Abstracts from the 11th American Conference on Pharmacometrics
ACoP11ISSN 2688-3953
ACoP11, November 9 – 13, 2020
Individual abstracts will be posted online post meeting.
For citation purposes please use the following
Citation: Author names, Abstract title, ACoP11, ISSN:2688-3953, 2020, Vol 2

TUE- 001
Updated Review of Vancomycin Population Pharmacokinetic Analyses in Adults
Abdullah Aljutayli1, Amélie Marsot2,3, and Fahima Nekka1,4,5
1Faculty of Pharmacy, Université de Montréal, Montréal, Canada
2
Laboratoire de suivi thérapeutique pharmacologique et pharmacocinétique, Faculté de Pharmacie, Université deMontréal, Montréal, Québec, Canada.
3
Centre de recherche, CHU Sainte-Justine, Montréal, Québec, Canada
4
Laboratoire de Pharmacométrie, Faculté de Pharmacie, Université de Montréal, Montréal, Québec, Canada.
5
Centre de recherches mathématiques, Université de Montréal, Montréal, Québec, Canada.
Background: Vancomycin is a drug of choice in the management of methicillin-resistant Staphylococcus aureus(MRSA) infections. Nonetheless, vancomycin optimal dosage regimens have, long, been controversial owing to thelarge between- and within-subject variability in its population pharmacokinetic (PopPK) profile.
Objectives: Our primary aim was to better inform vancomycin dosing by providing a comprehensive synthesis of
PopPK analyses of vancomycin to determine the most reported models, including the various sources of variability.
Methods: Systematic search for vancomycin PopPK studies was conducted on PubMed and EMBASE with a time
limit from January of 2011 to May of 2019. In addition, we inspected all relevant lists of references.
Results: Thirty analyses met the research criteria and were included in this review. One-compartment and twocompartment models best-described vancomycin PopPK in thirteen and fourteen studies, respectively. Further, threecompartment models were implemented in three studies to account for an additional cite of administration, i.e. in
cerebrospinal fluids. Common reported covariates were creatinine clearance and bodyweight (Table 1). Estimatedclearance (CL) and total volume of distribution (Vd) varied widely between the studies, ranging from 0.334 to 8.75
L/h (0.0054 to 0.1279 L/h/kg) and from 7.12 to 501.8 L (0.097-6.97 L/kg), respectively. An exponential interindividual
variability model was used in almost all the studies, and the highest inter-individual variability on CL and Vd were
99.2% and 101%, respectively.
Conclusion: We outlined the wide range of vancomycin parameter estimates and the covariates retained in thirty
different PopPK models developed in adults. This information might help guide the clinical dosing of vancomycin in
different subpopulations.
Table 1. Summary of recurrent and significant covariates including their frequencies on CL and Vd
Covariate

PK Parameter
Vd1

CL1
Creatinine clearance
Bodyweight
Age
Hemodialysis
Fat free mass1

Frequency

19
3
3
3
None

None
12
5
None
1

TUE-002

Pharmacokinetic Modeling and Simulation to Inform Dosing Regimen Selection for BIO 300 After
Subcutaneous Administration in Irradiated and Non-Irradiated C57BL/6J Mice
Ahmed M. Salem1, Isabel L. Jackson2, Michael D. Kaytor 3, Artur A. Serebrenik3, Zeljko Vujaskovic2, Jogarao
Gobburu1, Mathangi Gopalakrishnan1
1Center for Translational Medicine, Department of Pharmacy Practice, University of Maryland School of Pharmacy,
Baltimore, Maryland
2
Division of Translational Radiation Sciences, Department of Radiation Oncology, University of Maryland Schoolof Medicine, Baltimore, Maryland
3
Humanetics Corporation, Edina, Minnesota
OBJECTIVES: BIO 300, a nanosuspension of genistein, is being developed in accordance to the FDA animal rule
for the prevention and treatment of the delayed effects of acute radiation exposure (DEARE). A likely mechanism forradioprotection by BIO 300 is the activation of estrogen receptor beta (ERβ). Previous research indicated that
activation of ERβ requires an EC50 at least 24 ng/ml. The analysis objectives were to: (i) develop a pharmacokinetic(PK) model describing the disposition of BIO 300 in C57BL/6J mice following subcutaneous administration, (ii)perform PK simulations of multiple dosing to identify dose levels that are safe, therapeutic and do not cause significant
drug accumulation. An optimistic EC50 threshold of 24 ng/ml and a conservative EC50 threshold of 240 ng/ml were
explored as steady state Ctrough targets.
METHODS: The PK analysis included 162 genistein serum concentrations from 162 sex-matched C57BL/6J micefollowing single subcutaneous administration of 100 or 200 mg/kg in non-irradiated mice or 200 mg/kg in 14.5 Gy
total body irradiated mice with 2.5% bone marrow sparing. The data from irradiated and non-irradiated mice werepooled together as the radiation effect on PK was minimal to none from prior non-compartmental analysis (NCA).
Each animal contributed to one of the PK sampling times between 0.5 to 72 hours’ post-dose with triplicate
measurements per time point. Hence, the naïve-pooled approach was used to estimate the mean PK parameters. The
final PK model and the estimated mean parameters were used to simulate the following dosing scenarios: 25 mg/kg
to 200 mg/kg every 12, 24, 48 or 72 hours at steady state. The analysis was performed using Pumas v0.10.0(www.pumas.ai).
RESULTS: A two compartment PK model with first-order absorption and linear elimination adequately described
the mean concentration time-profile of BIO 300. The maximum concentration occurred at 0.5 hours. The mean
apparent volume of central and peripheral compartments was 40227 ml/kg and 107030 ml/kg, respectively indicating
extensive distribution of genistein due to high lipophilicity. Consistent with a previous NCA, the mean value ofapparent systemic clearance was 3873 ml/hr/kg. All the simulated dosing scenarios maintained steady state Ctroughlevels above the EC50 target of 24 ng/ml. However, only the regimens of 200 mg/kg dose, twice daily and daily
regimens of 50 mg/kg - 100 mg/kg were able to meet the Ctrough levels above the EC50 target of 240 ng/ml.
CONCLUSIONS: Assuming a conservative EC50 target of 240 ng/ml and adequate safety, 50 mg/kg to 200 mg/kg
daily subcutaneous doses would meet efficacy targets and avoid excessive fluctuation in concentrations. Given a
possibility of dose disproportionality at 200 mg/kg or higher, possible dose levels that could effectively activate ERβ
and be administered consecutively for several weeks are 50 mg/kg – 150 mg/kg administered daily subcutaneously.

TUE-003
Stimulation of erythropoiesis with ESA or blood donation: QSP model.

Alexander Stepanov1, Galina Lebedeva2
1
InSysBio, Moscow, Russia; 2InSysBio UK Ltd, Edinburgh, UK
Objectives: Erythropoiesis-stimulating agents (ESAs) enhance the red blood cells (RBC) formation by bone marrow.
They are used to manage anemic states caused by chemotherapy, bleeding, bone marrow’s malfunctions etc. To predict
the effect of pharmacological intervention on hematopoiesis-related clinical endpoints we propose the QSP model oferythropoiesis.
Methods: Multi-compartment ODE model of erythropoiesis was constructed to comprehensively describe cell
dynamics from hematopoietic stem cell to circulating red cells. The model includes variables corresponding to specificstages of cell development distinguished based on morphology and surface markers expression. Model processes arecell self-renewal, differentiation, proliferation, migration from bone marrow into circulation and cell death. Bindingof erythropoietin (EPO) and stem cell factor (SCF) to cell-surface receptors regulates cell dynamics with feedback on
the receptor expression modulated by interleukine-3 (IL-3). Regulation effects of EPO were implemented via explicit
description of EPO binding to its receptor followed by internalization leading to acceleration of cell proliferation anddifferentiation, and inhibition of apoptosis. Hypoxia-Inducible Factor (HIF) stabilizers (Roxadustat, Desidustat,
Daprodustat and Molidustat) included in the model prevent the degradation of HIF, thereby inducing synthesis of EPO
mRNA. Upregulation of EPO contributes to a negative feedback on HIF degradation through increased hemoglobin
production. The model was calibrated across published in vitro/in vivo data including cell expansion under growth
factors exposure in vitro, flow cytometry cell counting of bone marrow aspirates and clinical data of ESAs’
administration such as epoetins and HIF stabilizers. The QSP model was implemented in Heta language
https://hetalang.github.io. Modeling files were transformed to SBML, DBSolveOptimum and Simbiology format
using Heta-compiler [1].
Results: Data-driven model satisfactorily describes baseline levels of various cell precursors of erythroid branch inbone marrow and RBC in circulation of healthy subjects. The response of clinical outcomes such as reticulocytes
count, erythropoietin and hemoglobin levels to administration of growth factors (epoetins, SCF and IL-3) as well as
HIF stabilizers (Roxadustat, Desidustat, Daprodustat and Molidustat) were also well described by the model. The
model was validated on the data for erythropoiesis recovery after blood donation.
Conclusions: The proposed comprehensive human erythropoiesis QSP model describes clinical data and could beseen as predictive tool for investigation of potential pharmacological interventions including epoetins and HIF
stabilizers, and explanation of observed phenomena. The model is regarded as a branch of developing a generalplatform of human hematopoiesis.
[1] "Heta" is a New Declarative Language to Define the Large-Scale Systems Pharmacology and Systems Biology
Models. ACoP10, Orlando, October 2019, DOI: 10.13140/RG.2.2.14881.35682.

TUE-004

Characterization of Drug-Drug Interactions on the Pharmacokinetic Disposition of Busulfan in thePediatric Hematopoietic Stem Cell Transplantation Patient
Allison Dunn, Vijay Ivaturi, PhD, Hana Paek, PharmD, Amanda Gillespie, PharmD, Brady Moffett, PharmD, MPH,
Jogarao V.S. Gobburu, PhD, Robert Krance, MD
Center for Translational Medicine, Baltimore, Maryland, USA, Baylor College of Medicine, Houston, Texas, USA,
Texas Children’s Hospital, Houston, Texas, USA
Gobburu is a co-founder of Pumas-AI that commercializes Pumas software

Background: Busulfan is an alkylating agent used as a pretreatment for hematopoietic progenitor celltransplantation in chronic myelogenous leukemia. Busulfan is highly cytotoxic and its narrow
therapeutic index requires precise dosing.1 Doses too low can lead to transplant failure, while doses toohigh cause hepatic veno-occlusive disease and seizures.2 For this reason, accurate characterization of
drug-drug interactions (DDIs) is pivotal to clinician. With what is currently understood about busulfan
metabolism, there are a number of medications that theoretically impact busulfan clearance, but there
is insufficient data to determine clinical relevance.
Objectives: The primary objective is to investigate whether routine comedications meaningfully interact
with busulfan PK, using population analysis.
Methods: For this retrospective study patients were identified by querying the hospital electronic
medical records from January 1, 2011 to December 31, 2018 to identify patients who received 16 doses
of intravenous busulfan pretreatment with concentration monitoring after the 1st and 9th dose. A one
compartment model with first order elimination was developed in Pumas program (Pumas-AI,
Baltimore, MD). After adjusting for body size and maturation factors, the potential influence of DDIs,such as acetaminophen, phenytoin and azole anti-infective agents, were evaluated. The concomitantdrugs were binned into strong, moderate or mild categories for the following enzyme systems: CYP3A4,CYP2C9, CYP2C19, CYP2D6, and GST depletion.
Results: Data used in the analysis was comprised of 250 patients, both males and females, ranging from
5 weeks to 18 years of age with a weight ranging from 3 kg to 117 kg. The final model incorporated
covariates of weight and age in relation to their effect on both clearance and volume of distribution.
None of the evaluated covariates for DDIs produced significant results.

Conclusions: No clinically relevant DDIs were identified, which is inconsistent with the theoreticallyassumed interactions based on the metabolism of busulfan. This is the first time the maturation ofclearance and volume are quantified.
References:
1. Andersson, B. S., Thall, P. F., Madden, T., Couriel, D., Wang, X., Tran, H. T., … Champlin, R. E.
(2002). Busulfan systemic exposure relative to regimen-related toxicity and acute graft-versushost disease: Defining a therapeutic window for i.v. BuCy2 in chronic myelogenous leukemia.
Biology of Blood and Marrow Transplantation, 8(9), 477–485.
https://doi.org/10.1053/bbmt.2002.v8.pm12374452
2. Vassal, G., Deroussent, A., Hartmann, O., Challine, D., Benhamou, E., Valteau-Couanet, D.,
Brugières, L., Kalifa, C., Gouyette, A., & Lemerle, J. (1990). Dose-dependent neurotoxicity ofhigh-dose busulfan in children: a clinical and pharmacological study. Cancer research, 50 19,
6203-7.

TUE-007

Advanced Organ Modeling and Computer-Assisted Assembly of Platform PBPK Models for the Early
Evaluation of Target Druggability, Human Dose and Safety of Biologics
Armin Sepp1, Paul Michalski2, Dhaval K. Shah3
1

GlaxoSmithKline Plc., Stevenage, UK; 2GlaxoSmithKline, Upper Providence, PA, USA; 3University of Buffalo,

Buffalo, NY 14260, USA.
Objectives: Two-pore physiologically-based pharmacokinetics (PBPK) provides a mechanistic predictive
framework for assessing the tissue penetration, target engagement and elimination of biologics of any size according
to Three Pillars paradigm (1). This requires detailed modeling of organ physiology and target-mediated interaction
networks so that optimal modality, dosing and affinity requirements can be devised proactively, including flexible
incorporation of large protein-protein interaction networks both for the fluid phase as well as cell surfaces, if
required.
Methods: We evaluated the rodent data parameterized biologics PBPK framework (2) by comparing model
predictions with the experimental observations for a 89Zr-labeled albumin-binding domain antibody (AlbudAb)
GSK3128349 tissue and plasma concentrations in healthy human volunteers, as measured by positron emission
tomography-computed tomography (PET/CT) and mass-spectrometry respectively (3). In the case of CNS,
cerebrospinal fluid (CSF) barrier was incorporated in addition to the blood brain barrier in order account for the flowof CSF and the turnover clearance of biologics in these immune privileged organs (4).
Results: Using a Matlab™ -assembled SimBiology™-compliant two-pore biologics PBPK model we demonstratethat the organ-specific fractional lymph flow rates estimated from rodent data (2) predict well the human tissue andplasma concentration time course of 89Zr-GSK3128349 AlbudAb™, thereby extending the modelling frameworkfrom mice to humans (5). In the case of CNS, the exposures predicted from the CSF barrier incorporating PBPK
models were found to align well well with brain PK of mAbs from rodents to humans (4).
Conclusions: We have demonstrated the reliability of cross-species/cross-modality platform PBPK modelling byextrapolating biologics tissue distribution and elimination PK directly from rodents to humans and the value ofdedicated organ-specific layouts which capture specific features of organs and tissues like CNS, kidneys, lungs andsolid tumours.
References:
1.
2.
3.
4.
5.

P. Morgan et al., Can the flow of medicines be improved? Fundamental pharmacokinetic andpharmacological principles toward improving Phase II survival. Drug Discovery Today 17, 419-424 (2012).
A. Sepp et al., Computer-assembled cross-species/cross-modalities two-pore physiologically based
pharmacokinetic model for biologics in mice and rats. J Pharmacokinet Pharmacodyn 46, 339-359 (2019).
K. S. Thorneloe et al., The biodistribution and clearance of AlbudAb, a novel biopharmaceutical medicine
platform, assessed via PET imaging in humans. EJNMMI Research 9, 45 (2019).
H.-Y. Chang, S. Wu, G. Meno-Tetang, D. K. Shah, A translational platform PBPK model for antibody
disposition in the brain. J Pharmacokinet Pharmacodyn 46, 319-338 (2019).
A. Sepp, M. Bergström, M. Davies, Cross-Species/Cross-Modality Physiologically Based Pharmacokinetics
for Biologics: 89Zr-Labelled Albumin-Binding Domain Antibody GSK3128349 in Humans Manuscript in
preparation.

TUE-009

Modeling Dynamics of White Blood Cells (WBC) in Patients With Relapsed or Refractory Acute Myeloid
Leukemia (R/R AML) From a First-in-Human Study to Guide Dose and Schedule Exploration
Bishoy Hanna1, S.Y. Amy Cheung2, Eline M.T. van Maanen2, Maria Luisa Sardu2, Rik de Greef2, Michael
Pourdehnad3, Tonia J. Buchholz3, Simon Zhou1, Manisha Lamba1
1

Bristol Myers Squibb, Summit, NJ; 2Certara, Princeton, NJ; 3Bristol Myers Squibb, San Francisco, CA; USA.

Objectives: CC-90009 is a cereblon E3 ligase modulator and first-in-class small molecule that drives the binding of
a novel target protein, G1 to S phase transition 1, to cereblon, leading to AML cell death. WBC count was analyzed
as a surrogate marker of leukemic blasts and potential response. To provide quantitative metrics to guide the doseand schedule exploration in a first-in-human clinical study in patients with R/R AML (NCT02848001) throughmodel-informed drug development, the objective of this analysis was to develop a semi-mechanistic model to link
CC-90009 exposure to WBC count.
Methods: Data from the phase 1 dose-escalation study in patients with R/R AML were analyzed.1 The study
included 2 dosing schedules with CC-90009 administered intravenously on Days 1–5 or Days 1–3 and 8–10 of a 28day cycle; the total dose intensities per cycle ranged from 1.5–21.6 mg. The pharmacokinetic (PK) model was a 3compartment model with first-order elimination. A semi-mechanistic PK/pharmacodynamic (PD) model2 thatincorporated proliferation, maturation, and transit from bone marrow to peripheral blood was fitted. The inhibitory
effect of CC-90009 on the proliferation rate was adequately described by an Emax model with system-specificparameters (baseline WBC levels, mean transit time [MTT], and feedback governing proliferation of WBCs [γ]) anddrug-specific potency parameters (Imax and IC50) were estimated. Simulations were performed to explore the effect ofalternative dosing schedules on PK and WBC profiles.
Results: The PK model adequately described the observed PK profile; estimated central volume of distribution was
21.4 L; clearance was 5.6 L/hour. The PK–WBC model assumed that WBC data represented the total of mature
WBC and leukemic blasts, and that both cell types follow the same dynamic trend. The model was deemed adequate
based on goodness-of-fit plots and visual predictive checks, though a small bias remained at the population level thatmight be related to the nature of the total WBC data. Typical baseline WBC levels were estimated to be 4.68 ×

109/L, MTT was 5.51 hours, and γ was 0.51. Model simulations indicated a stronger schedule effect; increasing
treatment duration to 7 days (compared with 5 days) would result in prolonging leukemic blast suppression for
approximately 3–4 additional days.
Conclusions: The series of PK and PK/PD models adequately described the observed PK and WBC data from aphase 1 study of CC-90009 in patients with R/R AML. These models were used to simulate the impact of different
doses and schedules of CC-90009. The results support the exploration of extended consecutive dosing days in a 28day cycle to prolong exposures, potentially leading to a prolonged drug effect on leukemic blasts.
References: 1Uy GL, et al. Blood 2019;134(Suppl 1):232. 2Friberg LE, et al. J Clin Oncol 2002;20:4713-4721.

TUE-010

Title: Development and Verification of a Levonorgestrel PBPK Model for Enzyme- and Binding PartnerMediated Drug Interaction Studies in Healthy and Obese Women
Authors: Brian Cicali1, Karthik Lingineni1, Rodrigo Cristofoletti1, Thomas Wendl2, Joachim Hoechel2,
Herbert Wiesinger2, Ayyappa Chaturvedula3, Valvanera Vozmediano1, Stephan Schmidt1
Institutions:
1. Center for Pharmacometrics and Systems Pharmacology, Department of Pharmaceutics, College of
Pharmacy, University of Florida, Orlando, FL, USA
2. Bayer AG, Leverkusen, Germany
3. Department of Pharmacotherapy, System College of Pharmacy, University of Northern Texas Health
Science Center, Fort Worth, TX, USA
Objectives:
Levonorgestrel (LNG) is the active moiety in many hormonal contraceptive formulations. There isconcern that LNG’s widespread use in conjunction with its CYP3A4-mediated metabolism bares risk for
drug-drug interactions (DDI) resulting in unintended pregnancy or breakthrough bleeding. LNG is alsofrequently administered with ethinyl estradiol (EE) to increase the contraceptive efficacy and decrease sideeffects. Interactions between these two products both in terms of CYP3A4-mediated metabolism andprotein binding changes have made modeling these combinations difficult. Further, there is evidence thatrisk of unintended pregnancy is exasperated in obese women due to changes in protein binding partner
expressions and LNG exposure. Therefore, the objective of this study was to determine the plausibility andextent of DDIs for LNG and LNG+EE in healthy and obese women using a physiologically-based
pharmacokinetic (PBPK) modeling and simulation approach.
Methods:
Model development was conducted in PK-Sim (v8.0) using a combination of bottom-up and topdown approaches via literature-reported and optimized parameter values. Parameters were optimized using
patient-level data from Bayer AG following intravenous and oral administration of 0.03 mg LNG. The
model was then subjected to multiple verification steps including sensitivity analysis, visual predictive
checks at different doses with data not used for parameter fitting, and comparisons with clinical DDI datafor combinations of EE as well as concomitant administration of three different CYP3A4 inducers(carbamazepine, rifampicin and efavirenz). The verified model was then applied to an obese population(BMI ≥ 30 kg/m2) as well as two CYP3A4 inhibition DDI scenarios for investigation: clarithromycin toevaluate mechanism-based inhibition and itraconazole to evaluate competitive inhibition effects.
Results:
A whole-body PBPK model of LNG was verified for DDI studies both alone and in combination
with EE. In combination with carbamazepine, the predicted and observed AUC ratios (AUCR) indicatedgood predictive performance. The simulated LNG AUCR, when administered with EE, decreased to 0.50
(unbound: 0.45) in the presence of rifampin, whereas it increased to 1.45 (unbound: 1.50) for itraconazole.
These changes were further increased in obese populations, with AUCR of 0.38 (unbound: 0.40) and 1.60
(unbound: 1.25) in the presence of rifampin and itraconazole, respectively.

Conclusion:
Our results suggest that LNG shows moderate interactions with strong CYP3A4 inducers compared
to weak interactions with strong CYP3A4 inhibitors. The results of this analysis will be linked to exposureresponse models for unintended pregnancy and breakthrough bleeding events derived using a model-based
meta-analysis approach to determine how the predicted changes in total LNG exposure effect clinicaloutcomes.

TUE-012

Illustrating improved confidence in Phase II decision making using longitudinal Item Response Theory
analysis
Carolina Llanos-Paez1, Claire Ambery2, Shuying Yang2, Maggie Tabberer3, Misba Beerahee2, Elodie L. Plan1, Mats
O. Karlsson1
1. Department of Pharmaceutical Biosciences, Uppsala University, Uppsala, Sweden
2. Clinical Pharmacology Modelling and Simulation, GlaxoSmithKline plc., London, UK
3. Patient Centred Outcomes: Value Evidence and Outcomes, GlaxoSmithKline plc., Stockley Park, UK.

Objectives: To develop and assess an alternative methodology to compare clinical trial outcome measures using alongitudinal model-based item response model (IRM) which could potentially replace a standard analysis (mixed
model repeated measures - MMRM) of total score data as it may increase the statistical power and precision to
detect a drug effect.
Methods: Patient reported outcome data collected using the EXACT (Exacerbation of Chronic Pulmonary DiseaseTool) questionnaire in a randomized, placebo-controlled, phase II clinical trial [1] were analysed using an IRM.
EXACT consists of 14 items related to chronic obstructive pulmonary disease (COPD). The E-RS:COPD (Evaluating
Respiratory Symptoms in COPD) consists of 11 items from this EXACT instrument (sub-scales breathlessness, cough& sputum, and chest symptoms). The IRM was developed with a graded response model characterising item
parameters. Functions describing symptom-time courses were assessed, while Markov elements handled correlated
observations [2]. EXACT total scores (logit score-transformed) and the sum of the E-RS:COPD scores and E-RS
subscales were simulated at month 12 (N=2,000 with 5,000 subjects per arm) using the final parameter estimates withuncertainty. Specifically, the relationship between patient disease status and total score was derived and served as
the basis for the simulations. The 50th (2.5th, 97.5th) percentiles of the resulting 2,000 differences in average total score
between the trial arms at the end of the study were used to represent estimated drug effect (95%CI). These values
were compared with published MMRM results at month 12 [1]. Further, a relative sample size (NR) to MMRM CI(CIMMRM) values was calculated considering the precision obtained from the IRM (CIIRM) model as the desired marginof error (NR=(CIMMRM/CIIRM)2).
Results: Data from 93 patients who received either oral danirixin 75 mg twice daily (n=45) or placebo (n=48) on topof standard of care over 52 weeks were analysed. A step function best described the COPD symptoms-time course inboth trial arms. From the IRM, typical value (SE) of maximum response and time of response was -0.12 (0.18) and62.1 days (10.9) (danirixin arm) and 0.02 (0.13) and 98.6 days (14.6) (placebo), respectively. The IRM considerably
improved precision of drug effect compared to MMRM (Figure). A sample size of 2.8 and 3.7 times larger would be
required in the MMRM analysis to achieve the precision obtained with the IRM using EXACT and E-RS:COPD,
respectively.

Conclusions: The IRM in this example provided a considerably more informed basis for assessing the drug effect.
Thus, a longitudinal non-linear mixed-effect analysis based on item-level data may provide more precise estimates ofdrug effect than standard statistical analysis for the same endpoint, and thereby improving decision-making in Phase
II.
References:[1] GlaxoSmithKline. U.S. https://clinicaltrials.gov/ct2/show/NCT02130193?term=200163&draw=2&rank=1[2] Germovsek E et al. AAPS J (2019) 21, 60.

Figure. Mean (95%CI) difference in average total score between placebo and drug arm using a MMRM and IRM
analysis for different scales.

Funding Information: GSK funded this research in the form of a Research payment to Uppsala University.

Conflict of Interest: CL-P, ELP and MOK declare that they have no conflict of interest. CA, SY, MT and MB are
GSK employees and hold GSK shares.

TUE-013

Application of a dual PBPK-popPK model based approach across the age-population of
adults using bilastine as a probe drug
Chaejin Kim1+, Valentina Lo Re2+, Monica Rodriguez2, John C. Lukas2, Cristina Campo3, Aintzane Garcia3, Stephan
Schmidt1, Valvanera Vozmediano*1(1) Center for Pharmacometrics and Systems Pharmacology, Department of Pharmaceutics, University of Florida(2) Drug Modeling & Consulting (DMC), Dynakin, SL, Bilbao, Spain(3) Medical Department, FAES FARMA, S.A, 48940, Spain* Correspondence: valva@cop.ufl.edu
Contributed equally to this study+

Objective: Use of a dual physiologically-based pharmacokinetic model- population pharmacokinetic (PBPK-popPK)
model-based approach to integrate bilastine physicochemical, in vitro and in vivo data in young adults to: 1) enhance
the mechanistic understanding of intestinal transporters on drug PK, and 2) predict the PK in elderlies of different
biological age (i.e., young old (65~74 yrs), middle old (75~84 yrs) and oldest old (> 85 yrs)).
Methods: 1) PBPK model: Using GastroPlus 9.6® a PBPK model for young adults was developed considering apicalefflux and apical and basolateral influx transporters in the enterocyte, using PK data from young adults after IV (10mg
SD) and PO (20mg SOD)1. Transporters’ kinetics and colonic absorption parameters were optimized during the model
development process (due to insufficient experimental data available to inform the model). The model was qualified
using an external dataset containing data from 12 Phase-I studies with 13 different SOD and MOD2. The model wasthen used to extrapolate the PK to young olds, which also served to verify the predictive capacity of the model. 2)
PopPK model: A semi-mechanistic predictive popPK model for elderlies was develop in NONMEM version 7.2 using
a previously developed popPK model2 incorporating declining functions on different physiological systems(glomerular filtration, unbound fraction) and differences in body composition. Model predictive capacity wasevaluated using observations from young olds. Both models were qualified by comparing the predicted vs observed
PK parameters in elderlies, and by comparing the predicted concentration-time profiles to the clinical data. Bothmodels were then applied to assess the suitability of the therapeutic dose in middle and oldest olds.
Results: Final PBPK model predictions showed AUCpred/AUCobs ratios within 0.5 and 2 for all the doses (5mg 220mg). The final PBPK adequately predicted plasma concentrations in geriatrics (n=8 male; mean age= 69.75 yrs;
n=8 female: mean age = 67.625 yrs). Similar results were also obtained for the semi-mechanistic popPK model wheremore than 90% of observations were within the 5~95% of simulated confidence intervals. The application of bothmodels to middle and oldest old led to the conclusion than the 20 mg dose produces plasma concentrations of bilastine
within the known therapeutic margin3.
Conclusions: The PBPK model supports the hypothesis that basolateral influx transporters are involved in bilastine
PK (basolateral influx and apical efflux intestinal transporter were needed to adequately describe the PK) and ofregional differences on P-gp’s efflux capacity in the intestine. Both, PBPK and semi-mechanistic popPK models
indicate that 20 mg QD dose is appropriate (safe and effective) for geriatrics of any age.
References:[1] Sádaba et al. Clin Drug Investig. 2013;33:375-381. doi:10.1007/s40261-013-0076-y[2] Jauregizar et al. Clin Pharmacokinet. 2009;48(8):543-554. doi:10.2165/11317180-000000000-00000[3] Vozmediano et al. Eur J Pharm Sci. 2019;128(November 2018):180-192. doi:10.1016/j.ejps.2018.11.016

TUE-015

TITLE: Using Mechanistic Quantitative Systems Pharmacology (QSP) Models To Connect Biomarkers To ClinicalDisease Activity Scores – Examples In Atopic Dermatitis (AD) And Psoriasis (PsO).
AUTHORS: Christina Friedrich(1), Rebecca Baillie(1), Michael Weis(1), Meghan Pryor(1), Vincent Hurez(1),Katherine Kudrycki(1), Michael Reed(1)
INSTITUTIONS: (1) Rosa & Co. LLC, San Carlos, CA
OBJECTIVES: Many clinical trials use complex disease activity scores to assess patient response, and theconnections between biological components and these scores are often unclear. We explore how QSP modelingsupports elucidation of disease pathophysiology and better-informed extrapolation between biological components
and disease scores to facilitate prediction of clinical outcomes.
METHODS: Mechanistically informed relationships were identified to connect biomarkers to the Eczema Area &Severity Index (EASI) in an AD model and to the Psoriasis Area & Severity Index (PASI) in a PsO model.
RESULTS: AD and PsO QSP models were developed and qualified. Changes in tissue biomarkers upon treatment,e.g., skin immune cell infiltration, were particularly helpful in establishing links between disease pathology anddisease symptoms. The disease scores were modeled by (1) identifying the components of each disease activity
score, (2) formulating a biological rationale for associating specific biomarkers with each score component, and (3)calibrating the proposed function using clinical data from existing therapies. EASI and PASI score improvement
matched existing clinical trial data and the models were used to predict the clinical efficacy of novel interventions.
CONCLUSIONS: QSP models are valuable tools to integrate existing mechanistic and clinical data and generateplausible predictions of disease scores in response to novel interventions.

Figure 1. EASI score components linked to selected key biomarkers from an AD QSP model.

TUE-016

Title: Acceleration of Quantitative Systems Pharmacology Models Through Automatic Analysis of System
Structure and Simulation on Graphics Processing Units
Authors: Christopher Rackauckas1,2, Yingbo Ma4, Theodore R. Rieger3, Anna Sher3, Richard Allen3, Vijay
Ivaturi2, Viral Shah4, and C.J. Musante3

Affiliations:1
Department of Mathematics, Massachusetts Institute of Technology, Cambridge, MA, USA
2

College of Pharmacy, University of Maryland-Baltimore, Baltimore, MD USA

3

Quantitative Systems Pharmacology, Pfizer Inc, Cambridge, MA USA

4

Julia Computing, Cambridge, MA USA

Objectives:
Quantitative Systems Pharmacology (QSP) models often consist of stiff sets of ordinary differential
equations (ODEs) and are traditionally slow to simulate and difficult to parallelize. The lack of highthroughput simulation limits the types of research questions amenable to analysis by a QSP approach.
Here we addressed and improved some of the performance bottlenecks to extend the utility of QSP
models.

Methods:
We approached acceleration of QSP model simulations by:
1. Optimization of the numerical solution of ODEs through automatic Jacobian generation,
selection of solver, and stiffness detection.
2. Deployment of QSP models on Graphics Processing Units (GPUs) for high-throughput parallel
simulations.
We tested that our approaches in three separate QSP models: a smaller (14 ODEs) model of leucine
metabolism (unpublished), a model of cardiac metabolism and mechanics (54 ODEs) [1], and an ionic
cardiac model (105 ODEs) [2]. The simulations were performed using the Julia Language [3] and the new
methods have been incorporated into the relevant open-source packages. The performance of thesimulations was compared to existing “baseline” code for all three models. The leucine and Tewari et almodels were already fairly optimized as C-code using the SUNDIALS CVODE BDF solver. The Gong et al.
model was implemented in Matlab using ode15s and had not necessarily been fully optimized for
performance.

Results:
In the leucine model, application of automatic Jacobian generation resulted in a 7x, compared to thebaseline code. These speed-ups extended to parallel computing, with a near-linear gain per Central
Processing Unit (CPU) added (Figure 1). The time to perform global optimization of the model
parameters was reduced from 16 hours to 1 hour with a comparable final objective function value. The
Tewari et al. model accelerated 2x/CPU-used over the C-baseline code and the Gong et al. model gained
25x compared to simulations in Matlab.
We established a GPU acceleration strategy for parameter searches of small ODE systems (<200equations). The approach involved specifying the stacked ODE system as single program multiple dataover the parameters and specializing the implicit portion of the ODE solver to utilize a GPU-specialized(limited branching, partial pivoting) block LU factorization. The GPU kernel code is generated
automatically by the compilation process, allowing users to write standard code and have the code GPU
accelerated. Using a single GPU with 10,000 cores, the leucine model was accelerated 175x over thebase code (Figure 1).
Conclusions:
The newly developed tools showed significant performance gains for the QSP models tested. This
improved performance makes exploring uncertain parameter spaces of large QSP models that span
multiple timescales much more practical.
References:
1. Tewari et al. 2017.
2. Gong et al. 2020.
3. The Julia Language. https://julialang.org/

Figure 1. Relative speed-up of a simulation of the leucine QSP model vs. the base case. CPU-Serial- single
set of parameters on a single CPU, CPU-Parallel- simulation of multiple parameter sets using 6 CPU
cores, and GPU-Parallel-10,000 simultaneous runs across a GPU.

TUE-017

A target-mediated drug disposition-lymphocyte kinetics model simulates intricate interplay of
immunocytokine drug pharmacokinetics and cellular dynamics in blood and lymphoid tissues
Dan Lu1, Vittal Shivva1, Rajbharan Yadav1, John Desjarlais2, Matt Bernett2, Liz Bogaert2, Irene
Leung2, Coco Liu2, Pascal Chanu3, Saroja Ramanujan1
1Genentech, Inc., South San Francisco, CA, USA
Xencor, Inc. Monrovia, CA, USA
3
Genentech/Roche, France
2

Objectives
Immunocytokines such as engineered IL-2 and IL-15 are being developed as therapeutic agentsto induce proliferation of lymphocytes, alone or in combination therapy with other cancer
immunotherapy agents. XmAb24306 is an engineered IL15/IL15 Rα-Fc that binds to IL2/IL15 Rbg
subunits on natural killer (NK) cell and T lymphocytes and leads to a durable expansion of target
cells. XmAb24306 induced target cell expansion subsequently enhances the target-mediateddrug disposition (TMDD) of XmAb24306. The objective of this work is to build a semimechanistic model to simulate these interactions, to delineate the mechanisms underlying
observed PKPD, and to support translation and clinical development.
Methods
Serum concentration-time profiles of XmAb24306 and blood CD8+ T-cells and NK cells, wereobtained from single and repeated dose studies in cynomolgus monkeys (0.1 to 0.6 mg/kg,
intravenous administration). A semi-mechanistic model was developed with PK captured using
a 3-compartment model with linear and receptor-mediated clearance. Drug-receptor binding
and downstream PD is captured by modeling (i) dynamics of total cellular receptor levels as afunction of receptor turnover and cellular dynamics (lymphocyte turnover and traffic); (ii)affinity-dependent drug-receptor binding and complex internalization; (iii) complex-stimulated
lymphocyte margination (or exit from blood) and subsequent demargination (or re-entry); (iv)complex-stimulated lymphocyte proliferation in blood and tissue, and the resulted increases infree receptors, which in turn influences PK.
Results
Our model successfully describes the observed PK, CD8+ T-cells and NK cells dynamics, using
the number of drug-bound receptors per cell in blood and tissue as the driver for cellular PD.
Parameters were derived either from literature or were fit from cyno PKPD data; all parameter
estimates were within physiologically reasonable ranges. Both blood and tissue cell dynamics
appear to influence observed PK and PD in blood. The initial blood lymphopenia is resulted
from margination. Drug-receptor binding leads to lymphocyte proliferation in both blood andtissue with an estimated maximum proliferation rate corresponding to a cellular doubling timeof approximately 10 to 20 hours. The gradual demargination and trafficking of proliferated

lymphocytes from lymphoid tissues back into the blood explain the observed delay of maximal
blood lymphocyte expansion at day 7-8 after dosing, without invoking empirical delays in themodel. Furthermore, CD8+ T-cell and NK cell numbers alone, at physiological receptor
expression levels and turnover rates, are insufficient to account for the observed drug
clearance, suggesting a significant contribution of other IL2/IL15-Rbg expressing cells (e.g.,CD4+ T cells) to TMDD, which is also implemented in the model.
Conclusions
Based on the cynomolgus monkey data, a semi-mechanistic model was developed which can beleveraged for interpreting PK-PD in cancer patients and performing simulations to explore
different dosing regimens. The model can also be used to explore the optimal combination withother lymphocyte-mediated therapies.

TUE-018

Efficient simulation of clinical target isoboles applied to drug combinations for malaria

Daniel Lill (1,3), Anne Kümmel (1), Nathalie Gobeau (2), Henning Schmidt (1), Jens Timmer (3)1) IntiQuan GmbH, 2) Medicines for Malaria Venture, 3) University of Freiburg

Objectives: Dose selection in clinical trials is often based on the probability that the chosen doseachieves the desired clinical outcome. For combination therapies, clinical trial simulation for assessing
this probability is impeded due to combinatorial complexity: Compared to one optimal dose formonotherapy, many dose combinations, i.e., isoboles, lead to the same desired effect. Calculating these
isoboles is computationally demanding as many dose combinations must be simulated. The objectives ofthis work were two-fold: 1) to develop a fast algorithm able to calculate “isoboles” of the clinical target
for combination treatments, i.e. all the possible dose combinations of two drugs that allow to reach theclinical efficacy target; 2) to obtain the statistics of the isoboles over a large population (e.g. median andits 95% confidence interval) at these doses. In these calculations, the time-dependent pharmacokinetics
and pharmacodynamics of both compounds are predicted in a large number of individuals taking into
account inter-individual variability and parameter uncertainty to calculate the clinical target.
Methods: A fast algorithm to calculate population-level isoboles was developed. Instead of simulating
the model for all possible dose combinations, the algorithm iteratively refines the resolution of thedosing grid close to the actual isobole. To obtain statistical measures such as the median isobole and its
95% confidence bounds, an additional algorithm for two-dimensional quantile calculation wasdeveloped.
Results: We present properties of the algorithm and non-linear features of population isoboles bycomparison of three different models from malaria research. For each model, the dose combinations
yielding a 95% cure rate in the population, the target for antimalarial drug treatment, are determined.
The isobole algorithm cuts down the required simulations by more than a hundred-fold. We find that
population isoboles can differ remarkable from the isobole describing cure of a single subject,emphasizing the importance to consider population effects. Furthermore, the newly developed method
to obtain confidence intervals for isoboles is a powerful way to quantify model uncertainty for
combination therapies.
Conclusions: Population isoboles provide a visual, easy-to-communicate, as well as quantitative means
to assess a combination and its potential to achieve the desired clinical outcome. The computational
efficiency of the new algorithm allows its application to several anti-malarial combination therapies toselect the most promising one to be tested in a clinical trial. Compared to isoboles of a single typicalsubject, they are better able to capture extremal behaviors in the population. Population isoboles also
identify interesting dosing regimens which will help to prepare the design of the clinical trial.

TUE-019

Title: Literature review based modeling analyses to inform optimal clinical dose selection of chemotherapy
Authors: David J. Smith, Manash S. Chatterjee, Dinesh P. de Alwis, and Kapil Mayawala
Institutions: Quantitative Pharmacology and Pharmacometrics, PPDM, Merck & Co., Inc., Rahway, New Jersey
Objectives: While cytotoxic chemotherapy agents have traditionally been developed at their maximum tolerated
dose (MTD), an alternative dose below the MTD may be sufficient to maximize efficacy. Additionally, the safety
benefits at <MTD may return efficacy benefits through a reduction in patient dropouts.
A key challenge in the clinical investigation of chemotherapy <MTD is the possibility that such a dose may
not maximize efficacy. Here, we seek to motivate the study of reduced chemotherapy doses based on clinical datausing reverse translational principles.
Methods: We conducted a literature review to find studies reporting clinical efficacy and safety data at multiple
chemotherapy doses. We also leveraged published clinical exposure-response and population PK/PD models toassess the impact of dose reductions on both efficacy and safety. Specifically, we quantitatively assessed the effectof solvent-based paclitaxel dose reduction using established models of paclitaxel PK and chemotherapy-induced
myelosuppression (PD) [1], and gemcitabine dose reduction with and without infusion prolongation using anestablished PK model of the parent drug and active metabolite [2].
Results: Based on clinical studies of paclitaxel, we found that a 20-25% dose reduction on the q3w schedule
improved dose-limiting toxicities, neutropenia and neuropathy, while preserving overall survival (OS) in metastatic
breast cancer. Furthermore, our modeling analysis showed that the worst incidences of neutropenia are experienced
in the top quartile of unbound plasma paclitaxel exposure and that these patients are most benefitted by 20-25% dose
reduction.
Our investigation of gemcitabine clinical studies found that a 75% dose reduction improved
myelosuppression while preserving OS in pancreatic adenocarcinoma. Our modeling analysis showed how infusion
prolongation in tandem with dose reduction can offset the losses in active metabolite exposure in peripheral bloodmononuclear cells, a possible surrogate for efficacy, upon dose reduction and optimize active metabolite exposure
relative to parent drug exposure in plasma, a surrogate for toxicity.
We also identified agents such as carboplatin with rigorous exposure-response and dose optimization
efforts that do not support dose reduction due to losses in efficacy.
Conclusions: We found that existing clinical evidence supplemented with population PK/PD analyses supports dose
reduction of chemotherapy agents paclitaxel and gemcitabine. This work can be used to inform dose selection,
improving safety and potentially efficacy, in future clinical investigations involving chemotherapy.
References:
1. Kloft C, et al. Clinical Cancer Research. 2006;12:5481–90.
2. Zhang L, et al. Journal of Pharmacokinetics and Pharmacodynamics. 2006;33:369–93.

TUE-020

Population pharmacokinetics of magrolimab (5F9, Hu5F9-G4) in patients with solid tumors and lymphomas
Denise Chao-Yu Jin1, Bing Wang2, Branimir Sikic3, Ranjana Advani3, Mark Chao1, Chris Takimoto1, Balaji
Agoram1
1

Forty Seven, Inc., Menlo Park, CA; 2Amador BioScience, Pleasonton, CA; 3Stanford University School of
Medicine, Stanford, CA;
Objectives: Magrolimab (Hu5F9-G4) is a monoclonal IgG4 antibody that binds to CD47 and enables tumor cell

phagocytosis and activates an anti-tumor T-cell response. Magrolimab is being evaluated as an anti-cancer therapy
in multiple clinical trials both as a mono- and combination therapy. The objectives of this analysis were to develop a
population pharmacokinetics (PK) model of magrolimab to quantitate the effect of patient/disease characteristics on
PK.
Methods: Data were pooled from 2 studies: Study SCI-CD47-001 (Phase 1 monotherapy; solid tumors andlymphomas) and 5F9003 (Ph1/2; rituximab combination in lymphomas). A total of 3962 PK observations from 202
patients - dose range 0.1 to 45 mg/kg intravenous magrolimab - were used in this analysis. Population PK was
performed using a non-linear mixed effects modeling approach (NONMEM v7.4). Stepwise covariate building
approach was used to evaluate covariates.
Results: Magrolimab PK was described using a 2-compartment model with parallel first-order and MichaelisMenten clearances. All PK parameters and their variabilities were precisely estimated and physiologically plausible
for an antibody. The mean (standard error of estimate) estimates of linear clearance (CL) and central volume of
distribution (V1) were 0.273 L/day (0.012) and 3.70 L (0.06), respectively. Only body-weight was identified as asignificant covariate. Other demographic covariates, anti-drug antibody formation, and coadministration ofrituximab were not significant. In cancer patients with partial or complete tumor responses, a 28% decrease in CL
was observed (p<0.05).
Conclusions: A population PK model of magrolimab was developed and validated. Model indicated no need for
dose adjustments in any patient or disease sub-population.
Clinical trial information: NCT02216409 and NCT02953509.

TUE-021

A Computational Model of Cerebral Metabolism during Cerebral Ischemia and Reperfusion
Donald E. Lee1, Pawan Gupta1, Emin Maltepe2, Jogarao Gobburu1
1

Center for Translational Medicine, School of Pharmacy, University of Maryland, Baltimore, Baltimore, MD, USA

2

Department of Pediatrics, University of California, San Francisco, San Francisco, CA, USA

Objectives:
The brain consumes a disproportionate amount of total energy in the body and is particularly vulnerable to hypoxicischemic episodes. Currently, the only treatment for hypoxic-ischemic encephalopathy (HIE) is therapeutic
hypothermia, which provides modest reductions in brain injury and mortality. Novel treatment approaches are
therefore necessary. As metabolic and blood flow disturbances are fundamental to cerebral ischemia, we developed
a computational model to study cerebral metabolism during cerebral ischemia and reperfusion.

Methods:
The computational model is adapted from Jolivet et al1, with the neuron-glia-vascular functional unit, and was
implemented in the Julia language. Hodgkin-Huxley modeling of neuronal excitability, Buxton-Wang modeling ofvascular dynamics2, and modeling of metabolic pathways were incorporated.

Results:
Several forms of cerebral ischemia are simulated, with alterations in blood flow conditions. The model predicted
metabolic events consistent with experimental observations of cerebral ischemia, including lactate accumulation,buildup of extracellular potassium, and neuronal calcium influx.

Conclusions:
A computational model of cerebral metabolism was able to predict metabolic disturbances during cerebral ischemia
and reperfusion. Future models will incorporate time-varying parameters to match the varying metabolic conditions
during ischemia. Additionally, the cerebral metabolism model will be developed to include further pathophysiologic
mechanisms, such as free radicals and inflammatory messengers. Such a model may be able to accelerate drug
development for HIE by predicting responses to various treatments.

References:1
Jolivet R, Coggan JS, Allaman I, Magistretti PJ (2015) Multi-timescale Modeling of Activity-Dependent Metabolic
Coupling in the Neuron-Glia-Vasculature Ensemble. PLoS Comput Biol 11(2): e1004036. doi:10.1371/journal.
pcbi.1004036
2

Buxton, R. B., Wong, E. C., & Frank, L. R. (1998). Dynamics of blood flow and oxygenation changes during brain
activation: The balloon model. Magnetic Resonance in Medicine, 39(6), 855-864. Doi:10.1002/mrm.1910390602

TUE-022

TITLE: Effect of URAT1 inhibition with verinurad on proximal tubule intracellular lactate: A mathematicalmodeling analysis and hypothesis for antiproteinuric effect1
AUTHORS: Emily Nieves (1), Melissa Hallow, PhD (1), Peter Greasley (2), Joanna Parkinson (2)AFFILIATIONS: (1) College of Engineering, University of Georgia, Ga, United States (2) AstraZeneca
Gothenburg, Sweden---------------------------------------------------------------------------------------------------------------------------------------------OBJECTIVES: Verinurad lowers serum uric acid by inhibiting URAT1, a urate/ carboxylate co-transporterin the proximal tubule (PT) of the kidney. Verinurad reduced proteinuria in a recent clinical trial, but themechanism remains unknown. One proposed hypothesis is that the inhibition of URAT1 augments
protein reabsorption by increasing intracellular lactate in proximal tubule cells. The increased availabilityof lactate may lead to a restoration of the reabsorption of protein by increasing ATP levels and/ or
improving endocytosis of protein by lowering endosomal pH. The goal of this study was tomathematically evaluate the effect of URAT1 inhibition with verinurad on intracellular lactate
concentration.
METHODS: A mathematical model of urate and lactate transport in the rat proximal tubule1 of wasadapted to simulate transport in humans. The transporter model was further adjusted to represent
states of hyperuricemia and altered expression of uric acid transporters that likely occurs in
hyperuricemia. The transporter model was linked to a systems description of plasma uric acid and thepharmacokinetics and pharmacodynamics of verinurad were incorporated into the model.
RESULTS: The model was able to reproduce observed verinurad pharmacokinetics, as well as theobserved changes in plasma and urinary uric acid. The model predicted that URAT1 inhibition withverinurad increases intracellular lactate concentration in the human PT by 26% in normouricemic and
20% in hyperuricemic conditions.
CONCLUSIONS: Simulations indicate that verinurad may substantially increase the availability of lactate
inside PT epithelial cells. Since lactate is used as a fuel by the PT, increased availability of lactate may
help restore protein reabsorption capacity in fuel-deficient PT cells by increasing ATP levels. In addition,
increased intracellular lactate could help maintain the low endosomal pH necessary for protein
reabsorption.
REFERENCES:
1.
Edwards, A., et al. A model of uric acid transport in the rat proximal tubule. American Journal of
Physiology-Renal Physiology. 316(5), F934-F947 (2019).

1

This abstract will be published under the same title in the FASEB journal but presentation at the annual
Experimental Biology meeting was not given due to COVID-19.

TUE-025

Computational analysis of combination therapy of bispecific T cell engagers (BiTE®) with anti-programmed
cell death-1 (PD1) inhibitor for the treatment of hematological malignancies

Florencio Serrano Castillo1, Sameer Doshi1, Hansen Wong2, Vijay Upreti2, Fiona Chandra1, Khamir Mehta2
1

Clinical Pharmacology, Modeling and Simulations, Amgen Inc, Thousand Oaks, CA; 2 Clinical Pharmacology,

Modeling and Simulations, Amgen Inc, South San Francisco, CA
Objectives:
This work explores the development of a mechanistic systems model to explore possible therapeutic interactions
between bispecific T cell-engaging antibodies and anti-PD1 mAb for potential application in the treatment ofhematological malignancies. BiTE® molecules work by enhancing the immune response to the tumor by
circumventing the need to be activated by antigen presenting cells, while anti-PD1 treatments suppress the tumor
immune escape by blocking PD1 binding and decreasing the resistance to the cytotoxic activities of the immune
cells. Furthermore, tumor upregulation of 26mmune-regulating factors, including PD1, can potentially attenuate a
BiTE® molecule’s effectiveness and serve as a mechanism of tumor resistance. Both therapeutic modalities havedemonstrated efficacy across various tumor types and have the potential to synergize. In this work, we combine
proposed PK/PD models of BiTE® molecules and anti-PD1 mAbs, with prior knowledge about their respective
methods of action and relevant biology to computationally explore possible efficacy/safety/PK/PD profiles
following co-administration under different regimens.
Methods:
The proposed model consists of a system of nonlinear ordinary differential equations that incorporate the abovementioned mechanisms and the PK based on published PK models. The model tracks the target-based dynamics andcoverage for both compounds and includes regulatory feedbacks to explore the inhibitory effect of PD1 receptors
both in the absence of a checkpoint inhibitor and following their addition at different dosing schemes. Other states
include tumor growth, T-cell phenotypic dynamics, specifically, T-cell proliferation, and the transition between naïve
T-cells, activated T-cells and PD1+ T-cells. Figure 1 shows a schema of the model structure.

Figure 1. Schema of the multi-scale model structure implemented.
Results:
We explored model behavior using global sensitivity methods to understand the potential outcomes for different
mathematical structures for the PD1 regulatory feedbacks on T-cell tumor killing activity. As expected, model
simulations show a significant reduction of tumor burden with co-administration, compared to the monotherapy.
Furthermore, the model suggests higher baseline levels of PD1+ T-cells lower a BiTE® molecule’s cytotoxic activity,
which is rescued through the co-administration. Model also suggested the magnitude of cytokine release may be
influenced by two opposing elements: the suppression of T-cell regulation activity and the rapid decrease in tumor
burden. Thus, an optimal treatment regimen should balance the two phenomena to ensure lower risk of cytokine
storm but maintain the efficacy of tumor cell killing. We explore this idea through various simulated scenarios
based on initial cell counts and dosing regimens.

Conclusions:
Our model explores the possible interactions and potential synergisms between a BiTE® and an anti-PD1 mAb. It
proposes a mathematical framework to describe the underlying biology of the method of actions for thesecompounds and may provide valuable insight to facilitate the development of co-administration strategies forvarious hematological malignancies.

TUE-026

Population PBPK Modelling using the Quasi-Random Parametric EM Algorithm (QRPEM) andthe Nonparametric Adaptive Grid Method (NPAG) of the Simcyp Simulator
Janak Wedagedera1, Anthonia Afuape1, Siri Kalyan Chirumamilla1, Hiroshi Momiji1, Robert Leary1,
Mike Dunlavey1, Richard Matthews1, Khaled Abduljalil1, Masoud Jamei1, Frederic Y. Bois1.
1

CERTARA UK Limited, Simcyp division, Sheffield, UK.

Objectives: PBPK models usually include a large number of parameters because of their mechanistic
nature. The best-practice workflow for PBPK modelling in clinical studies involves in vitro to in vivo
extrapolation of parameter values. However, such extrapolations are uncertain and may benefit from
inclusion of clinical observations. Yet, when clinical inter-individual variability is high, or the data sparse,
it is essential to use a population pharmacokinetics framework. We compared the results obtained in sucha case with parametric (QRPEM) and non-parametric (NPAG) asymptotic methods implemented in the
Simcyp Simulator version 19 to those given by Bayesian numerical estimation.
Methods: The QRPEM and NPAG algorithms were used to analyse a canonical theophylline dataset(Trembath 1980; R-MEMSS package). A minimal PBPK model was used to describe theophylline
pharmacokinetics after administration of an immediate-release (IR) formulation to 18 healthy subjects.
We estimated the first-order absorption rate ka, the steady-state volume of distribution Vss, and the
maximum rate of metabolism through liver CYP1A2 N3-demethylation Vmax and their inter-individual
variability (IIV). Renal clearance was set at 0.31 L/h. The same data were analysed in a parametric
Bayesian framework using the same structural model using HMCMC (Stan software, Carpenter et al.
2017) and Metropolis-Hastings MCMC sampling (Bois 1997). We compared parameter estimates andmodel fits to the data.
Results: Simcyp version19 QRPEM and NPAG algorithms successfully fitted the PBPK model toobserved data to estimate the key parameters. Overall, QRPEM and NPAG showed consistency in theestimates except that for ka was two to four higher with the NPAG method. Run-times were within therange of 45 minutes to 3 hr. The comparison with Bayesian MCMC and HMCMC shows good agreementin the estimates for Vss and Vmax but ka was about 2-fold higher compared to the QRPEM estimates(HMCMC took about 60 minutes).
Conclusions: The QRPEM and NPAG methods implemented in the Simcyp Simulator version 19 gave
consistent population and individual parameter estimates (including IIV) and were in reasonableagreement with Bayesian HMCMC or MCMC estimates in this study. Relatively higher estimate forabsorption parameter ka with NPAG and MCMC or HMCMC seem to be due to sensitivity to priorinformation and measurement error, coupled with poor identifiability with this dataset. The run-times ofboth QRPEM and NPAG estimation methods were reasonable compared to the alternative Bayesian
MCMC estimation methods.
References:
Bois FY, Maszle D 1997. Journal of Statistical Software. 2, doi.org/10.18637/jss.v002.i09.
Carpenter B et al. 2017. Journal of Statistical Software. 76, doi.org/10.18637/jss.v076.i01.
Trembath PW, Boobis SW 1980. British Journal of Clinical Pharmacology 9:365-369.

TUE-027

Title: Simulation of first-in-human using an allometrically scaled population mechanistic TMDD model withpreclinical monkey data
Authors: Géraldine Ayral (1), Pauline Traynard (1), Monika Twarogowska (1)Affiliations: (1) Lixoft, Antony, France

Objectives:
The design of first-in-human clinical trials relies on the extrapolation of preclinical animal data to human
predictions. The ability to accurately predict the human pharmacokinetics (PK) of a drug candidate is therefore a key
challenge in drug discovery and development. For small molecules, simple allometric scaling of clearance andvolume is often sufficient. However, for many biologics, their nonlinear PK imposes the use of more advanced
methods. In this poster, we compare several model-based approaches for the first-in-human dose selection for the
fully human IgG2 monoclonal antibody PF-03446962, using cynomolgus monkey data and literature information.
Methods:
Drug concentration profiles after single and multiple doses of PF-03446962 for 14 monkeys are extracted from Luu
et al [1]. Three different PK models are fitted on the data using a population approach in Monolix: (1) a linear 2compartment model, (2) a simple target-mediated disposition (TMDD) model with linear and Michaelis-Menten
elimination and (3) a more complex TMDD model, where the complex internalization rate and the binding affinity
are fixed to literature values.
Each model is then used to simulate the human PK for a large range of doses using Simulx. Volumes and linear
clearance parameters are scaled using classical allometric scaling. For model 2 (simple TMDD), Km and Vm areassumed identical in monkey and human. For model 3, the parameter values are replaced by the literature values forhumans. The model predictions are finally compared with actual phase I human data.

First-in-human simulations using the complex TMDD model (green) and averaged phase I clinical data
(black)Results: The linear model is not able to capture the monkey PK profiles, but both the simple and more complex
TMDD models provide satisfactory diagnostic plots and good confidence on all estimated parameters. According to
the parsimony principle, one would favor the simpler TMDD model with linear and non-linear clearance. However,
the estimated Km and Vm parameters have no clear biological meaning and it is therefore difficult to decipher how
they scale to humans. On the opposite, the more complex TMDD model has parameters with clear physiologicalinterpretation, which in addition have been experimentally measured both in monkey and humans. This greatly
facilitates the choice of parameter values for the prediction of human PK. When compared to the actual PK profiles
in human from the phase I study, the more complex TMDD model shows the most accurate prediction, within 1 to
2-fold of observations.
Conclusion: This example illustrates how the incorporation of mechanistic information from the literature into amodel-based approach can help to obtain more accurate first-in-human predictions and thus inform the choice of thefirst-in-human doses.
References:[1] Luu et al. (2012). Journal of Pharmacology and Experimental Therapeutics, 341(3), 702–708.

TUE-029

Early prediction of response and survival using from a tumor growth inhibition (TGI) model in head andneck squamous-cell carcinoma (HNSCC) patients treated with immunotherapy
Ignacio González-García1, Vadryn Pierre2,3, Vincent F S Dubois1, Nassim Morsli4, Stuart Spencer5, Paul G
Baverel1,6, Helen Moore7
1

Clinical Pharmacology Safety Sciences, AstraZeneca, Cambridge, United Kingdom

2

Clinical Pharmacology Safety Sciences, AstraZeneca, Gaithersburg, MD, USA

3

EMD Serono Research & Development Institute, Inc. Billerica, MA, USA (current address)

4

Clinical Development, AstraZeneca, Cambridge, United Kingdom

5

Clinical Statistics, AstraZeneca, Cambridge, United Kingdom

6

Clinical Pharmacology, Roche Pharma Research and Early Development, Roche Innovation Center, Basel,

Switzerland (current address)
7

Applied Mathematics, Applied BioMath, Concord, MA, USA

Objectives
Predicting which cancer patients will respond to immuno-oncology (IO) therapy is of great interest for optimizing
clinical trial design as well as for patients. We developed a novel method based on a tumor dynamics modela
coupled with machine learning to give early predictions of best overall response (BOR) and 6-month overall
survival (OS6) in HNSCC patients treated with durvalumab (an anti-PD-L1 antibody). This work aims to evaluate
the predictive performance of the method.
Methods
A training data set was formed with longitudinal tumor size data (sum of longest diameters, SLD) from four clinical
trials with a total of 401 patients. A test data set with 482 patients from an additional clinical trial was designated forexternal validation. We fit the tumor dynamics model to the full training data set using nonlinear mixed-effects
modeling to obtain patient-specific parameters. These were used with known patient outcomes to train aclassification algorithm to predict BOR and OS6 categories. We finalized this classification model using five-foldcross-validation on the training data to select any informative covariates. We tested the method’s predictive
performance using test data truncated to 12, 18, and 24 weeks. The tumor dynamics model was fit to these truncateddata, the resulting individual parameters were input to the classification model, and the resulting predictions werecompared to the known eventual outcomes for each patient.
Results
During external validation, classification error rates were 9.75%, 7.05%, and 6.64% for BOR and 20.5%, 18.3%,
and 18.0% for OS6, for data truncated at 12, 18, and 24 weeks, respectively. The area under the receiver operating
characteristic curves was preserved for BOR (0.94, 0.97, and 0.97) and for OS6 (0.82, 0.84, and 0.85) at 12, 18, and24 weeks, respectively. These results suggest that the algorithm predicts response accurately based on data ≤12weeks.

Conclusions
We developed and evaluated a method that predicts individual cancer patient response and survival based on earlytumor size data and baseline characteristics. This method showed promising results using only twelve weeks oftumor size data for patients with HNSCC who received durvalumab. Further investigation for other cancers, and fortreatment with other IO therapies and non-IO therapies, will help determine the usefulness of this method in the
broader clinical setting.
The results in this abstract were previously accepted for an oral presentation at the Annual Meeting of the American
Society for Clinical Pharmacology and Therapeutics (ASCPT) planned for March 17-21, 2020 in Houston, TX, andpublished in the conference proceedings as abstract OIV-003.
Referencesa

Moore H. A new tumor dynamics mathematical model. Poster presented at: American Conference on
Pharmacometrics; Oct 23-26, 2016; Bellevue, WA.

Figure 1 Classification of best overall response (BOR, left) and 6-month overall survival (OS6, right) categories
using parameters k_d and k_g from the tumor dynamics model. The curves define the category predictions, and thecolors represent the actual recorded patient response category.

TUE-030

Development of a QSP model for ADAMTS13 andvon Willebrand factor interactions
Hoa Q. Nguyen 1 , Steven Yu 2 , Zhiwei Zhang 3 , Indranil Bhattacharya 1 , Majid Vakilynejad 1 , Andy Zhu 1
1 Takeda, Cambridge, MA, US; 2 LH Group, China; 3 RES Group Inc., Needham, MA, US

Objectives: Von Willebrand factor (VWF) is an adhesive and multimeric glycoprotein that plays anessential for maintaining hemostatic balance. VWF promotes platelet aggregation and clot formation atsite of endothelial injury. In order to prevent unnecessary clotting, VWF is negatively regulated by acatalytic enzyme ‘A Disintegrin and Mettaloproteinase with Thrombospondin Type 1 Motifs 13’(ADAMTS13). In this study, we developed a quantitative system pharmacology (QSP) model toinvestigate the complex dynamics of VWF and ADAMTS13 under pathophysiological condition ofcongenital thrombotic thrombocytopenic purpura (cTTP).

Methods: A biological process map was constructed to represent molecular interplay of criticalcomponents in ADAMTS13-VWF interactions, in which ADAMTS13 cleaves ultra-large VWF to smaller
VWF fragments under the inhibition by extracellular hemoglobin (Hb) and thrombospondin-1 (TSP-1).
Basal levels and kinetic parameters of VWF, ADAMTS13, Hb, and TSP-1 were extracted from theliterature using in vitro and in vivo studies for the model parameterization. Model performance was
evaluated using clinical data from first-in-human (FIH) study of recombinant ADAMTS13 (rADAMTS13) in(cTTP) patientsa.

Results: The molecular kinetics of ADAMTS13, VWF and Hb were established by comparing model
simulations with basal levels in healthy and TTP patients without drug intervention. With rADAMTS13
treatment, the model was able to describe and recapitulate observed VWF fragment, total VWF andactive VWF levels in cTTP patients.

Conclusions: A QSP model was built for ADAMTS13 and VWF interaction leveraging both biological
data in literature and clinical observations from cTTP patientsa, providing an amenable framework to
adapt and apply for other indications where rADAMTS13 could potentially influence the disease
pathophysiology.

Referencesa

Scully, Marie, et al. "Recombinant ADAMTS-13: first-in-human pharmacokinetics and safety incongenital thrombotic thrombocytopenic purpura." Blood, The Journal of the American Society of
Hematology 130.19 (2017): 2055-2063.

TUE-032

Mathematical Modeling of the Interactions between Radiation-Generated Reactive Oxidative
Species and Thrombopoietic System in Animal Models

Ibrahim Mustafa1, Allison Gibbs2, Isabel Jackson2, Joga Gobburu1, Mathangi Gopalakrishnan1
1Center for Translational Medicine, School of Pharmacy, University of Maryland, Baltimore, USA

2

The Department of Radiation Oncology, School of Medicine, University of Maryland, Baltimore, USA

Objectives: To build a systems biology model to investigate the effects of acute ionizing radiation (IR) doses onthrombopoietic system. The model aims to provide a mechanistic understanding of how IR generating reactive
oxidative species (ROS) interacts and mediates damage to platelet counts and to predict the dynamic behavior of thethrombocytes in different animal models of radiation. The interaction between ROS and damage to the thrombopoietic
system is a complex dynamic process that warrants a systems biological approach.
Methods: The systems biology model was built to elucidate key damage and dynamics of thrombopoiesis
accompanied by ROS in response to acute IR. The thrombopoietic system was composed of three compartments: twowere assumed to be radio-sensitive namely progenitors (proliferative cells) and megakaryocytes (non- proliferative
cells) in bone marrow, while the third compartment was assumed to be radio resistant, i. e., platelets in blood stream.
The ROS elucidating physiological changes was assumed to be radiation dose dependent. The model consideredfeedback effect for controlling the homeostasis in thrombocyte levels. The model was formulated in terms of ordinary
differential equations (ODEs) and all optimization, sensitivity analysis, and simulations were conducted using Pumas
v0.3.1 software. Dynamics of platelets as well as progenitors and megakaryocytes, in the presence of acute IR doses,
were examined and compared to observed platelet count in irradiated mice and rabbits.
Results: The radiation dose is a variable input parameter of the model. It is found that at low radiation doses, theplatelets count (as radio-resistance cells) can be recovered to the normal levels as both radio sensitive cells of bonemarrow namely: progenitors and megakaryocyte can be repaired, and proliferation can occur. However, at highradiation doses, platelets are completely damaged and unable to recover as high ROS levels lead to damaged
progenitors and megakaryocytes. Platelet counts reach nadir around 9000/μL on day 14 and 7200 /μL on day 10 and
did not recover at IR dose of 9.0 and 9.5 Gy in mice and rabbits respectively. The platelet counts in mice recovered
after 20 and 25 days after the irradiation exposure of 6.75 and 8.25 Gy respectively. While rabbits started recovering
after 11 and 17 days after exposure at 6.5 and 7.5 Gy respectively. The IR-ROS model adequately described theobserved platelets counts in two animal models of radiation.
Conclusion: A novel systems biology model was developed to investigate the interaction between ROS-induced byacute IR and the thrombopoietic system. The model adequately explained the influence of ROS on the components ofthe thrombopoietic system and predicted the behavior of the system in the presence of a wide range of IR doses inmice and rabbits successfully.

TUE-033

Title: Computational Systems Pharmacology of Antibody-Drug Conjugates
Authors: Inez Lam1, K. Phin Chooi2, Conor S. Barry2, Balakumar Vijayakrishnan2, Thaïs
Cailleau2, Peter C. Tyrer2, Philip W. Howard2, Rosalin Arends3, Kathryn Ball3, Feilim Mac
Gabhann1
Affiliations:
1
Institute for Computational Medicine and Department of Biomedical Engineering, Johns
Hopkins University
2
Spirogen (a member of the AstraZeneca group), AstraZeneca
3
Clinical Pharmacology and Safety Sciences, AstraZeneca
Objectives: Antibody-Drug Conjugates (ADCs) are engineered immunoconjugate drugs
composed of 3 core components: (1) a monoclonal antibody and (2) one or more cytotoxic small
molecules (known as warheads), attached via (3) a chemical linker. While ADCs have thepotential to assist in the fight against cancer, clinical success has been hindered by a lack ofunderstanding of the mechanisms driving ADC safety and efficacy, and difficulty in optimizationof each subunit individually and within the context of the entire ADC. Here, we apply
computational systems pharmacology approaches to study ADCs with pyrrolobenzodiazepine(PBD) as the cytotoxic drug entity, combining experimental data and mechanistic modeling toconduct simulations of ADCs in different experimental scenarios relevant to drug development.
This work is critical to understanding how ADC design translates to ADC function, enabling thecomparison of novel treatment scenarios and the development of better oncology therapies.
Methods: We have constructed a computational systems pharmacology model with cellular andintracellular mechanisms specific to ADCs with PBD warheads to understand the impact of ADC
characteristics on in vitro efficacy and toxicity. The model is comprised of coupled, deterministic,
nonlinear, ordinary differential equations (ODEs), outputting the concentrations of each molecule
or molecular complex over time. Model parameters are calibrated and validated using in vitro
experimental data provided by AstraZeneca for anti-HER2-PBD ADCs, which have antibodies
targeting the HER2 antigen and carry PBD payloads with differing properties. The model
describes ADC binding, internalization, recycling, intracellular trafficking and degradation,
warhead binding, efflux and influx of warhead.
Results: We conduct sensitivity analyses to identify the most influential parameters in thesystem, and find that sensitivity changes with time after treatment and receptor expression
levels. We also simulate different scenarios to understand the impact of key ADC
characteristics, such as the Drug-to-Antibody Ratio (DAR), warhead potency, and linker design,and connect these to ADC efficacy and toxicity in vitro to investigate how key design
characteristics can be manipulated to alter ADC performance. The computational model canalso track populations of extracellular and intracellular warhead, revealing additional insights
into warhead movement and mechanisms of action at the in vitro scale.
Conclusions: We have developed a computational systems pharmacology model of PBD
ADCs at the in vitro scale. This model includes both cellular and intracellular mechanisms
specific to ADCs with PBD warheads and contains parameters from literature or fit to AZ
experimental data. Using the relevant experimental data, this in vitro model can be expanded toincorporate whole-body pharmacokinetics, to serve as the basis for in vivo and clinical models
of PBD-ADCs. The resulting in models will describe whole-body pharmacology and can befurther developed to create a human clinical model, which can be used to run virtual clinical
trials.

TUE-034

RAS blockade and SARS-CoV-2 interaction through ACE2:
QSP evaluation of pro- and anti-inflammatory pathway activity.

Authors: Ishaan Dave1, Melissa Hallow, Ph.D.1
Affiliations: University of Georgia, Athens, GA, USA

Objectives: The renin-angiotensin system (RAS) consists of two arms, one that promotes inflammation through the
AT1 receptor and another that suppresses inflammation through the mas-receptor. ACE inhibitors (ACEi) and AT1
receptor blockers (ARBs) suppress the pro-inflammatory arm while promoting the anti-inflammatory arm. They
may do this in part by upregulating angiotensin converting enzyme 2 (ACE2), the membrane-bound enzyme bywhich SARS-CoV-2 infects cells. ACE2 levels are also altered with age, diabetes, and hypertension. Further
complicating things, SARS-CoV-2 may downregulate ACE2 after infection, promoting inflammation. Based on thesequalitatively understood directional effects, it has been speculated that that RAS blockers may either increase or
decrease risk of severe outcomes in COVID-19. This has introduced uncertainty in the management of diabetic andhypertensive patients, many who are already on RAS blockers. We aimed to develop a tool to quantify relativechanges in pro- and anti-inflammatory pathways under different treatments (e.g. ACE inhibitor or Angiotensin
Receptor Blocker (ARB)) and disease states (e.g. diabetes, hypertension, COVID-19).
Methods: A QSP model for the RAAS system was extended to account for the pathway involving conversion ofAngiotensin I and II to Angiotensin-(1-7) via NEP and ACE2, respectively, as well as Ang(1-7) binding to the mas
receptor. Literature data were used to inform model parameters, and the model was validated by simulatingAng(1-7) formation in response to labeled AngI infusion. A literature survey was used to define ranges for ACE2
upregulation or down-regulation under different conditions. The ratio between mas-receptor and AT1-receptor
occupancy was used as a measure of relative activation of anti-inflammatory to pro-inflammatory arms, and wassimulated over a range of ACE2 levels, with and without ACE inhibition.

Results: ACE2 activity is reported to be downregulated 65-75% with SARS-CoV-2 infection and with older age. As
illustrated in Figure 1, simulations suggest that this degree of downregulation will suppress the mas-AT1 ratio 3035%. In contrast, ACE inhibition alone, with normal ACE2 expression, increase this ratio 8-fold. ACE inhibition isreported to increase ACE2 activity 1 to 5 times normal. This would further increase the mas-AT1 ratio 1- to 3-fold.

Conclusions: These results indicate that the magnitude of the shift toward the anti-inflammatory RAS arm with
ACEi is large relative to the pro-inflammatory shifts due to ACE2 downregulation with age or SARS-CoV-2 infection.
Increased mas and decreased AT1 binding have been shown to be protective in animal models of acute respiratory
distress syndrome. Future work will evaluate effects of parameter uncertainty, and will analyze differences
between ACEi and ARBs, as well as combinations of RAS therapy, comorbidities, and timing of infection.

Figure 1. Effect of changes in ACE2 activity on the mas-receptor / AT1-receptor occupancy ratio.

TUE-035

Maribavir Dose Considerations for Treatment of Cytomegalovirus Infections During Co-administration of
CYP3A4 Inducers and Inhibitors, Based on Physiologically Based Pharmacokinetic (PBPK) Modeling

Manoranjenni Chetty1*, Sibylle Neuhoff1, Zoe Barter1, Karen Yeo1, Kefeng Sun2, Haojing Rong3, Ivy H Song4
1

Certara UK Ltd, Sheffield, UK; 2Shire, a Takeda company, Cambridge, MA, USA; 3Kymera Therapeutics,

Cambridge, MA, USA; 4Shire, a Takeda company, Lexington, MA, USA*Affiliation at time of research

Objectives: Maribavir is an oral antiviral drug in Phase 3 evaluation (NCT02927067, NCT02931539) for thetreatment of transplant recipients with cytomegalovirus infections. Maribavir is primarily metabolized by the isozyme
3A4 of cytochrome P450 (CYP3A4) and has demonstrated linear pharmacokinetics (PK) at doses up to 1,200 mg
twice daily (BID) [1]. The aims of this study were to develop a physiologically based pharmacokinetic (PBPK) model,
predict the impact of co-administration of CYP3A4 modulators on maribavir PK, and to provide guidance on dose
adjustments for maribavir.

Methods: A combination of in vitro and clinical PK data obtained from single oral doses of maribavir 400 mg wasused to develop the PBPK model, using the Simcyp® Simulator (Certara UK Ltd, Simcyp Division, Sheffield, UK;
Version 17 Release 1). Model verifications were performed against observed PK data following administration of
single oral doses of maribavir 800 mg and 1,600 mg, following a single oral dose of maribavir 400 mg with andwithout ketoconazole (400-mg single dose), and multiple oral 400-mg doses of maribavir with and without rifampicin(600 mg once daily [QD]). The model was then applied prospectively to predict interactions with other CYP3A4
inducers and inhibitors. Simulations were used to predict doses of maribavir necessary to maintain mean area under
the curve (AUC), maximum concentration (Cmax), and concentration 12 hours after dosing (C12h) in the desired range
when maribavir was co-administered with CYP3A4 modulators.

Results: Predicted likely outcomes of interaction with rifampicin 600 mg QD, phenobarbital 100 mg QD, phenytoin
300 mg QD, carbamazepine 200–400 mg QD, and efavirenz 600 mg QD in a healthy population indicated mean
maribavir Cmax ratios, with and without an inducer, of 0.56, 0.73, 0.69, 0.77, and 0.75, respectively. Corresponding
predicted maribavir AUC0–12h ratios, with and without an inducer, were 0.37, 0.61, 0.58, 0.71, and 0.58, respectively.
The predicted maribavir C12h ratios, with and without an inducer, were 0.10, 0.37, 0.36, 0.54, and 0.27, respectively.
CYP3A4 inhibitors such as ketoconazole, ritonavir, erythromycin, and diltiazem can increase maribavir exposure;
however, the estimated magnitude of change in maribavir exposure is less than two-fold.

Conclusions: Based on the known exposure–response relationship for efficacy and safety, maribavir can be dosedwith CYP3A4 inhibitors without dose adjustment. CYP3A4 inducers reduce maribavir exposure to a relevant extent;
therefore, to ensure antiviral efficacy (correlated to C12h), maribavir dose increase is necessary from 800 mg BID up
to 1,600 mg BID for various inducers. These data suggest co-administration of maribavir with rifampicin should be
avoided; potentially, alternative antibacterial therapies should be considered.

Funding:
This study was funded by Shire, a Takeda company.

References:[1] Lalezari JP, et al. Antimicrob Agents Chemother. 2002;46:2969–2976.

TUE-036
Characterization of Immunogenicity to a Monoclonal Antibody using a Mixed Hidden Markov ModelingApproach
Donald Irby1, Camille Vong2, Jatin Narula3, Timothy Nicholas4, Kevin Sweeney4, Ellen Wang5, Jae Eun Ahn3
1

Division of Pharmaceutics and Pharmacology, College of Pharmacy, The Ohio State University, Columbus, OH

2

Novartis Pharma AG, Basel, Switzerland

3

Pfizer Inc, Cambridge, MA

4

Pfizer Inc, Groton, CT

5

Pfizer Inc, New York, NY

Objectives: Anti-drug antibody (ADA) titers are often detected using semi-quantitative immunoassays.
Depending on the sensitivity of the assay, it may potentially detect low positive titers with no apparent clinicalimpact on PK, efficacy or safety. We aimed to characterize the ADA status of a subject using a previously describedprobabilistic framework1, 2, a two-state mixed hidden Markov model (MHMM) with data from a monoclonal
antibody (mAb) clinical program.
Methods: Two sources of data - ADA titers and PK residuals derived from a PK model for the mAb that did notaccount for the impact of ADA - were used in tandem to inform the hidden state parameter estimation. Differential
emission probabilities between states of ADA production (SADA) and no ADA production (SnoADA) were estimated. A
bivariate normal probability density function (PDF) was used for the two data types and their correlation wascharacterized. However, when time-matched records were not available, the emission probabilities were calculated
from the univariate normal PDF for whichever variable was present. The SAEM procedure in NONMEM 7.4.3 wasused for the parameter estimation, and the individual, hidden-state sequences were determined through the Viterbi
algorithm that was available as a post hoc subroutine in NONMEM3.
Results: A two-state, bivariate MHMM was developed, capable of producing individual ADA state sequence
predictions over time. The parameter estimates from the base MHMM were aligned with expected values (see Table1). The model predictions of the ADA incidence and the median time to first ADA positive record were also in
relative agreement with the observed values.
Conclusions: A two-state, bivariate MHMM was implemented on immunogenicity data from a mAb clinicalprogram, in which additional observations (PK residuals other than ADA titers only) were used to improve ADA
identification. The final parameter estimates of the MHMM were aligned with prior expectations based on
knowledge of the system, and the model performed reasonably well at recapitulating the observed ADA incidence
and the median time to first ADA positive record. Though still to be determined, this approach may help to interpret
ADA assay results and to identify possible covariate effects to inform clinical development strategies.

Table 1. Fixed and random effects parameter estimates of the MHMM.
Parameter

Description

Expectation

Estimate (RSE%)

IIV (RSE%)

ADAMES in SADA

Mode of the ADA distribution in the
two ADA states> 6.238.47 (1)0.0373 (7)3.12

3.12 FIX

0 FIX

Mode of the PK residuals distribution
in the two ADA states<0-0.744 (3)0.001 FIX†

~00.05 (15)0.001 FIX†

Correlation term of the bivariate
normal distribution in the two ADA
states<0-0.0262 (79)0.001 FIX†

0

0 FIX

0 FIX

ADAMES in SnoADA
PKRES in SADA
PKRES in SnoADA
ρ in SADAρ in SnoADA
φInit in SADA

Initialization probability for the ADA
producing state

0

0 FIX

0 FIX

πADA_noADA

Transition probability from the ADA
to non ADA producing states~ 0%-4.3* (1)0.0259 (65)

πnoADA_ADA

Transition probability from the non
ADA to ADA producing states~ 36%-1.37** (3)0.0928 (38)NA

2.12 (4)

0 FIX

~0

1 FIX

0 FIX

NA

0.917 (1)

0 FIX

0

0 FIX

0 FIX

1

1 FIX

0 FIX

~0

0 FIX

0 FIX

σ2ADA in SADA
σ2ADA in SnoADA
σ2RES
πnoADA_ADA non-sero
πADA_noADA non-sero
PKRES in SADA
non-sero

Variance of the ADA distribution in
the two ADA states
Variance of the PK residuals
distribution, shared between ADA
statesTransition probabilities between the
two ADA states for nonseroconverters
Mode of the PK residuals distribution
in the ADA producing state for nonseroconverters

RSE, relative standard error; IIV, inter-individual variability; PK, pharmacokinetic* transformed value ~ 1%** transformed value ~ 20%† omega was fixed to a small number to allow the SAEM algorithm to efficiently estimate parameters
References1.

Brekkan A, Jonsson S, Karlsson MO, Plan EL. Handling underlying discrete variables with bivariate mixed
hidden Markov models in NONMEM. Journal of pharmacokinetics and pharmacodynamics 46 591-604.
(2019)

2.

Brekkan AL, B,; Lledo-Garcia, R; Jonsson, S; Karlsson, M. O.; Plan, E.L. Characterization of Anti-Drug
Antibodies Using a Bivariate Mixed Hidden-Markov Model. PAGE 27; 2018.

3.

ICON. hmm.f90. [file] 2019 [cited]Available from: https://nonmem.iconplc.com/nonmem/hmm

TUE-037

Statistical-mechanistic modeling of a myocyte population of models to study effects of cardiac
inotropes via mapping pre-clinical data to parameters of myofilament contraction

Jaimit Parikh1*, Xenia Butova2, Tatiana Myachina2, Anastasia Khokhlova2, Tim Rumbell1, Sushmita Allam1, James
Kozloski1, Viatcheslav Gurev1
1

IBM T.J. Watson Research Center., Yorktown Heights, NY, USA

2

Ural Federal University, Yekaterinburg, Russia

Objectives: Biophysical models are increasingly explored to support clinical decision making, guide therapeutic
design, and provide early predictions of intervention outcomes and risks. However, application of these models
requires estimation of model parameter uncertainty and calibration to available pre-clinical and clinicalobservations, which are still open problems. The objective of our study is to a design robust method for constructionof a population of models of cardiac myocytes that characterize variability of the pre-clinical and clinical data andidentify differences in intrinsic contractile properties affected by drugs.
Methods: We employed a novel approach that integrates mechanistic models and machine learning methods toconstruct the population of cardiomyocyte models that captures variability in the pre-clinical data and thus provides
a better understanding of the effects of Omecamtiv Mecarbil (OM), a cardiac inotrope currently in phase III ofclinical trials. Resting myocyte length, time to peak contraction and amplitude of shortening features were extracted
from the experimental unloaded shortening data traces measured in the presence and absence of OM, and the
populations of models were constructed to match the joint density of these features. The core methodology used forparameter inference is based on the probability density mapping formulation proposed by Poole and Raftery (Poole
D, Raftery AE, J. Am. Stat. Assoc, 2000). Model parameters are sampled from probability densities represented by
Gaussian mixture models using an adaptive Markov chain Monte Carlo method and the surrogate AI model of thesarcomere.

Results: Using our approach, we matched the density of the unloaded shortening data features and generated
corresponding coherent distribution of model parameters linked to contractile machinery of myocytes (Figure 1).
The alterations in model parameters identified via calibration of the population of models by the unloaded
shortening data allowed us to evaluate changes in calcium sensitivity and cooperativity induced by OM. The
distribution of model parameters obtained not only captured the variability in the data but also highlighted aplausible mechanism of action of the drug, the cooperative activation of the thin filament.
Conclusions: We present a novel method for construction of a population of models coherent to the distribution ofobserved pre-clinical and/or clinical data. We demonstrate the application of the approach for improved
understanding of OM mechanism of action from simple in vitro experimental measures. The developed
methodology is highly suitable for enhanced analytics of multimodality data gathered across various phases of the
drug-development pipeline. Another application of the proposed approach for the identification of candidate
therapeutic regulation targets in Huntington’s disease phenotypes is covered in detail in related reports of thismethod.

Figure 1: Estimation of mechanistic model parameters coherent to experimental unloaded shortening data underaction of OM. (Black traces- Control condition, Green traces- OM)

TUE-038
Application of Physiologically-Based Pharmacokinetic Modeling to Evaluate the Potential Effect ofOxacarbazepine on Phenytoin Exposure During Concomitant Anti-Epileptic Drug Therapy in Adults

Jaydeep Sinha1, Fernando Carreño1, Andrea N. Edginton2, Daniel Gonzalez1
1
2

UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC, United States
School of Pharmacy, University of Waterloo, Waterloo, Ontario, Canada

Objectives: Oxcarbazepine (OXZ) and its pharmacologically active 10-monohydroxy derivative (MHD) are known
to inhibit cytochrome P450 (CYP) 2C19 in vitro, which in addition to CYP2C9, metabolizes phenytoin (PHT).
However, whether OXZ can lead to a clinically relevant drug-drug interaction (DDI) during adjunctive anti-epileptic
therapy with PHT has remained inconclusive, primarily because of contradictory reports [1]. Therefore, the aim of
this work was to systematically assess the clinical DDI potential of OXZ while concomitantly administered with PHT
using a physiologically-based pharmacokinetic (PBPK) modeling framework.
Methods: PBPK models of OXZ (including MHD) and PHT were developed for adults using the software PK-Sim®
(v. 8.0). The literature derived initial values of specific clearance (𝐾!"# ) and lipophilicity (LogP) were first optimized
using concentration vs. time (C-T) data and urinary excretion data following intravenous administration [2-3]. Next,
the PK-Sim® calculated specific intestinal permeability values were optimized using observed C-T data following oral
administration, where empirical Weibull models were applied for oral absorption. Predicted C-T profiles and therespective area under the curve (AUC) values were compared with the observed data. The reversible CYP2C19
inhibition constants of OXZ and MHD (𝐾$ ~15 𝜇𝑀) were used to develop the DDI model [4]. The ratio of AUC(AUCR) of PHT between the OXZ treated group (i.e., 1200 mg twice daily) and the control groups (300 mg and 600
mg single dose) were predicted for CYP2C9 wild type (WT: *1/*1) subjects and poor metabolizers (PM: *2/*2, *2/*3).
Results: The predicted C-T profiles captured the observed data well, and the predicted AUC values were mostly
within 1.2-fold of the observed values. For the PHT model, 83% of the dose was metabolized by CYP2C9 andCYP2C19, and their relative contributions were 𝑓%,'() = 95% and 𝑓%,'(*) = 5%, respectively. In addition, a 4-foldincrease in PHT dose from 300 to 1200 mg resulted in a 6.7-fold increase in AUC. Overall, these PHT model
predictions were in agreement with the literature information. The predicted AUCR values at the highest PHT dose
(i.e., 600 mg) were 1.04 and 1.16 for the CYP2C9 WT and PM groups, respectively.
Conclusions: The PBPK modeling results suggest that OXZ (at a maximum daily dose) is not likely to cause aclinically relevant elevation of PHT exposure that would warrant dose adjustment in both CYP2C9 WT (lower 𝑓%,'(*) )and PM (higher 𝑓%,'(*) ) groups. Therefore, the reported elevation of PHT exposure in a few previous studies might
have been caused by other concomitantly administered drugs and/or patient related factors (e.g., pathology), which
necessitates further investigation.
References:
1. Soskin et al., Psychosomatics, 51(6), 2010
2. Flesch et al., Drug Metabolism and Disposition, 39(6), 2011
3. Glazko et al., Clinical Pharmacology & Therapeutics, 10(4), 1969
4. Lakehal et al., Epilepsy Research, 52, 2002

TUE-039
Exceeding the Gastrointestinal Solubility of Abrocitinib:
An Example for Parallel First- and Zero-Order Absorption
Authors: Jessica Wojciechowski,1 Bimal K Malhotra,2 Xiaoxing Wang,1 Luke Fostvedt,3 Hernan Valdez,2 Timothy
Nicholas1
Institutions: 1Pfizer Inc., Groton, CT, USA; 2Pfizer Inc., New York, NY, USA; 3Pfizer Inc., Cambridge, MA, USA

Objectives: Abrocitinib is a selective Janus kinase 1 enzyme inhibitor being developed for the treatment ofmoderate-to-severe atopic dermatitis (AD). It is an orally bioavailable, Biopharmaceutics Classification System(BCS) Class II drug with apparent dissolution rate limited absorption after oral administration. Results of densely
sampled, healthy volunteer (HV) studies with abrocitinib suggested high intra- and interindividual variability in the
maximum concentration (Cmax), lack of proportionality at high doses, and accumulation somewhat higher than thatpredicted from superposition. The objective of this analysis was to develop a population pharmacokinetic (PK)model of abrocitinib in HVs, patients with psoriasis, and patients with AD.

Methods: The analysis data set was pooled from 7 phase 1 studies, 2 phase 2 studies, and 2 phase 3 studies. Doses
ranged from 3 mg to 800 mg administered as 4 formulations (suspension and 3 tablets) during clinical development.
A nonlinear mixed-effects model was developed using NONMEM. Simulations of dose proportionality and steadystate accumulation of Cmax and AUC were conducted using the final model, with reference to a 200-mg once-daily
regimen (highest dose in phase 3 program).

Results: A 2-compartment model with parallel first- and zero-order absorption, time-dependent bioavailability, anddose- and time-dependent clearance (CL) was developed. The parallel absorption model accounted for dissolution
rate limited absorption by implementing a parameter—AK1—that estimated the maximum amount absorbed by firstorder processes (the remainder of which is absorbed by zero-order). The estimate for AK1 was 121 mg, such that for
a 200-mg phase 3 tablet dose (highest effective dose assessed in phase 3 studies), 60.5% was absorbed rapidly via
first-order processes (ka; 4.01 hour−1 and the remaining 39.5% was absorbed by zero-order processes (k0; 75.3mg/h). First- and zero-order absorption processes were predominant at lower (3-100 mg; assumed solubilized state)and higher doses (400-800 mg; assumed supersaturated or precipitated states), respectively. AK1 was dependent ondifferent formulations (suspension increased AK1 by 117%) and the patient’s having eaten a high-fat meal (entirely
zero-order). Abrocitinib exposure increased in proportion with dose over the range of 3-400 mg for Cmax, and 100800 mg for AUC at steady state.

Conclusions: A semimechanistic approach to handling absorption was applied to describing the PK of abrocitinib.
Conditions altering the gastrointestinal pH (increased pH with high-fat meal) or particle size (formulations) orexceeding its intrinsic gastrointestinal solubility (doses sufficient for saturation or precipitation) significantly altered
the rate of abrocitinib absorption. Understanding dose- and time-dependent absorption was imperative before
evaluation of CL and impact of intrinsic patient factors on abrocitinib PK.

Figure 1. Schematic of Parallel First- and Zero-Order Absorption ModelTUE-040

Population pharmacokinetics modeling of Brincidofovir and the active metabolite cidofovir
diphosphate in healthy volunteers
Jie (Janet) Zhao1, Camila de Almeida1, Nathan Teuscher1, Tim Tippin2, John Dunn2, Tom Brundage2, Marion
Morrison2, Odin Naderer2, Virna Schuck1
1
2

Certara USA, Inc., Princeton, NJ, USA
Chimerix, Inc

Objectives: Brincidofovir (BCV) is an orally available lipid conjugate of cidofovir (CDV), which is active against
orthopoxviruses in vitro and is under development for the treatment of smallpox infections, one of the deadliest
infectious diseases in human history and a national security threat. Inside peripheral blood mononuclear cells(PBMCs), BCV is hydrolyzed to CDV and then converted via anabolic kinases to the active metabolite cidofovir
diphosphate (CDV-PP). Our aim was to develop an integrated parent-metabolite population pharmacokinetic (PK)model to describe the plasma BCV and intracellular (PBMC) CDV-PP concentration versus time profiles in healthy
subjects after oral and intravenous (IV) administration, and to evaluate the effect of intrinsic and extrinsic factors on
BCV PK. This model will contribute to the understanding of critical covariates and provide bases for BCV dose
selection in the smallpox indication.
Methods: This population PK model utilized data from 7 Phase I studies, with BCV doses of 100 mg and 200 mg
after oral administration and 10 to 50 mg after IV infusion. Plasma BCV and intracellular (PBMC) CDV-PP
concentration data were obtained from 224 healthy subjects. A sequential modeling approach from BCV to themetabolite CDV-PP was developed with NONMEM VII (v7.3). A broad range of covariates including weight, age,sex, and food status were selected for evaluation based on scientific and clinical relevance. Precision of estimationand the predictive performance of the model were assessed by visual predictive checks. Some data used in thismodeling was funded under BARDA contract HHSO100201100013C.
Results: Plasma BCV PK profiles were adequately described by a linear two-compartment model with a sequential
absorption process: zero-order appearance in depot compartment followed by a first order absorption into centralcompartment. Weight, formulation (tablet and suspension), sex, food, and route of administration were identified as
significant covariates. Bioavailability was estimated to be lower for tablets and fed state, and females were estimatedto have lower clearance than males. Distribution appears different for IV and oral administration, with Vp being 27%
lower after IV administration. The transit model (3 transits) was used to link and account for the delay between plasma
BCV to intracellular (PBMC) CDV-PP PK. The PK profiles of CDV-PP were described by a linear one-compartment
model, with sex as an important covariate on CDV-PP clearance.
Conclusions: The population PK model jointly described the plasma BCV and intracellular (PBMC) CDV-PP profiles
in healthy subjects after oral and IV administration. The developed model was applied to simulate BCV and CDV-PP
exposures at different clinical dosing scenarios, providing bases for BCV dose selection in smallpox patients.

TUE-041

Assessment of Cytochrome P450 3A4-Mediated Drug–Drug Interaction for Ipatasertib Using Physiologically
Based Pharmacokinetic (PBPK) Model
Jing Jing1, Rucha Sane1, Yuan Chen1, Kenta Yoshida1
1

Genentech, Inc., South San Francisco, CA, USA

Objectives: Ipatasertib is a potent and highly selective small-molecule inhibitor of AKT and it is primarily
metabolized by CYP3A4 in humans. The drug-drug interaction (DDI) risks for ipatasertib, as the victim or perpetrator,
have not been assessed at the proposed clinical dose of 400 mg. The aim of the study is to develop a PBPK model for
ipatasertib to evaluate the effect of CYP3A4 inhibitors (strong, moderate and weak) and inducers (strong, moderate)
on ipatasertib PK and the impact of ipatasertib on midazolam PK at the proposed clinical dose.
Methods: A PBPK model was developed for ipatasertib using in silico, in vitro, and clinical data in Simcyp. The
model was optimized and verified using DDI data [itraconazole-ipatasertib (100 mg single dose) and midazolamipatasertib (600 mg multiple dose)] and clinical PK data of ipatasertib which exhibited nonlinear PK at subclinicaldoses. The verified PBPK model was then used to predict the effect of CYP3A4 inhibitors and inducers on the PK
profile of ipatasertib and the impact of ipatasertib (400 mg) on midazolam exposures.
Results: The developed PBPK model reasonably captured observed DDIs and nonlinearity in ipatasertib PK. As thevictim of DDI, simulations showed that strong CYP3A4 inhibitors could increase ipatasertib AUC and Cmax by 3.3and 2.0-fold respectively and moderate inhibitors of CYP3A could increase ipatasertib AUC by ~ 2−2.5 fold and Cmax
by ~1.5~1.7-fold. Simulated exposures of ipatasertib at a reduced dose of 200 mg administered concurrently withmoderate CYP3A4 inhibitors were similar to the observed exposures of ipatasertib at the 400 mg dose alone. Basedon PBPK model simulation, strong and moderate inducers of CYP3A4 could decrease ipatasertib AUC by 86% and
74% and Cmax by 68% and 51%, respectively. As a perpetrator of DDI, the simulation showed a mild inhibitory effectof ipatasertib on midazolam (predicted AUC and Cmax ratios of 1.69 and 1.49) at the steady-state of the clinical doseof 400mg.
Conclusions: This study demonstrates the value of using PBPK model to assess the clinical DDI risks for ipatasertib
and to provide dosing strategy for the concurrent use of other CYP3A4 perpetrators or victims.

TUE-042

Pharmacokinetically-Guided Dosing of Oral Sorafenib in Pediatric Hepatocellular Carcinoma:
A Simulation StudyJohn C Panetta1, Olivia Campagne1, Jessica Gartrell2, Wayne Furman2, Clinton Stewart1
1
Department of Pharmaceutical Sciences, St. Jude Children’s Research Hospital, Memphis, TN; 2Department of
Oncology, St. Jude Children’s Research Hospital, Memphis, TN
Objectives: Pediatric hepatocellular carcinoma (pHCC), a rare malignancy accounting for 0.5% of all pediatricmalignancies, is treated with sorafenib, which improves outcomes in adult HCC. We have previously treated pediatricrefractory or recurrent solid tumors, including HCC, with a combination of sorafenib, bevacizumab, andcyclophosphamide and propose a prospective protocol in rare solid malignancies including HCC to evaluate theaddition of atezolizumab to this regimen. Two objectives of this protocol are to, a) test the ability of targeting sorafenib
exposure using pharmacokinetically-guided dosing and b) test the ability of pharmacokinetically-guided dosing to
reduce the rate of hand foot skin reaction (HFSR), a dose limiting toxicity of sorafenib. Due to the rare nature of HCC,
simulations were used to evaluate these objectives.
Methods: All pharmacokinetic simulations were based on the sorafenib pharmacokinetic and HFSR data from the St.
Jude ANGIO1 study (n=35; Inaba, H., Clin Cancer Res, 25:2019). Population pharmacokinetics (Monolix 5.1.0) alongwith individual post-hoc estimates (sampled from the conditional distribution) were used to simulate AUC0->12hr andCmin at steady-state given BID dosing of oral sorafenib at either a fixed dose (90 mg/m2/dose) or a pharmacokineticallyguided dose (adjusted to target a steady-state AUC0->12hr between 20 and 55 hr·µg/mL). The pharmacokineticallyguided dose was determined by adjusting the dose on day 4, based on day 1 pharmacokinetics, to obtain an exposure
by day 7 within the target range. The probability of HFSR toxicity as a function of sorafenib exposure was based ona Cox proportional hazard model. The percentage of simulated studies within the target range was assessed and the
95% confidence intervals were determined using bootstraps (n=1000). The power to detect the probability of successof the study objectives given a fixed number of patients (n=6 to 24) was assessed using study re-sampling withreplacement (n=1000).
Results: Simulations showed that 67% (95% CI: 62%-72%) of the pharmacokinetically-guided studies vs 50% (95%
CI: 45%-55%) of the fixed dose studies could be expected in the target range. The power to observe at least 4 of 6
patients in the target range was 72% vs 33% using pharmacokinetically-guided vs fixed dosing. Next, we compared
the probability of observing a grade 2/3 HFSR given either pharmacokinetically-guided or fixed dosing. Thesesimulations showed an expected grade 2/3 HFSR toxicity rate of 17% (95% CI: 15%-20%) given pharmacokineticallyguided dosing vs 22% (95% CI: 21%-26%) given fixed dosing. The power to observe less than 6 of 24 studies with agrade 2/3 HFSR toxicity was 80% vs 52% using pharmacokinetically-guided dosing vs fixed dosing.
Conclusions: Our simulation studies have helped guide a study design for our proposed pHCC protocol where
pharmacokinetically-guided dosing will both increase the percentage of studies in the target range and potentiallyreduce HFSR rates.

TUE-043(Found in) Translation – Cotton Rat Modelling and Validation with
Model Based Meta-Analysis (MBMA) for RSV
Jos Lommerse1, Michelle Green1, Amy Espeseth2, Kalpit A. Vora2, Antonios Aliprantis2, Lynn Finelli2, Radha Railka2,
Nele Mueller-Plock1, Brian M. Maas2, Luzelena Caro2, Li Qin1, Gerly van der Vleuten1, Marianne van Vugt1, Anna
Largajolli1, S. Y. Amy Cheung1, Jeffrey R. Sachs2
1

Certara Strategic Consulting, Princeton, NJ, USA. 2Merck Research Laboratories, Merck & Co., Inc., Kenilworth,

NJ, USA
Objectives: The cotton rat has enabled successful prediction of (passive) immunogenicity, safety, and efficacy in theclinic [1], and is therefore the preferred animal model for the development of novel vaccines and monoclonal
antibodies (mAb) for prophylaxis against respiratory syncytial virus (RSV) infections. This motivates development ofa translational model framework to demonstrate the applicability of cotton rat pharmacokinetic targets and biomarker
readouts to the clinical setting for other mAbs.
Methods: We focused on mAbs that target the pre-fusion antigenic sites [2]. Protocols for testing RSV mAbs (andvaccines) in cotton rat studies have become relatively standard [3]. Blood samples are taken shortly after passive
immunization to assess pharmacokinetics (mAb concentration). Efficacy data are obtained 4-6 days after inoculation(challenge infection) with RSV, which is the time of maximal viral replication (e.g. lung viral loads (VL)). Such nonclinical data were collected internally and from literature [4,5]. Data from RSV clinical trials were extracted fromliterature reports, in particular for pediatric populations.
The non-clinical data were modeled using a sigmoidal relationship between mAb concentration and lung VL:

𝑙𝑜𝑔2𝑉𝐿 = 𝑙𝑜𝑔2𝑉𝐿𝑚𝑎𝑥 ∙ 𝑒!"#(%&'()*+,)∙23445%&'()*+./%&'(6&-6 !

𝑠𝑢𝑝𝑝𝑟 = %&'(6&-6 ! 7%&'(89"#!

Where log2VL=log2(viral load) in cotton rat lungs and log2conc=log2(mAb concentration). Data were modeled using
R.3.6.3 using package nlme. In order to compare with human data, mAb concentrations were transformed into humanserum neutralizing titers (SN) and the lung viral load were scaled to RSV incidence rate in human.
Results: The cotton rat dataset had 179 data points. We obtained a robust relationship between mAb concentrationand lung VL, enabling prediction of efficacious doses or concentrations (Figure, accounting for covariate effects [6])The cotton rat efficacy curve was translated to a pediatric RSV-A SN versus incidence rate curve using an internallydeveloped clinical relationship between mAb concentrations and RSV-A SN. Furthermore, the maximum andminimum cotton rat lung viral loads were scaled to maximum and minimum clinical incidence rates for infections,
respectively. This provided a quantitative means to predict clinical incidence rates based on pre-clinical study
outcomes. The cotton rat curves agreed with previously reported clinical incidence rates at various concentrations.
Conclusions: Our analysis has shown that the cotton rat model can quantitatively predict clinical trial outcomes, byassuming that the RSV-A SN in human is predictive for RSV incidence rate. We aim to expand our non-clinical model
to include mAbs that target other RSV-A pre-fusion sites and to build a translational model to assess RSV F vaccine
candidates.

References:[1] The IMpact-RSV Study Group, Pediatrics. 1998 102(3 Pt 1):531-7 PMID:9738173[2] Graham BS, et al. Curr.Opin.Immunol. 2015 Aug;35:30-8. doi:10.1016/j.coi.2015.04.005.
[3] Boukhvalova, MS, et al. Antivir.Chem.Chemother. 2018;. doi:10.1177/2040206618770518[4] Zhu Q, et al. Sci.Transl.Med. 2017. doi: 10.1126/scitranslmed.aaj1928.
[5] Tang A, et al. 2019 Sep12;10(1):4153. doi:10.1038/s41467-019-12137-1.
[6] Lommerse J, et al. ACoP2018

TUE-045
Integration of Pharmacodynamic Biomarker with Modeling & Simulation for Eculizumab Precision Dosing
in Pediatric Patients with Hematopoietic Stem Cell Transplant Associated-Thrombotic Microangiopathy
Kana Mizuno1, Alexander A Vinks1,4, Christopher E. Dandoy2,4, Ashley Teusink-Cross3, Stella M. Davies2,4, andSonata Jodele2,4
1

Division of Clinical Pharmacology, 2Division of Bone Marrow Transplantation and Immune Deficiency, Cancer andBlood Disease Institute, 3Department of Pharmacy, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH;
4Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH
Objectives: Eculizumab, an anti-complement C5 antibody, is an effective therapeutic for the treatment of transplant
associated-thrombotic microangiopathy (TA-TMA) which is associated with poor survival after hematopoietic stem
cell transplantation (HSCT). We previously developed an eculizumab PK model and demonstrated the significant
benefit of PK/PD-guided dosing for survival improvement [1]. However, the current model only takes into accountelevated complement activation biomarker (sC5b-9) at start of therapy and has no capability to reflect contextual
changes in disease progression and improvement. This study aimed to refine the PK model using an enriched PK/PD
data set collected during the course of treatment by integrating time-varying sC5b-9 as a covariate on clearance. In
addition, optimal dosing strategies were explored using Monte Carlo Simulation (MCS).
Methods: Eculizumab and biomarker of response data (sC5b-9) were collected from 64 pediatric TA-TMA patientswith a median age of 6 years and until at least 100 days after transplant or until TA-TMA resolution [2]. Since diseaseprofiles are different in patients with bleeding complications, those patients (n=26) were excluded from the analysis(to be analyzed in a separate study). Consequently, 38 non-bleeding patients were included in the current population
PK model development using NONMEM 7.4. Potential covariates predictive of eculizumab PK were assessed
including age, sex, body weight, albumin, GFR, dose courses and sC5b-9 levels. PK target attainment (≥100 µg/mL)
for the current weekly dosing regimen was determined using MCS. Pediatric induction dosing regimens to improve
the target attainment was explored by shortening the time interval at a maximum dose of 900 mg.
Results: The mean eculizumab clearance was highest after the first dose (58.5 mL/h/70kg) among the treatment
courses. The clearance decreased over time in parallel with sC5b-9 reduction. The PK change was described byincorporating body weight, time-varying sC5b-9, and courses of dosing in the model as a covariate on clearance. The
MCS analysis showed that poor target attainment is achieved in patients with higher body weight and sC5b-9 levels.
The current weekly induction dosing protocol showed target attainment in only up to 25% of pediatric patients withbody weight ≥30kg, which was increased to 60% for our identified intensifying dosing regimen with dosing interval
ranging from 1-4 days depending on body weight.

Conclusions: We successfully refined the eculizumab PK model that captures the temporal changes in clearance byintegrating sC5b-9 and number of treatment courses. The developed model could be used to improve target attainment
by tailoring individual dosing regimens considering time-varying sC5b-9. This model-based optimized dosing
algorithm will be evaluated in a prospective study and can be incorporated into drug dosing strategies in clinic.
References
1. Jodele S et al. Biol Blood Marrow Transplant. 2016; 22: 307-315.
2. Jodele S et al. Blood. 2020; 135(13):1049-1057.

TUE-046

A Streamlined Approach to a Fully Validated, Part 11 Compliant, Pharmacometric Suite with a
Version Controlled Data Repository and FTP Server for Data Transfer

Authors: Kevin J. McConnell, Ronish Desai, Sachin Sanduja, Stephen A. Greene, Ry R. Forseth, Laurent
Vernillet
Affiliations:
1) Rudraya Corporation – KJM, RD, SS
2) SK Life Science, Inc. - SAG, JKL, RRF, LV

Objectives: Performing Pharmacokinetic (PK) and/or Pharmacodynamic (PD) analyses compliant withthe regulatory expectations of many global health authorities presents several operational and technicalchallenges. Regulatory requirements mandate that all PK/PD analyses that support drug approval
packages are conducted within a validated environment and with proper documentation to support themethodology used to validate said environment. With multiple software programs, data transfer
mechanisms, and servers utilized across the majority of PK/PD analyses, the validation of an analysis
environment requires assiduous planning and testing to meet regulatory expectations. As such, groups thatcarry the remit of validation often utilize systems that are inefficient and rate limiting in the drug
development process. Herein is presented a streamlined approach to construct a fully validated clinicalpharmacology (CP) computing platform (CPCP) to address the shortcomings of many validated
environments.
Methods: The instigated CPCP system is centered around a Linux application server containing a
version-controlled CP data repository (CPDR) called Sonic Projects ®, for storage of all data and analysis
files related to PK/PD analyses. The Linux server also houses commonly used PK/PD modeling software(NONMEM®, R/R studio, and Pirana®). A separate Windows application server was built for othercommonly used PK/PD modeling software, GastroPlus®, Phoenix® WinNonlin/NLME, R/Rstudio, andMicrosoft® Office. The CPDR housed on the Linux server is mounted on the Windows server to provide
the ability to move files from the Windows server to the data repository housed on the Linux server,
maintaining a centralized location for all PK/PD analysis files. Finally, an FTP server is integrated toallow for external vendors to place and obtain data. Access to the FTP server is restricted by IP address
and a username and password. The FTP server also allows for transfer of data between external vendors
and the CPDR via a single application (Sonic Client®) that’s controlled from the Linux server. Thisapproach restricts access to the CPDR from the FTP server to internal users.

Results: The fully validated CPCP system integrates many commonly used PK/PD analysis software
programs, a version-controlled data repository, and a fluid means of data transfer between the validated
environment and external sources. All servers are interconnected with appropriate security measures
between servers that have access from external venders.
Conclusions: The CPCP system provides a streamlined approach to PK/PD analyses from data transfer
with the use of an FTP server to data analysis on a Windows application server and Linux application
server and data management on a version controlled CPDR all while maintaining regulatory compliance(e.g, 21 CFR Part 11). The CPCP system provides a means to perform all necessary PK/PD analyses
required throughout a drug development program without the potential shortcomings of more traditional
systems.
Figure 1: Clinical Pharmacology Computing Platform Environment

TUE-047

A joint modeling workflow for the longitudinal and time-to-event data:
tools, statistical methods and advanced diagnostics
Kirill Zhudenkov1, Sergey Gavrilov1, Alina Sofronova1, Oleg Stepanov1, Kirill Peskov1,2
1

M&S Decisions LLC, Moscow, Russia; 2 Computational Oncology Group, I.M. Sechenov First Moscow State

Medical University, Moscow, Russia.

Objectives: In the last decades the amount of clinical study results constantly grows, though the efficient treatment
for many diseases is still being developed. Study biomarkers can be assessed both at baseline and longitudinal level.
Particular biomarker value achievement can be an endpoint in studies, thus statistical methods of biomarker trend
prediction may be useful. For late-phase studies, the analysis of event-type endpoints plays a crucial role (death,exacerbations, etc.), so study performance prediction and patient stratification, using interim data, is important for
new medicines design. To address all these items, the methods of joint modeling of longitudinal and time-to-event
data can be utilized [1]. The objectives of the current work were (1) to propose the generalized workflow for thedevelopment of joint models and (2) to compare existing tools able to cover it .

Methods: The workflow incorporates different statistical methods, that are controlled in R environment. Dependingon the analysis aims, following tools can be utilized: JM, JMBayes, rstanarm, Monolix, NONMEM, Stan [5,6]. Model
performance is initially assessed by means of statistical criteria (AIC, BIC). The workflow provides comprehensivegraphical diagnostics for the models – observed vs. predicted and residuals plots. Prediction performance can beassessed for both time-to-event data and longitudinal biomarkers using external validation and involving survival
discrimination by means of Receiver Operating Characteristics (ROC) curve and Brier Score. The workflow wastested using the datasets of oncology studies from the Project Data Sphere (NCT00312377, NCT00076388,
NCT00364351, NCT00322452, NCT00988208, NCT00617669).

Results: The developed workflow was successfully applied to various clinical endpoints and longitudinal biomarkers[2-4]: tumor size, blood biomarkers and patient survival. We obtained an adequate description of the data on the
diagnostic plots for all tested models. The long-term (12-24 months) validation performance of the models was tested

using interim (3-6 months) of longitudinal data. Each important longitudinal biomarker in multivariate models
provided 0.05-0.1 gain in area under ROC curve (with a typical range of 0.65-0.85 dependent on the model
complexity). Nonlinear multivariate models provided more efficient patient subgrouping and longitudinal biomarker
trend prediction. Stan tool appeared to have the highest performance and flexibility, while being the most complex in
implementation (Table 1).

Conclusions: Here we presented a workflow, incorporating the analysis of biomarkers and time-to-event data, thatcan be used in clinical study design and assessment. Utilizing the interim longitudinal biomarker data, the long-term
patient cohort survival, patient stratification and biomarker trends can be efficiently predicted.

References:
1. Rizopoulos D. CRC press, 2012, ISBN 9781439872864.
2. Gavrilov S. StanCon 2019 Conference.
3. Gavrilov S. PAGE Meeting 2019.
4. Zhudenkov K. ACoP9 Conference 2018.
5. Brilleman S. 38th ISCB Conference 2017.
6. Riglet F. The AAPS Journal (2020) 22:50.

TUE-048
Modeling of Tolerance to Opioid-induced Respiratory Depression in Chronic High-dose Opioid Users versus
Opioid-naïve Individuals
Algera MH1*, Olofsen E1*, Moss L,3 Dobbins R,2 Niesters M,1 van Velzen M,1 Groeneveld GJ,3 Laffont CM2#,
Dahan A1#* contributed equally; # contributed equally1

Department of Anesthesiology, Leiden University Medical Center, 2333 ZA Leiden, The Netherlands;

2

Indivior Inc., North Chesterfield, VA 23235, USA.

3

Centre for Human Drug Research, 2333 CL Leiden, The Netherlands.

Objectives: Prolonged use of opioids is associated with addiction, physical dependence and development oftolerance. While there is indirect proof for the development of tolerance to respiratory effects of chronic opioid use
in humans1,2, such tolerance has not been formally assessed or quantified. The respiratory effects of fentanyl inchronic opioid users and opioid-naïve subjects were measured in an experimental medicine study3 and population
pharmacokinetic/pharmacodynamic (PK/PD) modeling was conducted to quantify respiratory tolerance.
Methods: Fourteen opioid-naïve individuals (20-35 years) and 8 chronic opioid users (31-52 years, using ≥90 mg
oral morphine equivalents daily) received escalating doses of intravenous fentanyl (opioid-naïve: 75-350 µg/70 kg;
chronic users: 250-700 µg/70 kg). The study was conducted by anesthesiologists in a hospital setting and eachsubject had a facemask fitted to control inspiratory gases and to measure ventilation parameters underisohypercapnic conditions. Serial blood sampling was performed to measure fentanyl plasma concentrations.
Pharmacokinetic and pharmacodynamic data (minute ventilation) were analyzed with NONMEM 7.4 using asequential population PK/PD approach. Stimulated, nonspontaneous breathing data were set to zero (apnea).
Censoring at zero (M3 methodology4) was used to describe prolonged apnea events.
Results: A three-compartment model best described the fentanyl PK data. The time course of the respiratory
depressant effects of fentanyl was characterized using a biophase equilibration model in combination with an
inhibitory Emax model. C50, the effect-site concentration of fentanyl causing 50% ventilatory depression, was 0.42 ±0.07 ng/mL in opioid-naïve subjects and 1.81 ± 0.38 ng/mL in chronic opioid users, indicative of a 4.3-foldsensitivity difference. For similar fentanyl exposure, the predicted probability of apnea in chronic opioid users wasmany fold lower than in the opioid-naïve population

Conclusion: Differences in tolerance between populations were well described by PK/PD modeling. Althoughchronic opioid users showed higher tolerance to the respiratory effects of fentanyl, model predictions indicate thatfatal apneas may still occur at high doses reached during recreational use. The risk for these patients can be further
increased after a drug holiday which may reduce tolerance towards levels observed in opioid-naïve subjects.
References:1

Walsh TD, Rivera NJ, Kaiko R. Oral morphine and respiratory function amongst hospice in patients with advanced

cancer. Support Care Cancer 11, 780-784 (2003).
2

Santiago TV, Pugliese AC, Edelman NH. Control of breathing during methadone addiction. Am. J. Med. 62, 347-

354 (1977).
3

Wiest K, van Velzen M, Algera H, Moss L, Dobbins R (2019) High therapeutic buprenorphine levels reduce IV

fentanyl respiratory depression. Annual Meeting of the American Society of Addiction Medicine, April 4-7,
Orlando, FL.
4

Ahn JE, Karlsson MO, Dunne A, Ludden TM. Likelihood based approaches to handling data below thequantification limit using NONMEM VI. J Pharmacokinet Pharmacodyn. 2008 Aug;35(4):401-21.

TUE-049

Quantitative mechanistic model for amyloid beta and tau protein role in synaptic plasticity
Stepan Lerner, Tatiana Karelina
InSyBio, Moscow, Russia
Objectives:
Current therapies for amyloid beta (Ab) do not modify the progression of the Alzheimer’s disease (AD). Amyloid
hypothesis of AD is still debated. One of the most important property of neuron cells is its synaptic plasticity. Mostof the physiological studies of synaptic plasticity investigate LTP (long-term potentiation) and LTD (long-term
depression). The synaptic plasticity seems to underlie memory and learning formation. In animal models of AD, Ab
inhibits LTP. An increased intracellular calcium level, mediated by NMDA and nACh receptors, is responsible forappearance of LTP, which is determined by increased AMPA receptor phosphorylation. These processes may
contribute to tau hyperphosphorylation, further pathology progression. Understanding of these synaptic processes isparticularly important for better prediction of cognitive outcomes in clinical trials. Quantitative systems
pharmacology (QSP) model of synaptic plasticity was developed to investigate influence of amyloid beta on
receptors governing LTP and state of tau protein.
Methods:
The model describes calcium influx in postsynapse through acetylcholine and glutamate receptors. Type ofstimulation varies by changing the timing between cholinergic input and glutamatergic input. The model describes
activation of kinase/phosphatase cascade, tau phosphorylation and level of AMPA receptors phosphorylation. The
level determines the synaptic plasticity. Ab impairment of synaptic plasticity is modelled through impact on
receptors and glial extracellular glutamate uptake. Potential Ab targeting therapy is represented as Ab washout. At
the time of washout, Ab concentration is set to zero. Model is developed step by step by consecutive addition of new
mechanistic details, using in vitro data and in vivo mouse data. In vitro data from the literature concerning synaptic
plasticity (LTP) under various conditions are used for verification.
Results:
The model qualitatively describes LTP and its decrease in presence of Ab (Figure 1). It demonstrates that: 1)
amyloid effect on LTP in such a system is partially irreversible, and amyloid washout does not lead to complete
recovery of LTP 2) memantine protects from decrease of LTP 3) LTP recovers after exNMDAr blockers action,
because memantine decreases binding glutamate with receptors and, thus, decreases calcineurin activity 4) at small
concentration of amyloid beta (<10nM) with memantine, there is a complete rescue of LTP and decrease of tau
phosphorylation; at medium concentration of Ab (10nM<50nM<100nM), with memantine recover LTP and
decrease tau phosphorylation only after Ab washout; after influence of high Ab concentration (>100nM)
memantine does not provide recovery of LTP even after amyloid washout, but tau phosphorylation may decrease.

Conclusions:
The model demonstrates a partial irreversibility effect of amyloid beta on LTP even on short times, which may
partially explain failure of amyloid targeting and set up hypotheses for combinatorial treatments at different diseasestages. This QSP model may connect models of proteinopathies with cognitive outcome.

TUE-050

An integrated glucose-insulin-cardiorenal QSP model of glucagon-like peptide-1 receptor
agonist actions in the kidney
Lindsay E. Clegg1, Robert C. Penland2, David W. Boulton1, K. Melissa Hallow3
1
Clinical Pharmacology & Quantitative Pharmacology, Clinical Pharmacology & Safety Sciences, R&D,
AstraZeneca, Gaithersburg, USA
2
Clinical Pharmacology & Quantitative Pharmacology, Clinical Pharmacology & Safety Sciences, R&D,
AstraZeneca, Boston, USA
3
School of Chemical, Materials, and Biomedical Engineering, University of Georgia, Athens, Georgia, USA
Objectives:
Glucagon-like peptide-1 receptor agonists (GLP-1 RA) improve glycemic control by slowing gastric emptying,increasing insulin secretion, and decreasing glucagon. GLP-1 RA also promote natriuresis over short time-scales andreduce urinary albumin excretion in clinical studies. However, the effects of GLP-1 RA on long-term renal
outcomes remain unclear, as do the mechanisms through which GLP-1 RA exert their effects in the kidney. We built
a QSP model to explore by which of many posited mechanisms of action GLP-1 RA may drive the observed renal
effects, and what the implications of these mechanisms may be for long-term renal function.
Methods:
We integrated existing QSP and pharmacokinetic models of glucose-insulin dynamics[1], basal insulin
pharmacokinetics[2], GLP-1 RA pharmacokinetics and pharmacodynamics[3, 4], and cardiorenal hemodynamics[5](Figure) to study the effects of GLP-1 RA in acute renal studies performed under both fasting conditions[6] andfollowing a standardized meal test[7, 8] in subjects with type 2 diabetes.
Results:
In the fasting state, the GLP-1 RA exenatide increased sodium excretion and moderately increased mean arterialpressure (MAP) without increasing glomerular filtration rate (GFR)[6]. The QSP model suggests a combination of
decreased blood glucose, decreased proximal tubule sodium reabsorption, afferent arteriolar vasoconstriction, and ashort-term increase in cardiac output are required to simultaneously match the observed changes in sodium
excretion, MAP, and GFR. In the postprandial state, blood glucose increased less with the GLP-1 RA lixisenatide
than without, and a larger increase in MAP was observed than in the fasting state[9]. The model suggests thatslowing of glucose absorption from the gastrointestinal tract and decreased endogenous glucose production accountfor the observed effects on blood glucose, while similar mechanism appear to be acting in the kidney as in thefasting state.

Conclusions:This model allows us to explore which of the hypothesized mechanisms of action of GLP-1 RA may drive theeffects observed in the kidney. With further model development, we aim to provide key insight into thesemechanisms, how they may complement treatment with SGLT2 inhibitors and renin-angiotensin-aldosterone system
inhibitors, and what the implications of GLP-1 RA treatment on long-term renal outcomes in populations withvarying levels of renal function may be.

References:
1. Jauslin P, et al. (2011) JCP. doi:10.1177/0091270010362536.
2. Tham LS, et al. (2017) JCP. doi: 10.1002/jcph.899. PubMed PMID: 28394405.
3. Røge RM, et al. (2015) CPT: PSP. doi: 10.1002/psp4.11.
4. Frank T. (2013) J Pharm Drug Deliv Res. doi: 10.4172/2325-9604.1000112.
5. Hallow KM, et al. (2018) AJP-RP. doi: 10.1152/ajprenal.00202.2018.
6. Tonneijck L, et al. (2016) Diabetologia. doi: 10.1007/s00125-016-3938-z. PubMed PMID: PMC4901099.
7. Tonneijck L, et al. (2018) AJP-RP. doi: 10.1152/ajprenal.00432.2018.
8. Tonneijck L, et al. (2017) Diab. Obes, & Met. doi: doi:10.1111/dom.12985.

TUE-051

Zero-Inflated Beta Regression Exposure-Response Modeling of the Eczema Area andSeverity Index for Patients Taking Abrocitinib
Authors: Luke Fostvedt1, Jessica Wojciechowski2, Bimal K. Malhotra3, Xiaoxing Wang2, Hernan Valdez3, Ricardo
Rojo2, Timothy Nicholas2
Institutions: 1Pfizer Inc., Cambridge, MA, USA; 2Pfizer Inc., Groton, CT, USA; 3Pfizer Inc., New York, NY, USA
Objectives: Abrocitinib is a selective Janus kinase-1 inhibitor being developed for the treatment of moderate-tosevere atopic dermatitis (AD). The objective of this analysis was to develop a pharmacokinetic-pharmacodynamic
model to describe the time-course of the eczema area and severity index (EASI), one of the two primary efficacy
measures for the abrocitinib program.
Methods: EASI is a validated measure of disease severity for AD using values from 0 (no disease) to 72 (severe
lesions affecting the entire body) in increments of 0.1. Data from three randomized, double-blinded trials (one phase
2 and two phase 3 trials) and a non-randomized, open-label, run-in portion of an additional phase 3 trial were pooled
for modeling. As a bounded measure, with many patients at 0, a zero-inflated beta regression model was developedwith the EASI scores transformed to the (0,1) domain (beta distribution parameterized with a mean and precisionparameter).1 The mean parameter was modeled using an indirect response model with a maximum stimulatory effect(Smax) on kout. Smax was modeled as the summation of a drug effect, using an Emax parameterization with Cavg, and aplacebo effect. The zero-inflated probability was modeled using a logit transformation with the current mean fromthe beta distribution used as a predictor with a “cutpoint” parameter, estimated by the model, at which point theprobability of 0 is 0.5. The expected decrease in EASI scores and the percentages of subjects achieving 75%
decrease from baseline (EASI75) at Week 12 (the co-primary endpoint in the phase 3 registrational trials) werepredicted.
Results: Doses of 10 mg, 30 mg, 100 mg, and 200 mg once daily (QD) and placebo arms were included in themodel development. The EC50 was estimated to be 312 ng/mL. The Cavg exposures were 104 ng/mL and 246 ng/mL
for 100 mg and 200 mg QD doses after 12 weeks of treatment in a typical patient (white, male, 30 years old, 80 kg,
with a baseline EASI score of 26). The response was rapid with the time to half of the maximal effect of 15.5 and11.5 days for the 100 mg and 200 mg QD doses, respectively. The expected percent change from baseline of the
EASI score after 12 weeks of treatment was 62.2% and 73.3% for 100 mg and 200 mg doses in a typical patient,
respectively, resulting in a high proportion of patients achieving an EASI75 response of 38.6% and 55.9%,
respectively.
Conclusions: A zero-inflated beta regression exposure-response model was developed using abrocitinib Cavg tocharacterize the time course of the EASI scores and predict the EASI75 response at Week 12.
References: 1. Ospina R, Ferrari SLP. A general class of zero-or-one inflated beta regression models. Computat Stat
Data Anal. 2012:56(6):1609-1623.

TUE-052
Dynamic PBPK for Pharmacology in Intensive Care Medicine
Author: Marco Albrecht (1), Stephan Schaller (1)Institutions: (1) esqLABS GmbH
Objectives: Fluid therapy is a standard treatment at intensive care units (ICU). It has implications on the absorption,
distribution, metabolism, and excretion (ADME) properties in critically ill patients, leading to inadequate treatment
decisions at intensive care units (1). While current PBPK software has a good representation of the steady-state inphysiology, useful for most use-cases in drug development and regulatory authorization, the dynamic nature of rapidlychanging physiology in intensive care patients requires a likewise dynamic PBPK software to assess the pharmacology
of drugs in intensive care. Consequently, a modeling and simulation (M&S) framework is developed to predict theeffects of fluid resuscitation on molecule levels to inform drug therapy decisions at the ICU.
Methods: Parameters, relevant for the volume distribution, are automatically extracted from the individual basephysiology, as determined by the open-source and freely available PBPK software platform PK-Sim and MoBi. The
parameters are then updated by both occasionally generated lab data and continuously monitored vital values. The
quantitative shift in physiology is estimated by established equations for whole-body volume kinetics (2), endothelialtransport considering the endothelial glycocalyx layer (EGL) via the two-pore model (3-5), and a model representing
volume shifts between four compartments representing erythrocyte, plasma, interstitial, and cell volume (6).
Results: The implemented R-model of fluid resuscitation has been validated on data occurring in the series of papersof Matthew B Wolf as well as from publications on volume therapy and lymph-flow (2,5,6). The impact of volume
shifts on dexamethasone PK has been assessed and quantified to provide a prototype for further qualification studies.
Conclusions: An dynamic PBPK modeling framework is possible in information-rich ICU settings and necessary tocorrect for the most common physiological disrupters of drug levels. The PBPK framework will integrate data as theyoccur at the bedside, extrapolate treatment consequences, and visualize both physiological and pharmacological key
states for risk assessment. In the regulatory authorization perspective, the proposed framework might also be used asa physiological ICU stress test for refined and physiologically relevant safety margins.
446/450 Word
References:[1] Owen, Emily J, Gibson, Gabrielle A. and Buckman, Sara A. Surg. Infect. (2018) Pharmacokinetics andPharmacodynamics of Antimicrobials in Critically Ill Patients.
[2] Hahn, Robert. Anesthesiology (2010) Volume Kinetics for Infusion Fluids[3] Rippe, Bengt and Haraldsson, Börje. Physiol. Rev. (1994) Transport of Macromolecules Across
Microvascular Walls: The Two-Pore Theory.
[4] Niederalt, Christoph, et al. J Pharmacokinet Pharmacodyn (2018) A generic whole-body physiologically based
pharmacokinetic model for therapeutic proteins in PK-Sims.
[5] Levick, J. Rodney and Michel, C. Charles. Cardiovasc. Res. (2010) Microvascular fluid exchange and therevised Starling principle.
[6] Wolf, Matthew B. Am. J. Physiol. Renal Physiol. (2013) Whole-body acid-base and fluid-electrolyte balance: amathematical model.

TUE-053
Model-based Meta-analysis of Progression free survival in Pancreatic cancer with reconstructed individual
patient data; application to drug-development decision making.
Marion Bouillon-Pichault, Li Li, Alex Azrilevitch, David Yao, Akintunde Bello, Tarek Leil
1

BMS, Princeton NJ, USA.

Objectives: Second Line adenocarcinoma of the pancreas is one on the most aggressive cancers with patientssurviving a median of 6 months without treatment. Progression-free survival (PFS) is a widely used endpoint to assess
the potential efficacy of a new therapy for pancreatic cancer. The current standard of care treatments for advanced
pancreatic cancer are based on several chemotherapy combinations and have been extensively described in theliterature. However, there has not been a systematic assessment of the current therapeutic landscape. A modelingframework to evaluate the PFS of chemotherapy in advanced pancreatic cancer was developed; potential predictors
were identified, and the efficacies of different treatment options were quantified.
Methods: A database of randomized and non-randomized clinical trials of chemotherapy in Pancreatic cancer
published between 2000 and 2017 was used. Individual Patient Data (with events and censored) were reconstructedfrom digitized PFS curves from these trials using Guyot’s et al. algorithm (1). Fully parametric time-to-event (TTE)models were developed to describe the drop-out and the PFS survival curves and used to run clinical trial simulations
using Monolix Suite 2019.
Results: Data used in the analysis came from 97 trials (138 trial arms), representing over 13,000 reconstructed
patients. This model demonstrated that median PFS increased in later trial years and decreased with line of therapy.
The efficacy of different chemotherapy groups was quantified and ranked with mPFS and 6mo-PFS ranging from 27
weeks and 52% for Capecitabine-based combination chemotherapy to 22 weeks and 43% for 5-FU-based combination
chemotherapy for 1st line population.
ΔmPFS and Δ6moPFS were simulated for different clinical trial scenarios (different control arms, number of patients
per arms and assumptions for the efficacy of investigational treatment arms), to quantify the clinical probability ofsuccess and the false negative/false positive risks for those trial designs.
Conclusions: A model based meta-analysis was performed to describe the drop out and PFS for chemotherapy inpancreatic cancer on more than 13.000 reconstructed patients providing a quantification of the chemotherapy
landscape. This analysis was used internally to support decision-making for clinical development of assets inpancreatic cancer by quantifying the clinical probability of success vs. potential standards of care.

References:(1) Guyot P1, Ades AE, Ouwens MJ, Welton NJ., Enhanced secondary analysis of survival data:
reconstructing the data from published Kaplan-Meier survival curves. BMC Med Res Methodol. 2012 Feb
1;12:9. doi: 10.1186/1471-2288-12-9.

TUE-054
A Quantitative Systems Pharmacology Modeling Framework for Evaluation of Cytokine Release Mediatedby REGN1979/Cemiplimab Combination Therapies in Patients with B-cell Non-Hodgkin Lymphoma(B-NHL)
Masood Khaksar Toroghi, Min Zhu, Feng Yang, Nidal Huniti, John D. Davis, A. Thomas DiCioccio
Regeneron Pharmaceuticals, Inc., Tarrytown, NY, USA
Objectives: REGN1979 is a human immunoglobulin G4-based bispecific antibody that binds to CD3+ T-cells andCD20+ B-cells, targeting CD20+ tumor cells via T-cell-mediated cytotoxicity. The engagement of CD3 on T-cells
and CD20 on B-cells activates T-cells. During T-cell activation, inflammatory cytokines are secreted, which can
result in significant but temporary increase in the circulating cytokine concentrations and may lead to a systemic
inflammatory response; this is known as cytokine release syndrome. The objective of this work is to evaluate
cytokine profiles, as represented by interleukin (IL)-6, under REGN1979 monotherapy and in combination withcemiplimab, an anti-programmed death (PD)-1 inhibitor blocking PD-1/PD-L1 interaction, in patients with B-NHL.
Methods: A quantitative systems pharmacology (QSP) model was developed integrating pharmacokinetics of
REGN1979 and cemiplimab, dynamics of T-cells and B-cells, disease characteristics of NHL, and cytokine datafrom a REGN1979 monotherapy study (NCT02290951) and a REGN1979/cemiplimab combination study(NCT02651662). In the QSP model, mechanism of cytokine release was described as 1) the formation of
trimolecular synapse when REGN1979 binds to both CD3 on T-cells and target CD20 on B-cells, and 2) regulationof T-cell activation by the PD-1 pathway. To describe cytokine profiles under the treatment of
REGN1979/cemiplimab combination, we hypothesized that cemiplimab increased the stimulation of IL-6
production by activating more T-cells and reduced the inhibitory effect of the immune system to attenuate thecytokine release. Concentrations of IL-6, REGN1979, and cemiplimab from NHL patients under monotherapy andin combination, as well as in vivo/in vitro data (e.g. tumor growth rate, CD20/CD3 expression levels, T/B-cellbaseline levels, and drug affinity data), were used to calibrate the model. For simulation, 300 virtual patients withaggressive NHL were created using a Monte Carlo sampling approach.
Results: The model-based simulations indicate that, under REGN1979 monotherapy, IL-6 concentrations mostly
peaked on day 1 following an initial split dose of 1 mg in week 1. The cytokine concentrations reduced over timeeven with increased REGN1979 doses up to 10- and 100-fold in weeks 2 and 3, respectively. When REGN1979 andcemiplimab were dosed simultaneously on day 1 week 1, the mean peaks of IL-6 concentrations were higher thanthat of REGN1979 monotherapy due to the cemiplimab effect on PD-1/PD-L1 signaling pathway. The model
predicts that, when delaying cemiplimab dose to a later time following dosing REGN1979 alone, reduced IL-6
release is expected, and the IL-6 levels would not be affected by cemiplimab dose levels from 3 to 350 mg.
Conclusions: The developed QSP model provides a learning opportunity to evaluate the cytokine profiles of
REGN1979 and cemiplimab under different dosing sequences and dose levels, where REGN1979/cemiplimab
combination is modulating both CD20/CD3 and PD-1/PD-L1 signaling pathways.

TUE-055
Development and evaluation of a predictive model of hyperphosphatemia induced by inhibition of FGFR byextending an existing multiscale systems pharmacology
Matthew M. Riggs1, Howard A. Ball2, Kanji Komatsu3, Akihiro Yamada3
1

Metrum Research Group, Tariffville, CT USA; 2 Ball Pharma Consulting, LLC, Hartland, WI USA; 3 Astellas
Pharma Inc., Tokyo, Japan
Objectives: Fibroblast growth factor receptor (FGFR) inhibition has been investigated as a potential target fortreating cancer. Hyperphosphatemia (HP) has been observed clinically following FGFR inhibition due to its role inregulating phosphate (P) balance through FGF23, which regulates urinary P excretion and indirectly impacts dietary
P absorption and calcitriol (C) activation. An existing systems pharmacology model was leveraged to explore
whether HP circumvention can be achieved via intermittent dosing and concomitant P binders following
administration of ASP5878, an FGFR inhibitor investigated for treatment of solid tumors (NCT02038673).

Methods: A systems pharmacology model (Bone, 2010) was extended to describe changes in serum P, C,
parathyroid hormone (PTH), and FGF23 following oral ASP5878 administration. The model evaluated concomitant
P binder and impact of varied dosing regimens on exposure-related P changes. Analyses were conducted in R;
simulation and estimation included mrgsolve and minqa. QD and BID ASP5878 dosing, total daily dose, andintermittent, 5 days on / 2 off, and 4 days on / 3 off, regimens were considered.

Results: ASP5878 PK followed a 1 compartment model (typical t1/2 = 2.63h). Added mathematical descriptions
included: FGF23 control urinary P, PTH and C production, with feedback on FGF23 production from P and C. P
binder was estimated to decrease its dietary bioavailability by up to 32%. The extended model described the timecourse and magnitude of dose-related increases observed for P, C, FGF23 and PTH, including P > 6 mg/dL at doses>= 32 mg/day. P binder was predicted to mildly alleviate the increase at targeted doses. Efficacious response was
not obtained by any simulated regimen that minimized to acceptable P.

Conclusions: Results from the extended systems model supported program termination.

References:
The results in this abstract were previously accepted though not presented at canceled ASCPT, Houston TX March
2021 (canceled) and published in the conference proceedings as abstract PWI-002.

TUE-057

Development of a Mechanistic Model of Keratinocyte Dynamics and Skin Barrier
Function for Psoriasis Research

Authors: Michael Weis1,*, Douglas Chung1, Vincent Hurez1, Rebecca Baillie1, Katherine Kudrycki1, Michael Reed1
Affiliations: 1 Rosa & Co. LLC, San Carlos, CA
Objectives: While no longer considered the predominant factor, keratinocytes (KCs) have a central role in thepathophysiology of psoriasis(1). KCs become activated in response to extrinsic or intrinsic factors, producing
cytokines and chemokines that contribute to the inflammatory state in psoriasis. The psoriatic KCs undergo
hyperproliferation, resulting in thickened skin; and their abnormal desquamation results in squamous scales on theskin and itching. Understanding KC behavior and response to interventions is critical to the development of new
psoriasis therapies. We sought to develop a focused, mechanistic model of KC dynamics and their relation to skinbarrier function, which could be integrated within larger quantitative systems pharmacology (QSP) models of
inflammatory skin disorders such as psoriasis.
Methods: The model defines three distinct states of KCs representing the functional phenotypes observed in normal
and psoriatic skin: basal proliferating KCs of the stratus basale; non-proliferative differentiating KCs of the spinousand granular layers; and cornified, terminally differentiated KCs of the cornified layer or corneocytes. Fluxes
included proliferation and clearance of basal KCs, differentiation into suprabasal KCs, and final cornification into
corneocytes. These rates are regulated by various pro- and anti-inflammatory cytokines.
Results: The model assumes that strong stimulation of differentiation and cornification worsens the overall qualityof the resulting corneocytes and epidermal barrier function. To represent this, a calculation of corneocyte quality
tracks the epidermal turnover rates. This quality metric influences the rate of corneocyte shedding, the degree ofbarrier function, and clinical outcomes such as the “scaliness” component of the common clinical outcome, thePsoriasis Area Severity Index (PASI). The increased turnover rates in psoriasis results in a lower corneocyte quality
and reduced barrier function, further stimulating KC proliferation. The consequence of these effects is a net increasein the number of keratinocytes and corneocytes, but reduced quality, leading to thickened and scaling skin.
The model behavior was constrained using a variety of data types including: transepidermal water loss measured inhealthy and psoriatic subjects; cell numbers; cell-type specific mediator production; mediator turnover; cell
production, differentiation, cornification, and overall epidermal turnover rates in psoriasis vs. healthy subjects; themagnitude of effects of individual mediators on KC lifecycles; and the reversion to a quasi-homeostatic state
following therapies.
Conclusions: A focused QSP model was developed to capture the behavior of KCs in response to anti- and proinflammatory mediators in healthy and psoriatic conditions. This model has been successfully integrated withinseveral larger QSP models of psoraisis to facilitate evaluation of novel targets and standard of care therapies.
References:1.

Bata-Csörgö, Zsuzsanna, and Marta Szell. 2014. "The psoriatic keratinocytes." Expert Review ofDermatology 7 (5):473-481. doi: 10.1586/edm.12.48.

TUE-058

Implementation of the standard concentration - QTc study using MonolixSuite
applications on the example of Dofetilide.

Monika Twarogowska1, Géraldine Ayral1, Pauline Traynard1, Jonathan Chauvin1
1

Lixoft, Antony, France,

Objectives: In 2015, the International Council for Harmonisation (ICH) E14 guidance agreed to use a model-based
study of concentration-QT data as a primary analysis in the proarrhythmic risk assessment. Currently recommended
guidelines follow the methodologies presented in the “Scientific white paper on concentration-QTc modelling” by Ch.
Garnett et al.[1]. This work presents the implementation of the C-QT analysis in MonolixSuite following theserecommendations. Results, obtained on data of a class III antiarrhythmic agent Dofetilide, show the modelling
workflow and calculations of the standard criteria for a drug-induced QTc interval prolongation.
Methods:•••••

The dataset[2]: PK measurements and ECG recordings in a randomized, double-blind, 2-period crossoverclinical trial; single dose of Dofetilide or placebo.
Heart rate corrected QT (QTc) intervals were obtained with the Fridericia’s formula (the standard adopted
by the FDA). Derived baseline-adjusted QTc interval (ΔQTc) and baseline-adjusted placebo-corrected QTcinterval (ΔΔQTc) were used as model variables.
The pre-specified linear mixed effect models, given in the FDA guidelines and in [1], depend on theconcentration and include fixed effect parameters, influence of baseline, treatment and diurnal effects.
Monolix was used to implement models and estimate the parameters.
Risk assessment simulations - two-sided 90% confidence interval for ΔΔQTc - were performed in Simulx.

Results: Monolix was used to fit the Dofetilide PK data with a two-compartments model with a time delay for theabsorption and the baseline-adjusted QTc with the pre-specified linear mixed effect model. A joint modelling
approach, where both QTc and PK models parameters are estimated simultaneously, was compared with the FDA
recommended approach, where the C-QTc model uses directly the observed time-matched PK data. Both methods
yielded very close estimates. The estimated parameters and their standard errors were used to calculate the estimatedmean QTc change from the baseline and the 90% confidence interval for a large range of Cmax concentrations. For
Cmax concentrations above 0.5 ng/mL, the estimated mean QTc change from the baseline exceeded the 10 ms safety
threshold. The advantage of the joint PK-QTc model is that the QT interval prolongation can be studied for different
doses and designs (instead of different Cmax values). For instance, in case of a 125 mg q12h regimen, 88% of patients
had the QT prolongation longer than 10 ms.
Conclusions: The presented work shows step-by-step how the FDA recommended workflow for C-QTc analysis canbe implemented in Monolix. The procedure is generic and can be applied to other compounds in the same way.
References:[1] Garnett C. et al. Scientific white paper on concentration-QTc modeling. J Pharmacokinet Pharmacodyn. 2018 Jun;45(3):383-397[2] Johannesen L. et al. Differentiating Drug-Induced Multichannel Block on the Electrocardiogram: Randomized
Study of Dofetilide, Quinidine, Ranolazine, and Verapamil. Clin Pharmacol Ther. 2014 Nov; 96(5):549-58

TUE-059

A Bayesian Population Compartmental Absorption and Transit Modeling Approach to Support
Generic Drug Development: Application to Bupropion Hydrochloride Oral Dosage Forms
Nan-Hung Hsieh* , Frédéric Y. Bois' , Eleftheria Tsakalozou+ , Miyoung Yoon+ , Brad Reisfeld, , Weihsueh A. Chiu**

Department of Veterinary Integrative Biosciences, College of Veterinary Medicine and Biomedical Sciences, Texas
A&M University, College Station, TX, USA'
Simcyp Division, Certara UK Limited, Sheffield, UK+
Division of Quantitative Methods and Modeling (DQMM), Office of Research and Standards (ORS), Office ofGeneric Drugs (OGD), Center for Drug Evaluation and Research (CDER), U.S. Food and Drug Administration(FDA), Silver Spring, MD, USA,
Chemical and Biological Engineering and School of Biomedical Engineering, Colorado State University, Fort
Collins, CO, USA

Objectives: The study purpose was to develop and validate an open-source compartmental absorption
and transit (openCAT) model and apply a Bayesian population modeling approach to support establishingdissolution specifications for oral dosage forms of bupropion hydrochloride.
Methods: The openCAT model included six gut segments (stomach, duodenum, jejunum, ileum, cecum,
and colon) and the liver connected to a standard two-compartment model that empirically describedsystemic disposition and elimination. Prior information on model parameters for gut physiology,
bupropion physicochemical properties and drug product properties were from literature. The release of
bupropion hydrochloride from the immediate-, sustained- and extended-release oral dosage forms was
described by a Weibull function. To conduct the Bayesian analysis, we used our previously developed
global sensitivity analysis framework and determined influential parameters for the central compartment
and the openCAT model1. In-vitro dissolution data were used to assign priors to the in-vivo releaseproperties of the three bupropion hydrochloride formulations. To identify the inter- and intra-individual
variability, we used published bupropion datasets from healthy volunteers that included six plasma
concentration-time profiles (2 dose levels for each of the three formulations) for each subject2. One-thirdof subjects were used in model calibration, while the rest were used for external model validation.
Markov Chain Monte Carlo samples from the joint posterior of the calibrated parameters were then used
to simulate virtual crossover trials with multiple variant formulations. For the immediate-releaseformulation, Cmax and AUC estimates from the virtual crossover studies for formulation variants wereused to identify allowable variability in dissolution profiles in terms of a bioequivalence assessment(“safe space”). The study workflow is summarized on Figure 1.
Results: Parameters related to absorption (gut permeability, stomach transit time), distribution(distribution volume, intercompartmental rate constants and liver partition coefficient), elimination(clearance) and dissolution were chosen for calibration. The calibrated model was in good agreement with
the in-vitro dissolution and in-vivo exposure data and was able to describe inter- and intra-individual
variability2. The identified dissolution “safe space” suggested that dissolution may be delayed by up to
one and a half hour before impacting systemic exposure in a meaningful manner.
Conclusions: We demonstrated that a Bayesian population semi-physiological model can capture interand intra-individual variability observed following the administration of bupropion hydrochloride oral
dosage forms, and thereby characterizes the underlying physiological and substance-specific parameters.
The application of our population modeling framework provides a novel approach in supporting decisionmaking in generic drug product development.

References:1

Hsieh NH et al., 2018. Applying a global sensitivity analysis workflow to improve the computational
efficiencies in physiologically-based pharmacokinetic modeling. Front Pharmacol. 9:588.

2

Connarn JN et al., 2017. Pharmacokinetics and pharmacogenomics of bupropion in three different
formulations with different release kinetics in healthy human volunteers. AAPS J. 19(5):1513-1522.

Acknowledgements:
Funding for this work was made possible, in part, by the U.S Food and Drug Administration throughGrant 1U01FD005838. Views expressed in written materials or publications do not necessarily reflect theofficial policies of the Department of Health and Human Services; nor does any mention of trade names,
commercial practices, or organization imply endorsement by the United States Government.

Figure 1. Schematic diagram of the overall study workflow.

TUE-060

Glofitamab, a T-cell-engaging Bispecific Antibody, in Relapsed or Refractory B-cell Non-Hodgkin
Lymphoma: Population Pharmacokinetics and Exposure-response Analyses toInform Optimal Biologic Dose Selection
Nassim Djebli1, David Carlile2, Felix Jaminion1, Johann Laurent1, Francois Mercier1, Nicole A
Kratochwil1, Ann-Marie E Bröske3, Natalie Dimier2, Cristiano Ferlini1, Tom Moore1, Martin Weisser3,
Alexander Phipps2, Nicolas Frey1, Peter N Morcos4
1

Roche Innovation Center Basel, Roche Pharma Research and Early Development, Basel, Switzerland,
Roche Products Ltd, Welwyn Garden City, United Kingdom, 3Roche Innovation Center Munich, Roche
Pharma Research and Early Development, Penzberg, Germany, 4Roche Innovation Center New York,
Roche Pharma Research and Early Development, New York, NY, USA
2

Category: General pharmacometrics
Objectives: Glofitamab (RG6026) is a novel T-cell-engaging bispecific antibody with a ‘2:1’ molecularformat, comprising two binding regions for CD20 (on B cells) and one binding region for CD3 (on Tcells). In NP30179 (NCT03075696), an ongoing Phase I dose-escalation study in relapsed/refractory(R/R) non-Hodgkin lymphoma (NHL) patients, glofitamab has shown promising activity and manageable
safety.1 Glofitamab population pharmacokinetics (PopPK) and exposure-response (E-R) relationships for
efficacy/safety were investigated.
Methods: Indolent (i) and aggressive (a) R/R NHL patients received 1000mg obinutuzumab (G) as pretreatment 7 days prior to first glofitamab administration on Cycle 1 to mitigate for cytokine release
syndrome (CRS). Patients then received glofitamab 0.005−25mg Q2W or Q3W. A PopPK model wasdeveloped in NONMEM v7.4 using serial and spare PK data. To estimate glofitamab-receptor occupancy(RO%) in the presence of G concentrations competing for CD20 receptors over time, full G
concentration-time profiles were constructed using the G PopPK model.2 Using logistic regression, E-R
relationships were investigated between glofitamab time-averaged RO% (AvgRO%) up to C3D1 andcomplete response rates (CRR – Lugano criteria3) in aNHL patients who reached C3D1; and between
glofitamab AvgRO% over the first 24hours (majority of events occurred within 24hours of firstglofitamab administration) and CRS (ASTCT criteria4) in iNHL and aNHL patients combined.
Results: As of December 18, 2019, PopPK were analyzed in 165 iNHL and aNHL patients with ≥1 PK
sample. E-R relationships were analyzed in 87 aNHL patients with PK/efficacy data at C3D1, and in 139
iNHL and aNHL patients with PK/safety data. Glofitamab PK were best described using a twocompartment PK model with linear clearance and were comparable in patients with iNHL and aNHL. The
effect of body weight on volumes and clearances was retained. Positive E-R relationships were observed
between AvgRO% up to C3D1 and efficacy in aNHL patients (p<0.001; Figure 1A), and between
AvgRO% over the first 24 hours and Grade ≥2 CRS (p<0.001; Figure 1B). Based on the exposureresponse models, with an initial 10mg glofitamab dose at C1 (16mg thereafter), a CRR of 46.7% (41.5–
58.1%), and a Grade ≥2 CRS rate of 32.6% (29.0–37.7%) were predicted. Step-up dosing starting at
2.5mg (followed by higher doses) might improve the benefit/risk profile of glofitamab. It would result in
AvgRO% in the first 24hours of 0.160% (0.100%–0.290%), corresponding to a Gr ≥2 CRS rate of 22.1%(19.2%–26.3%).

Conclusions: We characterized glofitamab PopPK and E-R relationships for efficacy/safety. Thesemodels are being used to identify optimal glofitamab dosing that maximizes efficacy and minimizes CRS
risk.
References

1.
2.
3.
4.

Dickinson et al. Hematol Oncol 2019;37(S2):92–3.
Gibiansky et al. CPT Pharmacometrics Syst Pharmacol 2014;3:e144.
Cheson et al. J Clin Oncol 2014;32(27):3059-68.
Lee et al. Biol Blood Marrow Transplant 2019;25(4):625–38.

Acknowledgments: NP30179 is sponsored by F. Hoffmann-La Roche Ltd. Third party medical writing
assistance, under the direction of Nassim Djebli, was provided by Stephanie Lacey and Khalida Rizi of
Gardiner-Caldwell Communications and was funded by F. Hoffmann-La Roche Ltd.
Figure. Exposure-response analysis for: (A) the relationship between the probability of complete
response versus glofitamab AvgRO% up to Cycle 3 in aNHL patients (N=87), and (B) the relationshipbetween the probability of occurrence of Grade ³2 CRS (ASTCT criteria4) versus glofitamab AvgRO%
over the first 24 hrs in aNHL and iNHL patients (N=139). Q1–Q3 for AvgRO% up to Cycle 3 is 0.079%–
0.647%. Dots in graph A illustrate the observed individual patient response coloured by achieved
response. The proportion of responders (red circles) in panel A and associated 95% CIs (grey verticalsolid lines) are provided in each tertile of AvgRO% (vertical dashed lines). Numbers in panel B illustrate
the observed individual patient CRS event and associated grade. Black line=logistic regression; grey
shaded regions=95% CI for the logistic regression. P-values are obtained from the Chi-squared
test using ANOVA function of R, comparing the model to the null model. AIC, Akaike information
criterion; aNHL, aggressive non-Hodgkin lymphoma; AvgRO%, time-averaged receptor occupancy; CI,
confidence interval; CR, complete response; CRS, cytokine release syndrome; PD, progressive disease;
PET CT, positron emission tomography – computed tomography; PR, partial response; SD, stabledisease.

TUE-061
A Single Platform that Accommodates the Ever Expanding Pharmacometrics Toolkit –Challenging But Achievable
Navin Goyal1, Lorenzo Ridolfi2, Kevin McConnell3, Ronish Desai3, Sachin Sanduja3 and Stefano Zamuner2
1

CPMS, GlaxoSmithKline USA, 2 CPMS, GlaxoSmithKline UK, 3 Rudraya Corporation, USA

Objective: The current pharmacometric (PMx) toolkit has significantly expanded in recent years and continues to doso. The existing tools are not necessarily being replaced by the emerging technologies but are rather complemented insome cases. Based on the question that needs to be addressed, a user may want to employ several tools within a single
analysis thereby harness advantages of individual tools rather than being limited to a single tool. Therein lies thenecessity to have an infrastructure that allows utilizing multiple tools in a single environment while ensuring any
regulatory requirements around validation and compliance. Additionally, the setup should allow elastic capacity,
parallelization and grid support (e.g. slurm). Lastly, it has become increasingly critical to engage stakeholders i.e. nonPMx matrix team members and interact with the analyses in real time without getting deeply involved in model related
technical details (e.g. RSHINY).
Methods: A list of must have software tools along with predefined constraints and variables (e.g. scalability, system
flexibility and future proofing) was utilized as a starting point to develop the computational platform. A cloud-based
solution that met most of the requirements was implemented. Newer tools and capabilities were introduced within theplatform to allow their use within a single analysis.
Results: A customized enterprise platform hosted in the Amazon Web Services in a validated and encryptedenvironment was developed. The initial system included several open source and proprietary software tools such as
PsN®, R, RStudio®, NONMEM®, Pirana® and PHOENIX®. Newer tools can be easily added on the Linux andWindows server system embedded within the system architecture. The system easily allows the user to run an analysis
that calls several of the software tools while utilizing the grid capacity efficiently. Additional tools and capabilities
are being continuously added to the system. The SONIC Visualize feature (under evaluation) allows users to load
models wherein the user can create and share interactive and computationally intensive front ends (e.g. RSHINY) with
non-PMx matrix team thereby leading to increased interactions with respect to analysis outcome. The system separates
the model related technical details from the stakeholders while utilizing the platform capacity and capability behindthe scenes.
Conclusions: Updating any PMx toolkit with emerging tools can often present challenges regarding integration ofsuch tools and their use in tandem with existing tools. Adequate planning and domain expertise should allow setting
up a custom cloud-hosted platform that integrates multiple tools commonly used by our discipline. We present such asystem and share learnings with the community to implement such a comprehensive, high computing capacity andcost-effective platform solution.
Reference: Cloud-Based Computation Platform for Pharmacometrics – Challenges in Developing and Implementing
an Integrated Solution, Navin Goyal, Des Buke, Lorenzo Ridolfi and Stefano Zamuner, Journal of Pharmacokinetics
and Pharmacodynamics; 2018, 45

TUE-062

A library of tumor growth and tumor growth inhibition models for the MonolixSuite
Authors : Pauline Traynard (1), Geraldine Ayral (1), Monika Twaragowska (1)Institution: (1) Lixoft, Antony, France
Objectives:
A wide range of ordinary differential equations (ODEs) models for tumour growth (TG) and tumour growth
inhibition (TGI) is available in the literature and correspond to different hypotheses on the tumor ortreatment dynamics. In the absence of detailed biological knowledge, selecting the most appropriate
model is a challenge. Here, we present a modular TG/TGI model library that combines sets of frequently
used basic models and possible additional features. This library permits to easily test and combine
different hypotheses for the tumor growth kinetics and effect of a treatment, allowing to fit a large varietyof tumor size data.
The use of the library is illustrated with the modeling of a published experimental dataset comparing
different schedulings of bevacizumab and pemetrexed/cisplatin in non-small lung cancer xenografts inmice [1].
Methods:
The developed library allows to select:
•
•
•
•
•

a basic tumor growth model,
the use of the initial tumor size as a parameter to estimate or a regressor to read from the data
set,
additional features of angiogenesis or immune dynamics to combine to the basic tumor growth,
the type of drug exposure to be considered for the treatment effect if any, such as a constanttreatment or a standard PK model,
the log-kill or Norton-Simon hypothesis for the treatment effect and its dynamics.

The flexible selection workflow allows to combine all the choices above, leading to a wide variety ofmodels. In addition, the library includes a list of full pre-defined TGI models corresponding to common
models used in the field. It is documented with figures and guidelines to help the selection of an
appropriate model based on the characteristics of the data.
The library has been used to easily test different hypotheses and find the appropriate model for tumor
growth and treatment effect for the non-small lung cancer cancer dataset with a stepwise approach.

Results:
First different growth models were compared on the control group and the Simeoni model was found to
give the best fit. The effect of the chemotherapy was then considered and accounted for the modelling of
pemetrexed and cisplatin’s PK with parameters from the literature. The best model combined a log-killlinear effect with a delay induced by signal transduction. Further modification of the model allowed toinclude the increase in the drug’s delivery brought by bevacizumab and implemented in [1].
The final model shows a good predictive power for each treatment group. Simulations of the model allow
to test different schedules of treatment combinations.
Conclusions:
The MonolixSuite and the new TG/TGI library allow an efficient modeling and diagnosis of tumor size
data.

References:[1] Schneider, B. K., et al. (2019). CPT Pharmacometrics Syst Pharmacol, 8: 577–586.

TUE-063

A minimal brain PBPK model to support the preclinical development ofantibody therapeutics for neurological diseases.
Authors: Peter Bloomingdale1*, Suruchi Bakshi2*, Cesar Pichardo2, Christian Maass2, Eline van Maanen2,
Piet van der Graaf2, Nitin Mehrotra1
Affiliations: (1) Merck & Co. Inc, Kenilworth, New Jersey, US; (2) Certara UK Ltd.
* Authors have contributed equally to the work
Objectives: There are several antibody therapeutics in preclinical and clinical development, industrywide, for the treatment of neurological diseases. Our goal was to develop a minimal physiologicallybased pharmacokinetic model (PBPK) to enhance the quantitative understanding of antibody exposurein the brain of preclinical animal species and humans. Here we have reduced a full PBPK model to aminimal model with the goal of retaining physiological detail of the brain compartments without
appreciably affecting model predictability.
Methods: An existing multi-species platform brain PBPK model1 was reduced to a minimal PBPK model.
All non-brain compartments were combined into a single tissue compartment. Fourteen tissue reflection
coefficients were reduced to a single tissue-weighted value (σTissue = 0.92). Brain reflection coefficientsremain the same as in the full model. Tissue vasculature, endosomal, and interstitial volumes, besides
brain, were combined into single parameter values for the tissue compartment. Tissue plasma flow was
set to the difference between total cardiac output and brain plasma flow. The four cerebral spinal fluid
compartments were combined into one. A local sensitivity analysis was performed on the full andminimal PBPK models. Percent error in the AUC between the full and minimal PBPK model predictions
were determined for 1, 10, and 100 mg/kg IV doses in rats, monkey, and human.
Results: The minimal brain PBPK model contains 16 differential equations, compared to 100 in the full
PBPK model. The run time for the reduced model was approximately 15 times faster. Brain reflection
coefficients were identified as the most sensitive parameters on antibody exposure in the brain. The
rank order, from most to least, of sensitive parameters were conserved between the original andreduced model. The percent error in predicted AUC between the full and minimal model was <2% forserum, 1-2% for ISF, and between -15% to 2% for CSF.
Conclusion: The minimal brain PBPK model retained detailed physiological processes of the brain while
not significantly affecting model predictability. The minimal model can be expanded to include target
information for evaluating target engagement to inform clinical dose selection and more easily
implemented for population analysis.

References:

1

Chang, H.-Y., Wu, S., Meno-Tetang, G. & Shah, D. K. A translational platform PBPK
model for antibody disposition in the brain. Journal of pharmacokinetics andpharmacodynamics 46, 319-338 (2019).

TUE-064

Population PK of Rozibafusp alfa, a Bispecific IgG2 Peptide Antibody Conjugate Targeting T-cell and Bcell activity, Following SC Administration in Healthy Subjects and Rheumatoid Arthritis Patients
Po-Wei Chen1, Lubna Abuqayyas2, Sandeep Dutta1, Brett Houk1, Sameer Doshi1
Amgen Inc. 1Thousand Oaks. CA, 2Cambridge, MA
Objectives
Rozibafusp alfa is a bispecific IgG2 peptide antibody conjugate targeting both T-Cell and B-cell activity via
neutralization of B-cell activating factor (BAFF) and inducible T-cell co-stimulator ligand (ICOSL) and currently
in development for systemic lupus erythematosus (SLE). The objective of this analysis was to characterize thepharmacokinetics (PK) of rozibafusp alfa, including impact of target binding, after subcutaneous (SC)administration in healthy subjects (HS) and rheumatoid arthritis (RA) patients, and to develop a population
PK model to guide future clinical development of rozibafusp alfa in SLE and other autoimmune diseases.
Methods
Rozibafusp alfa PK was characterized using data obtained from three Phase 1 studies (n= 80) after single ormultiple (every 2 week) SC doses of rozibafusp alfa in HS (n= 54) or RA (n=26) patients (NCT02618967,
NCT03156023). Serum concentrations of rozibafusp alfa were analyzed using first order conditional estimation
with interaction in NONMEM® version 7.4.1 (ICON, Ellicott City, MD). Different structural PK models such as atwo compartment population PK models with linear or nonlinear absorption and parallel linear plus nonlinear
clearance including a target-mediated drug disposition (TMDD) model for a soluble target (BAFF) and amembrane bound target (ICOSL) (Gibiansky and Gibiansky 2010) were explored in parallel. Goodness of fit(GOF) and visual predictive checks (VPCs) were performed to compare structural models. Covariate analysis
evaluated the effect of body weight, sex, race, disease, and immunogenicity on PK model parameters. Final PK
model selection was based on improvement in objective function value using likelihood ratio test anddiagnostics.
Results
Rozibafusp alfa PK after single or multiple SC doses was best described by a two-compartment popPK modelwith linear plus nonlinear (Michaelis Menten, or MM) eliminations based on GOF and VPCs. The model wassuperior to other model estimations, as nonlinear clearance mechanisms best described the greater than doseproportional increases in exposure. The majority of conditional weighted residuals were between -4 to 4;
normalized prediction distribution errors were within -3 to 3. Apparent linear clearance was 0.4 L/day andapparent volume of distribution was 4.5 L. No statistically significant difference in PK parameters was identified
between HS and RA patients. Body weight was identified as the only significant covariate for linear clearance,
volume and Vmax of MM clearance; no other covariates were identified as significant.
Conclusions
A population PK model of rozibafusp alfa in HS and RA patients was successfully developed. The semimechanistic nature of the model incorporates target related information and may be used to guide future dose
selection for clinical development in SLE or other autoimmune diseases.
References
L. Gibiansky and E. Gibiansky. J Pharmacokinet Pharmacodyn (2010) 37:323-346.

TUE-065
A Multivariate Joint Model for Predicting Risk of End-Stage Renal Disease in Polycystic Kidney Disease

Authors: Rhoda Muse1, Klaus Romero1, Bob Stafford1, Jane Larkindale1, Jackson Burton1
Affiliations: Critical Path Institute, Tucson, AZ;
Objectives
Autosomal Dominant Polycystic Kidney Disease (ADPKD) is a rare disease that results in kidney failure, currently
with only one approved treatment that slows the progression to End Stage Renal Disease (ESRD). The objective of
this work is to develop a bivariate joint model to predict individualized trajectories of time to ESRD using thesimultaneous dynamic changes in mean Total Kidney Volume (TKV) and estimated Glomerular Filtration Rate(eGFR). To enable the effective development of treatments for ADPKD, understanding the progression of thedisease as it relates to the timing of ESRD is critical for optimizing the design of clinical trials. The use the annual
rate of change of eGFR, and changes in mean TKV as indicators of ADPKD progression have not been previously
assessed simultaneously.
Methods
Patient level data from 5 registries was previously standardized, curated, and integrated. A subset of data was
extracted comprising 587 individuals with 2392 longitudinal measurements for TKV, eGFR, and corresponding
timing of diagnosis of ESRD or right-censored times. The longitudinal markers were best modeled using linear
mixed effects, and the survival subcomponent was modelled using a Weibull baseline hazard. Covariates included
were age at baseline, sex, race, baseline chronic kidney status and body mass index. Other covariates including PKD
phenotype, anemia, hypertension, cholesterol levels, use of pain medication and smoking history were not tested due
to missing information. The analysis was conducted using the Bayesian ‘rstarnarm’ package in R.
Results
Results showing factors associated with the risk of ESRD are presented in Table 1. Predicted TKV and rate ofeGFR decline increased the risk of ESRD with log odds ratios of 1.0352 and 1.6903 respectively. The standard
deviations of the intercepts and slopes show evidence of subject specific trends and baseline values (shrinkages for
random intercept and slope respectively were 1.79%, 20.59% for TKV and 14.6%, 16.78% for eGFR). The
correlations between the random slope and intercept in TKV and eGFR equations were significant. The crossequation correlation between eGFR and TKV was due to significant correlation between the TKV intercept and theeGFR slope and intercept, hence the need for the multivariate model.
Conclusions
Modelling the eGFR and TKV jointly reduces bias in their effects on time to ESRD and quantifying the joint effectsof these clinically relevant biomarkers helps to predict time to ESRD more accurately compared to the markers
individually. The model developed, which allows for the prediction of individualized trajectories accounting forinter-subject variability, is intended to serve as the basis for a clinical trial simulation tool to help optimize efficacy
studies for ADPKD.

Table 1. Weibull PH joint model results of end stage renal disease with eGFR and TKV as longitudinalmarkers

VARIABLE

POSTERIOR
MEAN
2.3041
6.09920.07710.08591.0352
1.6903

STANDARD
DEVIATION
0.5742
1.07080.04060.03070.40410.3661

STAGE3A> STAGE 3A
BASELINE BMIBASELINE AGE
TKV: PREDICTED MEAN
EGFR: PREDICTED DECLINERATESLOPERANDOM EFFECTS SD AND CORRELATIONS (COR)
TKV EQUATIONINTERCEPT
0.645
0.0199SLOPE
0.04220.0199COR-0.1327
0.0585
EGFR EQUATIONINTERCEPT
0.05470.0024SLOPE
0.01
0.0006COR
0.319
0.0739CROSS EQUATION COR
TKV INT & EGFR INTERCEPT-0.2448
0.051
TKV INTERCEPT & EGFR
-0.5767
0.0478SLOPE
TKV SLOPE & EGFR
-0.02210.07
INTERCEPTTKV SLOPE & EGFR SLOPE-0.1272
0.0702

95% CREDIBLE
INTERVAL(1.2593, 3.489)(4.2737, 8.419)(-0.0021, 0.1576)(0.0317, 0.1551)(0.2749, 1.87)(1.0897, 2.5403)(0.6065, 0.6867)(0.0387, 0.0459)(-0.2433, -0.0178)(0.0501, 0.0594)(0.0089, 0.0112)(0.1726, 0.4653)(-0.3413, -0.1457)(-0.6666, -0.4771)(-0.1580, 0.1170)(-0.2584, 0.0180)

TUE-066

An in silico model of avadomide-induced neutropenia enables virtual clinical dose andschedule finding studies
Authors:
Roberto A. Abbiati1, Michael Pourdehnad2, Daniel W. Pierce2, Shailaja Kasibhatla3, Mark McConnell4, Soraya
Carrancio3, Alexander V. Ratushny4, Cristina C. Santini1
Institutions:
1

Bristol Myers Squibb, Celgene Institute for Translational Research Europe, Seville, Spain

2

Bristol Myers Squibb, San Francisco (CA), USA

3

Bristol Myers Squibb, San Diego (CA), USA

4

Bristol Myers Squibb, Seattle (WA), USA

Objectives:
Neutropenia is the most common adverse event experienced by clinical trial patients treated with avadomide, a
modulator of the E3 ubiquitin ligase complex and a potent antitumor and immunomodulatory agent. Preclinical datafrom an in vitro assay of neutrophil differentiation has previously supported the introduction of an intermittent 5/7day schedule which improved tolerability reducing frequency and severity of neutropenia (1-3). In this context,
mathematical models are a convenient tool to explore schedules that maximize efficacy and minimize toxicity.
Published models (4) of drug-induced neutropenia focus on chemotherapy and are inappropriate for avadomide given
the different mechanism of neutropenia. Avadomide leads to cereblon-mediated degradation of the hematopeoetictranscription factor Ikaros, leading to myeloid maturation arrest at a promyelocyte stage (3). Based on thisunderstanding, we developed a mathematical model for avadomide-induced clinical neutropenia to enable explorationof dosing schedules for avadomide as monotherapy or in combination.
Methods:
The mathematical model has three components: (i) neutrophil life-cycle, (ii) avadomide pharmacokinetics (PK), and(iii) pharmacodynamics (PD). The neutrophil life-cycle component captures maturation stages of neutrophil withinbone marrow, controlled cell retention/egress to systemic circulation, and cell death under both homeostatic andperturbed conditions. The PK/PD components determine avadomide concentration-dependent perturbations ofneutrophil homeostasis. The model is determined by a system of ordinary differential equations and is informed byexpert knowledge, literature, in vitro and clinical trial data (1-5). The model nominal parameter set for the homeostatic
condition is based on literature data. Disease-specific models are obtained by fitting relevant parameters from thenominal set to the absolute neutrophil count (ANC)-time profiles for avadomide-induced neutropenia in glioblastoma(GBM) and Diffuse Large B-cell Lymphoma (DLBCL) patients. Parameter value distributions were generated byfitting disease-specific models to individual patients from actual GBM or DLBCL cohorts. Virtual patient cohorts
were created by random sampling from these distributions.
Results:
The model was successfully fit to clinical data and quantitatively captured key characteristics of ANC-time profiles:(i) delayed ANC drop following avadomide administration, (ii) dose- and schedule-dependent changes of ANC-time
profile patterns, (iii) ANC recovery following treatment interruption, and (iv) disease specific (GBM vs DLBCL)
differences in neutrophil dynamics in blood. The model was validated by applying a Kolmogorov-Smirnov test to
confirm that model-generated ANC-time profiles and validation clinical dataset belong to the same continuous
distribution.

Conclusions:
We developed an in silico model of avadomide-induced neutropenia. The model was calibrated and validated onclinical data and applied to simulate a virtual patient cohort to explore avadomide dose/schedule regimens thatminimize neutropenia.

References:[1] Chiu et al., Br J Haematol. 2019_Feb_14.
[2] Santini et al., ISCB Comm. J:132(poster)_2019.
[3] Carpio et al., Blood. 2020_Jan_23.
[4] Friberg et al., J Clin Oncol. 2002_Dec_15.
[5] Carpio et al., ASH abstract#1494. 2015.

TUE-067

Exact solutions for the rapid expansion of blasting T cell populations
Authors: Ronny Straube & Brian J. Schmidt
Affiliation: Quantitative Clinical Pharmacology, Bristol Myers Squibb, Princeton, NJ, USA
Objectives: When lymphocytes encounter their cognate antigen in the thymus they become activated and undergo afinite number of cell divisions before they differentiate into memory or effector cells. We have previously proposedan age-structured model [1] together with preliminary solutions and fits to experimental data. Here, we provide new,time-dependent solutions for the case that the lymphocytes are activated from a pool of naïve cells undergoing a
rapid expansion and subsequent contraction phase. We show that our model can describe T cell expansions for awide range of input stimuli [2].
Methods: To describe the finite number of cell divisions of blasting lymphocytes we assume that theirdifferentiation rate increases with the number of undergone divisions while their proliferation and death rate remains
constant. As a result, we obtain an infinite dimensional (age-structured) system of ODEs which can be mapped to apartial differential equation for a generating function. The latter equation is solved for a linearly increasing
differentiation function and different choices for the activation rate function.
Results: If the activation process is not explicitly modeled the total cell number can be described by a double
exponential function. In the more realistic scenario, when T cell activation occurs from a pool of naïve (resting)cells, the total cell number can be expressed in terms of G-functions. Both solutions compare well with cell
expansion experiments where mouse CD8 T cells were stimulated either with different levels of IL-2 or
combinations of input stimuli [2]. The fits of our model to the data suggest that the initial propensity to differentiate
into an effector cell decreases with increasing stimulus which is consistent with previous findings that the division
destiny of a cell is set by the strength of the input signal [3]. Finally, we derive a solution for a constant (quasistationary) T cell activation rate which might be useful to parametrize QSP models.
Conclusions: Our model provides explicit time-dependent solutions for the rapid expansion and subsequent
contraction phase observed in blasting T cell populations. These expressions can be used to extract rate constants for
activation, proliferation, differentiation and apoptosis from in vitro proliferation assays using labeled lymphocytes.
The theoretically rigorous lymphocyte proliferation model proposed here may give rise to new QSP model
sensitivities and better predictive behavior than less rigorous approaches. We envision potential applicability for avariety of QSP models with an adaptive immune response component, including systems models of type 1 diabetes,
immunogenicity, and immuno-oncology.
References:[1] Straube R, Schmidt BJ, Thalhauser CJ. An age-structured model for blasting T cell populations. Poster
presentation, ACoP 10, Orlando (FL)[2] Marchingo JM et al. (2014) Science 346 1123 – 1127.
[3] Heinzel S, et al. Nat Immun 2017; 18, 96 – 104.

TUE-068
Comparative study of alternative models capturing Target Mediated Drug Disposition(TMDD) for CD40 agonists
PC Seshasai1, Anuraag Saini1, Mrittika Roy1 and Rukmini Kumar1
1
Vantage Research, India
Objectives:
1.

2.

Evaluate the trade-off between Target mediated drug disposition (TMDD) models versus simpler models
such as Michaelis-Menten (MM) or Quasi steady-state (QSS) with fewer parameters, in the context ofsparsely available clinical data.
Identify the most suitable model using available PK data from three CD40 agonist therapies to get
predictions on tumour concentrations.

Methods: A full TMDD modelling approach captures mechanistic interactions between antibody and receptor and is
standard for characterising the non-linear pharmacokinetic behaviour of a drug. However, TMDD models may beover-parameterized and difficult to converge when data is limited. In such cases, approximations of TMDD models
such as QSS and MM models can be used to estimate model parameters [1]. In this work, we have used a two
compartment TMDD model. The drug undergoes linear clearance from the plasma compartment and receptormediated endocytosis from both plasma and tumour compartment. We used plasma PK data of three CD40 agonists
namely CDX1140, CP870, 893 and ChiLob7/6 from various clinical studies and evaluated the fit and performancebased on the value of AIC from these alternative models. The model with the least AIC value was chosen as the bestmodel. Further, we predicted the amount of drug reaching the tumour compartment. AUC (area under theconcentration-time curve) analysis were done for the three CD40 agonists and they were ranked based on their
exposure in the tumour compartment.
Results: Based on our analysis the exposure of various CD40 agonists in the tumour compartment was estimated andcompared. CD40 agonists were ranked based on their exposure in the tumour tissue. The AUC for both plasma andtumour compartments is plotted against the doses for different model. The best model should have minimal numberof parameters estimated from the model to describe the data with acceptable precision [2]. TMDD model is overparameterized and some of the parameter estimates were not reliable. While MM model provides the most precise andunbiased parameter estimates and predictions for free drug concentration. Therefore, MM model is therefore the best
among the 3 models for predictions given a sparse dataset.
Conclusions: After analyzing 3 types of models for capturing TMDD, we arrived at the following conclusions. MMmodel is a good option when PK data is sparse as it explains the data with acceptable precision using minimal numberof parameters. QSS models, while having fewer parameters are often inconsistent with full TMDD model predictions.
References:
1. Gibiansky L, Gibiansky E, Kakkar T and Ma P (2008) Approximations of the target-mediated drug
disposition model and identiﬁability of model parameters. J Pharmacokinet Pharmacodyn
doi:10.1007/s10928-008-9102-8.
2. X. Yan, D. E. Mager, W. Krzyzanski (2010), Selection between Michaelis-Menten and target-mediated drug
disposition pharmacokinetic models. J Pharmacokinet Pharmacodyn 37:25-48.

TUE-069

A pharmacometrics approach to assess feasibility of capillary microsampling to replacevenous PK sampling in clinical studies
Authors: Sagar Bachhav1, Max Taylor2, Ana Martin2, Justin Green2, Stephan Duparc3, Katie Rolfe2, Hema Sharma2,
Lionel Tan2, Navin Goyal1

1

GlaxoSmithKline, Collegeville, PA, USA; 2GlaxoSmithKline London, UK; 3Medicines for Malaria Venture, Geneva,
Switzerland;
Objectives: Microsampling offers numerous advantages of being less invasive, requiring small blood volume (50100 μL) and well suited in vulnerable populations, as compared with conventional venous sampling in clinical studies.
Current regulatory guidance recommends bridging or correlative studies to enable microsampling as a PK sample
collection technique. A population pharmacokinetic (POP PK) approach is employed to assess the feasibility of blood
microsampling as an alternative to venous sampling. Interim data from a pediatric study evaluating tafenoquine (TQ),
a new single-dose anti-malarial for the radical cure of Plasmodium vivax (P. vivax) malaria in adults was utilized inthis analysis.
Methods: Pediatric subjects (bodyweight range 12-54 kg and age range 2-15 years) received TQ doses based on bodyweight. Sparse PK samples were collected using both venous and capillary microsampling around days 3, 15, 29 and
60. A direct comparison of TQ PK concentrations (venous and capillary microsampling data) was conducted using
correlation and Bland-Altman analysis. Subsequently, two separate POPPK models were developed and qualified
using bootstrap, goodness-of-fits and predictive checks. POPPK model post-hoc exposures (AUC, Cmax) were
estimated for each individual based on venous and microsampling data. Simulations were conducted to predict TQ
exposures and dose recommendations across pediatric weight bands and compared against each of the blood sampling
methods.
Results: The correlation and Bland-Altman analysis did not meet the a priori established criteria - positive correlation(ρ≥0.98) and limits of agreement within 5% of the median concentration in each quartile across entire concentration
range. This is potentially attributed to the underlying stochastic variability in the data. Each POPPK model adequately
described TQ PK using a 2-compartment model with bodyweight based on allometric scaling of apparent clearance
and volume of distribution. The two models were qualified based on results of bootstrap analysis, goodness-of-fit plots
and visual predictive checks. TQ PK parameter estimates were very similar across the two models with slightly higher
between-subject variability (BSV) in PK data from capillary microsampling. The median predicted AUC acrossdifferent dose regimens and weight bands were comparable across the two models (Figure 1). Predicted exposure
range across the body weight bands overlapped with slightly wider prediction intervals for capillary microsampling
given the higher BSV (Figure[b]). The two models recommended the exact same dosing regimen.

Conclusions: The current analysis based on interim data demonstrated that a POPPK approach successfully evaluatedthe performance of an alternative PK sampling technique. The pharmacometric approach provides a robust solutionin scenarios where random variability in PK data collected via venous and microsampling does not result in a 1:1
correlation. The findings establish that, when employed appropriately, microsampling techniques can potentiallyreplace the conventional venous sampling method. Availability of such alternative PK sampling methods is highly
useful across any drug development program.

●
Figure 1: Comparisons of predicted exposure based on microsampling and venous sample model. Simulations (a)with post hoc individual estimates at different doses, (b) with population estimate vectors from bootstrap runs(the solid line in forest plot indicates 95% prediction interval [PI] and point ‘●’ or ‘●’ indicates median).

TUE-070

Mechanistic TK-TD Modeling of Agonist Anti-TrkB Therapeutic Antibody for Biomarker
Rationalization and Safety Risk Assessment
Seung-Wook Chung, James Smith, Qihong Huang, Warren Ku
Nonclinical Drug Safety US, Boehringer Ingelheim, Ridgefield, Connecticut, United States
Objectives
Tyrosine receptor kinase-B (TrkB) signaling is known to play a vital role in the survival of neuronaltissues, and thus an agonist therapeutic antibody targeting TrkB is expected to protect neurons and glial
cells in the retina of diabetic patients. However, it has also been reported that TrkB agonists (bothendogenous ligands like brain-derived neurotrophic factor (BDNF) and therapeutic antibody) can increase
food intake and body weight in non-human primates (NHPs) [1, 2]. In this study, we demonstrate how
toxicokinetic-toxicodynamic (TK-TD) modeling and simulation (M&S) can inform preclinical safety
evaluation decisions with regard to biomarker and assay selection, and risk assessment.

Methods
First, to determine whether BDNF can be an appropriate surrogate of systemic target engagement for
toxicology studies, a mechanistic TK-TD model for intravenous (IV) injection was developed based onliterature findings including reported PK-PD observations of TAM-163 (Pfizer) [1] and TrkB-BDNF
biology (e.g., target expression/shedding/turnover, target-ligand/-drug binding, etc). Next, to assess a
potential clinical risk of drug-mediated obesity in diabetic retinopathy patients, different TK-TD models
for intravitreal (IVT) administration in monkeys and humans were developed with respect to body weightgain as a TD endpoint [1, 2].

Results
Key predictions obtained from the first model are as follows: (1) The systemic BDNF does not seem asensitive target engagement marker due to its minimal post-dose elevations; (2) Free and total BDNF arealmost identical due to the very minimal impact of soluble TrkB binding to BDNF; (3) Post-dose serum
BDNF elevation can be sustained for long because of prolonged target engagement. The second model
predicts that after monthly intravitreal administration of agonist TrkB mAb (e.g., TAM-163), body weightgain may be noticeable (~6-10%) in NHPs but almost negligible (~1%) in humans. The result implies a
low obesity risk in diabetic retinopathy patients and thus, additional de-risking studies with regard toobesity are not recommended.

Conclusions
M&S can guide the design of preclinical toxicology studies by helping select right biomarkers, rightassay, and right sample collection times. M&S can also serve as a safety de-risking tool.

References[1] Vugmeyster Y. et al. (2013) mAbs 5:3, 373–383[2] Perreault M. et al. (2013) PLos One 8(5): e62616

TUE-071
A Population Pharmacokinetic and Pharmacodynamic Analysis of Anti-ADAMTS-5 Nanobody to
Support the Phase II Trial Design
Lanke Shankar PhD1, Girard Pascal PhD 2, Flavie Moreau1, Christoph Ladel PhD3, Hans Guehring MD3, Goteti
Kosalaram PhD1

1

EMD Serono, Inc., Billerica, MA, USA, a business of Merck KGaA, Darmstadt, Germany; 2Merck Institute ofPharmacometrics, Lausanne, Switzerland, an affiliate of Merck KGaA, Darmstadt, Germany; 3Merck KGaA,
Darmstadt, Germany

Objective: To develop a population pharmacokinetic (PK) and pharmacodynamic (PD) model of anti-ADAMTS-5(M6495) nanobody and simulate phase II trial dosing scenarios.

Methods: Data from phase I single ascending dose (SAD) and phase Ib multiple ascending dose (MAD) studies wereused for PK/PD model development. The SAD study included 54 healthy volunteers that received a single dose of
subcutaneous M6495 (1, 5, 20, 75, 150 and 300 mg) or matching placebo (n=3 in the 6 dose groups). The MAD study
included 32 patients with symptomatic knee osteoarthritis who received M6495 75, 150 and 300 mg every other week,
300 mg weekly, or matching placebo (n=2 in the 4 dose groups) for six weeks. PK (serum) and PD (Alanine-ArginineGlycine-Serine [ARGS] biomarker) samples were assessed at prescheduled intervals. Nonlinear mixed effects
modeling was performed using NONMEM (version 7.3.0) and R (version 3.5.1) for data manipulation, dataset
simulation, and for plots. The First Order Conditional Estimation with Interaction (FOCEI) estimation method wasused. Criteria for model selection were based on a likelihood ratio test with p<0.01 for inclusion in the model and
p<0.001 for backward elimination. The PK/PD model was used to simulate phase II dosing scenarios.

Results: The M6495 concentration-time profiles were well captured by the two-compartmental base model, with azero and first order absorption process and a linear plus non-linear drug elimination process observed. The model
improved with inception of a lag in the absorption process (decrease in objective functional value by 21 points). No
significant covariate effect was observed on any PK parameters. Interindividual variability (IIV) was tested on allparameters and were retained on clearance, volume of distribution of central compartment, and absorption rate
constant. The target PD (ARGS level) was described effectively with an indirect response model in which

(predicted) M6495 concentrations inhibited ARGS production. IIV was tested on all parameters and were retainedon baseline ARGS (E0). IIV was not retained on other PD parameters. The dose- and regimen-stratified VPCs
showed that the observed PK/PD profiles were well within the respective model based on simulated 90% prediction
intervals.

Dosing scenario simulations showed that greater than 70% ARGS suppression was observed with a dose of greaterthan 75 mg once a month. A dose of 25 mg M6495 was distinguishable from placebo. The dose that elicited two
thirds of the effect compared to the top dose was 25 mg M6495. A monthly dosing injection scenario appears to befeasible.

Conclusions: Based on the totality of available data, a population PK/PD model was successfully developed todescribe the M6495 PK and ARGS levels in healthy volunteers and patients. This preliminary PK/PD model predicted
the emerging PK and ARGS profiles in osteoarthritis patients.

TUE-072
Comparison of pharmacokinetics and QTc effect of Quizartinib in Japanese and non-Japanese patients with
Relapsed/Refractory (R/R) FLT3-ITD positive acute myeloid leukemia (AML) using population
pharmacokinetic (PopPK) analyses
Shintaro Nakayama1, Masaya Tachibana1, Elizabeth Ludwig2, David Jaworowicz2, Hannah Huang2, Jill FiedlerKelly2, Dongwoo Kang3, Malaz Abutarif3, Ophelia Yin3, Hitoshi Ishizuka1, Kazutaka Yoshihara1
1

Daiichi Sankyo Co., Ltd. Tokyo, Japan; 2 Cognigen Corporation, a Simulations Plus company, Buffalo, NY; 3
Daiichi Sankyo, Inc., Basking Ridge, NJ

Objectives: The PopPK and C-QTc analyses were conducted to compare the pharmacokinetic (PK) profiles and
QTc effect of quizartinib in Japanese and non-Japanese patients.
Methods: Data for PopPK analysis were obtained from the participants in quizartinib treatment arms of the 7 global
studies and 2 Japanese studies. The previously developed PopPK models for quizartinib and its metabolite AC886
were updated using the pooled data. The affect of Japanese population was then evaluated as a covariate on relevant
clearance and volume parameters. A sensitivity analysis was performed with only Japanese studies. Mean of
triplicate ECG measurement and observed time-matched concentrations of quizartinib and AC886, obtained from 1
Japanese study and 1 global study, were used for concentration-QT interval corrected using Fridericia’s
formula (C-QTcF) analysis. To establish the relationship between quizartinib and AC886 concentrations and QTcF,
a previously developed C-QTcF model was used (Dongwoo Kang et. al., 2019). A full model was formed byinclusion of the affect of Japanese population on baseline QTcF and Emax for quizartinib and AC886.
Results: The PK of quizartinib in healthy volunteers and non-Japanese and Japanese patients with AML was wellcharacterized by a 3-compartment model with sequential zero- and first-order absorption and linear elimination. The
time course of quizartinib and AC886 concentrations in Japanese patients was adequately described using the samestructural popPK model with updated parameter estimates. Japanese population was not found to be a statisticallysignificant covariate on quizartinib and AC886 PK parameters. The estimate of quizartinib CL in the sensitivity
analysis model was consistent with the CL estimate in the final PopPK model, suggesting that the model was robust
enough to accurately characterize quizartinib disposition in the Japanese population alone. The C-QTcF relationshipin Japanese patients was adequately described by the same structural model developed for the global study withparameters updated. Covariate of Japanese population was evaluated on baseline QTcF, Emax of quizartinib, andEmax of AC886 and were not statistically significant. The predicted median ΔQTcF at the geometric mean peakplasma quizartinib concentration (378 ng/mL) and corresponding AC886 concentration (205 ng/mL) on Cycle 1 Day
28 after once-daily dosing of quizartinib at 60 mg using actual dosing history in patients was 19.9 msec [90% CI:
17.8, 22.0 msec].
Conclusions: The PopPK analysis demonstrated similar PK profiles of quizartinib and AC886 in Japanese and nonJapanese AML patients. There were no significant differences in the C-QTcF relationships for quizartinib andAC886 between the Japanese and non-Japanese AML patients. These results support the same dosing regimen for
quizartinib in Japanese and non-Japanese AML patients.
Reference: Dongwoo Kang et. al., (2019) 24th Congress of European Hematology Association. Population
pharmacokinetics and concentration-QTc analysis of quizartinib in patients with FLT3-ITD positive
relapsed/refractory acute myeloid leukemia.

TUE-073
Population Pharmacokinetics of Melphalan for Pediatric Patients Undergoing Allogenic
Hematopoietic Cell Transplantation
Shuhui Li1, Danna Chan2, Christopher C. Dvorak3, Alexis Melton3, Elyes Dahamane1, Jogarao Gobburu12, Vijay
Ivaturi1*, Janel Long-Boyle3,4
1

School of Pharmacy, University of Maryland, Baltimore, MD, USA; 2ASTEX Pharmaceuticals, Pleasanton, CA,

USA; 3Department of Pediatrics, Division of Allergy, Immunology, and Bone Marrow Transplantation, University ofCalifornia San Francisco, San Francisco, CA, USA; 4Department of Clinical Pharmacy, University of California San
Francisco, San Francisco, CA, USA.

Objectives: Melphalan is an alkylating chemotherapeutic agent widely used in combination pretransplantconditioning prior to hematopoietic stem-cell transplantation (HCT). The pharmacokinetics (PK) of melphalan
exhibits significant interpatient variability. This study was the first to characterize the PK of melphalan in the pediatricallogenic HCT (alloHCT) patient population, including evaluation of potential patient-specific covariates impacting
drug exposure.

Methods: Twenty-nine patients (1.4 -18 years old) who received melphalan as part of conditioning were recruited ina single center, prospective, open-label, non-interventional study. The most common dosing regimen (25/29) was 140
mg/m2, given as either one single dose (140 mg/m2 x 1) or two equal doses on two consecutive days (70 mg/m2 x 2).
Five samples per patient were taken up to 3 hours after infusion. Non-linear mixed effects modelling was utilized tocharacterize the 145 observations, using Pumas v0.10.0 (Pumas-AI Inc., Baltimore, MD, USA). Patient-specificcovariates were tested to explain the variability in PK parameters, including actual body weight, lean body mass, andmarkers for renal function. The final covariate model was used to evaluate the doses of melphalan over a wide rangeof ages as compared to the conventional dosing of 70mg/m2 x 2 doses.

Results: A 2-compartment model with proportional residual error model best fitted the data with the final estimationin agreement with the previously reported values: clearance 18.3 L/hr/25 kg, volume of the central compartment 7.7
L/25 kg, volume of the peripheral compartment 5.7 L/25 kg, intercompartmental clearance 12.1 L/hr/25/kg, and 12.3%

2

Gobburu and Ivaturi are co-founders of Pumas-AI that commercializes Pumas software.

residual unexplained variability. Actual body weight was found to explain a significant portion of the variability inthe parameters with the reduction of the between-subject variability up to 62% (CV%); thus, was retained in the final
model. Variability for all the PK parameters was less than 30% in the final covariate model. The median daily AUC
for 70mg/m2 cohort was estimated to be 3.3 mg·hr/L that was used as the therapeutic target to project the doses forpediatric patients with body weight range of 10.0 kg to 64.7 kg (age: 2 – 17 years old). The model-informed dose wasshown to be slightly lower than typical body surface area dosing.

Conclusions: In summary, a population PK model was developed to describe the PK of melphalan in a pediatricalloHCT population. Among all covariates tested, actual body weight was the most significant covariate to explain
the variability in the PK parameters. These results suggested that individualized, model-based dosing of melphalan
can optimize melphalan exposure, reduce the variability in exposure, and have the potential to improve clinicalefficacy while minimizing the regimen related toxicity.

TUE-074
Development of a PK/Efficacy model supplemented with mixture and logistic regression models fordescribing the heterogeneity of tumor growth inhibition in tumor-bearing mice in immuno-oncology
Siak-Leng Choi1, Xavier Boulenc1, Christine Mauriac1, Delphine Valente1
1
Sanofi, R&D DMPK France, Alfortville, France
Objectives: Heterogenous distribution in responding to tumor growth inhibition (TGI) can lead to different
subpopulations exhibiting distinct TGI responses after receiving repeated dosing in immuno-oncology (IO).
Although the underlying mechanism of action is unclear, developing a semi-mechanistic population PK/TGI model
supplemented with both mixture and logistic regression models is necessary to predict the population TGI response
being impacted by the heterogeneity for exploring different dosing schemes.
Methods: Plasma PK and longitudinal TGI data following oral and intravenous (IV) dosing in mice were collected.
The semi-mechanistic PK/TGI model adopts a TGI model described by Simeoni et al.[1], incorporating with amixture model [2] to estimate the proportion of subpopulations (i.e., responders and non-responders). Subsequently,
the discrete data of subpopulation grouping assigned by mixture model was analyzed by logistic regression model
for its correlation with PK metrics (Cmax and AUC). Thereafter, the resultant logistic regression model was
combined with the semi-mechanistic population TGI model for the simulations. These analyses were performed
using NONMEM® version 7.3 (ICON Development Solutions, Ellicott City, MD, USA).
Results: PK observations from 79 mice following single- and repeated dosing were collected. Whereas, TGI
observations collected from days 7 to 36 from 146 MC-38 tumor bearing mice, receiving different dosing schemesof oral and IV repeated dosing, from three efficacy studies were pooled. A two-compartment PK model links with asemi-mechanistic TGI model, incorporating with a mixture model to estimate the second-order tumor killingparameter (kkill) for each subpopulation by assuming 50% population probability of being responder, successfully
distinguishes responders' TGI profiles from non-responders. This model estimates typical kkill in responders is about
60-fold higher than non-responders. Based on the logistic regression model, total Cmax (Cmax∙ Number of doses)
correlates significantly with the proportion of responders. Combining with the resultant logistic regression model,
the semi-mechanistic TGI model allows an adequate prediction of the partial- and complete tumor responder rates,
the population median regression rate in relative to vehicle group (dT/dC), and the probability of a dosing scheme tobe efficacious.
Conclusions: The model adequately predicts the heterogeneity of population TGI and is warranted to be utilized for
informing the active dose selection in IO at the preclinical setting.
References :[1] Simeoni M et al. (2004) Predictive pharmacokinetic-pharmacodynamic modeling of tumor growth kinetics inxenograft models after administration of anticancer agents. Cancer Res. 64(3):1094-101.
[2] Carlsson KC et al. (2009) Modeling subpopulations with the $MIXTURE subroutine in NONMEM: finding theindividual probability of belonging to a subpopulation for the use in model analysis and improved decision making.
AAPS J;11(1):148-54.

TUE-075

PK/PD Model Prediction of Platelet Reduction as a Guide for Dose-Escalation Design of Navitoclax
Added on to Ruxolitinib in a Phase 2 Study
Silpa Nuthalapati1, John Hayslip3, Rajeev Menon1, Sven Mensing2

1

Clinical Pharmacology and Pharmacometrics, AbbVie, North Chicago, IL, USA; 2Clinical Pharmacology andPharmacometrics, AbbVie, Ludwigshafen, Germany; 3Clinical Development, AbbVie, North Chicago, IL, USA

Objectives: Navitoclax, a first-in-class BCL-2/BCL-XL inhibitor is being investigated in combination with thecurrent standard of care for myelofibrosis, ruxolitinib (JakafiTM, JAK1/2 inhibitor) in primary and secondary
myelofibrosis. Both navitoclax and ruxolitinib cause thrombocytopenia via different mechanisms: navitoclax byperipheral platelet apoptosis and ruxolitinib by slowing platelet production. We report a model-based approach toquantitate the magnitude of thrombocytopenia for the navitoclax and ruxolitinib combination to recommend
navitoclax starting dose and ramp up doses.

Methods: A single arm, open-label Phase 2 proof-of-concept study was designed to evaluate safety and efficacy ofnavitoclax added to ruxolitinib in patients with primary or secondary myelofibrosis (NCT03222609). Subjects mustbe currently on a stable dose of ≥ 10 mg BID of ruxolitinib without achieving an adequate clinical response toruxolitinib. A mechanistic model modifying the literature reported Friberg model for neutrophils was developed toevaluate the effect of navitoclax and ruxolitinib on platelets count based on navitoclax data from previous studies
and literature data for mean platelet profiles with ruxolitinib. Navitoclax effect was modeled to increase theelimination of circulating platelets while ruxolitinib inhibitory effect was on platelet proliferation. The combined
PK/PD parameters were used to perform simulations of the add-on effects of navitoclax at steady dose of ruxolitinib(10 mg BID) to aid in navitoclax dose escalation planning for different dosing scenarios of navitoclax

Results: The mechanistic PK/PD model was fitted to individual navitoclax platelet count data from 256 subjects
receiving oral navitoclax (dose range 10–475 mg) as a 14/21-day schedule or a continuous once daily (QD) scheduleand literature data for mean platelet profiles for ruxolitinib.

Simulations conducted using the combined PK/PD model to evaluate weekly or bi-weekly dose escalations ofnavitoclax from 50 mg QD to 300 mg QD added on to 10 mg BID of ruxolitinib showed that:§

The maximum decrease in platelet count was predicted to be comparable between weekly dose escalation
versus biweekly dose escalation.

§

Dose escalating weekly achieved target dosing of 300 mg 4 weeks earlier.

§

A starting dose of 25 mg showed only a minor median difference in platelet counts when compared to 50
mg dose.

The recommendation was to start with 50 mg navitoclax QD co-administered with a stable dose of ruxolitinib BID
and weekly escalation of navitoclax, with careful platelet monitoring in the Phase 2 study.
Conclusions: A mechanistic PK/PD model was developed to predict the effect on platelets for combination drugs
causing thrombocytopenia by different mechanisms. Based on the model-based approach, a navitoclax starting doseof 50 mg QD followed by weekly dose ramp up to 300 mg QD at a steady dose of ruxolitinib was chosen in the
proof-of-concept study to manage thrombocytopenia and maximize exposures to navitoclax.

Clinical trial information: NCT03222609
Author disclosures:
All authors are AbbVie employees and may hold stock or options

AbbVie disclosures:

AbbVie provided financial support for the study and participated in the design, study conduct, analysis andinterpretation of data as well as the writing, review and approval of the abstract.

TUE-076

In vitro–In vivo Relationship and Bioequivalence Prediction of ziprasidone Capsules
based on a PBPK Model.
Authors: Frederico S. Martins (1), Fabricia Ré (2), Fábio P. de Souza (2), Francine A. de Castro (3), Stephan Schaller(1), Rodrigo Cristofoletti (4), Marli M. Lima (2), Andrea Diniz (2)Affiliations: (1) esqLABS GmbH, Saterland, Germany; (2) State University of Maringá, Paraná, Brazil, (3) Universityof São Paulo, São Paulo, Brazil; (4) Center for Pharmacometrics & Systems Pharmacology ,University of Florida, FL,
US;
Objectives: The aim of this study is the comparison of two immediate release formulations containing ziprasidone(ZIP) using PBPK-based virtual bioequivalence (BE) trials.
Methods: The in-vitro dissolution assessment was performed in flow-through cell dissolution apparatus (USP
apparatus-4) Sotax CE 7 Smart and the obtained dissolution profiles were incorporated into a validated ZIP-PBPK
model, which was previously developed in PK-Sim/MoBi [1]. The comparison of dissolution profiles was conductedby calculating the difference factor (f1), and the similarity factor (f2). . To perform the virtual BE assessment, a
population of 1000 healthy adults, representative of the gender ratio and demographics of the sampled cohort
recruited for the in vivo study were simulated. The in vivo BE study was an open-label, 2-period, randomized, single
dose, crossover study in 40 healthy adults from both sexes with 21-50 years old. In each period, subjects received
either the Brazilian reference listed drug, Geodon® (40 mg of ZIP hard capsule) or the test formulation (40 mg ZIP
capsule). For each subject and formulation, Cmax and AUC of the study data were computed by NCA, and thegeometric mean and CI-90% were derived for each formulation.
Results: The PBPK model was qualified with i.v and oral dose data within single and multiple dose regimens. The
simulated PK parameters were consistent with the observed values; the minimum folder error (MFE) values for the
AUC and Cmax ranged from 0.7-1.2 0.8-1.2, respectively (Figure1). A model is considered robust when MFE values
are well within a range of 0.5 - 2.0. The similarity factors between both formulations were f1=12.4 and f2=65.2,
values of f1 ≤15 and f2≥50 are considered within the similarity range defined by the guidelines[2]. Twoformulations are considered bioequivalent when the CI-90 of AUC and Cmax are within 80-125% [2] .For the present
study, the CI of Cmax lies within 112-128% for the clinical data and within 122-129% for the virtual population,
which indicates non-bioequivalence.
Conclusions: The confirmation of the observed CI for Cmax by the PBPK analysis through the performed analysis
demonstrates, that a validated PBPK absorption model is an appropriate tool for the translational evaluation of invitro dissolution properties to in vivo product pharmacokinetic performance.
References:1

Biesdorf C, Martins FS, Sy SKB, Diniz A. Physiologically-based pharmacokinetics of ziprasidone in pregnant
women. Br J Clin Pharmacol 2019; 85: 914–923

2

Fda, Cder, Purdief. Guidance for Industry Bioavailability and Bioequivalence Studies Submitted in NDAs or
Ihttp://www.fda.gov/Drugs/GuidanceComplianceRegulatoryInformation/Guidances/default.htm

Figure 1: Simulated and observed plasma concentration proﬁles for oral administration of Geodon and genericformulation.

TUE-077
Meta-analysis of dapagliflozin dosing to assess influence on low density lipoprotein (LDL) cholesterol in type2 diabetes mellitus (T2DM)
Srinivas Martha, Preethi Hepzibah Jangam, Suraj G. Bhansali
ClinPharm, Excelra Knowledge Solutions, 6th & 7th Floor, Wing B NSL SEZ ARENA, IDA Uppal, Habsiguda,
Hyderabad, Telangana State 500039, India.
Objectives:
Dapagliflozin is widely used novel oral anti-diabetic drug for type-2 diabetes mellitus (T2DM) patients whose glucose
levels are inadequately controlled by metformin along with diet and exercise. A systematic literature review (SLR)
was performed and meta-analysis was done to evaluate the effects of dapagliflozin on LDL cholesterol in T2DM.
Methods:
SLR was conducted using MEDLINE, EMBASE, and the Cochrane Library from 1950 to January 2020 for trials ofat least 12 weeks duration in which diabetic patients were treated with either dapagliflozin monotherapy or as an addon therapy. Data on change in LDL cholesterol levels were pooled in a meta-analysis. Data from dose-comparison
trials were separately pooled.
Results:
A total of 44 trials were identified for the analysis and 6 trials for the dose comparison analysis. Dapagliflozin
monotherapy (1, 2.5, 5, 10, 20 mg) increased LDL cholesterol by 2.5 mg/dL (95% CI 1.17 to 3.81; I2 = 0%) versus
placebo or active comparator; dapagliflozin added to oral therapy increased LDL cholesterol by 2.59 mg/dL (-1.53 to
6.72; I2 = 74%) versus placebo or active comparator. Dapagliflozin monotherapy (10 mg) increased LDL cholesterolby 3.67 mg/dL (1.61 to 5.73; I2 = 6%) versus placebo or active comparator. There was a slight increase in LDLcholesterol by 0.91 mg/dL (-4.34 to 6.17; I2 = 68%) with 10 mg dapagliflozin compared with 5mg dapagliflozin.
Conclusions:
Dapagliflozin therapy increases LDL cholesterol when used as monotherapy and in combination with other therapeutic
agents. Increase in LDL cholesterol levels are higher with 10 mg as compared to 5 mg of dapagliflozin.

TUE-078
Using longitudinal tumor size (TS) modeling to power phase 1b oncology dose finding trials

Tarjinder Sahota1, Vincent Haddad2 and Ghada F. Ahmed3
1

Clinical Pharmacology and Safety Sciences, BioPharmaceuticals R&D, AstraZeneca, Cambridge, UK.

2

Oncology Biometrics R&D, AstraZeneca, Cambridge, UK

3Formerly Clinical Pharmacology and Safety Sciences, BioPharmaceuticals R&D, AstraZeneca, Cambridge, UK.

Objectives: Dose optimization for targeted anti-cancer agents requires exploring several dose levels early indevelopment. Typically, large sample sizes are needed for adequate dose differentiation power making theconsequent delay to phase 3 a challenge. To address this, we demonstrate how exposure-TS modeling forsavolitinib (AZD6094, HMPL-504, volitinib), a potent and selective cMET inhibitor can maximize dose selection
power without inflating the sample size for the dose finding trial.
Methods: TATTON was a multi-part phase 1b study that included savolitinib+osimertinib arms in patients with
NSCLC1. Initial expansion arms (N~40/arm) explored savolitinib 600 mg QD. One subsequent expansion sought toexplore 300 mg QD for alternate dose finding. However, 80% power to detect a 20% drop in objective response rate(ORR) with 300 mg, assuming 10% 1-sided α, required 150 subjects/arm, a study deemed unfeasible. TS change
from baseline (∆TS) was used as a surrogate biomarker for ORR. To explore power using exposure-TS, subjects
were sampled with replacement from the 600 mg pharmacokinetic and TS profiles. Trials (600 vs 300) were
simulated (300/scenario) with 10, 25 and 80 patients/arm and 0-50% ∆TS loss applied for the 300 mg profiles. An
E-TS model (exponential function with first-order shrinkage rate, Keff) was fitted to the trials and a likelihood ratio
test was used to conclude significance of exposure on Keff (ΔOFV=3.84,ꭕ2df=1). The power function was calculated
as percentage of times statistical significance was achieved. A threshold of acceptable ∆TS loss with 300 mg was set
at <20% after clinical team consultation. The procedure was repeated using the dose-RR model. Resampling/reestimation was conducted in NONMEM 7.3.1 and automated via the NMproject interface2.
Results: A total of 51 TS profiles were available from the 600 mg cohort. Seventy-eight and fifty-three percent ofsubjects had at least 6 and 12 weeks follow up, respectively, thus the data reflected early change in target lesion size.
E-TS provided ~70% power to detect 20% loss in ∆TS (N=25/arm) (Figure 1). In collaboration with statistics, adose selection framework based on the widely-utilized ORR and biomarker-based Exposure-TS analyses is proposed(Figure 1).

Conclusions: Longitudinal TS modeling offered meaningful power to detect modest but relevant drops in efficacy
with 300 mg. This provided assurance for a robust dose decision where ORR would have been inconclusive forsmall but relevant effect sizes and justified an expansion arm of at least 25 subjects for 300 mg1. The E-TS model islimited by exclusion of non-target and new tumor lesions. However, the composite dose selection framework
combined multiple analyses to guide dose decision and was enabled by effective cross-functional collaboration
between clinical pharmacology-pharmacometrics and statistics.

References:1.
2.

Sequist LV, et al. Lancet Oncol.2020 Mar;21(3):373-386.
PAGE26 (2017) [www.page-meeting.org/?abstract=7357], https://github.com/tsahota/NMproject

Figure 1. Power function (N=25) using exposure-TS and dose-RR models and a framework for a composite dose
selection criteria

TUE-079
Characterisation of dapagliflozin efficacy as an adjunct-to-insulin therapy in patients with type 1 diabetes
mellitus: a semi-mechanistic exposure-response analysis
Tatiana Yakovleva1, Joanna Parkinson2, Robert C. Penland3, David W. Boulton4, Kirill Peskov1,5, Victor Sokolov1,
Weifeng Tang4
1 – M&S Decisions LLC, Moscow, Russian Federation
2 – Clinical Pharmacology & Quantitative Pharmacology, Clinical Pharmacology and Safety Sciences, R&D,
AstraZeneca, Gothenburg, Sweden
3 – Clinical Pharmacology & Quantitative Pharmacology, Clinical Pharmacology and Safety Sciences, R&D,
AstraZeneca, Boston, USA
4 – Clinical Pharmacology & Quantitative Pharmacology, Clinical Pharmacology and Safety Sciences, R&D,
AstraZeneca, Gaithersburg, USA
5 – I.M. Sechenov First Moscow State Medical University, Moscow, Russian Federation
Objectives: Dapagliflozin is a highly potent, selective inhibitor of sodium-glucose cotransporter 2 that was approvedin Europe and Japan for the treatment of type 1 diabetes mellitus (T1DM) as an adjunct to insulin therapy based on
the results of Phase 3 DEPICT programme [1, 2]. The aim of this work was to characterize the joint effect of
dapagliflozin and total daily insulin dose (TDID) on daily glucose and HbA1c using a semi-mechanistic exposureresponse model.
Methods: Data from Phase 2 (MB102-072, NCT01498185) and Phase 3 (DEPICT-2, NCT02460978) studies wereused to develop a non-linear mixed effects (NLME) model. External model qualification was performed on the datafrom Phase 3 (DEPICT-1, NCT02268214) trial. The following baseline covariates were evaluated: age, sex, diabetes
duration, body weight, BMI, fasting plasma glucose, haemoglobin, HbA1c.
The model was developed in a stepwise fashion to consecutively characterize TDID as a function of dapagliflozin
exposure (1), continuous glucose measurements (CGM) response to TDID changes (2), dapagliflozin effect on CGM(3), and the link between CGM and HbA1c (4). The forward covariate search as well as the mixture of distributions

based on a categorical covariate were performed at the last stage of model development. The models were evaluatedby convergence, precision/stability of estimates, improvements in objective functions, and visual predictive checks.
The Monolix software (version 2018R2) was used for NLME modelling. Data visualization and forward simulations
were performed in R software (version 3.5.1).
Results: The decrease of TDID with dapagliflozin treatment was successfully characterized by an Imax equation(Imax = 0.0813, IAUC50 = 19 ng/mL*h). The gradual increase of CGM with time (0.139 mg/dL/week) and the
TDID impact on CGM were calibrated using placebo data, while the effect of dapagliflozin exposure was estimatedusing the data from the active arms. Due to poor convergence of the model, IAUC50 was fixed at the previously
defined value for CGM. The changes in CGM alone were insufficient explain the HbA1c response. The adequate
description of HbA1c response and successful model validation was achieved when glucose-independent impact of
dapagliflozin was introduced to the model structure.
Conclusion: We have successfully established a quantitative link between the dapagliflozin exposure, TDID,
plasma glucose and HbA1c in patients with T1DM. An additional glucose-independent dapagliflozin impact on
HbA1c decrease was identified during model development.
References:1.

Dandona P et al. Efficacy and safety of dapagliflozin in patients with inadequately controlled type 1 diabetes(DEPICT-1): 24-week results from a randomised controlled trial. Lancet Diabetes and Endocrinol. 2017:5;84617

2.

Mathieu C et al. Efﬁcacy and Safety of Dapagliﬂozin in Patients With Inadequately Controlled Type 1 Diabetes(the DEPICT-2 Study): 24-Week Results From a Randomized Controlled Trial. Diabetes Care 2018;41:1938–
1946

TUE-081

Population Pharmacokinetics (PopPK) and Exposure-response (ER) Analysis of
Nirsevimab in Healthy Preterm Infants
Vadryn Pierre1, M Pamela Griffin2, Yuan Yuan2, Therese Takas2, Mark T Esser3, Tonya
Villafana2, Lorin Roskos1, Rajesh Narwal1, Anis A Khan1
1

Clinical Pharmacology and Safety Sciences, AstraZeneca, Gaithersburg, MD
Late RIA, AstraZeneca, Gaithersburg, MD
3
Microbial Sciences, AstraZeneca, Gaithersburg, MD
2

Objectives: Nirsevimab is a human IgG1κ monoclonal antibody with YTE-extended half-life
properties, which binds with nanomolar affinity to antigenic site Ø on the prefusion
conformation of the RSV-F protein. In a recent placebo-controlled Phase 2b study(NCT02878330), a single, fixed 50 mg nirsevimab intramuscular dose demonstrated clinicalefficacy as RSV prophylaxis in healthy preterm infants against MA-LRTI (Medically-attended
Lower Respiratory Tract Infection)1. In this work, we aim to characterize the popPK and ER
properties of nirsevimab in healthy preterm infants in order to optimize the dosing across a wider
age and weight groups of infants.
Methods: The popPK and ER characterization of nirsevimab was conducted in a pooled dataset
consisting of serum PK, ADA and demographic information from the Phase 1 study(NCT02114268; n=137) in healthy adults, the Phase 1b/2a (NCT02290340; n=71) and Phase 2b(NCT02878330; n=968) studies in infants. The median (range) baseline postnatal andpostmenstrual age ranged from 3 months (0.1 – 11.9 months) and 13.9 months (7.4 – 19.7
months), respectively. Baseline bodyweight ranged from 1.6 kg to 11.1 kg with a median of 6.8
kg. The ER properties of nirsevimab were explored using a multivariate hazard model with
popPK-derived continuous and ordered categorical quartiles of exposure metrics such as, serumconcentration C(t), Cmax, and AUC implemented as linear or sigmoidal relationships along withclinically pertinent covariates.
Results:
The popPK covariate model accounted for the impact of allometrically-scaled bodyweight onclearance and volume parameters as well as the effects of maturation on systemic clearance2. The
model was internally qualified using goodness of fit and visual predictive check plots (Fig 1).
The hazard analysis demonstrated that infants with serum AUC>Q1 have a lower risk of
MALRTI with an instantaneous hazard ratio (HR) and 95% confidence interval (CI) of 0.17
(0.09, 0.32) compared to those with lower exposure or placebo. Notably, only 40% of the infants
weighing ≥5 kg had serum AUC>Q1 compared to 97% of those weighing <5 kg. Although theordered categorical quartiles AUC model resulted in the lowest objective function value, thesigmoidal model with continuous Cmax further corroborated the protective effect of higher
exposure. Hence, a fixed weight-band dosing strategy is proposed, which results in a geometricmean ratio (GMR) of nirsevimab serum AUC 0.985[0.948, 1.02] for infants weighing<5kg
versus those weighing≥5kg. Additionally, simulated profiles indicated that the proposed dosing
will result in over 90% of infants achieving serum AUC>Q1 and a Cmax >EC80 using the weighband dosing scheme.
Conclusion: Model-based approaches resulted in identifying an optimal weight-band dosing
strategy to ensure protective exposures across a wide range of age and body weights in late stage
trials.
References: 1. Griffin P. ID week 2019, 2. Robbie G AAC. 2012

Figure 1: Visual Predictive Check of Adults (a) and Infants (b) PK Data by Scheduled Visit

TUE-083

Model-informed optimization of titration regimens to reduce nausea incidence under treatment
with GLP-1 analogues
Veronika Voronova1, Victor Sokolov1, Binbing Yu2, Holly Kimko3, Jing Li3, Marcella Petrone3, Gabriel Helmlinger3,
Kirill Peskov1, Rosalin Arends3
1 – M&S Decisions LLC, Moscow, Russia
2 – Oncology Biometrics, Oncology R&D, AstraZeneca, Gaithersburg, USA
3 – Clinical Pharmacology and Quantitative Pharmacology, Clinical Pharmacology & Safety Sciences,
BioPharmaceuticals R&D, AstraZeneca
4 – Sechenov University, Moscow, Russia
Objectives:
The administration of glucagon-like peptide-1 receptor agonists (GLP-1 RA) is associated with nausea as the mostcommon treatment-related adverse event [1]. Nausea incidence decreases over time as a result of tolerance
development over time. Therefore, dose titration is used as an effective measure to mitigate nausea risk [1]. In thiswork, we aimed to quantify the relationship between GLP-1 receptor occupancy and nausea incidence dynamics whentreating with various GLP-1 RA and explore optimal titration scheme for a novel dual glucagon-GLP-1 RA cotadutide.
Methods:
The model is based on aggregated data from 17 randomized clinical trials in healthy subjects and T2DM patients
receiving placebo, exenatide immediate or extended release (IR, ER) formulations, oral or subcutaneous semaglutide
formulations, liraglutide, or cotadutide and is represented by a system of ordinary differential equations reflecting thechange over time in plasma drug concentration, GLP-1 receptor occupancy and nausea incidence. Tolerance
development was captured by a time-dependent Emax equation. PK parameters were taken from the literature or
estimated based on publicly available data. Pharmacodynamic (PD) parameters were estimated using the data on
nausea incidence dynamics. The model compilation was done using R-based mrgsolve package.
Results:
The model diagnostics were adequate, though significant variability was observed in nausea incidence even in theframework of a single compound. The estimated adaptation rate was different across the drugs with rates ranging fromfast to slower for exenatide ER, followed by cotadutide, liraglutide, exenatide IR, and both subcutaneous and oral
formulations of semaglutide.

Different titration schemes were simulated for cotadutide, starting with daily administration of 50 or 100 ug dose andsubsequent weekly or bi-monthly increase in dose with an increment of 50 to 100 ug until the maximum dose level of
600 ug. Starting dose was found to significantly impact maximum nausea incidence (15% and 20% of patients for 50
and 100 ug respectively), while the titration schedule mostly affected the nausea during the first 2-3 weeks of thetreatment (Figure 1).

Figure 1. Predicted nausea incidence dynamics for various cotadutide titration schemes. Dose escalation was done
every week (QW) or every two weeks (Q2W)

Conclusion: We have successfully established a quantitative link between change over time in GLP-1 receptor
occupancy and nausea incidence for six GLP-1 receptor agonists. The resulting PK-PD model was used to explore
dynamics of tolerance development with different treatment regimens to inform titration strategies for cotadutide.
References:1.

Aroda VR, Ratner R. The safety and tolerability of GLP-1 receptor agonists in the treatment of type 2
diabetes: a review. Diabetes Metab Res Rev. 2011;27(6):528–542.

TUE-084
Mechanistic PK/PD Modeling in Support of a Patient Convenient Once-Weekly Dosing Regimen of
KYPROLIS (carfilzomib)
Authors: Yago MR1, Bhagwat S1, Mehta K1, Klippel Z2, Obreja M3, Chopra VS4, Dutta S1, Upreti VV1
Institutions: 1 Amgen, Inc., Clinical Pharmacology, Modeling & Simulation; 2 Amgen, Inc., Global Development; 3Amgen, Inc., Global Biostatistical Science; 4 Amgen, Inc., Clinical Biomarkers & Diagnostics
Objectives: Carfilzomib (CFZ) is an irreversible proteasome inhibitor approved for treatment of relapsed or refractory
multiple myeloma. Demonstrating exposure-response for once-weekly (QW) and twice-weekly (BIW) regimens withtypical modeling and simulation approaches is challenging due to CFZ’s unconventional PK properties. A mechanistic
PK/PD model was developed to evaluate the feasibility of less frequent CFZ dosing regimens based on its mechanismof action.
Methods: The model, based on mechanism of action, drug binding kinetics, and proteasome turnover, used acompartmental population PK model to drive irreversible binding kinetics (synthesis, recovery, homeostasis) ofproteasome inhibition (PI). The model predictions were compared with observed PI data of CFZ and bortezomib(BTZ; reversible PI) from the phase 3 ENDEAVOR study and their correlation with observed efficacy metrics wasevaluated for QW and BIW CFZ regimens.
Results: Observed CFZ and BTZ PI was in agreement with model predictions. Deeper and longer lasting PI was
predicted for CFZ versus BTZ, consistent with superior efficacy of CFZ in the ENDEAVOR trial. PI simulationsfor 70 mg/m2 QW and 56 mg/m2 BIW at steady-state were comparable (>98%), and consistent with cross-study
efficacy comparisons between 70 mg/m2 QW from the phase 3 ARROW trial and 56 mg/m2 BIW fromENDEAVOR (ORR and PFS). Expectedly, based on the irreversible mechanism of action of CFZ, higher Cmax of
CFZ with 70 mg/m2 QW (approximately 25%) compensates for the approximately 40% lower AUC with 56 mg/m2
BIW.
Conclusions: The mechanistic model demonstrated comparable PI between CFZ 70 mg/m2 QW and 56 mg/m2
BIW, which correlated well with comparable clinical efficacy between the regimens. Overall the results of themechanistic model indicate CFZ is amenable to less frequent dosing.

TUE-085

Usage of a validated environment to support modeling andsimulation activities
Vincent Buchheit1, Michael Hackl3, Katja Kuldszun2, Guillaume Schmitt2, Christian Flandorfer3(1) Roche Pharma Research and Early Development, Pharmaceutical Sciences, Roche Innovation Center
Basel, Switzerland(2) Roche Pharma Research and Early Development, Pharmaceutical Informatics, Roche Innovation Center
Basel, Switzerland(3) scinteco gmbh, pharma IT company, Vienna, Austria

Objective: Given the importance and impacts of quantitative clinical pharmacology contribution to drug
development, it has become crucial to move from an open shared drive to a more robust and validated
platform.
The objective of this abstract is to highlight the user’s perspectives on using a validated environment tosupport modeling and simulation activities and how it further improves efficiency and quality of thequantitative clinical pharmacology analyses.

Methods: Together with our IT colleagues, we defined user requirements and explored several solutions.
Quickly we noticed that improve had the potential to fulfill our needs. improve was designed to support
PKPD analysis and provides a valuable and convenient interface with modeling software.
Results: improve is a repository allowing scientists to perform their daily work, in a secured validated andversioning control environment. improve combines a global, secure file repository with a robust and versatilemodeling management interface for 3rd party tools like NONMEM®, Monolix®, Pumas, SAS®, R, PsN,
Rstudio….No matter which analysis you choose, improve will keep track of the "who, what, when, where andwhy" of your data.
improve automatically extracts key information from the users NONMEM® run, such as objective function
value, thetas, omegas, and sigmas. improve provides also a graphical representation of your analysis flowusing steps. You can flag steps to easily identify your base, full or final models.
At any time, you can go back to a previous version of your script, input data or output files. You can also
compare versions of files, or versions of outputs. This also works for entire runs, so you can compare inputs,
outputs and estimates of specific versions of a run or other runs.
Current Limitations: Besides the client, there is the improve.office Module that enables easy access to theclient repository without having to use the client itself. This is particularly useful for code development inSAS® for example. We are also using this module to save Phoenix Project Files via improve.office into theclient. A loose integration of the improve.client and improve.office led to high disc space usage andconflicts due to the manual synchronisation process. A new version is currently developed to allow a morefine-grained control which files actually are needed on the user hard drive and gives direct feedback if data in
the repository has been changed.
Conclusions: improve is currently used to support modeling and simulation activities, as well as NonCompartmental Analysis (NCA). During the course of the year, we are planning to use improve to store all
Clinical Pharmacology documents, making this new platform the single source of data and analysis for all
Quantitative Clinical Pharmacology activities.

References:www.scinteco.com

TUE-086
Development of a PBPK Model for TAK-018 (EB8018/Sibofimloc), a Bi-mannoside Compound Targeting
FimH Expressing Bacteria in Intestinal Lumen to Inform Dose Selection for theTreatment of Crohn’s Disease
Wan Sun1, Abdul Naveed Shaik2*, Grace Fraczkiewicz2, Suresh K. Balani1, Scott Butler1, Ajit Suri1, Jean-Michel
Paillarse3, Vijay Yajnik1, Maria Rosario1†
1
Takeda Pharmaceuticals, Cambridge, MA, USA; 2Simulations Plus, Inc., Lancaster, CA, USA, 3Enterome, Paris,
France*Employee of Simulations Plus at the time work was conducted†
Employee of Takeda at the time work was conductedObjectives:
TAK-018 is an oral FimH blocker being developed for Crohn’s disease (CD) treatment. Since the therapeutic site ofaction is gut mucosa, local gut exposure is more relevant for efficacy than systemic, but more difficult to measure.
Physiologically-based pharmacokinetic (PBPK) model enables predicting drug exposure at different gut regions inorder to inform the dose selection for a phase 2 study.
Methods:
The model input parameters were combination of in vitro measurements, structure-based predictions, and fitted to invivo data. The model was calibrated using single and multiple dose (50-3000 mg daily) PK data from a first-inhuman study conducted in healthy subjects. Nonlinear PK was captured through saturable absorption via an active
transporter. To account for binding of TAK-018 to mucosal lining of the gut wall as well as bacteria in the gut
lumen, enterocyte binding was used as a surrogate and fraction unbound in enterocytes was fitted to match theobserved plasma concentrations. Biliary secretion was modeled as linear flux of TAK-018 from hepatocytes to bilewith excretion rate defined through permeability-surface area product and fitted against in vivo data. Renal secretion
was estimated as product of glomerular filtration rate and fraction unbound in plasma. Enterohepatic circulation of
TAK-018 was used to describe the prolonged terminal phase of PK profiles. Human gut concentrations were
calculated from the simulated amount of TAK-018 in gut lumen (accounting for the volume of liquid in eachcompartment) and the bound amount to enterocytes. Based on published data showing increased permeability of gut
in CD patients, both plasma and gut exposure of TAK-018 were predicted in CD patients with adjustment on theintestinal permeability. The target efficacious concentration was based on the IC99 from in vitro pharmacology
studies and compared with predicted gut concentration profiles of TAK-018 to evaluate different dose regimens.
Results:
The final model reasonably described the available plasma concentrations across a wide range of doses, andprovided predicted segmental and total lumen concentrations of TAK-018 in human. Projected permeability increaseby 4- or 10-fold for patients resulted in higher plasma concentrations and lower gut concentrations. The gut
concentrations after 300 mg BID dosing appeared to be consistently well above IC99 including trough level.
Therefore, dose levels of 1500 and 300 mg will be evaluated in the phase 2 study to explore the efficacious dose
range of TAK-018.
Conclusions:PBPK model with mechanistic absorption provided prediction of local drug exposure in the absence of observed
data in gut. The model developed with healthy subjects data allows PK projections of patient population based on
known physiological changes caused by a disease. Such projections are of great value at early stage of development,
when clinical data is limited, providing aid in dose selection.

TUE-087

Novel Mechanistic PBPK Model to Predict Renal Clearance in Varying Stages of CKD by
Incorporating Tubular Adaptation and Dynamic Passive Reabsorption
Weize Huang and Nina Isoherranen
Department of Pharmaceutics, University of Washington, Seattle, WA, USA.
Objectives: Chronic kidney disease (CKD) has a multiplicity of effects on renal clearance (CLr) of drugs.
Empirically, CLr is considered to decrease proportionally with GFR, assuming all renal drug handling pathways
decline with renal function in parallel. However, CLr was shown to decrease more than proportionally with GFR instage 4/5 CKD for highly secreted drugs via OAT1/3,1 and to decrease much less than proportionally with GFR instage 4/5 CKD for permeable and renally reabsorbed drugs.2 To predict the complicated CKD effects,
physiologically-based pharmacokinetic (PBPK) models have been recently used to specifically address active
secretion and CLr for hydrophilic nonpermeable compounds. However, no studies have shown systematic PBPK
modeling of renal passive reabsorption or CLr for hydrophobic permeable drugs in CKD. The objective of this study
was to develop a novel adaptive kidney model to universally predict changes in CLr in CKD for permeable andnonpermeable drugs that accounts for the dramatic nonlinear effect of CKD on renal passive reabsorption.
Methods: To simulate the CKD effect on passive reabsorption, we expand our previously developed and verified
mechanistic kidney model3 to incorporate the physiologically-based tubular changes of reduced water
reabsorption/increased tubular filtrate flow per remaining functional nephron for each renal subsegment acrossvarying CKD stages. The final adaptive kidney model enables the translation from in vitro drug permeability into
degree of passive reabsorption and renal clearance value and was validated using a set of 20 compounds withdiffering properties without any optimization or empirical scaling throughout the progression of CKD from healthy
to the end stage.
Results: The final adaptive kidney model successfully captures the physiological changes in renal tubular filtrateflows of remaining functional nephrons in CKD patients, resulting in a predicted urine flow of 0.6 mL/min thatmatches with observed data in CKD patients with GFR of 10 mL/min.4 The simulation results showed successful(absolute-fold-error (AFEA) all<2) prediction of renal passive reabsorption and CLr for 20 permeable andnonpermeable test compounds across the stages of CKD. In contrast, the proportional GFR reduction approachwithout addressing tubular compensation processes in CKD generated unacceptable CLr predictions (AFEP=2.617.35) in severe CKD. Finally, the adaptive kidney model accurately predicted CLr of PAH and memantine, twosecreted compounds, in CKD, suggesting successful integration of active secretion into the model, along with
passive reabsorption.
Conclusions: The final adaptive kidney model enables mechanistic predictions of in vivo CLr for secreted, nonsecreted, permeable, and non-permeable drugs and metabolites throughout CKD progression from in vitro data
without any empirical scaling factors, facilitating dosing regimen optimization and clinical trial design prior to renal
impairment studies.
References:1.
Hsueh, et al. (2016).
2.
Houghton, et al. (1985).
3.
Huang and Isoherranen. (2018).
4.
Nechita, et al. (2015).

Figure 1: (Panel A) Schematic presentation of the mechanistic kidney model structure. (Panel B) The renal tubular
filtrate flow (TFF, in mL/min) for each individual subsegment of the kidney model (a total of 12). Three sets ofphysiologically-based TFF shown here are for healthy subjects (GFR 120 mL/min, in green) and for the
representative CKD patients who have residual GFR of 10 mL/min using the proportional model (in blue) and theadaptive model (in red). (Panel C-H) Simulation and verification of renal drug clearance (CLr) of 3 representative
test compounds that are permeable and highly renally reabsorbed: pefloxacin (C and D), metronidazole (E and F),
and minocycline (G and H), across multiple stages of CKD (Panel C, E, and G). Model performance wasquantitatively evaluated at CKD stage 4/5 (GFR ≤ 30 mL/min) using calculated AFEA (shown in red) and AFEP
(shown in blue). The observed CLr are shown in black, the simulated CLr values using adaptive and proportionalmodel are shown in red and blue, respectively.

TUE-088
Exposure–Response of TAK-906 in Patients with Diabetic or Idiopathic Gastroparesis and Healthy Subjects
Chunlin Chen1, Cecilia Scimia1*, George Dukes1, Saurabh Gupta1*; Wenwen Zhang1, Mukker, Jatinder1, Cristina
Almansa1, William S. Denney2
1

Takeda Pharmaceuticals International Co., Cambridge, MA, USA; 2 Human Predictions, Cambridge, MA, USA;*At the time of the study
Objectives: TAK-906 is a novel oral D2/D3 receptor antagonist currently under development for chronic treatment ofmoderate to severe diabetic and idiopathic gastroparesis (GP). The objective of this analysis was to characterize
exposure–response (E-R) relationships for prolactin (pharmacodynamic engagement marker) and relevant exploratory
endpoints (gastroparesis symptoms) assessed via American Neurogastroenterology and Motility Society Gastroparesis
Cardinal Symptom Index-Daily Diary (ANMS-GCSI-DD).
Methods: This analysis was based on data from first-in-human (FIH) study in healthy volunteers (N=72) and proof
of concept (POC) study in subjects with GP (N=51). In the FIH study, participants received doses between 5-300 mg
or placebo in single-ascending dose phase, and b.i.d dosing of (50 or 100 mg) or placebo on Days 1-4 followed bysingle dose on Day 5 in multiple-ascending dose phase. In the POC study, participants (N=12-14/cohort) received
TAK-906 (5, 25 or 100 mg) or placebo for 9 consecutive days. Prolactin was assessed at baseline, following a single
dose, and after 7 days dosing in subjects with GP. Effect of TAK-906 on GP symptoms (nausea, postprandial fullness,
early satiety and abdominal pain) was assessed via ANMS-GCSI-DD prior to Day 1 and continued daily throughout
the trial.
Exposure-prolactin model was based on combined data from FIH and POC studies without placebo arm and using‘nlme’ package (version 3.1-137) in R (version 3.5.1). Exposure-nausea model was based on data from subjects withGP including placebo arm and using ‘rstan’ package (version 2.18.2) in R (version 3.5.1). Nausea was exploratory
endpoint with most relevant effect and therefore selected for the E-R analysis. Models were selected by a combinationof biological plausibility and goodness of fit.
Results: Exposure-prolactin model was a mixed-effects Emax model with hill slope, a different E0 by disease, diurnal
variation in baseline, a different Emax by sex, and a different residual error by disease. Residual error was log-normally
distributed. Indirect effect models with and without a precursor pool were also tested. Female prolactin Emax was 117.1
ng/mL and male prolactin Emax was 59.32 ng/mL, respectively.
ANMS-GCSI-DD nausea model was an ordered logistic model with linear effect of baseline, linear placebo effect bytime after dose, and baseline-adjusted Emax effect of PopPK estimated AUC on nausea score (0 to 4 scale). The
maximum effect depended on baseline severity, and from a baseline of 3.5 points, the estimated placebo-adjusted
change from baseline in the ANMS-GCSI-DD nausea score was approximately 1 point. Visual predictive checks
suggest very severe category is underpredicted in favor of less severe categories, but E-R profile appears to bereasonably captured.
Conclusions: TAK-906 increases prolactin levels with an Emax trend, suggesting maximal receptor antagonism had
been achieved. The analyses indicate TAK-906 appears to improve ANMS-GCSI-DD nausea symptom score morethan placebo over time.

TUE-089

Population Exposure Response Analysis of Evinacumab in Patient with Homozygous Familial
Hypercholesterolemia Using an Indirect Response Model toPredict Low-Density Lipoprotein Cholesterol Lowering
Xia Pu, Feng Yang, Yi Zhang, Hong Yan, John D. Davis, Nidal Huniti
Regeneron Pharmaceuticals, Inc., Tarrytown, NY, USA
Objectives:
Evinacumab, a human monoclonal antibody against Angiopoietin-like protein 3 (AngPTL3), significantly lowers
low-density lipoprotein cholesterol (LDL-C) levels. This analysis aimed to develop and qualify a population
exposure response model for evinacumab in patient with homozygous familial hypercholesterolemia(HoFH), using
pooled patient data obtained from three phase II/III clinical trials. This model was used to provide supporting
evidence for dose selection.
Methods:
The exposure response (ER) analysis was performed sequentially where individual evinacumab concentrations
where predicted using a population pharmacokinetic model. Data from one phase-II and two phase-III studies(n=95 HoFH patients) were used to develop indirect response model, parameterized with decreasing LDL-C
production, to relate evinacumab concentrations to LDL-C values. The model was parameterized with a zero-order
rate constant for production of LDL-C (Kin), the maximum drug-induced inhibitory effect (Imax), and theevinacumab concentration inducing 50% of Imax (IC50). Covariates were selected based on clinical relevance andbiological plausibility and tested using a stepwise approach. Post-hoc prediction was performed to calculate the
LDL-C reduction on week 24 of the patients included in the analysis.
Results:
The population ER analysis allowed the characterization of the time course of LDL-C levels in HoFH and estimationof individual LDL-C levels and derived pharmacodynamic parameters (the percentage change of LDL-C frombaseline at week 24). The final PopPK/PD model included two covariates on Imax, one on IC50. A smaller IC50
was observed in patients with a higher baseline LDL-C, suggesting greater sensitivity to treatment. An increase inpotential maximum effect (Imax) was observed in patients who are white and/or have a lower baseline weight.
Compared to the base model, the inclusion of the covariates reduced parameter variability (CV%) of Imax and IC50
by 0.3% and 14.4%, respectively. The predicted percentage of patients reaching LDL-C reduction are close to theobserved values reported in pivotal trial, confirming the model performance.
Conclusions:This model adequately characterized of the population pharmacokinetic/pharmacodynamic properties of evinacumabin its target population and the estimation of individual LDL-C levels, to assist in dose selections of evinacumab.

TUE-090
Population Pharmacokinetic/Pharmacodynamic (PK/PD) Modeling of the Effect ofAbrocitinib on QT Intervals in Healthy Volunteers
Authors: Xiaoxing Wang1, Pankaj Gupta2, Saleem Ashley Farooqui3, Vu H. Le1, Jessica Wojciechowski1, Bimal K.
Malhotra2, Timothy Nicholas1
Institutions: 1Pfizer Inc., Groton, CT, USA; 2Pfizer Inc., New York, NY, USA; 3Pfizer R & D UK Ltd., Sandwich,
UK
Objectives: Abrocitinib is a selective Janus kinase-1 enzyme inhibitor in development for the treatment ofmoderate-to-severe atopic dermatitis (AD). The objectives of this study were to assess the relationship between
plasma concentrations of abrocitinib and two electrocardiogram- related measures: heart rate-corrected QT (QTc)
and heart rate (HR); and to investigate the effect of abrocitinib on QTc and HR at the supra-therapeutic
concentration in atopic dermatitis (AD) patients.
Methods: The study enrolled 36 healthy volunteers into 3 crossover treatments: single doses of abrocitinib 600 mg,
placebo, and moxifloxacin 400 mg. The relationship between change from baseline in Fridericia-corrected QTc(∆QTcF) values and abrocitinib plasma concentrations was explored graphically and modeled using a pre-specified
linear mixed effects model.1 The two-sided 90% confidence interval (CI) for time-matched placebo-corrected∆QTcF (∆∆QTcF) was calculated from the model parameter estimates. The upper end of the 90% CI at the supratherapeutic concentration in AD patients was used to assess whether the QTc prolongation exceeded the regulatory
threshold level (10 milliseconds [ms]).1 As abrocitinib is eliminated primarily by CYP2C19/2C9/3A4 metabolism,
the supra-therapeutic concentration was estimated from the predicted maximum plasma concentration (Cmax) for AD
patients using a population PK model, multiplied by the increase in Cmax observed in the drug-drug interaction study
with fluconazole (a potent CYP2C19, moderate CYP2C9, and CYP3A inhibitor). The same procedure was followed
to derive predictions of time-matched placebo-corrected change from baseline in HR (∆∆HR) with 90% CI at thisconcentration.
Results: The model-predicted 90% CI of the mean ∆∆QTcF for the supra-therapeutic concentration in AD patients(2156 ng/mL) was 6.00 (4.52, 7.49) ms. The mean ∆∆HR (90% CI) at the supra-therapeutic concentration in AD
patients was 6.51 (5.23, 7.80) beats per minute (bpm). Graphical assessments showed no apparent hysteresis and noapparent nonlinear relationship between the PK profile and QTcF. In the concentration-QTc analysis, a statisticallysignificant slope (90% CI) was estimated (0.0026 [0.0018, 0.0035] ms/[ng/mL]) indicating an effect of abrocitinib
concentrations on ∆QTcF. Similarly, abrocitinib was found to have a positive effect on ∆HR with statisticallysignificant slope (0.0031 bpm/[ng/mL] with 90% CI of [0.0024, 0.0038]).
Conclusions: At the predicted supra-therapeutic concentration of abrocitinib in AD patients, there was no clinically
relevant effect on QTc interval; the upper bound of the 90% CI of predicted ∆∆QTcF was less than 10 ms. In
addition, there was no clinically relevant effect of abrocitinib on ∆∆HR. The concentration-QTc/HR modelingdemonstrated that QTc interval and HR increased with increasing abrocitinib concentration after administration of asingle dose of abrocitinib 600 mg.
References:1.

Food and Drug Administration. Guidance for Industry: E14 Clinical Evaluation of QT/QTc Interval
Prolongation and Proarrhythmic Potential for Non-Antiarrhythmic Drugs.
https://www.fda.gov/media/71372/download. Published October 2005.

TUE-091

Model Based Pharmacometric Analyses of Nivolumab and Ipilimumab Combination in Chinese Patients with
Previously Treated Advanced Solid Tumors including Microsatelite Instability-High Colorectal Cancer

Yali Liang1*, Pradeep Vuppala1*, Amol Tendolkar1*, Sunney Li1, Heidi Wang1, Joyce Xu2, Akintunde Bello1, Amit
Roy1, and Jennifer Sheng1
1

Bristol-Myers Squibb, Princeton, NJ, USA; 2Bristol-Myers Squibb China, Shanghai, China*

Co-leading authors

Objectives: Nivolumab, in combination with ipilimumab, is indicated for the treatment of multiple tumor types in the
US, EU, and other countries, but combination therapy is not yet approved in China. With ongoing regulatory reforms,
the China National Medical Products Administration requires the assessment of a product’s “ethnic sensitivity”,
through evaluating pharmacokinetics (PK), pharmacodynamics, safety and efficacy outcomes that can be potentiallyinfluenced by ethnic factors following the principles of ICH E5 and E17. Nivolumab, as a single agent, is not ethnicallysensitive in Chinese, non-Chinese Asian and non-Asian patients1. This work presents the pharmacometric analyses of
ipilimumab monotherapy and nivolumab and ipilimumab combination therapy in Chinese patients including colorectal
cancer (MSI-H CRC).
Methods: Non-compartmental analysis (NCA) was performed using data from a Chinese Phase 1 safety/PK study fornivolumab and ipilimumab combination treatment in solid tumors including MSI-H CRC. Population PK (PPK)
analyses were conducted using data from 12 and 15 clinical studies of nivolumab and ipilimumab, respectively. The
PPK models were developed by re-estimating parameters of previously developed models and then incorporation of
additional covariates to assess the magnitude of covariate effects on model parameters. PPK model evaluation was
performed using prediction corrected visual predictive checks.
Results: The PK parameters Cmax and AUC(TAU) derived by NCA exhibited a logical rank order in terms ofmagnitude for nivolumab and ipilimumab (Nivolumab 3 mg/kg Q2W > 3 mg/kg Q3W > 1 mg/kg Q2W and ipilimumab
3 mg/kg Q3W > 1 mg/kg Q3W > 1 mg/kg Q6W). The PK of nivolumab or ipilimumab was well described by a linear
two-compartment model with time-varying clearance (CL) that decreased with time. The PK of nivolumab andipilimumab when administered in combination were similar to that seen with monotherapy in Chinese subjects.
Nivolumab exposures in Asian subjects were similar to those in non-Asian subjects with MSI-H CRC treated withcombination therapy. Nivolumab exposures with 3 mg/kg Q2W, 240 mg Q2W or 480 mg Q4W dose, during themaintenance phase of combination treatment for CRC, were maintained below the corresponding exposures observedwith the well tolerated 10 mg/kg Q2W dosage. Ipilimumab exposures were marginally (£ 22%) lower in Chinese
subjects as compared to non-Asian subjects for combination therapy, which is not expected to be clinically relevant.

Conclusions: Neither nivolumab nor ipilimumab showed ethnic sensitivity with regards to PK when administered in
combination, between Chinese, non-Chinese Asian and non-Asian MSI-H CRC patients. Ipilimumab, as monotherapy,
is ethnically insensitive in Chinese, non-Chinese Asian and non-Asian patients.
References:1.

Zhang J, Cai J, Bello A, Roy A, Sheng J. Model-Based Population Pharmacokinetic Analysis of Nivolumab
in Chinese Patients With Previously Treated Advanced Solid Tumors, Including Non-Small Cell Lung
Cancer. J Clin Pharmacol. 2019 Oct;59(10):1415-1424.

TUE-092
Model-based Cellular Kinetic Analysis of Chimeric Antigen Receptor-T Cells in Humans
Can Liu1, Vivaswath S. Ayyar2, Xirong Zheng2, Wenbo Chen2, Songmao Zheng2, Hardik Mody2, Weirong Wang3,
Donald Heald2, Aman P. Singh2*, Yanguang Cao1,4*1Division of Pharmacotherapy and Experimental Therapeutics, School of Pharmacy, University of North Carolina at

Chapel Hill, NC 27599, USA; 2 Discovery and Translational Research, Biologics Development Sciences, Janssen
Biotherapeutics, Spring House, PA, USA; 3 Clinical Pharmacology and Pharmacometrics, Janssen Research &Development, Spring House, PA, USA; 4Lineberger Comprehensive Cancer Center, School of Medicine, University ofNorth Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
Objectives: Chimeric antigen receptor (CAR)-T cell therapy has achieved considerable success in treating B-cell
hematologic malignancies. However, the challenges of extending CAR-T therapy to other tumor types, particularly
solid tumors, remain appreciable. The cellular kinetics of CAR-T may correlate with patient responses, but which
factors determine CAR-T cellular kinetics remain poorly defined. Herein, we developed a cellular kinetic model toretrospectively characterize CAR-T kinetics and systematically compared CAR-T kinetics across patient populations
and tumor types.
Methods: An empirical cellular kinetics model was developed to capture multiphasic CAR-T cellular kinetics(distribution, expansion, contraction, and persistence) in 218 patients from 7 trials. Cellular kinetic parameters
were further taken to evaluate substantial variabilities in CAR-T cellular kinetics across CAR-T products, dosing
regimens, patient responses, disease types, tumor burdens, and lymphodepletion conditions.
Results: Based on our analysis, CAR-T cells exhibited a significantly higher cell proliferation rate constant andcapacity but a lower contraction rate constant in patients who responded to treatment. CAR-T cells showed higher
proliferate potential and lower contraction rate constant in hematologic malignancies than in solid tumors. The
pre-infusion CD4:CD8 ratio in CAR-T product and lymphodepletion conditions showed a certain degree of influence
on CAR-T cellular kinetics and patient response. Within the assessed dose ranges (107‒109 cells), CAR-T doses wereweakly correlated with CAR-T cellular kinetics and patient response status, suggesting steep dose-response curves.
Conclusions: The developed CAR-T cellular kinetic model adequately characterized the multiphasic CAR-T cellularkinetics and supported systematic evaluations of the potentially influencing factors, which can have significant
implications for the development of more effective CAR-T therapies.

Figure 1.
Model structure for CAR-T cellular kinetics and schematic diagram of three-step workflow for modeling and analysis.

TUE-094
Population Pharmacokinetic/Pharmacodynamic (Pop PK/PD) Modeling of Dupilumab on Nasal Polyps Score(NPS) in Patients with Chronic Rhinosinusitis with Nasal Polyposis (CRSwNP)Yue Gao1, Zhicheng Qian1*, Christine Xu1, John D. Davis2, Vanaja Kanamaluru1, Qiang Lu1
1
Sanofi, Bridgewater, NJ; 2Regeneron Pharmaceuticals, Inc., Tarrytown, NY; USA*Affiliation at time of study
Objectives: Dupilumab is a fully human, VelocImmune-derived monoclonal antibody, that binds to the shared
receptor component for interleukin (IL)-4 and IL-13, cytokines that are key drivers of type 2 inflammatory diseases.
The binding of dupilumab to human IL-4 receptor alpha (IL-4Rα) results in blockade of the functions of IL-4 and IL13 signal transduction. Dupilumab is approved for subcutaneous (SC) treatment of adult patients with CRSwNP as
well as patients aged 12 years and older with moderateto-severe atopic dermatitis or with moderate-to-severe asthma. This analysis aimed to develop a Pop PK/PD model tocharacterize the relationship between dupilumab exposure and time course of NPS response, and to identify intrinsic
and/or extrinsic factors significantly contributing to NPS response variability in CRSwNP patients.
Methods: The Pop PK/PD model was developed using data pooled from one phase 2 study (NCT01920893) and twophase 3 studies (NCT02912468 and NCT02898454) in CRSwNP patients who received SC dupilumab 300 mg every
week, 300 mg every two weeks, or 300 mg every two weeks for 24 weeks followed by 300 mg every four weeks.
Estimated individual PK parameters from a previously published Pop PK model[1] were used to simulate PK exposure
to drive NPS inhibition in an indirect response model. Demographic variables, baseline disease characteristics, type 2
inflammation biomarkers, immunogenicity and asthma history were tested as covariates by forward selection andbackward elimination. The model was validated with visual predictive check and bootstrap.
Results: The Pop PK/PD dataset included 5357 NPS observations from 777 CRSwNP patients (N=312 treated withplacebo, N=465 treated with dupilumab). The placebo effect for NPS was found to be minimal (mean observed NPSchange from baseline at end of treatment was <±5%) and was not included in this Pop PK/PD model. Baseline age
and nasal congestion (NC) were identified to have statistically significant influence on NPS response variability incovariate analysis, but not deemed to be clinically relevant based on simulations. None of the other tested covariates,
including body weight, gender, race, region, baseline type 2 inflammation biomarkers and anti-drug antibodies, were
statistically significant. Concomitant use of common nasal polyposis medication (systemic corticosteroids) and othercomorbidities (asthma history, exacerbated respiratory disease history and allergic rhinitis history) did not show effecton NPS response based on covariate or post-hoc analysis.
Conclusions: The NPS response over time in CRSwNP patients treated with dupilumab was well characterized byan indirect response model. Baseline age and NC were identified as statistically significant, but not clinically
relevant, covariates on dupilumab treatment effect in CRSwNP patients.
References:[1] Gao Y, Xu C, Davis JD, Kanamaluru V, Lu Q. Population pharmacokinetic analysis of dupilumab in adult patientswith chronic rhinosinusitis with nasal polyposis. ACOP10, Orlando, FL, ISSN:2688-3953, 2019, Vol 1.
Acknowledgments:
Research sponsored by Sanofi and Regeneron Pharmaceuticals, Inc. ClinicalTrials.gov Identifiers: NCT01920893,
NCT02912468 and NCT02898454.
Disclosures:
Gao Y, Xu C, Kanamaluru V, and Lu Q: Sanofi - employees. may hold stock and/or stock options in the company
of Sanofi.
Qian Z: Sanofi - employees at time of study. have nothing to disclose.
Davis JD: Regeneron Pharmaceuticals. Inc. - employee and shareholder.

TUE-095

A novel webtool, MTXPK.org, uses a three-compartment high-dose methotrexate
population pharmacokinetic model to inform glucarpidase use
Authors:
Zachary L. Taylor1,2,3, Tomoyuki Mizuno3,4, Nieko C. Punt5, Balaji Baskaran6, Adriana Navarro
Sainz6, William Shuman6, Nicholas Felicelli6, Alexander A. Vinks2,3,4, Jesper Heldrup7, Laura B.
Ramsey2,3,4
Affiliations:1
Department of Molecular, Cellular, and Biochemical Pharmacology, University of Cincinnati,
Cincinnati, OH
2
Division of Research in Patient Services, Cincinnati Children's Hospital Medical Center,
Cincinnati, OH
3
Division of Clinical Pharmacology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH
4
Department of Pediatrics, University of Cincinnati, Cincinnati, OH
5
Medimatics, Maastricht, The Netherlands
6
Division of Biomedical Informatics, Cincinnati Children’s Hospital Medical Center, Cincinnati,
OH
7
Department of Pediatrics Oncology, Lund University Hospital, Lund, Sweden
Objective:
There is significant inter-patient pharmacokinetic (PK) variability associated with high-dose
methotrexate (MTX). Patients who experience delayed renal elimination are at an increased risk
for severe nephrotoxicity. Glucarpidase is a lifesaving rescue agent for these patients, but
determining the appropriate administration is difficult due to challenges interpreting the labeledindication and consensus guideline. The objective of our study was to create a web-based
clinical decision support tool, MTXPK.org, that uses real-time drug concentrations and Bayesianestimation to facilitate model-informed post-infusion care and use of glucarpidase for patients
receiving HD MTX.

Methods:
A population pharmacokinetic (PK) model was developed using 31,717 MTX plasma
concentrations obtained from 772 pediatric patients with acute lymphoblastic leukemia receiving
HD MTX on Nordic Society of Hematology and Oncology protocols. Two- and threecompartment structural models with first-order conditional estimation were explored withdemographic and laboratory data being evaluated as potential covariates to predict clearance.
Model preference and covariate inclusion were based off a change in objective function value(OFV) > 3.54. The final population PK model was validated on two external datasets withdiverse age range, indication, and dosing schedules prior to its integration into MTXPK.org. A
module for Bayesian estimation is included in the MTXPK.org platform. The Edsim++ model
designer compatible PK-engine runtime enables the Bayesian estimation of individual PK
parameters.

Results:
The default population PK model for MTXPK.org is a three-compartment structural model since
it significantly improved the description of the data (ΔOFV = -6,136.43, p < 0.001) and the
population prediction performance compared to the two-compartment structural model (R2:
0.83 vs. 0.63, respectively, p < 0.001). Body surface area and serum creatinine were significant
covariates and included in the final model. MTXPK.org uses these model parameters as priorknowledge for a posteriori Bayesian estimation of a patient’s elimination profile, which is
generated once the user enters the patient’s demographics, dose administered, infusion
duration, MTX concentrations, and serum creatinine levels into the tool. Then, MTXPK.org plots
the profile along with the population’s average concentration-time curve and associated ± 2
standard deviation, which illustrates the labeled indication of glucarpidase, and glucarpidase
guideline thresholds at 24, 36, 42, and 48 hours. The visual and numerical information
generated by the tool provides feedback of a patient’s MTX clearance and can be used to inform
post-infusion care and use of glucarpidase. MTXPK.org went live in December 2019; since then,
the webtool has been used by >900 unique users in at least 35 countries.

Conclusions:
MTXPK.org is a free, web-based clinical decision support tool that uses a patient’s demographics
and real-time drug concentrations to provide model-informed post-infusion care and use of
glucarpidase. This tool has the capability to improve patient outcomes for all patients receiving
high-dose MTX treatment.

TUE-096

Modeling the dynamics of antibody-target binding in living tumors
Yu Tang1, Yanguang Cao1 #Division of Pharmacotherapy and Experimental Therapeutics, UNC Eshelman School of Pharmacy, University ofNorth Carolina at Chapel Hill, Chapel Hill, North Carolina. 27599, United States

Objectives: Antibody binding to its target is the first and most critical step for the treatment efficacy. However, ourunderstanding of their binding dynamics in living tumors remained limited. We had developed a bioluminescenceresonance energy transfer (BRET) imaging approach that directly supports the measurement of the antibody-target
engagement in the native tumor environment. In the present study, we developed a spatially resolved computationalmodel to analyze the BRET imaging data and then explore the mechanisms of a biphasic binding dynamics between
a model antibody cetuximab and its target epidermal growth factor receptor (EGFR) in living xenograft tumors.
Methods: A sequential modeling strategy was implemented in which the pharmacokinetic model was first
optimized against plasma data and then fixed during the second step of tumor binding characterization. In thespatially resolved mathematical model, the tumor was conceptually dissected into two anatomical compartments: astroma-rich and a stroma-poor area, to account for the spatial histological heterogeneity. Two competing hypotheses
were tested to evaluate which one makes more consistent predictions to the observed binding data: (1) the antibodies
bind to the targets differentially across two tumor areas (the heterogeneous binding model, HBM) and (2) the
antibodies are distributed differentially into two tumor areas, but with the same binding profile (the heterogeneous
distribution model, HDM).

Results: Compared to the heterogeneous antibody distribution model (HDM), the heterogeneous binding (HBM)
better explained the biphasic and dose-shifted binding dynamics of cetuximab-EGFR in the living tumors. The
antibody bound differently to its target in the stroma-rich areas than in the stroma-poor regions. Antibodies had amuch slower dissociation rate in the stroma-rich areas, which was confirmed by immunofluorescence staining. We
also observed that cetuximab bound to EGFR to a “slower-but-tighter” degree in living tumors in comparison to in
vitro, demonstrated by a significantly slower association rate (kon) and a disassociation rate (koff) compared to thesurface plasma resonance measurements.
Conclusions: We demonstrated the spatial heterogeneity in antibody-binding dynamics in living tumors. Thesefindings improve our understanding of the complex antibody targeting process and should aid in the design ofantibodies that show more favorable targeting properties (NIH: GM119661).

WED-001

Mechanistic mathematical model to optimize drug delivery via intratumoral injection
Abed E. Alnaif1, Frederic J. Reu1, Craig J. Thalhauser1,2, Brian J. Schmidt1, Yash Gandhi1, George T. Currier3, MarkS. Howansky3, Tarek A. Leil1
1

Bristol-Myers Squibb, Princeton, NJ, USA. 2Genmab U.S., Princeton, NJ, USA. 3Bristol-Myers Squibb, Princeton,

NJ, USA.
Objectives: Intratumoral (I-TUMOR) injection is thought to enable effective delivery of drug to the site of action
while limiting risk related to systemic exposure. However, although I-TUMOR injection is currently being employed
in several clinical trials [1], and even in an approved therapy [2], little experimental work has been done to optimize
the parameters of I-TUMOR injection in terms of amount of drug delivered to the tumor. Furthermore, commonlyused mouse experimental models are of questionable relevance to addressing this problem given the substantial
differences in biomechanical properties of tumors, and examination of tumor pharmacokinetics in humans istechnically highly challenging and therefore not part of standard clinical development in studies that administer small
molecules intratumorally. Given these obstacles to characterizing I-TUMOR injection experimentally, we developed
a mechanistic mathematical model in an effort to understand and predict how various I-TUMOR injection parameters
may affect the success of drug delivery.

Methods: Unsuccessful I-TUMOR injections provide minimal tumor drug exposure due to the drug flowing out fromthe tumor along the injection tract. Thus, we modeled this as an expandable, leaky annular “pipe”, whereby the lossof drug occurs by flowing out along the annular space between the needle and tumor (the injection tract; Figure 1).
This was modeled according to a partial differential equation (PDE) model in which the pressure of the drug can
expand the annular space (by compressing the tumor), and in which leakage of the drug into the tumor can happen
across the outer surface of the injection tract. This leakage of the drug into the tumor is what we sought to maximize,as it represents delivery of the drug into the tumor.

Results: We used the model to understand and predict the effects of various injection parameters on the efficacy of
drug delivery. Specifically, the model was used to predict the impacts on drug delivery of varying both tumor-specific(tumor malleability, tumor hydraulic conductivity, and intratumoral pressure) and needle/drug-specific (injection
speed, needle penetration depth, needle diameter, needle opening configuration, and drug viscosity) parameters.

Conclusions: Using this platform, we predicted the influence of various parameters on the efficacy of drug delivery
via I-TUMOR injection. Several of the model’s predictions went against the intuition we had at the start of thisexercise, which has prompted the development of experiments to validate the model. If experiments validate themodel, it may allow tailoring I-TUMOR injection methods to achieve maximum drug delivery in human tumors.

References:1.

Marabelle, A et al. Ann Oncol. 2018 Nov; 29(11): 2163–2174.

2.

Conry, RM er al. Hum Vaccin Immunother. 2018; 14(4): 839–846.

Figure 1: Diagram of the model of drug delivery via I-TUMOR injection, illustrating the undesirable loss of injectate along theannular space formed by the injection tract.

WED-003
PKPD Exploratory Graphics (xGx) Cheat Sheet
Alison Margolskee1, Fariba Khanshan1, Andrew Stein1, Camille Vong2, Yu-Yun Ho3 and Michael Looby2
1

Novartis Institutes for Biomedical Research, Cambridge, MA, USA; 2Novartis Pharmaceutical Company, Basel,
Switzerland; 3Novartis Pharmaceutical Company, East Hanover, NJ, USA

Objectives:
Checklists are useful tools not only for beginners, but also for experts in any field. As John D. Cook stated: “Weresist checklists because they insult our intelligence, and yet they greatly reduce errors. Experienced people in every
field can skip a step, most likely a simple step, without some structure to help them keep track.”(https://www.johndcook.com/blog/2019/12/03/distracted/)

As pharmacometricians, we sometimes jump into complex modeling before thoroughly exploring our data.
Exploratory plots help uncover issues with data, useful insights, or aspects to explore further, and can sometimes
answer questions without the need for complex models.

To help pharmacometricians remember the key steps in exploring PKPD data, we created the PKPD Exploratory
Graphics (xGx) Cheat Sheet(https://opensource.nibr.com/xgx/Resources/PKPD_Exploratory_Graphics_(xGx)_Cheat_Sheet.pdf) a 1-page (front
and back) question-based reference sheet, designed around the principles from the Exploratory Graphics (xGx) tool,
an open-source R-based tool available on GitHub (https://opensource.nibr.com/xgx/)

Methods: The key steps in dose-exposure-response exploration were organized by structured PK, PD, and PKPD
exploration. Key questions and example graphs were created to accompany each step. Guiding Principles from xGx
were simplified and organized into Technical Considerations. Commonly used functions from the xgxr package
were compiled along with brief descriptions of their usage. Finally, a high-level checklist was written to summarize
the key steps in PKPD data exploration. All of these components were streamlined and optimized to fit into a 1-pagecheat sheet.
Results:
Key steps in PKPD exploration include:
•
•
•
•
•
•
•
•
•
•

Identify data type and choose appropriate graph types (PD)
Identify axis scale that reflects distribution of data (PK, PD)
Provide an overview of the data (PK, PD)
Determine whether data corrections are needed (PD)Assess trends over time (PK, PD)
Assess trends by dose (PD)Assess PK linearity (PK)Assess extent and sources of variability (PK, PD)
Get an overview of the relationship between exposure and response (PKPD)
Explore delays between exposure and response (PKPD)

Technical Considerations include: extent and sources of variability (between vs. within subject, explained vs.
unexplained), PD data corrections (e.g. baseline correction), and effective use of scales.
Useful functions include: xgx_theme(), xgx_check_data(), xgx_summarize_covariates(), xgx_geom_ci(),
xgx_geom_pi(), xgx_scale_y_log10(), xgx_scale_y_reverselog10(), xgx_scale_y_percentchangelog10(),xgx_annotate_status(), xgx_annotate_filenames(), and xgx_save_table().

Figure 1: Front page of PKPD Exploratory Graphics (xGx) Cheat Sheet(https://opensource.nibr.com/xgx/Resources/PKPD_Exploratory_Graphics_(xGx)_Cheat_Sheet.pdf)

Conclusions: The PKPD Exploratory Graphics (xGx) Cheat Sheet was created to help pharmacometricians follow a
structured approach to PKPD data exploration. The key steps to PKPD exploration along with questions andexample graphs were compiled into a 1-page reference sheet. The cheat sheet also includes technical considerations,useful functions from the xgxr package, and a high-level checklist. Cheat sheets, similar to checklists, can be usefulfor people of all levels of experience, from beginners learning PKPD, to experts who can sometimes skip simple but
important steps.

WED-004

The use of PBPK modelling from clinical drug-drug interaction studies with inhibitors and inducers of
CYP3A4, CYP2C9 and OATP1B1 for BIIB093 (IV glibenclamide) to support regulatory decision making
Anas M Fathallah1, Alice Ke2, Lilly East3, Himanshu Naik1 and Eric Masson1
1

Biogen, Inc., Cambridge, MA, USA. 2SimCYP LTD. Sheffield, UK. 3Previosly with Biogen, Inc., Cambridge, MA,
USA

Objectives: Construct and verify a PBPK model of BIIB093, an investigational product under development for thetreatment and prevention of severe cerebral edema, using published clinical PK data with oral glibenclamide, in houseclinical data with BIIB093 as well as in vitro data to evaluate DDI potential of BIIB093 in-silico with
inhibitors/inducers of CYP3A4, CYP2C9 and OATP1B1.
Methods: Literature and inhouse clinical and in-vitro data were used in the PBPK model development. The model
incorporated a first-order absorption model and hepatic OATP1B1 uptake followed by metabolism split between
CYP3A4 and CYP2C9. Due to limitations of scaling of CYP2C9 mediated metabolism from in vitro data, the clinicaldata on the systemic exposure of oral glibenclamide in subjects with differing CYP2C9 polymorphic status was used
to estimate fmCYP2C9. A scalar (RAF) for the OATP1B1-mediated hepatic uptake was optimized based on matching
the reported IV clearance for glibenclamide. The hepatic OATP1B1 component was subsequently verified against
single-dose rifampicin DDI in the literature. The CYP3A4 component was verified against clarithromycin DDI datain the literature. The multiple-dose rifampicin DDI effects were assessed by incorporating the induction of CYP2C9,OATP1B1 and CYP3A4. An intestinal P-gp component was not considered in the model initially, due to reported highbioavailability of the oral formulation (>=0.89). The recovery of PK profiles for BIIB093 following various IV dosing
regimens was also demonstrated.
The model was prospectively applied to predict the likely outcomes of interaction of BIIB093 with ketoconazole(strong CYP3A4 inhibitor), fluconazole (strong CYP2C9 inhibitor and moderate CYP3A4 inhibitor), rifampicin 600
mg SD (OATP1B1 inhibitor) and QD (CYP3A4/CYP2C9/OATP1B1 inducer) following IV infusion of BIIB093 (3and 5 mg/day)
Results: The final PBPK model captured the mean AUC and Cmax ratios of glibenclamide reported in several
publication within the criteria described by Guest et al1. The model recovered the PK profiles for BIIB093 from 2
phase I clinical studies in healthy subjects within 0.88 to 1.1-fold. The predicted changes in plasma exposure of
BIIB093 during coadministration of single dose rifampicin (strong OATP1B1 inhibitor) and daily rifampicin(CYP3A4/CYP2C9/OATP1B1 inducer) are modest (AUCr or Cmax r > 2 and <5). Simulations with the strong
CYP2C9 inhibitor fluconazole and with the strong CYP3A4 inhibitors predicted weak interactions (AUCr ≥ 1.25 and< 2) and (AUCr ≥ 1.25 and < 2) respectively.
Conclusions: A PBPK model constructed from literature and inhouse clinical and in-vitro data was developed andvalidated. The approach is supportive of regulatory decision-making and elimination of additional clinical DDI
studies.
Guest EJ, et al (2011) Critique of the two-fold measure of prediction success for ratios: application for the assessmentof drug-drug interactions. Drug Metab Dispos 39:170-173
1

WED-005
Population Pharmacokinetic Modeling of Belinostat and its Major Metabolite in Advanced Cancer Patientswith varying degrees of Liver Dysfunction
Anitha Suram1, Naoko Takebe2, Alice Chen2, Shivaani Kummar3, Richard Piekarz4, Brian Kiesel5, Nancy Moore1,
James Doroshow3, Jan Beumer5, Jogarao Gobburu1
1

Center for Translational Medicine, School of Pharmacy, University of Maryland, Baltimore, MD, USA; 2Early
Clinical Trials Development Program, National Cancer Institute, Bethesda, USA,3Division of Cancer Treatment andDiagnosis, National Cancer Institute, Bethesda, USA;4Cancer Therapy Evaluation Program, National Cancer
Institute, Bethesda, USA; 5Cancer Therapeutics Program, UPMC Hillman Cancer Center, Pittsburgh, PA, USA.
PurposeBelinostat, a second-generation histone deacetylase (HDAC) inhibitor, is an anticancer drug. Belinostat is
extensively metabolized by the liver; and a recent phase I pharmacokinetic (PK) safety and tolerability study inpatients with declining liver function demonstrated significant differences in belinostat clearance and exposure of its
metabolites (Takebe et al., 2018). The aim of this study is to develop a simultaneous population PK model forbelinostat and its major metabolite (glucuronide) to delineate the effect of liver dysfunction, and to investigate thesources of variability in the PK parameters.
Methods
Data consisted of 64 advanced cancer patients with normal, mild, moderate, and severe hepatic impairment. A single
dose of 400 mg/m2 belinostat was administered by IV infusion over 30 minutes. Nonlinear mixed effect modellingof belinostat and glucuronide PK data was conducted using Pumas (www.pumas.ai). FOCEI method was used forthe maximum likelihood estimation (MLE). The point estimates, between subject (BSV), and residual variabilities of
the PK parameters were estimated. The influence of declining liver dysfunction, body weight, and CRCL (creatinine
clearance) on the PK parameters were determined.
Results
A two- compartment model for both belinostat and glucuronide with combined proportional and additive error
model best described the data. Belinostat clearance (Cl) was parameterized to formation clearance of glucuronide(Clpmet = Cl*fm) and rest clearance Cl*fr (fr = 1-fm). The fraction of parent converted to the metabolite (fm, 65%)
was derived from the mass balance study.
Allometric scaling of clearance and volume parameters to body weight (75 kg) significantly improved themodel fit (delta OFV = -45, p<0.05). Liver dysfunction status on Clpmet and CRCL on glucuronide clearance(Clmet) further improved the model fit (delta OFV = -52.4 and - 55.9, respectively; p<0.05). The cohorts with mild,moderate and severe liver dysfunction demonstrated 5%, 11 %, and 27 % reduction in Clpmet respectively, relative
to normal status. The coefficient for the effect of CRCL on Clmet was estimated to be 0.35. Overall, covariates
reduced 25% BSV in the clearance of belinostat, 6% BSV in central volume of belinostat and glucuronide, 2% BSV
in Clmet. The final parameter estimate of belinostat central volume is 8.6 L/75 kg, total clearance is 56.6 L/hr/75 kg
in the normal cohort.
Conclusions
We characterized the PK of belinostat and its major glucuronide metabolite from the data collected in advanced
cancer patients and evaluated the effect of liver dysfunction on the metabolic pathway and CRCL on the metabolite
clearance. The population PK model proposed can be leveraged to facilitate dosing adjustment in patients withsevere liver dysfunction.
Reference
Takebe et al., A phase 1 pharmacokinetic study of belinostat in patients with advanced cancers and varying degrees
of liver dysfunction. Br J Clin Pharmacol. 2019, 85(11):2499-2511

WED-007A META EXPOSURE-RESPONSE ANALYSIS OF TAFENOQUINE IN PATIENTS WITH
PLASMODIUM VIVAX MALARIA
Ashwin Karanam1, Nilay Thakkar2, Justin Green3, Stephan Duparc4, Lionel Tan3, Navin Goyal2
1

Department of Experimental and Clinical Pharmacology, University of Minnesota, MN, USA;
GlaxoSmithKline, Collegeville, PA, USA; 3GlaxoSmithKline London, UK; 4Medicines for Malaria
Venture, Geneva, Switzerland
2

Objectives:
Tafenoquine (TQ) is an anti-malarial for the radical cure (prevention of relapse) of Plasmodium vivax (P.
vivax) malaria in combination with a blood stage antimalarial (chloroquine [CQ]). The goal of theseanalyses was to characterize the TQ exposure-response (recurrence-free at 6 months) in P. vivax malaria
patients across 3 clinical trials.
Methods:
Subjects randomized to TQ + CQ and CQ-only arms from three trials (TAF112582 Parts I and II;
TAF116564) were included in this analysis. TAF112582 Parts I and II (N = 613) were used as training
datasets for model building and TAF116564 (N = 166) as test dataset for model evaluation. Multivariate
logistic regression and time to event (TTE) analyses were performed in NONMEM using the Laplacemethod. Model evaluation was based on multiple criteria and included simulation-based visual predictive
checks. A classification and regression tree (CART) analysis was performed to identify key determinants
impacting P. vivax recurrence.
Results:
Logistic Regression and CART Analysis: TQ AUC and country (Thailand vs Others) were found to bestatistically significant (p < 0.01) predictors of clinical response based on multivariate logistic regression
analyses. After accounting for country, the odds of being recurrence-free at 6 months increased byapproximately 44% (95% CI, 28 – 60%) for each 25-unit increase in AUC above the median value of 74µg·h/ml. Adequate model performance was established by comparing the predictions against test data
outcome (Figure 1). CART analyses identified a breakpoint for AUC (48.35 µg·h/ml) indicating higher
likelihood of being recurrence-free at 6 months in subjects who achieved exposures > the breakpoint
AUC as compared to those who did not.
TTE Analysis:
A Weibull distribution hazard model with a delay function adequately described the data. TQ AUC(Emax model) and country (Thailand vs Others) were found to be statistically significant (p < 0.01)
predictors of time to relapse with a potency estimate (EC50) of 65.3 µg·h/ml. After accounting forregion/country, the risk of relapse at 6 months decreased by 46% (95% CI, 37 – 54%) for every 25µg.h/ml increase in TQ AUC compared to the CQ-only group.

Conclusions:
The results demonstrate that TQ + CQ therapy improves recurrence-free rates at 6 months compared withthe subjects with CQ-only therapy. A clinically relevant exposure threshold of TQ AUC of 48.4 µg·h/ml
was estimated impacting recurrence outcome and was comparable to the previous estimate (56.4 µg·h/ml)
based on analysis of TAF112582 Part I data only. The analyses also identified differences in recurrence
rates in Thailand as compared to rest of the world. Tafenoquine exposure is directly related to an
improved efficacy outcome and significantly influences the probability of preventing malaria relapse. The
model can be employed to perform further clinical trial simulations and predict outcomes under different
dosing regimen scenarios.

Figure 1: Simulated versus observed proportion of subjects for determining relapse-free status at 6 months

WED-009

Population pharmacokinetics of R and S methadone inchildren undergoing major surgery
Authors:
Blessed W. Aruldhas1,3, Brian R. Overholser1,2, Sara K. Quinney1, Andrea R. Masters1, Christine M. Bach1,
Senthil Sadhasivam1

Author affiliations:
1. Indiana University School of Medicine, Indianapolis, IN, 2. Purdue University College of Pharmacy,
Indianapolis, IN, 3. Christian Medical College, Vellore, India

Objectives:
Methadone is a racemic (50/50 of the R and S enantiomers) synthetic opioid used for postoperative
analgesia in addition to its widespread use in opioid abuse disorder. The individual enantiomers vary intheir pharmacokinetic and pharmacodynamic properties. The optimal dose required to achieve
therapeutic concentrations for effective analgesia in children is not fully known. This study aims todevelop pharmacokinetic models of R and S methadone in children undergoing surgery.

Methods:
The study was conducted at Riley Hospital for Children, Indianapolis, IN. A total of 38 children 8 to 17.9
years of age undergoing specific surgeries were recruited after obtaining informed consent. Methadone
was given both intravenously in the operating room and orally at a dose of 0.05 to 0.1 mg/kg every 12hours. Blood samples were collected 6 to 10 times over 3 to 5 inter-dose intervals for each child. Rmethadone, S-methadone and AAG (acid alpha glycoprotein) concentrations were measured using liquid
chromatography. The pharmacokinetic data were analyzed using a nonlinear mixed effect model
approach in NONMEM(v7.4) using FOCE method with interactions. Covariates were graphically explored
and were added by a stepwise process. Model qualification was performed using visual predictive check(VPC) and bootstrap.

Results:
The pharmacokinetics of both R and S-methadone were described by two-compartment disposition
models with linear elimination and first order absorption. Bodyweight was added allometrically toclearance and volume parameters raised to a fixed exponent of 0.75 and 1 respectively. AAG (alphaacid-glycoprotein) was identified as a covariate on central volume for both the enantiomers (Δ OFV =11.5 and 12.4 respectively). The eta and epsilon shrinkages of the final models were low (Table 1). Visual
predictive checks were done using 1000 samples generated from the final models showed a good fit
with the observed data. A bootstrapping procedure generated with 1000 random samples from the data
found parameter estimates to be reliable with acceptable relative standard errors (Table 1).

Table 1.

KA (hr-1)

R Methadone
Population
estimates(%RSE)
0.111 (26.6)

CL (L.hr-1)

29.8 (14.7)

Vc (L)

234 (18)

Q (L.hr-1)
Vp (L)
AAG on Vc
BMI on Vc

110 (20)348 (21.2)-0.0032 (19.7)-

RUV

0.202 (0.122)

Parameters%CV for
BSV(%
RSE)
92.5
(0.277)60.3(0.229)85 (0.18)

0.312

S Methadone
Population
estimates(%RSE)
0.131 (25.7)

0.2

29.4 (10.3)

0.183

219 (16.6)

Shrinkage%CV for
BSV(%
RSE)
91.7
(0.25)
54.3(0.198)54.9(0.328)

Shrinkage
0.273
0.148
0.271

74.1 (18.3)
138 (24.5)-0.00289 (35.9)-0.0454 (41.9)
0.08

0.206 (16.5)

0.081

Conclusions:Pharmacokinetic models were successfully created for both R and S methadone in children. AAG and
BMI were important covariates describing the volume of central compartment in children. Additional
pharmacokinetic/pharmacogenomic/pharmacodynamic modelling is required to personalize and further
optimize dosing of methadone for each child.

References (if applicable): NA

WED-010
Simulations of Orally-Administered Ivermectin Utilizing A Minimal Physiologically-Based Pharmacokinetic(mPBPK) Model: Potential COVID-19 Therapeutic Option
Brian Jermain1, Patrick Hanafin1, Carter Cao1, Gauri Rao1
1

Division of Pharmaceutics and Experimental Therapeutics, Eshelman School of Pharmacy, University of North

Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
Objectives: Ivermectin has been shown to inhibit SARS-CoV-2 replication in vitro with an IC50 value of 1750
ng/mL.1 SARS-CoV-2 infects the lungs resulting in COVID-19. Hence, concentrations of ivermectin in the lungs
help us understand the therapeutic potential of ivermectin against COVID-19. The only FDA-approved formulationof ivermectin for systemic use in humans is an oral tablet. The objective of the study was to utilize a developed
mPBPK model to simulate a dosing regimen of oral ivermectin that would achieve lung concentrations above 1750
ng/mL for 50% of the 5-day treatment period.
Methods: Simulations of an orally-administered loading dose (LD) and maintenance dose (MD) were performed in
Phoenix® (8.2, Certara) utilizing the mPBPK model developed based on a single-dose pharmacokinetics study ofivermectin.2 The duration of time when ivermectin lung concentration were above 1750 ng/mL was assessed.
Single dose simulations were performed using the FDA-approved dose for strongyloidiasis (12 mg) and the highest
dose shown to be safe in clinical trials (120 mg) as references for assessing the finalized regimen. A 5-day course oftreatment was selected to resemble the treatment duration of a clinical trial of remdesivir.3 The 50% time above IC50
cutoff was selected in the absence of standardized pharmacodynamic endpoints for antiviral treatment.
Results:
FDA-approved single dose (12 mg): Lung Cmax = 59.6 ng/mL, Plasma Cmax = 29 ng/mL
Highest single dose proven safe in clinical trials (120 mg)4: Lung Cmax = 596 ng/mL, Plasma Cmax = 289.5 ng/mL
Simulations with a 480 mg LD followed by 180 mg MD every 12 hours resulted in ivermectin lung concentrations
above 1,750 ng/mL for 53.2% of the 5-day treatment period. Lung and plasma Cmax resulting were 2,384.5 ng/mL
and 1,158.1 ng/mL respectively; the plasma Cmax is 4-fold higher than that after a 120 mg single dose.

Conclusions: Orally-administered ivermectin as monotherapy is likely not ideal for COVID-19 treatment given thehigh plasma concentrations required to achieve sufficient lung concentrations. The 480 mg LD followed by 180 mg
MD every 12 hours results in consistently high ivermectin plasma concentrations which may cause central nervous
system toxicity.4 Additionally, ivermectin is a lipophilic drug with a long half-life (~24 hours) which may potentiate
the risk of this adverse effect. Reformulating ivermectin for inhalational delivery should be considered as this mayachieve adequate concentrations in the lung while minimizing its systemic uptake and related adverse effects.
References:1

Caly et al. Antiviral Res. 2020.

2

El-Tahtawy et al. PLoS Neglected Tropical Diseases. 2008

3

ClinicalTrials.gov Identifier: NCT04292899

4

Guzo et al. J Clin Pharmacol. 2002.

Figure 1: Schematic of the developed mPBPK model for ivermectin following oral dose administration in humans.
The plasma compartment (dashed line) on the left is identical to the plasma compartment on the right. The mPBPK
model comprises two tissue compartments that represent the entire body (lung and other) along with the plasmacompartment. The delay following oral dosing is described by a transit compartment (a0) with transit rate Ktr;
absorption rate Ka; cardiac output, Qco; lung partition coefficient, Kp,lung; fraction of cardiac output, Fraction; tissue
partition coefficient, Kp,other; plasma clearance, CLp

WED-011

EXPOSURE-RESPONSE MODEL TO DESCRIBE THE RELATIONSHIP BETWEENBARICITINIB AND THE TEMPORAL RESPONSE PROFILES IN SYSTEMIC LUPUSERYTHEMATOSUS (SLE) PATIENTS FROM A PHASE 2 TRIAL
C. Steven Ernest II, Rodney Decker, Robert Hoffman, and Maria Silk. Eli Lilly and Co, Indianapolis, IN

BACKGROUND/OBJECTIVE:
Baricitinib is an orally available, selective JAK inhibitor with excellent potency/selectivity for
JAK1/2 and less potency for JAK3/Tyrosine kinase 2 that may inhibit cytokines implicated in SLE
such as type I IFN, type II IFN-g, IL-6, and IL-23 as well as others. A Phase 2 trial was conducted
to evaluate the effect of baricitinib on efficacy measures in SLE patients. Two of the efficacy
measures assessed in this trial were the proportion of patients achieving resolution of arthritis or
rash as defined by the SLE Disease Activity Index 2000 (here after referred to as SLEDAI-2K
response) and proportion of patients achieving SLE Responder Index-4 (SRI-4) response at Week
24 compared to baseline. The results presented characterize the exposure-response relationshipof these efficacy measures for dose assessment for upcoming Phase 3 studies.
METHODS:The database comprised 1 Phase 2 study in SLE patients receiving placebo, 2- or 4-mg baricitinib
daily up to 24 weeks. The corresponding pharmacokinetics (PK) and SLEDAI-2K and SRI-4
response rates were analyzed using NONMEM. Models were developed to describe the timecourse of SLEDAI-2K and SRI-4 responses following administration of baricitinib or placebo up to
24 weeks. Patient specific factors (age, body weight, etc.) affecting the response rates were
explored.
RESULTS:The observed response rates were 48.6%, 53.4% and 65.0% for SLEDAI-2K and 47.6%, 52.4%,
and 65.0% SRI-4 at week 24 for placebo, 2-mg and 4-mg baricitinib QD dosing regimens,
respectively. Compared to placebo, a statistically significant improvement in efficacy wasobserved at Week 24 for the baricitinib 4-mg group but not the baricitinib 2-mg group. The PK/PD
models adequately characterized the exposure-response time course of SLEDAI-2K and SRI-4
responses. Both baricitinib and placebo exhibited an indirect inhibitory effect on a latent variable
describing the disease progression of SLE with a 2-compartment model describing the plasma
baricitinib concentrations. Model-estimated exposure-response analyses demonstrated thebaricitinib 2-mg and 4-mg dosing regimen produce a higher predicted response than placebo atweek 24 and predict a slight increase in responses between dose groups after 52 weeks oftreatment.
CONCLUSIONS:The exposure-response analyses support examination of both 2-mg and 4-mg baricitinib QD for
the treatment of SLE in longer term Phase 3 studies and suggest that both doses could be
efficacious.

WED-013
Exposure-Response (E-R) analyses for therapeutic dose selection of belantamab mafodotin in patients withrelapsed/refractory multiple myeloma (RRMM)
Chetan Rathi1, Jon Collins2, Herbert Struemper2, Joanna Opalinska1, Roxanne C Jewell2, and Geraldine FerronBrady1
1

GlaxoSmithKline, Collegeville, PA; 2GlaxoSmithKline, Research Triangle Park, NC

Objectives:
Belantamab mafodotin (GSK2857916) is a first-in-class B-cell maturation antigen (BCMA)-targeting
immunoconjugate with a humanized afucosylated anti-BCMA monoclonal antibody conjugated to a cytotoxic
payload mafodotin (MMAF) by a protease-resistant maleimidocaproyl linker (mc). This antibody-drug conjugate(ADC) binds to BCMA and eliminates MM cells by a multimodal mechanism. It delivers MMAF to MM cells
which inhibits microtubule polymerization, resulting in apoptosis, enhances antibody-dependent cellular cytotoxicity
and phagocytosis, and induces immunogenic cell death. The objective was to determine the relationships between
ADC and cys-mcMMAF exposure and key efficacy and safety endpoints in support of the monotherapy dose
justification.
Methods:
Individual Cycle 1 exposure measures from population PK models of ADC and cys-mcMMAF [1] were used for ER analysis. Data were included from heavily pretreated RRMM patients who received intravenous (IV) doses
ranging from 0.03 to 4.6mg/kg Q3W in a phase 1 study (DREAMM-1; n=73) and doses of 2.5 or 3.4mg/kg Q3W in
the pivotal phase 2 study (DREAMM-2; n=218). Exposure-efficacy analyses evaluated probability of response(PoR), progression-free survival (PFS), and time to response or best response (TTR, TTBR). Exposure-safety
analyses evaluated probability of any grade or ≥2 keratopathy (KP), blurred vision (BV) and dry eye (DE), grade ≥3
thrombocytopenia (TCP) or neutropenia (NTP), and infusion-related reactions (IRR). Generalized linear models
were used to assess event probabilities, while Cox proportional hazard models were used to assess time-to-event
endpoints. Covariates were identified using a step-wise model building procedure.
Results:
In DREAMM-2, PoR and PFS were not related to exposure and inversely related to baseline disease factors
including soluble BCMA (sBCMA) and β2-microglobulin being the most significant predictors, respectively. In
DREAMM-1, PoR and PFS were significantly related to Cycle 1 ADC trough concentration (Ctau). Afternormalizing for baseline patient characteristics including sBCMA and IgG, model-predicted PoR increased from
38% at 2.5mg/kg to 40% at 3.4mg/kg for an increase in typical ADC Ctau from 2.2µg/mL (2.5mg/kg) to 3µg/mL(3.4mg/kg). TTR was significantly inversely related to ADC Ctau, whereas TTBR was exposure-independent. KP
was strongly associated with exposure. Increasing dose from 2.5 to 3.4mg/kg increased the model predictedprobability of any grade KP from 80% to 89% and grade ≥2 KP from 57% to 67% after normalizing for othersignificant covariates (history of dry eye and sBCMA). Time to any grade or grade ≥2 KP was inversely related to
ADC Ctau. Probability of TCP was significantly related to cys-mcMMAF Cmax. BV, DE, NTP and IRR wereindependent of ADC or cys-mcMMAF exposure.
Conclusions:Overall, E-R relationships for KP showed a greater increase with increasing exposure than the efficacy endpoints,
which supported monotherapy dose selection of 2.5mg/kg.
References: [1] Collins et al., ACoP2020
Results in this abstract have been presented in part at AACR, 2020.

WED-014
Mathematically modeling CD8+ T cell-mediated drug-induced liver injury (DILI):
from ovalbumin (OVA) to amodiaquine in mice
Christina Battistaa, Zackary R. Kenza, Lisl K.M. Shodaaa

DILIsym Services, Inc., a Simulations Plus Company, Research Triangle Park, NC

Objectives: Idiosyncratic drug-induced liver injury (iDILI) is poorly understood, but there is evidence to support
that some iDILI events may be immune-mediated. Amodiaquine (AQ), a malaria medication, is thought to cause
immune-mediated iDILI due its delay between treatment initiation and DILI onset and its ability to induce a more
rapid injury upon drug re-challenge.1 Previous work has been devoted to representing T cell-induced cytotoxicity inresponse to ovalbumin (OVA)-expressing hepatocytes in mice.2 The current work aims to translate the OVA
representation to recapitulate AQ-mediated hepatotoxicity in mice.
Methods: An existing quantitative systems toxicology (QST) model (DILIsym®) was previously expanded tocharacterize CD8+ T cell responses to hepatocyte-expressed OVA.2 This model has further been expanded toincorporate exposure of AQ via a physiologically-based pharmacokinetic (PBPK) representation which includes AQ
metabolism to reactive metabolites (RMs). AQ RMs induce hepatocellular ER stress3 as well as oxidative stress4.
These mechanisms for intrinsic toxicity were evaluated for their potential to alter the hepatic microenvironment,
making it more permissive for a CD8+ T cell response. The model representation of T cell interaction with
hepatocyte-presented antigen was also updated to incorporate the putative lower T cell receptor affinity of AQ
compared with OVA and to represent a function of cytokine/co-stimulation levels which modulate T cell activity.
These modifications, along with the pathway for T cell exhaustion already built into the OVA model, allow for
representing interference in pathways implicated by CTLA4 and PD-1 compared with a wild-type mouse.
Simulations aimed to reproduce CD8+ T cell-mediated DILI in a PD-1-/- mouse model (bred on a C57BL/6
background) exposed to AQ with anti-CTLA4 treatment.5,6
Results: Simulations successfully captured differences in CD8+ T cell responses under different experimental
conditions. No ALT elevations were predicted in simulated wild-type mice treated with AQ. Simulations designed
to mimic inhibition of regulatory pathways, i.e., PD-1-/- mice treated with anti-CTLA4, predicted CD8+ T cellmediated ALT elevations consistent with levels seen in experimental data.5,6. Variations in cytokine/co-stimulationlevels were simulated to assess T cell response sensitivity and range of response. These abstract results have beenpreviously presented in part at Immunology2020, Honolulu, HI, 5/2020 and published in the conference proceedings
as abstract 2395.
Conclusions: The results provide confidence in the QST model translation of OVA hepatotoxicity to AQ-induced
DILI in mice. Results provide a basis for exploring AQ-induced DILI in humans using QST modeling with the goalof investigating immune-mediated iDILI risk for future compounds.
References:
1. Larrey. Ann. Intern. Med. 1986. 104(6):801;803.
2. Kenz. ACoP9 Poster W-076. 2018.
3. Zhang. Front Pharmacol. 2018. 9:388.
4. Heidari. Res Pharm Sci. 2014. 9(2):97-105.
5. Mak. Chem Res Toxicol. 2015. 28(8):1567-1573.
6. Metushi. Hepatol. 2015. 61(4):1332-1342.

WED-015
Population Pharmacokinetic Simulations of Sarilumab to Support Dose Selection in a Pediatric Population
with polyarticular-course juvenile arthritis (pcJIA)
Christine Xu1, Qiang Lu1, and Vanaja Kanamaluru1
1

Sanofi, Bridgewater, NJ, USA

Objectives: To guide pediatric subcutaneous (SC) dose selection of sarilumab using simulations in pediatric patientswith pcJIA using the allometrically-scaled population pharmacokinetic (PopPK) model developed in adult patientswith rheumatoid arthritis (RA).
Methods: The pharmacokinetics of sarilumab in adult patients with RA were described previously using a
2-compartment, target-mediated drug disposition model with parallel linear and non-linear (ie., Michaelis-Menten)
elimination and first order absorption (1). A virtual pediatric population based on typical weight and sex distributionin pediatric patients with pcJIA was created. The PopPK model in adult patients with RA was adapted to a pediatricpopulation by allometrically scaling with body weight, using scaling exponents of 0.85 for clearance (CL/F) and
1 for volume of distribution. Sarilumab exposure following multiple doses was then simulated using the pediatric
PopPK model to select the pediatric dose regimens achieving similar exposure as the dose regimens tested in phase
2/3 studies in adults with RA. A sensitivity analysis of the model was carried out by applying an allometric scaling
exponent of 0.75 instead of 0.85 on CL/F.
Results: Based on steady-state exposure predictions (AUC0-14 days, Cmax, and Ctrough) from PopPK simulations,
pediatric patients with polyarticular-course JIA (pcJIA) were divided by body weight into 2 groups, A (30–60 kg)
and B (10–<30 kg). Group A/B doses of 2.0/2.5 mg/kg every two weeks (q2w); 3.0/4.0 mg/kg q2w; and2.0/2.5 mg/kg once weekly (qw) were predicted to achieve similar exposure to adult RA doses (150 mg q2w,
200 mg q2w, and 150 mg qw, respectively). Sensitivity analysis showed a small impact of the allometric scaling
exponent (0.75 vs 0.85 for CL/F) on predicted pediatric exposure across the range of pediatric weights, but with no
impact on the dose recommendations. Compared to flat SC dosing, individual body weight-based dosing (mg/kg)
showed lower PK variability of simulated exposure in pediatric patients. The observed PK data in the phase 2pediatric study subsequently confirmed that doses recommended using PopPK simulations had similar exposures inthe two weight groups in pcJIA and achieved the targeted adult exposures.
Conclusions: Model-based simulations provided a quantitative approach to guide a sarilumab SC doserecommendation by body weight groups in pediatric patients with pcJIA.
Reference:(1) Population Pharmacokinetics of Sarilumab in Patients With Rheumatoid Arthritis. 26th Population Approach
Group in Europe Meeting; Budapest, Hungary; June 6-9, 2017.
Disclosures: Christine Xu, Qiang Lu, and Vanaja Kanamaluru are employees of Sanofi and may hold stock and/or
stock options in the company.

WED-016
Population pharmacokinetics of blonanserin, an atypical antipsychotic drug,in adolescent patients with schizophrenia.
Daisuke Nemoto, Takeshi Takagaki, Atsushi Kitamura, Yoshiko Tomita, Hiroyoshi Kakuyama, Katsunori Maruta
Sumitomo Dainippon Pharma Co., Ltd., Tokyo, Japan;
Objectives: Blonanserin is an atypical antipsychotic drug with high affinity, selective and full antagonisms for
dopamine D2, D3 and 5-HT2A receptors and was approved in Japan, Korea and China for treatment of adult patientswith schizophrenia. Double-blind, placebo-controlled study (DB study) and open-label extension study (OLE study)
were conducted to evaluate the efficacy, safety, and pharmacokinetics of blonanserin in adolescent patients withschizophrenia. The objectives of this population pharmacokinetic (PPK) analysis were to evaluate the characteristicsof blonanserin pharmacokinetics in adolescent patients with schizophrenia and to compare their pharmacokinetic
parameters with adults.
Methods: PPK analysis was conducted using 330 plasma concentration data from 131 adolescent patients (12 to 18
years) in two studies. DB study was a 6-week, randomized, three arm (placebo, 4 mg BID, 8 mg BID) study. OLE
study was a 52-week, flexible dose (4 to 24 mg/day) study. For the structural model, one and two compartment models
with first-order absorption were tested. The impact of age, weight, sex, dose, or concomitant drugs on apparent
clearance (CL/F) and that of weight or BMI on apparent volume of distribution were tested by stepwise forward
addition and backward elimination. Final model was validated by goodness of fit plots, visual predictive check (VPC)and bootstrap analysis. Area under the concentration-time curves during 24 hours at steady state (AUC24,ss) inadolescents estimated by PPK analysis were compared with those in adult patients estimated in the previously
conducted PPK analysis for adult patients with schizophrenia. PPK analysis was performed using NONMEM 7.3 with
PDx-Pop 5.0.
Results: Blonanserin pharmacokinetics were described by a 2-compartment model with first-order absorption. Typical
population estimates of CL/F, central (Vc/F) and peripheral (Vp/F) apparent volume of distribution, apparent intercompartmental clearance (Q/F) and absorption rate constant (Ka) were 0.916 kL/h, 10.5 kL, 91.0 kL, 0.494 kL/h, and1.23 h-1, respectively. Inter individual variability was 29.1% for CL/F. Shrinkage was reasonable for CL/F (11.2%).
None of the tested variables was detected as covariate in the model, including age or weight, and the final model waswell validated. There was no noticeable relationship between age and CL/F in adolescents. Most of estimated AUC24,ss
in adolescents were comparable with that in adults at the same dose.
Conclusions: A 2-compartment model with first-order absorption has been developed that can adequately describe
the blonanserin plasma concentration profile in adolescent patients with schizophrenia. Age and weight were not
identified as a covariate in the final model. The distribution of estimated AUC24,ss in adolescent patients wascomparable to that in adult patients with schizophrenia. This PPK model can be used to understand and predict
pharmacokinetics of blonanserin in adolescent patients with schizophrenia.

Figure 1: Relationship between dose and AUC24,ss (95% CI) in adult and adolescent patients of schizophrenia

WED-017

Development of a Tumor Growth Inhibition Model for Dacomitinib in Patients with EGFR
Mutant Non-Small Cell Lung Cancer
Authors: Dana Nickens2, Luke Fostvedt1, Weiwei Tan2
Institutions: 1Global Product Development, Pfizer Inc., Cambridge, MA, USA; 2Global ProductDevelopment Pfizer Inc., La Jolla, CA, USA
Objectives:
Dacomitinib is a second-generation, irreversible EGFR tyrosine kinase inhibitor (TKI) for the
first-line treatment of patients with metastatic non-small cell lung cancer (NSCLC) and EGFRactivating mutations. The recommended starting dose of dacomitinib is 45 mg QD with dose
reductions of 30 mg QD and then 15 mg QD for adverse reactions. Due to a high rate of dose
reduction or dosing interruption in the pivotal trials, a longitudinal tumor growth inhibition (TGI)model was developed to support the dacomitinib dosing regimen. This work supported the recent
NDA approval by the FDA with a starting dose of 45 mg QD.
Methods:
Several TGI models were evaluated to characterize the change in tumor size over time in patientswith NSCLC and EGFR-activating mutations taking dacomitinib at a starting dose of 45 mg(N=250) or 30 mg (N=16) QD. A time-varying exposure metric,1 (Cavg) was incorporated into themodel using a power parameterization (i.e. Cavg^theta) calculated as the cumulative AUC up to
the time of each tumor measurement divided by the length of time since the first dose based on
post hoc estimates from the population PK model. The Claret model2 was chosen for the final
analysis, which best characterized changes in tumor size over time. Using the model, the effectof dose reductions and drug holidays on the tumor shrinkage over time was assessed.
Results:
The exposure-response model for dacomitinib presents a fast kill rate (KD=1.002 years-1) withslow drug-resistance (λ= 14.47 years-1) which leads to a prolonged tumor shrinkage. The mediantime to maximum tumor shrinkage was estimated to be 5.27 months with 5th and 95th percentilesof 1.57 and 16.69 months, respectively. The estimated exposure effect was 0.454 (i.e. Cavg0.454)
indicating greater tumor shrinkage and longer duration of tumor growth inhibition associatedwith increased dacomitinib exposure (Figure 1). The effect of the 45 mg versus 30 mg QD
starting dose on tumor shrinkage, with and without dose reductions, was simulated using theobserved median times of the first and second dose reductions. The results reveal that a patient
who receives 45 mg QD with 1 or 2 dose reductions has greater and prolonged tumor
suppression, compared to a starting dose of 30 mg QD without dose reductions.
Conclusions: The tumor growth inhibition modelling and simulation played an important role injustifying the clinical dose regimen with a starting dose of 45 mg QD by showing a greater andmore prolonged tumor shrinkage.

References:
1. Jingyu Yu, “Regulatory Perspectives on Quantitative Clinical Pharmacology in Oncology
Drug Development”, AAPS Meeting, 2017
2. Claret, L. et al. Model-based prediction of phase III overall survival in colorectal cancer on thebasis of Phase II tumor dynamics. J. Clin. Oncol. 27, 4103–4108 (2009).
Figure 1: Predicted Percent Change in Tumor Size by Regimen

WED-018

BCMA-CD3 Bispecific Antibodies - A Modeling Framework to Characterize Kinetics of Bispecific
Antibody, T Cell, Cytokines, and Serum M-Protein.
Chandra Udata, David Dai, Sibo Jiang, Alison Forgie, Wenlian Qiao, Cynthia Musante, Donghua Yin
Institution: Pfizer Inc.
Objectives:
The B-cell maturation antigen (BCMA) is an attractive target in multiple myeloma (MM) since it is more
selectively expressed on myeloma cells (1). T-cell–engaging BCMA-CD3 bispecific antibodies (BsAbs)
simultaneously bind to CD3 on T-cells and BCMA on tumor cells, activate T-cells, and redirect T-cells’
cytotoxicity against tumor cells (2). BCMA is shed from the surface of plasma cells by gamma-secretase–mediated cleavage, leading to detectable, soluble BCMA (sBCMA) in circulation. Higher concentrationsof sBCMA in patients with myeloma are associated with lower objective response rate (3). Furthermore,sBCMA in circulation may potentially impact pharmacokinetics, target engagement on tumor cells, andefficacy when it is present in a relatively higher concentration compared to the drug concentration (4).
Quantification of therapeutic response often relies on surrogate measurements such as serum Mprotein and free light chains. The objective of this work is to develop a quantitative systems
pharmacology (QSP) model to characterize the kinetics of BsAb, T-cell, cytokines and serum M-protein
upon BsAb treatment based on the published Bispecific T cell Engager (BiTE) data (5).
Methods:
We developed a QSP model (Figure 1) to capture T cells engagement to malignant cells expressing BCMA
that leads to selective cell death of MM cells, and cytokine release. To quantify the influence of sBCMAon efficacy of CD3-BCMA-bispecific therapies, we accounted for the formation of sBCMA-drug dimersthat reduced the free drug concentration. The model was calibrated to published data of BiTE
pharmacokinetic profiles, serum M-protein dynamics and efficacy across the clinically evaluated dose
range.
Results:
The QSP model was able to capture the pharmacokinetic and serum M-protein profiles of the calibration
data, across the clinical dose range. The simulations also predicted sBCMA dynamics of the responders
treated with BiTE. We found that the absolute concentration of circulating sBCMA was associated withdecreased efficacy, especially at lower doses. Despite the presence of sBCMA, we determined thathigher BCMA binding affinity increased efficacy.
Conclusion:
The model serves as a foundation to better understand the impact of sBCMA, tumor burden, T-cellabundance and redistribution, trimer levels, and cytokine on efficacy and safety. This model could assist
in the design of clinical dosing strategies for CD3-BCMA BsAb programs. In addition, the framework canbe adopted as a platform for optimizing therapeutic outcome of other CD3-BsAb programs.

Figure 1. BCMA-CD3 QSP Model Schematic

References
1. Cho, S. F., Anderson, K. C., & Tai, Y. T. (2018). Targeting B cell maturation antigen

2.

3.

4.

5.

(BCMA) in multiple myeloma: potential uses of BCMA-based immunotherapy. Frontiersin immunology, 9, 1821.
Shah, N., Chari, A., Scott, E., Mezzi, K., & Usmani, S. Z. (2020). B-cell maturation antigen(BCMA) in multiple myeloma: rationale for targeting and current therapeutic
approaches. Leukemia, 1-21.
Ghermezi, M., Li, M., Vardanyan, S., Harutyunyan, N. M., Gottlieb, J., Berenson, A., ... &Udd, K. (2017). Serum B-cell maturation antigen: a novel biomarker to predict outcomesfor multiple myeloma patients. Haematologica, 102(4), 785-795.
Chen H, Li M, Xu N, Ng N, Sanchez E, Soof CM, et al. Serum B-cell maturation antigen(BCMA) reduces binding of anti-BCMA antibody to multiple myeloma cells. Leuk Res.
2019;81:62–6.
Topp, M. S., Duell, J., Zugmaier, G., Attal, M., Moreau, P., Langer, C., ... & Beutner, K.
(2019). Evaluation of AMG 420, an anti-BCMA bispecific T-cell engager (BiTE)
immunotherapy, in R/R multiple myeloma (MM) patients: Updated results of a first-inhuman (FIH) phase I dose escalation study.

WED-019

Physiologically-Based Pharmacokinetics of Dexamethasone in Rats
Dawei Song1#, Le Sun1#, Debra C DuBois 1,3, Richard R Almon 1,3, Shengnan Meng 2, William J Jusko 1
1

Department of Pharmaceutical Sciences, School of Pharmacy and Pharmaceutical Sciences, State University of
New York at Buffalo, Buffalo, NY, 14214, USA; 2 Department of Pharmaceutics, School of Pharmacy, China
Medical University, Shenyang 110122, China; 3 Department of Biological Sciences, State University of New York at
Buffalo, Buffalo, NY, 14260, USA; # Authors have contributed equally to these studies

Objectives: The tissue distribution of dexamethasone (DEX) in male and female rats was assessed in order to develop
a physiologically-based pharmacokinetic (PBPK) model for the purposes of: 1. Thorough investigation of multi-organ
PK profiles of DEX to gain better understanding of determinants for DEX whole-body PK. 2. Comparison of tissue
partition coefficients between females and males to augment previous PK studies of DEX in relation to sex in rats. 3.
Obtaining a data base for possible scaling the PBPK of DEX to other species.
Methods: Blood/plasma and 11 tissue samples were collected from groups of male and female Wistar rats after
subcutaneous (SC) infusions to achieve steady-state (SS) and after a SC bolus of 2.25 mg/kg of DEX. In vitro and invivo assessments were made of plasma protein binding and RBC partitioning. Quantitation of DEX was done by liquid
chromatography-tandem mass spectrometry (LC-MS/MS). The observed plasma and tissue profiles were describedby a traditional PBPK model using appropriate differential equations with the Adapt software. The tissue partition
coefficients (Kp) of DEX were estimated by PBPK model fitting and compared to those obtained from SS infusionand published in silico methods (GastroPlus).
Results: The plasma clearance of DEX averaged 0.198 in males and 0.178 L/h/kg in female rats, consistent with
hepatic CYP3A expectations. The blood-to-plasma ratio of DEX was 0.664 in males and 0.725 in females indicating
limited entry of unbound (fraction unbound = 0.175) drug from plasma into RBC. Despite its expected highpermeability (LogP=1.83), limited partitioning of DEX into brain (Kp = 0.06/0.10 males/females) was found andmodeled based on known P-glycoprotein-mediated efflux. Distribution of moderately lipophilic DEX into adipose
tissue was low (Kp = 0.16/0.10 males/females). The PBPK-fitted SC-dosed Kp values were close to those from SS
infusion data. The PBPK-estimated liver Kp was 6.76, reflecting appreciable liver partitioning and binding similar toother corticosteroids. Various Kp values obtained from male and female rats were generally similar (within 1.6-fold).
By using female Kp and clearance values and physiological parameters, DEX plasma PK profiles were well captured
in female rats. Use of three in silico prediction methods for Kp produced a range of values that were in reasonableagreement with in vivo values for all tissues except brain, fat, and liver.
Conclusions: The tissue distribution and PK of DEX differed only modestly between male and female rats. Our PBPK
model well describes the experimentally determined tissue and plasma DEX PK profiles in rats and can serve as thePK driving force for receptor-mediated pharmacodynamic effects in various tissues.

WED-020
Representation of Efavirenz-mediated Drug-Induced Liver Injury (DILI) Using
Quantitative Systems Toxicology (QST)
Diane M. Longo1, Kyunghee Yang1, Brett A. Howell1, Jeffrey L. Woodhead1
1

DILIsym Services Inc., Research Triangle Park, NC, USA.

Objectives: Efavirenz is a non-nucleoside reverse transcriptase inhibitor (NNRTI) used in combination with otheragents to treat human immunodeficiency virus (HIV) infection.

Efavirenz treatment is associated with a low

frequency of serum enzyme elevations. To understand the hepatotoxicity mechanisms underlying clinically observed
liver signals, efavirenz was represented in DILIsym®, a QST model of DILI.
Methods: The potential for efavirenz to inhibit bile acid transporters was assessed using transporter-overexpressing
vesicles and cells. The potential for efavirenz to induce mitochondrial dysfunction or oxidative stress was assessed in
HepG2 cells. These in vitro data were used to define DILIsym hepatotoxicity parameters for efavirenz. A previously
constructed GastroPlus physiologically based pharmacokinetic (PBPK) representation was used to predict efavirenz
exposure. Simulated populations (SimPops) that include variability in hepatotoxicity mechanisms and in efavirenz
exposure were used to simulate the in vivo response in humans to efavirenz in DILIsym.
Results: Mechanistic in vitro assays indicated that efavirenz inhibits the mitochondrial electron transport chain (ETC),
increases oxidative stress, and inhibits bile acid transporters (BSEP, NTCP, and MRP4). Combining in vitro
hepatotoxicity data with predicted exposure, DILIsym predicted infrequent ALT elevations (1-2%) in SimPops
following administration of efavirenz (600 mg QD) for 12 weeks.
Conclusions: Using in vitro mechanistic data to determine efavirenz toxicity parameters, DILIsym predicted
infrequent hepatotoxicity, consistent with the low rate of ALT elevations seen clinically for efavirenz.

Efavirenz 600 mg QD oral

Observed ALT >5X ULNSimulated ALT >5X ULN

1% to 8% (LiverTox)

1% to 2%

Table 1. Comparison of observed vs. simulated hepatotoxicity for efavirenz.

WED-021
Using a QSP model of Rheumatoid Arthritis calibrated to capture clinical response to Adalimumab to
perform population analysis
Tanvi Joshi, Prakash Packirisamy and Rukmini Kumar
Vantage Research, India
Objectives:
1.
2.

Develop a QSP model of Rheumatoid Arthritis (RA) capturing mechanistic pathways and clinical responses
to Methotrexate, Adalimumab and Tocilizumab.
To derive insights from population analysis about differences between Adalimumab responders and nonresponders.

Methods:
The Vantage RA QSP model [1] represents an inflamed RA joint at steady state. Modular design approaches used to
set up common immunological subsystems (cell life cycles and cytokine effects), enable repurposing to other autoimmune diseases.
Using Ordinary Differential Equations (ODEs), the model captures lifecycle and cellular interactions of Fibroblast
like Synoviocytes (FLS), B cells, T cells and macrophages and relevant pro and anti-inflammatory cytokines like IL6, TNF-α, TGF-β. The model parameters are constrained by clinical trial data (top down constraints) and by bottom
up data (cell proliferation rate, cytokine secretion rates). ACR and DAS-28 scores in Virtual Population are
calibrated to match clinical trials for Methotrexate, Adalimumab (anti-TNF-α therapy) and Tocilizumab. Population
analysis was performed on the calibrated virtual population to derive insights about differences between
Adalimumab responders and non-responders.
Results:
The Vantage RA QSP model was developed to capture multiple physiological pathways in RA and post therapy
effects on disease severity scores. The virtual population was calibrated to capture the clinical trial entry
characteristics, such as DAS-28 ranging from 5 to 7 at baseline, and match the clinical trial readouts, such as
remission and response percentages for three therapies studied in the Williams et al. 1985, OPTIMA and ROSE
trials. [2, 3, 4]
Conclusions:
The Vantage RA QSP model captures the physiology of RA, the clinical outcomes, and the response to threestandard therapies. Modular model design enables model repurposing to other auto-immune diseases with similar
pathophysiology. Population analysis helps derive insights about Adalimumab responders and non-responders.
Future efforts include determining differences between different responders of Adalimumab and Infliximab andexploring response to JAK inhibitors.

References:1.

Tamara Ray, Madhav Channavazzala, Dinesh Bedathuru, Maithreye Rengaswamy, Rukmini Kumar. QSP
Model of Rheumatoid Arthritis, capturing range of clinical responses to Methotrexate and anti-TNF-atherapies. PAGE 28 (2019) Abstr 9081 [www.page-meeting.org/?abstract=9081]

2.

Williams, H. J., et. al. (1985). Comparison of low-dose oral pulse methotrexate and placebo in thetreatment of rheumatoid arthritis. A controlled clinical trial. Arthritis & Rheumatism: Official Journal of
the American College of Rheumatology.

3.

Kavanaugh, A., et. al. (2013). Clinical, functional, and radiographic consequences of achieving stable low
disease activity and remission with adalimumab plus methotrexate or methotrexate alone in earlyrheumatoid arthritis: 26-week results from the randomised, controlled OPTIMA study. Annals of therheumatic diseases.

4.

Yazici Y, et. al. (2010). Efficacy of tocilizumab in patients with moderate to severe active rheumatoid
arthritis and a previous inadequate response to disease-modifying antirheumatic drugs: the ROSE study.
Annals of the rheumatic diseases.

WED-022
Pharmacometric Modeling with the Zero-Order Hold
Violeta Rodriguez-Romero1*, Rudy Gunawan1*, and Eric Haseltine1
1

Vertex Pharmaceuticals Incorporated, Boston, MA, United States*

These authors contributed equally to this work.

Objectives:
Solving models of ordinary differential equations (ODEs) in NONMEM using ADVAN6 or ADVAN13 typicallyrequires substantially longer run times than models with analytical solutions. Often the need to use these methods
results from PK variations over the dosing interval preventing derivation of an analytical solution, as is the case forindirect-response models and enzyme induction models. As long run times hinder model development, it isdesirable to derive suitable approximations to speed up model solutions.
Methods:
The zero-order hold1, a concept used in the field of advanced process control to optimize control decisions, provides
an attractive approximation for these situations. Consider as an example the indirect-response model shown in
Figure . The PK component of the model can be solved analytically using ADVAN2. The continuous PK profile isdiscretized over a pre-specified interval using a suitable metric, such as Cavg – this approximation is the zero-orderhold (seeFigure , right). Subsequently, the nonlinear ODE for the indirect response model becomes a linear ODE over theinterval as Cavg is constant. This linear ODE can be solved analytically by providing Cavg values in the NONMEM
input file, which eliminates the need to solve for the PK in the PK/PD model. Note that this approach differs fromthe one reported by Petersson et al.2, who applied a forward-difference approximation for the ODEs. In contrast, we
exactly solve all ODEs but discretize the PK response over a pre-specified interval.
Results:
Two examples are considered: an indirect-response model and an enzyme induction model. Results are simulated
from an ADVAN13 model, and then refit using either the same model or the zero-order hold approximation. It isshown that the zero-order hold approximation provides a substantial reduction in computational time without unduly
biasing the parameter estimates. For the indirect-response model, the speed-up is ~50-fold with comparable
estimated parameters.
Conclusions:
Applying the zero-order hold is an attractive method for efficiently solving models where time-varying PK preventsan analytical solution. Although this approach ignores PK variation within the discretization interval, the results
provide two examples where such an approximation does not adversely impact the estimated parameters. The
approach does require thoughtful selection of the PK metric used to drive the response, and if the approximation isappropriate for the situation. If appropriate, computational expense can be substantially reduced for estimating
models typically solved with ADVAN6 or ADVAN13.
References
1.

Ogunnaike, B. A. & Ray, W. H. Process Dynamics, Modeling, and Control. (Oxford University Press, 1994).

2.

Petersson, K. J. F., Friberg, L. E. & Karlsson, M. O. J. Pharmacokinet. Pharmacodyn. 37, 493–506 (2010).

Sponsored by Vertex Pharmaceuticals Incorporated

Figure 1: Indirect-Response Model with Zero-order Hold Approximation (Cavg - red line)WED-023
Population Pharmacokinetic Analysis of JP-1366, a Novel Potassium-Competitive Blocker, in Healthy Subjects
Eunsol Yang1, Inyoung Hwang, MD1, SeungHwan Lee, MD, PhD1

1

Department of Clinical Pharmacology and Therapeutics, Seoul National University College of Medicine andHospital, Seoul, Republic of Korea

Objectives: JP-1366 is a novel potassium-competitive acid blocker under clinical development for the treatment of
acid-related disorders. This analysis aimed to establish a population pharmacokinetic (PK) model for JP-1366.
Methods: PK data from a first-in-human study of JP-1366 (single ascending dose part (n=38); multiple ascending
dose part (n=30)) in healthy Korean subjects was used for the analysis. The dataset for model development included
1495 plasma concentrations from 68 subjects. Population PK modeling was carried out using NONMEM® version
7.4.4, and PK parameters were estimated by first-order conditional estimation method. Also, potential covariates suchas body weight, albumin, alanine aminotransferase and aspartic acid transaminase were evaluated. Final population
PK model was validated by goodness-of-fit (GOF) plots and Visual Predictive Check (VPC).
Results: A two-compartment model with Erlang-type absorption (n=6), first-order elimination and a proportional error
model well described PK profile of JP-1366. The population parameter estimates for absorption rate constant (Ktr),
apparent clearance (CL/F), apparent inter-compartmental clearance (Q/F), apparent central volume of distribution(Vc/F) and apparent peripheral volume of distribution (Vp/F) were 13.6 h-1, 29.5 L/h, 29.4 L/h, 97 L and 147 L,
respectively. None of the covariates had significant effects on CL/F and Vc/F. The GOF and VPC plots showedrobustness and predictability of the developed final population PK model.
Conclusions: The population PK model for JP-1366 successfully described the observed PKs of JP-1366. This model
will be utilized for designing further clinical studies and establishing dosing regimen.

WED-024
A Population Pharmacokinetic/Pharmacodynamic/Exposure Response Model to Relate Complement 4
Antigen Concentrations and Risk of Attacks in Hereditary Angioedema Patients
Authors: Fiona Glassman1, Ziheng Hu1, Theresa Yuraszeck1, Ingo Pragst2, Dipti Pawaskar1
Affiliations: 1CSL Behring, King of Prussia, PA, USA; 2CSL Behring GmBH, Marburg, Germany

Objectives: Hereditary angioedema (HAE) is a rare genetic disease caused by deficiency in functional C1 esterase
inhibitor (C1-INH[f]). Uncontrolled activation of the classical complement pathway leads to low concentrations ofcomplement 4 (C4), which is a less varying biomarker of HAE than C1-INH(f). While the relationship between HAE
attack risk and C1-INH(f) is well established, its relation to C4 antigen is unknown. This analysis aims to explore therelationship between C4 antigen and risk of attack in HAE patients.

Methods: C1-INH(f) and C4 antigen were measured in COMPACT Phase 2, 3 and open-label long-term safety clinicalstudies. A population PK/PD model was developed to characterize the relationship between C1-INH(f) and C4 antigen,which was described using an indirect response model. An exposure-response (ER) model was also developed to relate
C1-INH(f) and risk of HAE attack. The relationship between C4 antigen concentration and risk of HAE attack wasevaluated based on combined simulation results of the PK/PD and ER models of C1-INH(f) and risk of HAE attack.

Results: The relationship between C4 antigen concentration and risk of HAE attack was investigated with a combined
population PK/PD/ER model previously developed. A positive linear relationship was observed between C4 antigen
concentrations and C1-INH(f) until C1-INH(f) of ~50%, at which point signs of saturation were apparent. In addition,
an inverse relationship was observed between C1-INH(f) and risk of HAE attack. Similarly, HAE attack hazard
decreased with the increase of C4 antigen concentrations.

Conclusions: The inverse relationship between the risk of HAE attack and C4 antigen concentrations in HAE patientscan be described by a population PK/PD/ER model. This can provide a basis for the use of C4 antigen in addition to
C1-INH(f) for assessing drug efficacy.

WED-027
Application of different approaches to generate virtual patient populations for QSP model of Erythropoiesis
Galina Kolesova1, Oleg Demin1, Alexander Stepanov1
1

InSysBio, Moscow, Russia

Objectives: In the study we propose and compare four different techniques to generate virtual patient populations
basing on experimentally measured mean data and statistics.

Methods: QSP model of erythropoiesis [1] was constructed to comprehensively describe cell dynamics fromhematopoietic stem cell to circulating red cells. The model describes cell self-renewal, differentiation, proliferation,
migration from bone marrow into circulation and cell death. Binding of growth factors such as stem cell factor (SCF)
and erythropoietin (EPO) to cell-surface receptors regulates cell dynamics modulated by interleukin-3 (IL-3). The
model was calibrated across published in vitro/in vivo data.

Data describing time series of plasma reticulocyte count in response to single dose erythropoietin administered to 5
healthy subjects is used to find out final population of virtual patients (VP). Experimental data are given in the formof mean and standard deviation (SD).

Four different approaches were applied to generate virtual patient populations (VPpop): (1) Monte-Carlo Markov
Chain, (2) Model fitting to Monte-Carlo sample, (3) Population of clones, (4) Stochastically bounded selection. 36
parameters of the erythropoiesis model were chosen to be responsible for variability in observed clinical data. Initial
VPpop was generated based on a priori distribution of the selected parameters. Number of VPs equal to that specified
in clinical data and allowing to satisfactory describe both mean and SD of reticulocyte values measured clinicallywere chosen from initial VPpop on the basis of each of the approaches.

Results: In addition to approaches (1), (2) and (3) presented earlier [2] we have developed an approach (4) entitled as“Stochastically bounded selection”. The approach includes (i) finding minimal (RETmin) and maximal (RETmax)
values of reticulocyte count in particular time point among initial VPpop, (ii) building a truncated lognormal
distribution for reticulocyte count, the distribution is concentrated at [RETmin, RETmax] and has mean and SD equal
to those of experimental data, (iii) generation of S random values of the endpoint (in our case reticulocyte count andS=5 equal to number of patients in experimental data), (iv) finding 5 VPs in initial VPpop which reticulocyte values
are closest to the 5 randomly selected values at step (iii). Fig 1 illustrates application of the approach (4). Similar
results were obtained for other approaches.

Conclusions: The approaches proposed are capable for reproducing the endpoint distribution characteristics. The
choice of a specific technique is determined by characteristics of the particular problem. Namely, in case of distribution
with relatively small SD all methods work similarly. However, to reproduce distributions with relatively large SD
second, third and fourth methodologies are preferable. But for large models, the fourth method can preferentially be
used. This method allows to include in generation of VPpop any number of variable parameters.

WED-028

Novel Quantitative Approach for the First-in-human Dose in an Oncology Setting for the Antiprogrammed Cell Death-1 Monoclonal Antibody, AMG 404
Hansen L. Wong1, Khamir Mehta1, Sandeep Dutta2, Vijay V. Upreti1.
1
Amgen Inc., South San Francisco, CA, USA; 2Amgen Inc., Thousand Oaks, CA, USA.
Objectives: AMG 404 is a novel monoclonal antibody (mAb) that blocks programmed cell death-1 (PD-1)
from binding to its ligands, programmed death-ligand 1 and 2 (PD-L1 and PD-L2, respectively). We
present here a strategy for first-in-human (FIH) starting dose selection that integrates preclinicalassessments and human pharmacokinetic (PK) predictions of AMG 404 with existing literature data ofanti-PD-1 mAbs.
Methods: The key motivation of this effort was to recommend a starting dose that provides similar
pharmacological activity as the existing, generally well-tolerated and efficacious exposures of anti-PD-1
mAbs in patients. By this strategy as shown in Figure 1, the proposed dose was expected to beefficacious. AMG 404 exposure targets [steady-state average concentration (Cavg,ss)] were predicted byscaling the known clinical Cavg,ss values of multiple approved anti-PD-1 mAbs (also referred to as anti-PD1-drugs) to AMG 404 using corrections for molecular weight (MW) and PD-1/PD-L1 blockade activity
from Jurkat T cell-based reporter gene and HEK293T cell-based assays (as assessed by concentrations forhalf-maximal effect [EC50] and half-maximal inhibitory effect [IC50], respectively) using the equation:
Target Cavg,ss,AMG 404 = Cavg,ss, Anti-PD-1 Drug x Average IC(EC)50 ratio of AMG 404/Anti-PD-1 Drug x
Average MW ratio of AMG 404/Anti-PD-1 Drug
AMG 404 doses were predicted in conjunction with human PK parameters derived from allometricscaling of cynomolgus monkey PK using a 2-compartment model with linear elimination.
Results: Using derived human PK parameters, AMG 404 dose range for intravenous administration was
estimated to achieve the predicted target Cavg,ss exposures. A starting dose was proposed near the mean
estimate that was expected to be well-tolerated and efficacious in light of the prior experience with
anti-PD-1 mAbs, though potential regulatory feedback was fully expected. Favorable regulatory
feedback was received: starting dose was reduced only 2-fold from proposed. The results in thisabstract have been previously presented in part at the 2020 annual meeting of the American Society ofClinical Pharmacology and Therapeutics and published in the conference proceedings as abstract PII059.

Conclusions: This work showcases a successful FIH dose selection strategy towards rapid evaluation of a
new drug which considers literature results of other anti-PD-1 mAbs that was accepted by regulatory
agencies and can be used for future programs.

Figure 1. Proposed Starting Dose of AMG 404 with Similar In Vitro Pharmacological Activity of
Efficacious Doses of Other Anti-PD-1 mAbs

WED-029
pbpkme: A Flexible Fit Matrix-Based Framework for PK and PBPK Models in R
Authors: Hillary R. Husband and Eric A. Sherer
Institutions: College of Engineering and Science, Louisiana Tech University

Objectives: The pbpkme package is designed for solving classic compartmental or physiologically-based
pharmacokinetic (PBPK) models. These models are often characterized by large systems of linear ordinary
differential equations (ODEs), but the dimensionality of PBPK models can be burdensome and inefficient. The
objectives of this work are to:••Construct a matrix-based framework for efficiently solving large systems of differential equations as often
used to define PBPK models
Openly provide this framework in the form of an R package, pbpkme, which takes in rate coefficients,
initial conditions, and dose information and returns drug concentrations in all compartments

Methods: This package uses the eigenvalue/eigenvector solution to a system of linear ODEs. Matrix manipulation iscomputationally fast, making it ideal for model fitting/parameter estimation, especially for large models. The
equation for the analytical solution is#𝟎𝐱(𝑡) = 𝛟(𝑡)𝐱 + 𝛟(𝑡) 2 𝛟.𝟏 (𝑠)𝐠(𝑠)𝑑𝑠,#!

where 𝐱 is a vector of amounts or concentrations, 𝛟(𝑡) is the fundamental matrix derived from theeigenvalues/eigenvectors of the linear ODEs, 𝐱 𝟎 is the vector of initial conditions, and 𝐠(𝑠) is the non-homogenous
portion of the system, if one exists, representing an infusion profile.1 It is common for larger systems to produce
complex conjugate pairs in their solutions, which pbpkme handles automatically. The method by itself solves for asolution to the model over the specified time; its limitation being it only produces a solution for the last time point.
However, we are able to use that solution as the initial condition for the next iteration of the function. The method iscomputationally fast; therefore, the solution over a sufficiently small time interval 𝑡 can be calculated many times
very quickly (which reduces propagation of error – a common flaw of numerical time-stepping methods).2

Results: The method was validated against an example with a known solution. Results for varying time-steps
illustrating the method’s accuracy and speed are shown in Figure 1. To showcase pbpkme’s computation speed
compared to traditional approaches, a three-compartment PK model was computed in R using the fourth-order
Runge-Kutta method (RK45) and the pbpkme function.1 The pbpkme function was 43%-94% faster than RK45 at
step-size 0.01 and 0.1, respectively. Finally, ODEs for a forty compartment PBPK model were converted into matrix
form and its predictions were accurate compared to the original model’s predictions, demonstrating the flexibility ofthis function.3

Conclusions: Using a matrix-based approach to model systems of first-order ODEs is a computationally efficientand accurate technique. The pbpkme package simplifies the use of this mathematical method for general use in thepharmacometrics community.

References[1] Boyce, WE and RC Prima.1996. New York. John Wiley & Sons.
[2] Hegarty, G and SB Duffull. 2012. School of Pharmacy, University of Otago.
[3] Dubbelboer, et al. 2017. Mol. Pharmaceutics, 14(3):686-698.

WED-030

Application of a QSP Model for Recombinant Human ADAMTS13
in Sickle Cell Disease
Hoa Q. Nguyen 1 , Steven Yu 2 , Zhiwei Zhang 3 , Andy Zhu 1 , Indranil Bhattacharya 1
1
Takeda, Cambridge, MA, US; 2 LH Group, China; 3 RES Group Inc., Needham, MA, US

Objectives: Sickle cell disease (SCD) is an inherited blood disorder characterized by sickle hemoglobin
formation, leading to rigid and deformed sickle shaped red blood cells. These sickle shaped blood cells
cannot traverse the microcirculation, creating blockages (vaso-occlusion) leading to tissue hypoxia andexcruciating pain (vaso-occlusive crisis, VOC). In VOC state, due to increased VWF multimers, it ishypothesized that administration of recombinant human ADAMTS13 (rADAMTS13) could regulate
homeostasis in VOC state through improved VWF-ADAMTS13 interaction. A QSP approach was used to
guide dose selection for SCD indication.

Methods: A previously developed QSP model for congenital thrombotic thrombocytopenic purpura wasadapted for SCD pathophysiology. Basal levels of endogenous ADAMTS13, VWF and Hb were adjusted
for SCD patients during steady state as well as in painful crisis accordingly. A combination of apharmaco-statistical and QSP approach was used to generate a virtual patient population (n = 1000)
accounting for inter-individual variability in drug exposure (PK) and basal level of VWF. The model wasthen used to simulate the reduction in active VWF for different dose levels of rADAMTS13 in SCD
patients at steady state and those experiencing VOC.

Results: The model-predicted VWF, Hb levels were comparable to reported baseline estimates. Basedon model simulation for virtual SCD patient population, 40 – 160 U/kg was predicted to result in 23-52 %reduction of active VWF compared to its VOC level without treatment in the first 5-15 hours after
dosing. At 160 U/kg, active VWF is expected to stay under remission during 3-8 days. Active VWF and
VWF fragment were shown to be sensitive to rADAMTS13 dosage and are potentially informative clinicalbiomarkers.

Conclusions: An ADAMTS13 QSP model adapted for SCD patients demonstrated the reduction of active
VWF with drug intervention. The model supported a recommendation of rADAMTS13 40 – 160 U/kg
dose for SCD patients in the currently ongoing phase 1/2 study.

WED-031
Measurement and Quantitative Characterization of Whole-Body Pharmacokinetics of MMAE in Mice

Hsuan-Ping Chang 1, Dhaval K. Shah 1
1

Department of Pharmaceutical Sciences, School of Pharmacy and Pharmaceutical Sciences, The State University of

New York at Buffalo, Buffalo, NY, USA.
Objectives: The tubulin inhibitor, auristatins monomethyl auristatin E (MMAE) is one of the most studied payloads
in antibody drug conjugates (ADCs). Although MMAE-conjugated ADCs significantly improves efficacy comparedto naked antibodies, the highly potent payload can cause on-target off-tumor toxicities and target-independent
toxicities. We therefore investigated whole-body pharmacokinetic (PK) of MMAE in mice and developed aphysiological-based pharmacokinetic (PBPK) model to better understand the toxicity profiles of MMAE-conjugated
ADCs.
Methods: A total of fifteen N87 tumor-bearing nude mice were treated with single 0.1 mg/kg of MMAE intravenously.
Blood, tumor, and tissue (heart, liver, lung, kidney, spleen, pancreas, fat, muscle, skin, bone, and brain) samples werecollected at 5 min, 1, 6, 24, and 168 h post-dose. MMAE in plasma, blood cell, tissue, and tumor homogenates wereanalyzed by LC-MS/MS method. The PBPK model included 17 tissue compartments and a tumor compartment, witheach of tissue divided into plasma, blood cells, endosomal, interstitial, and cellular sub-compartments. Tissue
distribution of MMAE between plasma-blood or interstitial-cellular sub-compartments was assumed to bepermeability-limited and connected via permeability-surface area product (PS) values. Partition of MMAE to tissue
cellular space and blood cells were characterized via partition coefficient (Kp) calculated by ratio of tissue to plasma
area-under-the curve (AUC0-t). MMAE was mainly eliminated by CYP3-mediated metabolism and biliary excretion,and thus the PBPK model only included hepatic clearance (CL).
Results: Plasma concentration dropped rapidly with 0.3% of injected MMAE remaining after 5 minutes. Blood-toplasma AUC ratio was 5.46, indicating MMAE distribution into blood cells. Kp values were greater than 18 (range
from 17.9 to 35.3) in highly perfused tissues (lung, kidney, heart, and spleen), around 2 (range from 1.20 to 2.40) inmuscle, skin, bone, adipose, pancreas, and liver, and 8.21 in tumor, indicating MMAE significantly distributes into
tissues and binds to the tubulin. The PBPK model well captured the MMAE PK profiles in plasma, tissues, and tumor(Figure 1), with percent predictive error (%PE) less than 10% for all tissues except liver (%PE 38%). The estimatedhepatic CL (107 mL/h, CV% 8.92) corresponded to the calculated CL (109 mL/h) using IVIVE based on human liver
microsome intrinsic CL. The PS values for individual tissues were estimated with good precision (%CV < 40%).
Conclusions: The whole-body distribution data indicated MMAE distributes into blood cell and supported MMAE
binds to tubulins and thus retains in tissues for a prolonged period. The PBPK model well characterized PK data of
MMAE, which can further be utilized to build MMAE-conjugated ADC model and to predict disposition of MMAE
in higher species after scaling up.
Figure 1. Observed and PBPK model predicted MMAE concentration profiles in plasma, tissues, and tumor.

WED-032
Sub-Compartmental Systems Biology Modeling of Acute Radiation effects on theThrombopoietic System in Animal Models
Ibrahim Mustafa1, Allison Gibbs2, Isabel Jackson2, Joga Gobburu1 , Mathangi Gopalakrishnan1
1Center for Translational Medicine, School of Pharmacy, University of Maryland, Baltimore, USA

2

The Department of Radiation Oncology, School of Medicine, University of Maryland, Baltimore, USA

Objectives: The nature of the interaction of ionizing radiation (IR) with biological systems is not well understood.
The research objective was to develop a sub-compartmental systems biology model to describe the potential
mechanism of the interaction between IR and thrombopoietic system in terms of reactive oxidative species (ROS).The
model aims to investigate time delays of the system considering feedback mechanisms in two animal models ofirradiation and compare with the experimental data.
Methods: The thrombopoietic mechanistic model considered three main compartments: progenitors, megakaryocytes(MKs), and platelets. The ROS are dose dependent and triggers alterations in the structure of the system. Bothprogenitors and MKs were assumed to be radiosensitive while platelets are radio resistant. The MKs compartment
was divided into 10 sub-compartments while the platelets compartment was divided into 9 sub-compartments toinvestigate the time delay in response to different doses of acute IR. Parameters values were obtained by optimization
procedures using dynamic platelet data or from literature. The model was externally validated using experimental datafrom mice and rabbits that was not used during the parameters estimation process. The model was formulated in termsof ordinary differential equations (ODEs). All numerical analysis, and simulations were conducted using Pumas v0.3.1
softwareResults: The model showed that the thrombopoietic system is a complex multistage dynamic process with potential
differences between the radio-sensitive stem cells and radio-resistant platelets. The model showed that the nadir times
of progenitors and MKs in rabbits at IR dose of 2 Gy are reached on day 2 while platelets are reached on day 10
following acute IR exposure. Both progenitors and MKs recovered on day 7 while platelets recovered on day 10. The
model allowed for comparison of the system's response across species and can simulate the average response in miceand rabbits following irradiation. At IR dose of 7.5 Gy in rabbits, platelet recovered on day 10, whereas in mice at IR
dose of 7.5 Gy platelet did not recover, in agreement with the experimental results. Variation in platelet dynamics wasdue to the sensitivity of stem cells to ROS. The model was successfully validated with observed platelet count frommice and rabbits.
Conclusion: The sub-compartmental system biology model realistically predicted thrombocytes behavior following
acute IR in a wide range of doses: 2.0-9.5 Gy and explained the variation of platelets response of rabbits and mice toradiation which is attributed to the sensitivity of stem cells to ROS. It is found that MKs could be the rate limiting
components in the thrombopoiesis process to IR. The model could be useful in drug development of radiation medical
countermeasures.

WED-033
A Quantitative Systems Pharmacology Model of Individualized NeoAntigen Specific Immunotherapy (iNeST)
Iraj Hosseini1,*, Tom Snowden2, Jaehak Oh1, Yoko Oei1, Sophie Lehar1, Jill Schartner1, Lelia Delamarre1, Saroja
Ramanujan1
1
2

Genentech Inc., South San Francisco, CA
Certara QSP, Canterbury, UK

Objectives: To develop and apply a systems pharmacology model to support research and clinical development of
individualized neoantigen specific immunotherapy (iNeST).
Methods: Cancer is characterized by an accumulation of somatic mutations, which can generate cancer-specificneoantigens. Preclinical and clinical studies have shown the feasibility of increasing the immune response to tumors
by using a vaccination strategy that encodes cancer-specific neoantigens [1-2]. iNeST is an individualized
therapeutic approach that consists of mRNA encoding patient-specific neoantigens, protected by a lipoplex
nanoparticle formulation. The particles are designed for preferential uptake in the spleen to drive antigen
presentation and anti-tumor immunity. We developed a systems model to describe the mechanism of action of
iNeST and its interaction with the innate and immune response. The model includes three physiologicalcompartments (peripheral blood (PB), spleen, and tumor), and the following processes: pharmacokinetics of RNA
lipoplex in PB and spleen; RNA uptake by antigen presenting cells (APCs in spleen; TLR7/TLR8 stimulation by
RNA and release of cytokines by APCs; maturation of dendritic cells; antigen presentation on MHCI molecules;
interaction of DCs with CD8+ T-cells; T cell priming, differentiation, activation and memory generation; and finallytumor killing by activated CD8+ T-cells. The model was developed in SimBiology and Matlab (MathWorks, NatickMA).
Results: The model was calibrated using preclinical data in mice treated with GFP-, OVA- or gp70-encoded RNA
lipoplex [1]. The model successfully reproduced RNA uptake, translation, and antigen presentation at different doselevels, and replicated the effects of RNA-lipoplex on cell maturation and dynamics of DCs and macrophages.
Additionally, the model captured the complex dynamics of antigen-specific T-cells in response to a wide range ofdose levels, different regimens, and antigens. Importantly, the model also reproduced the behavior of antigen-specificT-cell response when only a fraction of RNA-lipoplex encodes meaningful antigen and the remaining RNA isirrelevant. The model has the potential to evaluate different dosing strategies in patients and to inform combinationsof iNeST with other molecules.
Conclusions: The mechanism-based systems model of innate and adaptive immune response accurately reproduces
the pharmacodynamics of iNeST in preclinical settings in response to different antigens at different dose levels andregimens. The model provides mechanistic insight into how iNeST works and has the potential to inform researchand clinical questions.
References:[1] Kranz, L. M., et al. Nature (2016) 534.7607: 396.
[2] Sahin, U., et al. Nature (2017) 547.7662: 222.

WED-034

Practical Identifiability Analysis of QSP Models with LikelihoodProfiler
Ivan Borisov1, Evgeny Metelkin1
InSysBio, Moscow, Russia

1

Background: Identifiability analysis is a crucial step in improving reliability and predictability of QSP models.
Various methods of practical identification have been proposed including Monte Carlo simulations, Fisher information
matrix, Profiler Likelihood, etc. Profile Likelihood (PL) is a reliable though computationally expensive approach.
Confidence Intervals by Constraint Optimization (CICO) algorithm based on Profile Likelihood was designed toreduce computational requirements and increase the accuracy of the estimated parameters confidence intervals.
Objectives: The software package LikelihoodProfiler implementing CICO algorithm was tested on real QSP models
and compared to other approaches to identifiability analysis. The results of these analyses will be presented.
Methods: CICO algorithm has several advantages over original stepwise PL implementations. Stepwise approach
implies visualizing parameters profiles and deriving confidence intervals endpoints as profiles intersections with thesignificance level threshold. This approach often produces inaccurate endpoint estimates and is computationallyexpensive. CICO algorithm tests parameters identifiability by estimating their confidence interval endpoints with somepreset tolerance. The algorithm does not recover the whole profile to obtain confidence intervals endpoints (or to state
non-identifiability), which results in less likelihood function evaluations and increased performance in comparison
with stepwise PL approach. CICO translates confidence intervals estimation problem into an optimization problem
and solves it by converging to confidence interval endpoint. The algorithm does not require likelihood function
derivatives and enables modeler to choose derivative-free or gradient-based optimization algorithm.
Results: CICO algorithm is proposed for complex QSP models where each likelihood estimation can becomputationally expensive and some parameters are non-identifiable. The algorithm was tested on SB (Systems
Biology) and QSP models described in published studies, including Cancer taxol treatment model [1], TGF- β pathway
model [2], etc. Confidence intervals endpoints estimated with CICO correspond with the values reported in the articles.
It was shown, that on average the algorithm overperforms stepwise PL approach especially in case of non-identifiable
parameters. Detailed analysis of each model can be found on github: https://github.com/insysbio/likelihoodprofilercases. The CICO algorithm is available in a free software package LikelihoodProfiler based on Julia(https://github.com/insysbio/LikelihoodProfiler.jl) and Python (https://github.com/insysbio/LikelihoodProfiler.py).
Conclusions: The proposed algorithm and software package can be used as a means of identifiability analysis andconfidence intervals evaluation for SB and QSP models of different complexity.
References:[1]

M. C. Eisenberg and H. V. Jain, “A confidence building exercise in data and identifiability: Modeling cancer
chemotherapy as a case study,” J. Theor. Biol., vol. 431, pp. 63–78, Oct. 2017, doi:
10.1016/j.jtbi.2017.07.018.

[2]

A. Gábor, A. F. Villaverde, and J. R. Banga, “Parameter identifiability analysis and visualization in large-scale
kinetic models of biosystems,” BMC Syst. Biol., vol. 11, no. 1, p. 54, Dec. 2017, doi: 10.1186/s12918-0170428-y.

WED-035
Using Population Pharmacokinetic Modelling Methods in Hemophilia A to Guide Individualized Dosing for
Clotting Factor Concentrates
Jacky K Yu1, Alfonso Iorio2,3, Andrea N Edginton1
1

School of Pharmacy, University of Waterloo, Waterloo, ON, Canada; 2Department of Health Research Methods,

Evidence, and Impact, McMaster University, Hamilton, ON, Canada; 3McMaster-Bayer Research Chair in Clinical
Epidemiology of Congenital Bleeding Disorders, Department of Medicine, McMaster University, Hamilton, ON,
Canada.
Objectives:
Hemophilia is a bleeding disorder caused by a lack of clotting factor. Due to the development of extended half-life(EHL) clotting factor concentrates (CFC), hemophilia A patients may switch their prophylactic treatment to another
CFC that is dosed less frequently. When switching, CFCs are prophylactically dosed based on international units perweight, however this approach does not account for the large pharmacokinetic (PK) variability between
individuals. Our goal is to develop and evaluate methods to predict individual PK on efmoroctocog alfa (Eloctate)using population PK (PopPK) models.
Methods:
PK data from subjects taking Eloctate as well as Adynovate or a standard half-life (SHL) CFC was collected fromthe Web-Accessible Population Pharmacokinetic Service – Hemophilia (WAPPS-Hemo.org) database. The PopPKmodels used were two-compartment models with fat-free mass and age as covariates, with between-subject
variability terms (eta, or η) on clearance and volume. Method 1 used a PopPK model for Eloctate based on
covariates only (e.g. age, weight) while Method 2 included knowledge of η, the individual deviation from a
population for any PK parameter, calculated from the prior CFC PopPK model (Adynovate or SHL CFC).
Individual PK outcomes including half-life and time-to-2% were obtained. Percent differences of predictedindividual PK outcomes of Eloctate compared to observed PK outcomes were derived for each method.
Results:
Data used in the analysis included 231 subjects. The half-life geometric mean absolute percent differences werelower by up to 4.1% for Method 2 compared to Method 1. Individuals with ηCL or ηV1 below the 20th percentile andabove the 80th percentile benefited the most from Method 2, with half-life and time-to-2% estimates up to 25%
relative error lower than Method 1 when compared to observed.
Conclusions:
During the time of switching, PK-guided dosing using PK knowledge from prior CFC (η’s) may improve the abilityof clinicians to target appropriate CFC activities in patients.

WED-036
POPULATION PHARMACOKINETICS OF THIOTEPA AND ITS ACTIVE METABOLITE, TEPA, IN
PEDIATRIC PATIENTS UNDERGOING HEMATOPOIETIC CELL TRANSPLANTATION (HCT)
Jagan Parepally, PhD1, Jayesh Patel, MS1, Christopher C. Dvorak, MD2, Alexis Melton, MD2, Christine Highan,
MD2, Ying Lu, PhD2, Beth Apsel Winger, MD, PhD3, Joga Gobburu, PhD1*, Janel Long-Boyle, PharmD, PhD2,4
1

Center for Translational Medicine, School of Pharmacy, University of Maryland, Baltimore, MD, USA;
Department of Pediatrics, Division of Allergy, Immunology, and Bone Marrow Transplantation, University ofCalifornia San Francisco, San Francisco, CA, USA
3
Department of Pediatrics, Division of Hematology and Oncology, University of California San Francisco, San
Francisco, CA, USA,
4
Department of Clinical Pharmacy, University of California San Francisco, San Francisco, CA, USA.
2

Objectives: ThioTEPA is an alkylating agent used in pretransplant combination chemotherapy prior tohematopoietic cell transplantation (HCT) to support engraftment of donor stem cells. The primary aims of this study
were 2-fold: (1) to develop a combined population PK model for thioTEPA and its active metabolite, TEPA and (2)to identify sources of variability that contribute to patient disposition including drug clearance and exposure. An
exploratory objective was to estimate and compare PK parameters obtained from using nonlinear mixed effects(NLME) modeling via NONMEM and those obtained using the Pumas software.
Methods: Plasma time-concentration data for thioTEPA and TEPA were collected in 14 pediatric patients as part ofa single center, prospective, open-label, non-interventional study. Multiple dosing strategies were evaluateddepending on diagnosis and included 300 mg/m2, 5 mg/kg or 10 mg/kg administered by a continuous IV infusion
over 1 to 2 hours. Following a single dose of thioTEPA a total of 4 blood samples were collected were collected asdetermined through an optimal sampling strategy. The FOCEI maximum likelihood approximation (MLE)
estimation method was used for the model-building process to estimate PK parameters, along with estimation of
between-subject variability (BSV), and residual unexplained variability. Patient-specific covariates, including
gender, age, actual body weight and laboratory markers for hepatic and renal function, were evaluated to quantify
the contribution to variability in PK parameters.
Results: A two- compartment PK model for both thioTEPA and TEPA with proportional error model best
described the data. An empirical allometric scaling of clearance and volume parameters significantly improved themodel fit (delta OFV = 8.964, p<0.05). The population mean volume of distribution and clearance of thioTEPA
from the final population PK model are 12.6 L/15 kg and 2.5 L/h/15 kg, respectively, and were in good agreementwith those previously reported by TEPADINA prescribing information (18 L and 9 L/hr). Actual body weight was
identified as the predictor of clearance and volume of distribution of thioTEPA and TEPA, which explained 33%
and 161% of the BSV in central volume and clearance, 43% of intercompartmental clearance of thioTEPA in the
final model. Most of the PK parameter estimates were comparable between the Pumas and NONMEM software.
Conclusions: A two-compartment combined population PK model for both thioTEPA and TEPA with proportional
error with standard allometric scaling of all fixed parameters best described the data. The population PK model canbe applied to evaluate and optimize dosing strategies within the pediatric autologous and allogeneic HCT patient
population with future studies.
*Disclaimer: Dr. Gobburu is a co-founder of Pumas-AI that commercializes Pumas software.

WED-037
Exposure-Response Analyses of Idecabtagene Vicleucel (Ide-Cel; bb2121), a Novel Anti-BCMA CAR T Cell
Therapy, in Patients With Relapsed/Refractory Multiple Myeloma
Jamie Connarn1, Han Witjes2, Benjamin Rich3, Marielle van Zutphen-van Geffen2, Rik de Greef2, Timothy
Campbell1, Liping Huang1, Kristen Hege1, Shari Kaiser1, Maria Palmisano1, Simon Zhou1, Manisha Lamba1
1

Bristol Myers Squibb, Princeton, NJ, USA
Certara Strategic Consulting, Oss, The Netherlands
3
Certara Strategic Consulting, Montreal, Quebec, Canada
2

Objectives: Ide-cel is an immunotherapy consisting of T cells transduced with an anti-BCMA chimeric antigen
receptor (CAR) lentiviral vector. In the phase 2 KarMMa study (NCT03361748), ide-cel demonstrated an overall
response rate (ORR) of 73%, and complete response rate (CRR) of 31%, in heavily pretreated patients with multiple
myeloma who received ≥3 prior therapies, including an immunomodulatory agent, a proteasome inhibitor, and ananti-CD38 antibody [1]. The objective of this analysis was to evaluate exposure-response (ER) relationships of idecel efficacy endpoints and safety events in KarMMa.
Methods: Ide-cel exposure data were available from 127 treated subjects at target doses of 150, 300, and 450 × 106
CAR+ T cells in KarMMa. Key exposure metrics including area under the transgene curve from 0–28 days (AUC028days), area under the transgene curve from 0–3 months (AUC0-3M), and maximum transgene level (Cmax) were
calculated via noncompartmental methods. ER evaluation analyzed multiple efficacy endpoints, including ORR,
very good partial response rate or better, and CRR, as well as safety events, such as cytokine release syndrome(CRS), CRS that required concomitant medication with tocilizumab and corticosteroids, investigator-identified
neurotoxicity, and cytopenias. ER trends were examined by evaluating the proportions of responders for quartiles ofthe exposure distribution across the dose range. Upon observation of an apparent ER trend, candidate models weredeveloped with logistic regression, fitting either linear, maximum effect (Emax), or sigmoid Emax functional forms.
Covariates were also evaluated for potential effects on the likelihood of response using stepwise forward selection
followed by backward elimination, with significance levels of 0.05 and 0.01, respectively. Established ER models
were finally used to simulate dose-response relationships and evaluate the impact of identified covariates.
Results: There was wide overlap of exposures across the target doses. ER relationships were observed for each of
the efficacy endpoints, with higher response rates associated with higher exposure. Model-based evaluations
identified statistically significant covariates and baseline demographics and disease characteristics for each of theefficacy endpoints, in addition to the impact of the exposure parameter. Specifically, female sex was predictive of ahigher ORR (p=0.003) and baseline serum monoclonal-protein ≤10 g/L was predictive of a higher CRR (p<0.001).
ER relationships were observed for safety events of CRS requiring tocilizumab or corticosteroids; however, no ER
trends were observed for grade ≥3 CRS, investigator-identified neurotoxicity, or cytopenias.
Conclusions: The observed ER relationships were primarily driven by lower response rates being associated withthe lowest quartiles of exposure. Established ER models were used to predict dose-response relationships, whichshowed a positive benefit-risk assessment across the range of ide-cel exposures associated with a dose range of 150–
540 × 106 CAR+ T cells.

Reference: 1. Munshi et al. J Clin Oncol 38:2020 (suppl; abstr 8503).

WED-038

Quantitative Systems Toxicology Modeling Supports Safety Determination
for Ubrogepant, a Novel CGRP Inhibitor
Jeffrey L. Woodhead*, Brett A. Howell*, Brenda Smith**, Josh M. Rowe**, Scott Q. Siler**DILIsym Services, Inc., a Simulations Plus company, RTP, NC, USA**Allergan Plc, Irvine, CA, USA
Objectives: CGRP inhibitors are a class of drugs that meet a significant unmet medical need for migraine
treatments. However, two CGRP inhibitors, telcagepant and MK-3207, failed in clinical trials due to liver
toxicity (1,2). Telcagepant, MK-3207, and the next-in-class compound ubrogepant were represented in
DILIsym, a quantitative systems toxicology (QST) model of drug-induced liver injury, in order to predictwhether ubrogepant would be a safer alternative.
Methods: In vitro experiments were undertaken determining the potential for the three compounds toinhibit bile acid transporters, cause mitochondrial dysfunction, and produce oxidative stress. The resultsof the in vitro assays were used to produce a representation of each compound in DILIsym, along with a
PBPK model of each compound. The clinical doses of telcagepant and MK-3207 at which liver toxicity
was observed were simulated, as well as a range of potential ubrogepant clinical protocols.
Results: Each of the molecules tested displayed signals in the in vitro assays for bile acid transporter
inhibition, oxidative stress generation, and mitochondrial dysfunction. DILIsym correctly predicted theliver toxicity of telcagepant and MK-3207, while ubrogepant was predicted to be safe at doses up to 10X
the proposed clinical dose (Table 1). Subsequent clinical trials demonstrated that ubrogepant was
indeed safe (3), and the drug was approved by the FDA for the acute treatment of migraine.
Conclusions: QST modeling can prospectively differentiate between toxic and non-toxic drugs within thesame class and identify which drugs within a class carry less risk of toxicity. These applications cancontribute to success in the clinic and regulatory approval of new drugs.
References: 1. Ho TW, Connor KM, Zhang Y, Pearlman E, Koppenhaver J, Fan X, et al. Randomized
controlled trial of the CGRP receptor antagonist telcagepant for migraine prevention. Neurology.
2014 Sep 9;83(11):958–66.
2. Holland PR, Goadsby PJ. Targeted CGRP Small Molecule Antagonists for Acute Migraine Therapy.
Neurotherapeutics. 2018 Apr;15(2):304–12.
3. Ailani J, Lipton RB, Hutchinson S, Knievel K, Lu K, Butler M, et al. Long-Term Safety Evaluation of
Ubrogepant for the Acute Treatment of Migraine: Phase 3, Randomized, 52-Week Extension Trial.
Headache. 2020 Jan;60(1):141–52.

Table 1. Simulated ALT elevation frequency in a 285-individual model population using DILIsym v7A.

CompoundMK-3207

Dosing Protocol

Simulated*
ALT > 3X
ULN**

200 mg q2h, 2 daily doses (400mg daily dose), for 14 days

3.5%
(10/285)

300 mg q2h, 2 daily doses (600mg daily dose), for 14 days

7%
(20/285)

450 mg q2h, 2 daily doses (900mg daily dose), for 14 days

10.2%
(29/285)

280 mg BID 12 weeks

12.6%(36/285)

3.2% (8/265)

140 mg BID 12 weeks

0%
0/285

1.9% (5/263)

100 mg qd, 2 days on, 2 days off
for 56 days, 28 total doses

0%(0/285)

No ALT elevations
observed

1000 mg qd, 2 days on, 2 days off
for 56 days, 28 total doses

0%(0/285)

N/A

Telcagepant

Ubrogepant

Clinical ALT > 3X
ULN***
42% (5/12) amongst
individuals dosed
for more than 1
week; most
responding were
given 600 – 900 mg
per day

WED-039

Population Pharmacokinetic-Pharmacodynamic Modeling of Platelet Time Courses
After Administration of Abrocitinib
Authors: Jessica Wojciechowski,1 Bimal K. Malhotra,2 Xiaoxing Wang,1 Luke Fostvedt,3 Hernan Valdez,2 Timothy
Nicholas1
Institutions: 1Pfizer Inc., Groton, CT, USA; 2Pfizer Inc., New York, NY, USA; 3Pfizer Inc., Cambridge, MA, USA
Objectives:
Abrocitinib is a selective Janus kinase 1 inhibitor being developed for the treatment of moderate-to-severe atopic
dermatitis (AD). Transient decreases in platelet counts were observed in phase 2 psoriasis and AD patient studies.
The objectives of this analysis were to develop a population pharmacokinetic-pharmacodynamic (PK-PD) model todescribe the time course of drug-induced platelet reduction after abrocitinib administration.
Methods:
The platelet analysis data set was pooled from 2 phase 2 studies and 3 phase 3 studies (duration; 12 weeks, dose
range; 10 mg once daily [QD] up to 400 mg QD). Abrocitinib concentrations for time-matched platelet observations
were predicted for analysis individuals based on empirical Bayes estimates of parameters from a population PK
model or assumed the population-typical values for individuals when abrocitinib concentrations were not available(ie, placebo). The semimechanistic platelet model was based on the chemotherapy-induced myelosuppression
model.1 The effect of abrocitinib concentrations on platelet proliferation was described by an Emax relationship, andthe implementation of tachyphylaxis (additional to inherent feedback mechanisms) was considered. Intrinsic patient
factors on the platelet time course were assessed by stepwise covariate modeling procedures. The impact of baseline
platelet count on the probability of thrombocytopenia at the nadir and week 12 after administration of the highest
therapeutic dose of 200 mg QD in phase 3was assessed by simulation.
Results:
The final estimate for the concentration at 50% of the maximum effect (EC50) on platelet proliferation was 55.3
ng/mL. For the average AD patient administered 200 mg QD at steady state, the typical time above EC50 was 15.3
hours, and the time delay between dosing and a decrease in circulating platelets was 7.21 days, corresponding to atime of nadir of 24 days. Significant predictors of lower baseline platelets counts were disease-type (psoriasis, 12%
lower), lower baseline white blood cell count (4300/μL [5th percentile], 9% lower), sex (male, 6% lower), older age(64 years [95th percentile], 5% lower), higher baseline hematocrit (49% [95th percentile], 3% lower). For example,
an individual with a baseline platelet count of 170,000/μL was predicted to experience a net decrease in platelets
after 12 weeks of 26.2%, and a probability of grade 1 (75,000-150,000/μL; 76.2%) and grade 2 thrombocytopenia(50,000-75,000/μL; 16.3%) at the nadir after administration of 200 mg QD. The probability of grade 3 (25,00050,000/μL) and grade 4 (<25,000/μL) thrombocytopenia was <3%.
Conclusions:
For a typical AD patient given 200 mg QD for 12 weeks, the probability of grade 1 thrombocytopenia was <10% at
the nadir and <1% at week 12. The probability of higher grades of thrombocytopenia was approximately 0%.
References: 1. Friberg LE et al. J Clin Oncol. 2002;20:4713-4721.

WED-040

Usage of In vitro Metabolism Data for Drug-drug Interaction Prediction in Physiologically-Based
Pharmacokinetic (PBPK) Modeling Submissions
Jieon Lee1,2, Yuching Yang1, Xinyuan Zhang1, Jianghong Fan1, Manuela Grimstein1, Hao Zhu1, Yaning Wang1
1

Office of Clinical Pharmacology, Office of Translational Sciences, Center for Drug Evaluation and Research, U.S.

Food and Drug Administration
2

Oak Ridge Institute for Science and Education

Objectives: The physiologically-based pharmacokinetic (PBPK) models are usually developed by combining
information from in vitro and in vivo studies. Intrinsic clearance (CLint) and fractional contribution of themetabolizing enzyme toward metabolism (fm) are the key parameters for the prediction of DDI. We aimed tosummarize the accumulated knowledge from new drug applications (NDAs) to understand the current usage of invitro data in PBPK models used for DDI prediction.
Methods: We collected the in vitro data in approved NDAs containing PBPK models which were submitted to the
Office of Clinical Pharmacology, U.S. Food and Drug Administration from 2016 to 2018. The collected information
includes physicochemical properties, in vitro metabolic profiling methods and data, and the methods used in the final
model for determining CLint and fm.
Results: We screened all approved 51 NDAs including PBPK models. 35 and 26 NDAs were included for evaluationof CLint and fm respectively. For CLint, most of the PBPK model (86%) used modified value based on in vivo data.
A limited number of the evaluated models used CLint scaled from in vitro data without modification. For fm, 38.5%of the models modified the values with modification ranging -65%-178.6% from in vitro test. When using in vitro
CLint and fm, without modification or optimization, the model resulted in up to 12.1-fold, 2.9-fold underprediction inarea under concentration of substrate (AUC%) or AUC of substrate with and without inhibitor/inducer administration(AUCR), respectively.
Conclusions: Our results can be used to leverage the accumulated knowledge from these applications to better
understand current usage of in vitro data and in PBPK models. Alignment between industry, academia, and regulatory
agencies in developing the best practices for usage of in vitro data in PBPK modeling is warranted.
Keywords: PBPK modeling, in vitro metabolism, drug-drug interaction

WED-041

Clinical Trial Simulation to Inform Phase 1b/2a Study Design in Infants to Accelerate Development of an
RSV Monoclonal Antibody (MK-1654)
Authors: Jingxian Chen, Brian Maas, Wei Gao, Ferdous Gheyas, Luzelena Caro
Affiliation: Merck & Co., Inc., Kenilworth, NJ, USA
Objectives: MK-1654 is a monoclonal antibody being developed for the prevention for Respiratory
Syncytial Virus (RSV) in infants. A Phase 1b/2a dose-ranging study in infants is currently ongoing. Aninterim analysis is planned for this study to facilitate a Go/No-Go to Phase 2b/3 decision. The Go decision
criteria under consideration for the interim analysis requires adequate pharmacokinetic (PK) data to
predict with high confidence that a typical infant will have a true geometric mean concentration 150 days
after a single dose (C150) above the pre-established PK target. Concentration on Day 150 was chosen as
the PK endpoint since it covers the typical length of an RSV season. The current analysis aims to determine
the minimum sample size required to enable an early interim analysis decision using clinical trial
simulation (CTS).
Methods: An established adult population pharmacokinetic model describes MK-1654 PK using a twocompartment model with first-order absorption [1]. Infant PK was predicted from the adult model using
allometric scaling. Time-varying PK resulting from infant growth was taken into account by predicting bodyweight over time using modified growth chart equations developed by U.S. Centers for Disease Control
and Prevention (CDC). Clinical trials were simulated for scenarios with varying sample sizes. In addition,
uncertainty in adult-infant PK extrapolation was explored through a sensitivity analysis by varying
allometric scaling exponents on clearances and volumes of distribution (separately and jointly) andincluding a maturation function [2]. For each scenario, 200 clinical trials were created accounting forparameter uncertainty, and C150 for the total number of infants in each trial were simulated with intersubject and residual variability. Study power was estimated as the percentage of simulated trials meetingthe prespecified criteria, i.e. the geometric mean of simulated C150 within this trial exceeding the PK target.
Results: Clinical trial simulation (CTS) showed a >90% power to make a Go/No-Go decision for the all
sample sizes under the most likely scenario. Sensitivity analyses indicated that study power based on thecurrent Go/No-Go criteria is sensitive to allometric scaling parameters. In each allometric scaling scenario,
only modest improvement in power (approximately 10%) was gained by increasing the sample size fromthe minimum to the maximum being considered. Therefore, delaying the interim analysis to enroll
additional infants was considered unnecessary.
Conclusions: CTS analyses provide confidence that the interim analysis can be triggered with fewer
subjects than originally planned. This analysis highlights the application of clinical trial simulation to help
accelerate development timelines and provides a framework that can be leveraged in other pediatric drug
development programs.
References:
1) Maas, BM, et al. Open Forum Infectious Diseases, 2018, Nov, Volume 5, Issue suppl_1, S424–S425.
2) Pierre V, et al. American Conference for Pharmacometrics. 2018. Abstract # W-082.

WED-042

Population pharmacokinetics of belantamab mafodotin, a BCMA targeting agent inpatients with relapsed/refractory multiple myeloma (RRMM)

Authors: Jon Collins1, Chetan Rathi2, Herbert Struemper1, Joanna Opalinska2, Roxanne C Jewell1, andGeraldine Ferron-Brady2
Institutions: 1GlaxoSmithKline, Research Triangle Park, NC; 2GlaxoSmithKline, Collegeville, PA;

Objectives: Belantamab mafodotin (GSK2857916) is a first-in-class B-cell maturation antigen (BCMA)targeting immunoconjugate with a humanized afucosylated anti-BCMA monoclonal antibody (mAb)
conjugated to a cytotoxic payload mafodotin (MMAF) by a protease-resistant maleimidocaproyl linker(mc). This antibody-drug conjugate (ADC) binds to BCMA and eliminates MM cells by a multimodal
mechanism. It delivers MMAF to MM cells which inhibits microtubule polymerization resulting in
apoptosis, enhances antibody-dependent cellular cytotoxicity and phagocytosis, and induces
immunogenic cell death. The objective was to develop a population PK model for belantamab
mafodotin, to identify covariates of clinical relevance, and to estimate relevant exposure measures for
E/R analysis[1].
Methods: Nonlinear mixed-effects models were developed to characterize the PopPK of ADC, total mAb
and cys-mcMMAF after intravenous infusion at 0.03 to 4.6 mg/kg Q3W in 291 heavily pretreated RRMM
patients enrolled across a phase I (DREAMM-1, n=73) and the pivotal phase II study (DREAMM-2,
n=218). Sequential modeling was performed, with individual post hoc parameter estimates from thefinal ADC model being used for model development of total mAb and cys-mcMMAF models. Covariates
were assessed using forward inclusion (p< 0.01), backward elimination (p< 0.001) modelling procedures.
Covariates evaluated included demographics, clinical chemistries, organ function, soluble BCMA(sBCMA) concentration, and drug configuration.
Results: A linear, two-compartment PopPK model with time-varying clearance described the PK of ADC.
The sum of ADC and naked antibody formed via deconjugation of cys-mcMMAF using a first-order rate
constant accounted for total mAb concentration. Cys-mcMMAF concentrations were described with alinear two-compartment model linked to ADC; the input rate was governed bydeconjugation/intracellular proteolytic degradation of ADC modulated by an exponentially decreasing
MMAF:mAb ratio (DAR) after each dose. Baseline sBCMA, IgG, albumin (inverse), and body weight were
found to impact clearance, whereas baseline albumin (inverse), body weight, and gender affected
central volume of ADC. Baseline sBCMA and IgG impacted the central volume of distribution of cysmcMMAF. The initial geometric mean(%CV) ADC clearance in patients from DREAMM-2 was 0.924 L/day(42%) with a half-life of 11.8 days (31%). Over time, clearance was reduced to 0.725 L/day (50%),
resulting in a longer half-life of 14.0 days (37%). The time to 50% of maximal change in clearance was~50 days. The time to 50% reduction in DAR was 10.3 days after an administered dose.
Conclusions: ADC, total mAb, and cys-mcMMAF concentration–time profiles in RRMM patients were
well described by their respective PK models, with disease-related characteristics the most significant
factors associated with exposure. Mild to moderate renal impairment, mild hepatic impairment, age,
African American race, prior treatments and drug configuration were not found to significantly impact
ADC or cys-mcMMAF pharmacokinetics.
References: [1] Rathi et al., ACoP2020
Results in this abstract have been accepted in part for AACR, 2020.

WED-043

Leveraging Data from a Phase 1 Study of Loncastuximab Tesirine in Adults with Relapsed/Refractory
Non-Hodgkin Lymphoma for Pediatric Dose Prediction
Xiaoyan Zhang1, David Ungar1, Yajuan Qin1, Joseph Boni1*
1

ADC Therapeutics America Inc., Murray Hill, NJ, USA*Corresponding Author E-mail: joe.boni@adctherapeutics.com
Objectives: ADCT-402-101 is a first-in-human Phase 1 study of loncastuximab tesirine (Lonca; previously ADCT402) in adult patients (≥18 years old) with relapsed or refractory B-cell non-Hodgkin lymphoma (NHL). Lonca is a
novel antibody-drug conjugate comprising a humanized monoclonal antibody of immunoglobulin G1 (IgG1)
directed against human CD19, stochastically conjugated with a potent pyrrolobenzodiazepine (PBD) dimer toxin.
This analysis aims to use a pharmacostatistical model of Lonca in the adult population to determine a dosing
regimen for the initial treatment of pediatric patients with NHL.
Methods: Serum concentrations of Lonca PBD-conjugated antibody from 183 adult patients were used to establish alinear two-compartment model with inter-occasion variability for pediatric dose estimation. The population
pharmacokinetic (PK) model added body weight (WT) as a covariate on both clearance (linear elimination clearance[CL], distribution clearance [Q]), and volume of distribution (central compartmental [V1] and peripheralcompartment [V2]). Allometric scaling constants of 0.75 on clearance and 1 on volume of distribution were
employed to predict the exposure of conjugated antibodies in the simulated pediatric population. CDC NHANES
datasets from 1999–2016 were used to derive and sample the distribution of WT for pediatric age groups from ages
6 months to 17 years. The model was used to stochastically simulate PK profiles for 2000 virtual pediatric patientsin each age stratification group to determine the corresponding dose that matched the target exposure for the adult
Lonca dosing regimen in NHL (150 µg/kg every 3 weeks [Q3W] for 2 doses [x2], followed by dose reduction to 75µg/kg Q3W x4).
Results: The simulations provided reasonable comparability of the predicted area under the curve at steady state(AUCss) and peak concentration (Cmax) values within the 90% prediction interval (PI) of the corresponding adultexposure range across the various pediatric age/WT bands. For the first 2 cycles of treatment, predictions from theexposure metrics indicate that Lonca doses required to achieve target exposure within the 90% PI of adult exposure
following 150 µg/kg Q3W x2 treatment are 170 µg/kg Q3W x2 for children aged 6 months to <24 months and age 2
years to <6 years, and 150 µg/kg Q3W x2 for children aged 6 years to <12 years and adolescents aged 12 to <17years. For cycles 3–6, Lonca doses would be commensurately reduced by 50% for the respective subgroups.
Conclusion: Through pharmacostatistical modeling, rational dosing regimens for the initial treatment of pediatricpatients with NHL are proposed.

WED-044

Quantitative Systems Pharmacology Dissection of IL-4/IL-13 Contribution to
Human Allergic IgE Production
Kamau Pierre1, Randolph J. Leiser1, Yui-Hsi Wang2, Mengdi Tao1, Christine Xu3, Haobin Luo4, John
Pappas4, Zhiwei Zhang4, Susana Zaph1, Spyros Stamatelos1, Paul Bryce2 and Panagiota Foteinou1
1

Translational Disease Modeling, Digital Data Science, Sanofi, Bridgewater, NJ
Type 2 Immunology & Fibrosis Cluster, Sanofi, Cambridge, MA
3
Pharmacokinetics, Dynamics and Metabolism US, Sanofi, Bridgewater, NJ
4
RES Group, Inc, Needham, MA
2

Objectives: Immunoglobulin E (IgE) antibodies play a crucial role in the pathogenesis of allergic diseases such asasthma, a disease affecting as many as 339 million people worldwide. Studies on the immunological basis of allergic
diseases have indicated that aberrant production of the prototypical type 2 cytokines, interleukin (IL)-4 and IL-13
contribute to enhanced IgE synthesis and the development of clinical atopy. Despite tremendous scientific inquiry, therelative contribution of these cytokines to the production of IgE in allergic patients has remained poorly characterized,
partly due to lack of suitable experimental models. Innovative approaches such as Quantitative Systems Pharmacology(QSP) modeling are valuable in deciphering such complexity by quantifying in vivo functional interactions within abiological network. On this basis, a human QSP model for type 2-driven IgE regulation was developed. A uniqueaspect of this modeling framework is that the simulated human (asthmatic) IgE levels emerge as a result of a complex
inflammatory network spanning multiple scales (molecular, cellular, tissue).
Methods: The QSP model is multiscale in design, allowing the mechanistic representation of key biological processes
underlying the production of human allergic IgE in their appropriate organ compartments (lymph node, blood). At themolecular level, the model represents explicitly the physicochemical interactions between IL-4/IL-13 cytokines andtheir receptor subunits (γC, IL-4Rα, IL-13Rα1) located in the lymph node, particularly on the surface of humanmemory B cells. Serum IgE is, in turn, produced by plasma cells arising from B cell proliferation/differentiation,induced by the upstream cytokine-receptor signaling. During the model development phase, datasets from diverse
sources (experimental and clinical) were used to parameterize and validate the model, with most of these data derived
exclusively from clinical studies. These include quantitative in vitro (e.g. dose-dependent) data derived from humancells, as well as receptor and cytokine expression levels, tissue cell numbers, half-lives, and cytokine-receptor
affinities. In terms of clinical validation, the model was benchmarked against its capability to reproduce IgE responses
as seen clinically in published lebrikizumab studies.
Results: A mechanistic QSP model for IgE production in allergic asthma has been constructed and clinically calibrated
to relevant experimental data. In terms of application, two exploratory case studies were performed to better
understand the relative contribution of endogenous IL-4 vs. IL-13 signaling on aberrant IgE secretion. Interestingly,
the model predicts greater sensitivity of asthmatic IgE levels to endogenous IL-4 than IL-13 signaling, due to a greater
potency of IL-4 signaling in the lymph node (~4-fold difference).
Conclusions: A robust multiscale QSP strategy has been successfully implemented to capture key aspects of type 2
inflammation in IgE production. The proposed model has identified counterintuitive cytokine sensitivities – a novel
hypothesis difficult to experimentally dissect – that have the greatest influence on IgE production in asthma.

WED-045
Towards Achieving Regulatory Endorsement of a Disease Progression Model-based Clinical Trial Simulation
Tool for Duchenne Muscular Dystrophy– Models of Progression of Timed Motor Function Tests
Karthik Lingineni1, Juan Francisco Morales1, Daniela J Conrado2, Camille Vong3, Nathan Hanan2, Diane Corey2,
Jackson Burton2, Jane Larkindale2, Klaus Romero2, Stephan Schmidt1, Sarah Kim1,*, on behalf of the Cooperative
International Neuromuscular Research Group (CINRG) investigators and Duchenne Muscular Dystrophy
Regulatory Science Consortium (D-RSC) members
1

Center for Pharmacometrics and Systems Pharmacology, Department of Pharmaceutics, College of Pharmacy,
University of Florida, Orlando, FL, USA
2
Critical Path Institute, Tucson, AZ, USA
3
Novartis Pharma AG, Basel, Switzerland*
Corresponding author’s email: sarahkim@cop.ufl.edu
Objectives: The objective of this research was to optimize the design of clinical trials for Duchenne Muscular
Dystrophy (DMD), through quantitative disease progression modeling and simulations, using 3 timed motor function
tests, i.e. velocities of the 4 stair climb test, stand from supine test, and 10 meter (or 30 foot) walk/run.
Methods: Disease progression models were built from an aggregation of longitudinal patient-level data comprising 9
studies that include natural history and clinical trials with a total of #patients (#observations): 842 (4797) for velocity
climb, 677 (3724) for velocity stand, and 825 (4538) for velocity walk/run. Patients were randomly divided intotraining and test datasets at a ratio of 4:1 for each endpoint, while maintaining the covariate composition in thecomposite dataset. Non-linear mixed effects modeling was utilized to describe disease progression. Several
mathematical functions were tested to describe the longitudinal dynamics of endpoints in the training dataset, with thetest dataset reserved for model validation. Missing observations resulting from the inability to perform tests due todisease severity were censored using the likelihood-based M4 method, conditional on the observation being greaterthan zero. Modeling was performed in Monolix (2019R2) using the SAEM algorithm and COnditional Sampling use
for Stepwise-Approach based on Correlation tests (COSSAC) approach for covariate analysis1.
Results: The product of the Chapman-Richards growth function and a sigmoid Emax model best captured the timecourse of all endpoints. The estimated DPmax (approximate maximum fractional decrease in score) was found to be 1
for all endpoints (Table). Age and velocities at first visit were significant covariates for all endpoints on Gmax
(approximate maximum possible score), DP50 (approximate age at which score is half of its maximum decrease) andgamma (shape parameter). Mutation (skip-44/del_3-7 or others) was identified as a covariate on DP50 for all endpoints,
while race (Asian or others) was incorporated on DP50 for velocity stand and velocity climb. If a patient is having
either skip-44 or del_3-7 mutations, this resulted in delayed onset of decline in endpoint. On the other hand, onset of
decline occurred at younger ages in patients of Asian origin. However, this predicted effect of race could be due todifferences in standard of care. Visual predictive checks of longitudinal endpoint observations and fraction of missing
observations due to inability showed good agreement with observed data.
Conclusions: The final models adequately capture longitudinal changes of all timed motor function tests in DMD
patients, including both increase and decline phases. These models may be used in clinical trial simulations to
optimize selection of inclusion/exclusion criteria and other trial design parameters. We will extend these models toincorporate time-to-event models for predicting the probability of dropouts in clinical trials.
Reference:1

Monolix version 2019R2. Antony, France: Lixoft SAS, 2019.

Table: Parameter estimates of models using 3 timed motor function tests

Velocity climb
Value R.S.E.(%)

Velocity standValue R.S.E.(%)

Velocity walk/run
Value R.S.E.(%)

Fixed Effects
GMAX_pop

0.97

1.18

0.363

1.92

2.95

0.923

beta_GMAX_logtBAGEAA
beta_GMAX_logtBSCOREAADPMAX_pop

-1.12
0.909

2.81
1.52

-0.882
0.831

5.16
2.97

-0.708
0.97

2.18
1.47

1

0.006

DP50_pop

10.7

1.1

10.1

1.02

11.4

0.971

beta_ DP50_Mutation

0.178

20.3

0.196

23.1

0.198

18.1

beta_ DP50_RACE-0.093924-0.0795

27.3

NE

NE

beta_ DP50_STUDY_TYPE-0.038736.7-0.0334

47.1

NE

NE

beta_ DP50_logtBAGEAA

0.531

4.8

0.612

4.53

0.528

4.81

beta_ DP50_logtBSCOREAA

0.053617.9

0.0927

13.8

0.155

13.2

1.04
5.17
42.2
9.85
7.72

0.094618.4
NE
1.28-0.593

2.2
5.42
NE
14.4
15.8

0.104
12.4
NE
1.01
-1.39

1.29
4.3
NE
11.7
7

5.03
4.37
10.8
6.34

0.205
0.126
0.109
0.666

4.43
4.77
14.1
6.31

0.0632
0.16
0.069
0.584

7.69
4.38
12
4.91

NE

NE

NE

-0.5388.87

1DPG_pop
0.037
GAMMA_pop
14.3
beta_GAMMA_STUDY_TYPE
0.181
beta_GAMMA_logtBAGEAA
1.39
beta_GAMMA_logtBSCOREAA-0.682Standard Deviation of the Random Effects
omega_GMAX
0.151
omega_DP50
0.14omega_DPG
0.0692omega_GAMMA
0.546
Correlations
corr_GAMMA_DP50
NEError Model Parameters
b
0.207
1.44

1

0.216 1.66

0.126 1.41

R.S.E.(%): Relative Standard Errors, Gmax: approximate maximum possible score, g: power constant, DPmax:
approximate maximum fractional decrease in score, DP50: approximate age at which score is half of its maximum
decrease, gamma: Hill coefficient or shape factor, beta_parameter_logtBAGEAA: power coefficient for age at first
visit on parameter, beta_parameter_logtBSCOREAA: power coefficient for velocity at first visit on parameter, beta_
DP50_Mutation: coefficient for mutation (skip-44/del_3-7 or others) as categorical covariate on DP50, beta_
DP50_RACE: coefficient for RACE (Asian or other) as categorical covariate on DP50, beta_ DP50_STUDY_TYPE:
coefficient for study type (natural history or clinical trial) as categorical covariate on DP50, and NE: Not estimated.

WED-046
Computational analysis of dynamic interactions between soluble shed target and pharmacokinetics of free
drug: A case study with Bispecific Tcell engagers for cancer immunotherapy
Authors: Khamir Mehta, Sergey Ermakov, Vijay Upreti and Sandeep Dutta
Institution: Clinical Pharmacology, Modeling and Simulation, Amgen Inc, South San Francisco, CA
Background: Cell surface molecules representing targets for bispecific drugs often can be found circulating in plasma
in a soluble form, for example, shed by tumor cells. The presence of shed target can interfere with drug action by
preventing it from binding to the target on the surface of the tumor cell and can affect the observed free drug exposures.
Additionally, the drug can also impact the levels of the shed target, by either direct effect and/or via the mechanismof action that includes the modulation of shedding rate, target expression, and finally the lysis of tumor cells. The
dynamics of these various simultaneously occurring processes can make the characterization of the pharmacokinetics,
and therefore, the prediction of an efficacious and safe dosing regimen quite challenging.
Objectives: In this effort, we explore the impact of shed target and its direct and indirect interactions with thebispecific monoclonal antibodies using a computational model. The key objective of the work is to elucidate the impactof this interactions on the pharmacokinetics of the free drug.
Methods: We have developed a mechanistic modeling framework that incorporates the dynamics of the mechanisms(Figure-1) in form of a system of ordinary differential equations. The model explicitly tracks the pharmacokinetics
and biodistribution of the drug and the target-based interactions with tumor cells, whereby the tumor cells canpotentially alter the target expression, increase the shedding rate or internalization, and undergo lysis owing to theaction of the activated immune cells.
We parameterize the base model with reasonable estimates of parameters based on published clinical data for acrossdifferent monoclonal antibodies and different shed target levels. Using computational experiments, we systematically
explore the impact of the shed target levels on the availability of the free drug as a function of various system anddrug related parameters.
Results:
Expectedly, our analysis shows a sharp decline in the free drug exposures when the levels of shed target increase
above the dissociation constant of binding (Kd). Furthermore, the impact of the tumor growth, drug induced immune
cell mediated lysis of tumor cells, target shedding rate can introduce time dependence on the pharmacokinetics of thedrug, which may warrant a time varying drug dosing regimen to ensure optimal efficacy. Finally, we also demonstrate
the potential impact of drug induced target shedding/internalization on the pharmacokinetics, which can give rise to
several non-intuitive pharmacokinetic profiles.

Conclusions: We have developed a platform to theoretically investigate the complex interplay of tumor growth,immune action, antigen shedding and intrinsic drug pharmacokinetics to understand the potential impact on the drug
efficacy and safety. This model can provide valuable insight to facilitate the development of various bispecific drugs
across different immunotherapeutic areas for cancer treatment.

Figure 1: Relevant Mechanisms governing pharmacokinetics of free drugin presence of soluble/shed target

WED-047

Optimization of dose selection using multiple surrogates of toxicity as acontinuous variable in Phase I cancer trial
Se Yoon Lee1, Shankar Lanke1, Alain Munafo2, Pascal Girard2, Kosalaram Goteti1
1
EMD Serono, Inc., Billerica, MA, USA, a business of Merck KGaA, Darmstadt, Germany;2
Merck Institute of Pharmacometrics, Lausanne, Switzerland, an affiliate of Merck KGaA, Darmstadt,
Germany
Objective: Using novel methodology for achieving maximum tolerated dose (MTD) for anti-cancer drugs
with narrow therapeutic range during Phase I oncology trials.
Methods: In this paper, we consider a fully sequential design setting where patients are introduced tothe trials individually and sequentially, and each patient is assigned with an optimal estimate of MTD
based on the accumulated patient’s information at interim. To protect patients from being overdosed, adrug is initiated at a very low dose and slowly escalated toward the targeted MTD as the trials go on.
Typically, two pioneering Bayesian adaptive designs for the phase I cancer clinical trials are continual
reassessment method (CRM) and escalation with overdose control (EWOC). Two drawbacks of CRM andEWOC are that, first, adverse event is recorded with a binary response, DLT (dose limiting toxicity) or
non DLT, and second, they cannot accommodate grade information for multiple adverse events (AE). To
accommodate grade information from multiple AEs, recently, EWOC using normalized equivalent
toxicity score (EWOC-NETS)1, one of expansion of EWOC, has been developed: however, the resultscannot be reproduced by simulation because EWOC-NETS is based on a pseudo-Bernoulli likelihood. In
this paper, we upgrade one parameter linear dose-finder (1PLD)2 into the two- parameter linear dosefinder (2PLD) to address a continuous toxicity response in the phase I cancer clinical trials. The 2PLD
assumes a linear relationship between a toxic reaction and a dosage (or an exposure), and treats the
toxicity response as a continuous variable, aimed at utilizing the Common Toxicity Criteria for Adverse
Events provided by the National Cancer Institute. The proposed search procedure suggests an optimal
exposure to each patient by using accrued patients’ information while controlling the posterior
probability of overdose.
Results: The simulations show that this novel 2PLD method has good operating characteristics andimproved accuracy in achieving MTD. Generally, simulations showed that the 2PLD requires a fewer step
to converge to the targeted MTD than using adaptive clinical trial design methods that are based on
binary responses such as EWOC or CRM.
Conclusions: This novel 2PLD can be an attractive tool for clinical scientists because of its parsimonious
description of a toxicity-dose curve, medically interpretable hyperparameters, and an automatedposterior computation.
References:
1. Zhengjia Chen, Mourad Tighiouart, and Jeanne Kowalski. Dose escalation with overdose
control using a quasi-continuous toxicity score in cancer phase i clinical trials. Contemporary
Clinical Trials, 33(5):949–958, 2012.
2. Benjamin H Eichhorn and S Zacks. Sequential search of an optimal dosage, i. Journal of theAmerican Statistical Association, 68(343):594–598, 1973.

WED-048

Pharmacokinetics of betamethasone is not associated withrespiratory distress syndrome in preterm birth
Larissa Lachi Silva1, Hayley Krzych Trussel1, Andrea R Masters1, Christine Bach1, David M. Haas1, Sara
Kay Quinney11

Indiana University School of Medicine, Indianapolis, IN, USA.

Objectives: Antenatal glucocorticoids, including betamethasone (BMZ), are administered to women at risk of preterm
birth to accelerate fetal lung maturation. This drug administration is known to protect the neonates from developing
respiratory distress syndrome (RDS). However, the association between the pharmacokinetic disposition of maternal
BMZ and neonatal outcomes remains unclear. This study will evaluate whether maternal pharmacokinetic (PK)parameters are important in predicting the risk of neonatal RDS after antenatal BMZ administration.
Methods: Pregnant women with singleton 23-34 weeks gestational age (GA) and anticipated preterm birth receiving
BMZ were enrolled following informed consent. Two doses of BMZ were administered intramuscularly as 6 mg BMZ
sodium phosphate and 6 mg BMZ acetate 24 hours apart. Blood was drawn prior to dosing and at intervals up to 72
h after administration of BMZ. Plasma samples were analyzed by a validated LC-MS/MS method. A population PK
model of BMZ was developed using NONMEM 7.4.3. First-order conditional estimation method with interaction(FOCE-I) was used to estimate structural and variance parameters. PsN and Xpose were employed for model
evaluation. The effects of GA at dosing, weight, BMI, race, and ethnicity were evaluated as covariates on CL/F and
Vd/F. The final model was used to simulate the total exposure from the first dose to delivery time, using the function
exposure in the mlxR library for R. Multiple linear regression was used to assess the relationship between neonatal
RDS and maternal BMZ PK parameters after accounting for GA at delivery using R 4.0.
Results: A total of 931 concentrations from 211 pregnant women were used for model development. The PK of BMZ
in pregnant women was adequately described by a one-compartment model with first-order absorption andelimination, a constant error model and log-normal between-subject variability (BSV) on CL/F and Vd/F. The BSV in
Vd/F could be in part (~8%) explained by incorporation of weight as a covariate in the model. No other covariates
were found to be significant. The population estimates were absorption rate constant (ka) of 1.45 ± 0.13 h-1, CL/F of
32.20 ± 1.08 L/h and Vd/F of 334 ± 11 L. Delivery occurred 2.6 hours to >2 weeks after BMZ administration.
Simulated maternal AUC from the first dose to delivery ranged from 27.25 to 2583.46 h*μg/L. There was no
statistically significant relationship between maternal PK parameters and neonatal RDS after adjusting for GA atdelivery.
Conclusions: Overall, there was no statistically significant association between maternal PK parameters and the riskof neonatal RDS after antenatal BMZ administration.

WED-049

Author:Leticia Arrington(1,2), Mats O. Karlsson(1), Sebastian Ueckert(1)Title:

Software Performance Comparison of NONMEM and mirt for the Estimation
for Item Response Models

Institution:(1) Uppsala University, Uppsala Sweden (2) Merck& Co., Inc., Kenilworth,
NJ, USA,

Objectives
Item response theory is a statistical approach that evaluates the relationshipbetween the underlying hidden trait and item level responses measured by anassessment. Pharmacometric (i.e. longitudinal) IRT modeling acknowledges thechange in these responses over time and presents a framework to combine different
disease outcomes into a joint disease progression model. NONMEM is the classicalsoftware used for longitudinal IRT models. To date there has not been an evaluationof item parameter estimation, recovery and overall model performance in NONMEM
compared to the alternative software platforms. The objective of this work was toperform a systematic comparison of two software, NONMEM and the R package
mirt (multidimensional item response theory) and evaluate the estimation
performance and item parameter recovery for a set of scenarios with varying
number of subjects, items and latent variables.
Methods: A simulation study evaluating three sample sizes (N=50,100,500), twoassessment lengths (item=5 and 20) and one or two latent variables scenarios atbaseline was performed. Ordered categorical items were simulated with 5 categories
of responses (0-4). The item parameters were randomly sampled from a lognormal
distribution (0,0.52) for the discrimination parameter or uniform distribution for
threshold parameters. Replicate (N=1000) datasets were simulated for each sample
size and item scenario using mirt from the simulated item parameters. IRT model
estimation was performed using the Laplace estimation method in NONMEM andstochastic EM with fixed Gaussian quadrature in mirt. Item parameter recovery fromtrue parameters was evaluated using estimation error and root mean square error(RMSE). Furthermore, model fit in terms of log-likelihood were considered.

Results: Overall mirt and NONMEM performed similarly with relatively low

estimation error (median near zero) across all scenarios, indicating reasonable item
parameter recovery. As the number of subjects and items were reduced theestimation precision decreased. The discrimination (a1) parameter was more
precisely estimated in mirt than NONMEM. However, for the threshold (b)parameters NONMEM appeared to be more precise (Figure 1). Model fit (i.e. loglikelihood) was similar between software for the scenario with 500 subjects. Whenthe number of subjects reduced to 100 or 50 and 5 items, mirt performed better inapproximately 10% and 25% of the cases, respectively. Computationally mirt is fasterthan NONMEM with an average model estimation runtime of approximately 19s
seconds compared to approximately 5 minutes in NONMEM for a simulated datasetwith 500 subjects and 20 items.
Conclusions:
Item parameter recovery and overall model fit were similar between NONMEM andmirt. The ease of use and speed of estimation in the existing mirt R package make it awell-suited alternative to provide item parameter estimates for a pharmacometric
IRT analysis.

Figure 1 Software Comparison of Item Parameter RMSE by Subject and Item Scenario

WED-051

WED-051
QSP Model Insights into the Role of Energy Demand Dependent Substrate Selection in Myocardial
Mitochondria Metabolism
Lyndsey Meyer1, Daniel A. Beard2, CJ Musante1, Anna Sher1
1

Quantitative Systems Pharmacology, Early Clinical Development, Pfizer Worldwide Research, Development andMedical, Cambridge, MA, USA
2
Department of Molecular and Integrative Physiology, University of Michigan, MI, USA
Background: The metabolic hallmarks of heart failure include diminished ATP hydrolysis potential indicated by areduced PCr/ATP ratio, alterations in myocardial energy substrate metabolism, such as the switch in substrateutilization from fatty acid (FA) oxidation to carbohydrate oxidation and reduced metabolic flexibility [1]. However,the underlying biological mechanisms and their relative contributions to the impaired mitochondrial oxidative flux arepoorly understood.
Objective: The objective of this work is to develop a comprehensive quantitative systems pharmacology (QSP) modelof the myocardial mitochondrial metabolism in order to evaluate therapeutic targets modulating FA and glucose
substrate selection to improve heart function.
Methods: Published models of oxidative phosphorylation and the TCA cycle [2,3] are combined with a model of βoxidation [4] and are modified to incorporate an updated representation of the enzyme pyruvate dehydrogenase (PDH)that accounts for the important role PDH plays in substrate selection. β-oxidation enzyme parameters are refit tooxygen consumption data following the addition of acyl-carnitines to isolated cardiac mitochondria from male Wistar
rats (400-600 g, n = 5, 12-16 weeks old). The model is then simulated under varying conditions of available pyruvate
and palmitoyl-CoA to determine the predominate oxidation pathway contributing to ATP synthesis. In addition, themodel is simulated with increasing energy demand to evaluate substrate selection under exercise conditions, and withvarying pools of energetic substrates to explore disease state conditions.
Results: The model predicts, as expected, carbohydrate oxidation predominates in the fed state and palmitoyl-CoAoxidation predominates in the fasted state. In the healthy myocardium, changing available substrate does not affect
PCr/ATP ratio. Reductions in the pools of total adenine nucleotides, total creatine, and total phosphate that areassociated with heart failure result in a reduced PCr/ATP ratio and a 35% increase in carbohydrate oxidation in theresting state compared to healthy conditions.
Conclusions: The newly developed in silico model of mitochondria metabolism reproduces key biologicalexperiments including substrate selection (i.e., FA vs. pyruvate) under different levels of workload in healthy
conditions. We found the mechanism of substrate dependent switch is the result of increased PDH activity driven by
feedback from the products and reactants of PDH in healthy and heart failure conditions. However, under the simulated
low adenine nucleotides pools, the increase in PDH flux did not impact cardiac energetics measured by the PCr/ATP
ratio.
References:[1] Karwi, Q.G. et al. Fontiers in Cardiovascular Medicine. 2018; 5: p. 68.
[2] Wu, F., et al. Journal of Biological Chemistry. 2007; 282(34): p. 24525-24537.
[3] Wu, F., J. Zhang, and D.A. Beard. Proceedings of the National Academy of Sciences. 2009; 106(17): p. 7143.
[4] van Eunen, K., et al. PLOS Computational Biology. 2013; 9(8): p. e1003186.

WED-052
Pharmacokinetic-Pharmacodynamic Modeling of a Cell-Penetrating Peptide Phosphorodiamidate
Morpholino Oligomer in mdx MiceMarie Claire Mukashyaka1, Leslie Wu1, Jianbo Zang1, Jenna Wood1, John R. Hadkock1, Mohammad Shadid1
1

Sarepta Therapeutics, Inc., Cambridge, MA, USA

Objectives: Phosphorodiamidate morpholino oligomers (PMO) and cell-penetrating peptide conjugated PMOs
(PPMO) have shown promise in treating Duchenne Muscular Dystrophy (DMD) owing to their ability to skip exonsin mutated dystrophin genes restoring the reading frame. This technology produces an internally truncated yetfunctional dystrophin protein in DMD patients. The objective of the current work is to build a semi-mechanistic PKPD model characterizing the relationship between tissue drug concentrations, exon skipping of mutant dystrophin
pre-mRNA and produced dystrophin protein over time. A mouse PPMO (RC1001) that skips exon 23 (whichcontains a point mutation resulting in a stop codon) to restore the open reading frame in dystrophin gene of mdx
mice was used in the study.
Methods: MDX (C57BL/10ScSn-DMDmdx/J, Jackson Labs) mice were administered single and once-every fourweeks (Q4W) multiple dose of RC1001 at 20, 40 and 80 mg/kg. A PK-PD model was built to characterize plasma
and tissue PK, and PD effects (exon skipping and dystrophin protein) in tissue (skeletal muscle). Sequential
modeling approach was performed: a 2-compartment plasma PK model was first developed to estimate a set ofparameters that were fixed in subsequent models. Tissue PK-PD model was built to characterize tissue PK and PD(skipped and unskipped message and dystrophin protein) and estimate parameters. 2-compartment tissue PK model
was used to describe tissue concentration data. Tissue concentration was assumed to be the driver of observed PD,
and semi-mechanistic indirect response models were used to characterize PD data. Multiple dose dataset was used toverify the developed model. Monolix 2019R2 software was used to fit data and estimate parameters.
Results: The developed PK-PD model was able to characterize plasma PK and tissue PK-PD data as shown indiagnostic plots, and other assessment of goodness-of-fit tools. Additionally, the model was able to accurately
estimate parameters as shown by low relative standard error (R.S.E %) values of each estimated parameter. For
model verification, the developed model and estimated parameters were able to predict multiple dose dataset.
Conclusions: This is a first semi mechanistic PK/PD model that describes the exon skipping of antisense
oligonucleotide in a DMD disease model. The developed PK-PD model well-characterized single and multiple dose
data. The developed model will be helpful to predict and characterize the time-course of PK and PD biomarkers in
mdx mice. Additionally, the developed model frame has a potential to be extended to other exon skipper moleculesand species.

Funding Statement: This study was sponsored by Sarepta Therapeutics, Inc.
Disclosures: all authors are employees of Sarepta Therapeutics, Inc. and may own stocks in the company. Products
are investigational only.

WED-054

Conditional individual weighted residuals for categorical and count data models
Authors: Yasunori Aoki (1), Joakim Nyberg (2), Mats O. Karlsson (2)Institutions: (1) National Institute of Informatics, 2-1-2. Hitotsubashi, Chiyoda-ku, Tokyo, Japan,(2) Department of Pharmaceutical Biosciences, Uppsala University, Sweden
Objectives: To implement and investigate the individual conditional weighted residuals for the categorical or countdata, where the model is given as a probability mass function. To demonstrate the utility of this quantity for model
structure diagnosis and covariate model building is demonstrated using two clinical datasets.
Methods: We define the conditional individual weighted residual for count and categorical models as:
CIWRESlike(y; fx) =Ф-1(U(0,fx(0;b))),
CIWRESlike(y; fx) =Ф-1(U(Σi=0..y-1 fx(i;b), Σi=1..y fx(i;b))),for y=0
for y=1,2,…

where b is a vector of the empirical Bayes estimates, fx(y;b) is the probability mass function given the empirical
Bayes estimates and U(m,n) is the probability density function of the uniform distribution for a range m to n.
A few examples were used to illustrated the performance of the CIWRESlike quantity, both using a simulated data
example where the CIWRESlike was compared to Pearson’s weighted residuals (PWRES) and using two publishedmodels. The models used were a proportional odds model for Likert pain score data [1] and a Seizure count model[2].
Results: With the seizure count model, when using simulated data, i.e. no model misspecification, CIWRESLikeapproximately follows the standard normal distribution. On the other hand, PWRES does not follow the standard
normal distribution even when there is no model misspecification. Using real data, CIWRESLike correctly identified
that including a Markovian element would improve the fit of the model (fig 1). For the Likert pain score model,
CIWRESlike was correctly indicating the model fit is improved by including Paracetamol rescue medication bystratification of quantile-quantile- CIWRESLike by the Paracetamol rescue medication covariate.

Conclusions: A conditional individual weighted residual like quantity for the statistical model that can be written asa probability mass function is presented. A similar idea, but more comparable to a population residual, has beendeveloped by [3-5] using NPD/NPDEs. However, as we have demonstrated through two pharmacometric models;
CIWRESlike can be used for model structure adequacy analysis and covariate model building of nonlinear mixed
effect models.

References:
1. Schindler E, Karlsson MO. A minimal continuous-time Markov pharmacometric model. The AAPS journal
2017; 19(5):1424–1435.
2. Trocóniz IF, Plan EL, Miller R, Karlsson MO. Modelling overdispersion and Markovian features in count data.
Journal of pharmacokinetics and pharmacodynamics 2009; 36(5): 461.
3. Nguyen THT, Comets E. Mentré F. Extension of NPDE for evaluation of nonlinear mixedeffect models inpresence of data below the quantification limit with applications to HIV dynamic model. J Pharmacokinet
Pharmacodyn (2012) 39:499–518
4. RJ. Bauer. NONMEM user guide. Introduction to NONMEM 7.4.3, ICON Plc. Gaithersburg, Maryland, 2018.
5. Cerou M, Lavielle M, Brendel K, Chenel M, Comets E. Development and performance of npde for theevaluation of time-to-event models. Pharmaceutical Research (2018) 35: 30.

WED-055

Physiologically-based pharmacokinetic modeling of sublingual buprenorphine in newborns with neonatal
abstinence syndrome: Implication of biliary clearance ontogeny on pharmacokinetic variability
Matthijs W. van Hoogdalem1,5, Trevor N. Johnson3, Brooks T. McPhail1,7, Suyog Kamatkar2,8, Scott L. Wexelblatt2,6,
Laura P. Ward2,6, Uwe Christians4, Henry T. Akinbi2,6, Alexander A. Vinks1,6, Tomoyuki Mizuno1,6
1

Division of Clinical Pharmacology, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH, USA; 2Perinatal
Institute, Division of Neonatology, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH, USA; 3Certara
UK Limited, Sheffield, UK; 4iC42 Clinical Research and Development, University of Colorado, Aurora, CO, USA;
5
James L. Winkle College of Pharmacy, University of Cincinnati, Cincinnati, OH, USA; 6Department of Pediatrics,
College of Medicine, University of Cincinnati, Cincinnati, OH, USA; 7University of South Carolina, School of
Medicine Greenville, Greenville, SC, USA; 8Community Hospital East, Indianapolis, IN, USA
Objectives:
Prolonged in utero exposure to opioids causes neonatal abstinence syndrome (NAS) in newborns. NAS is a withdrawal
syndrome that results in a variety of central nervous system and respiratory disturbances, associated with severe
neonatal and early childhood morbidity [1, 2]. Buprenorphine is used in NAS to control symptoms in those infants
with severe withdrawal. However, the development of an effective evidence-based dosing protocol is impeded bylarge variability in buprenorphine pharmacokinetics (PK) and pharmacodynamics. The aims of this study were (1) todevelop and assess the predictability of a physiologically-based pharmacokinetic (PBPK) model of buprenorphine inneonates and (2) to identify the sources of variability in buprenorphine PK.
Methods:
A PBPK model for buprenorphine was developed in Simcyp (v18) using physicochemical properties and in vitro
enzyme kinetics data for CYP3A4, 2C8, UGT1A1, 1A3, 2B7 and 2B17 with additional biliary clearance [3], asbuprenorphine is known to be predominantly excreted in the bile. After model validation with adult data, the prediction
in neonates was assessed using 52 blood concentrations obtained from 19 term and preterm infants treated with
sublingual buprenorphine solution. Biliary clearance ontogeny was estimated by minimizing concentration residuals
as a function of postnatal age (PNA). Performance was evaluated by comparing predicted and observed concentrations
and clearance (CL). Predicted CL was determined by simulating a standard buprenorphine dose in 1000 virtual
individuals per patient, using Virtual Twin methodology [4]. Observed individual CL was estimated by Bayesianestimation in MwPharm++ (v1.8) using a previously developed population PK model.
Results:
The PBPK model adequately predicted observed concentrations in neonates with 71% and 96% of predictions fallingwithin 2- and 3-fold of the observed values, respectively. In 17 out of 19 neonates (89.5%), predicted CL fell within
2-fold of the observed value. Increasing biliary clearance as a function of PNA increased the model’s predictive
performance, indicating minimal clearance at birth and reaching full maturation at 4.5 days PNA (Figure).
Conclusion:
A minimal PBPK model of sublingual buprenorphine in neonates was successfully developed. This study underscores
the importance of establishing biliary clearance ontogeny profiles, since the results indicate that this can be an
important determinant of buprenorphine clearance and PK variability in neonates.

References
1.
2.
3.
4.
FigureLisonkova et al. Pediatrics. 2019;144(2):1-11.
Witt et al. J Perinatol. 2017;37(10):1124-1129.
Johnson et al. Drug Metab Dispos. 2016;44(7):1090-1098.
Polasek et al. Br J Clin Pharmacol. 2018;84(3):462-476.

WED-056
Analysis of continuum and compartmental modeling approaches for intrathecally administered drugs to
improve monkey-to-human model scalability
Michael Monine1 and Ivan Nestorov1
1

Clinical Pharmacology and Pharmacometrics, Biogen, Cambridge, MA, USA

Objectives:
Complex subarachnoid space geometry and slow cerebrospinal fluid (CSF) turnover limit distribution of intrathecal(IT) drugs within the central nervous system (CNS). Animal data suggests that concentration gradients along the spinal
CSF persist over several hours after IT administration to the lumbar area [1,2]. These gradients may become more
dramatic in human because of the lengthier spinal column and upright position. The objectives of this work are to 1)
describe the spinal column as a continuum one-dimensional (1D) system; 2) explore scaling of the model with thespinal column length.
Methods:
In a physiological-based pharmacokinetic (PBPK) framework, the spinal column is treated as either a continuum orcompartmental system (Fig. 1). In a continuum model representation, the spinal CSF is described by an advectiondiffusion equation in 1D. Local concentrations in the spinal cord, brain, plasma and peripheral tissues are represented
by a system of homogeneous compartments described by ordinary differential equations. The effective diffusion
coefficient (D) and net flow speed (u) of a drug in the CSF are estimated based on non-human primate (NHP) data
generated in imaging studies with a radioactive tracer [2] and PK studies with antisense oligonucleotides (ASOs). The
continuum model accounts for actual lengths of the spine segments in cynomolgus monkey. The model is implemented
in R with the 1D part discretized using ReacTran package [3].
Results:
Simulations of the continuum model for varied values of D, u and length were compared to time- and space-dependent
concentration data from the imaging studies [2]. The analysis suggests that after IT administration, the effective rateof diffusion in the spinal CSF is relatively high (10-3-10-2 cm2/s) due to forcing factors such as IT injection, heartbeat
and breathing. The estimated net flow rate is consistent with the literature-reported estimates for slow CSF turnover[4]. The continuum model simulations also agree with the NHP PK data for IT-injected ASOs.
Conclusions:Intercompartment rate constants are empirical parameters that are hard to scale. The main scalable parameter of thecontinuum model is the spinal column length. The continuum and compartmental spinal CSF modeling approaches
agree when diffusion is fast (close to well-mixing approximation). After benchmarking the compartmental model tothe continuum model, we conclude that the number of homogeneous compartments in the former model can be scaledby the same factor as the spinal column length in the latter model.

References:[1] Krupp JL and Bernards CM (2004) Anesthesiology, 100:315–22.
[2] Tangen K et al. (2019) IEEE Trans. Biomed. Eng. 2019 Jul 23. doi: 10.1109/TBME.2019.2930451.
[3] Soetaert K and Meysman F (2012) Reactive transport in aquatic ecosystems: Rapid model prototyping in the open
source software R Environmental Modelling & Software, 32, 49-60.
[4] Pardridge WM (2016) Expert Opinion On Drug Delivery, 13:963-975.

Figure 1. A PBPK modeling framework for IT-injected drugs and different spinal column models.

WED-057
QSP modeling shows efficacy of an NK3R antagonist to reduce treatment-induced vasomotor symptoms
Chris Schelling1, Rick Ridgewell1, Christina Friedrich2, Katherine Kudrycki2, Rebecca Baillie2, Douglas Chung2,
Mike Reed2
1

Acer Therapeutics Newton, MA USA; 2Rosa & Co. LLC, San Carlos, CA USA

Objectives: Vasomotor symptoms (VMS, hot flashes and night sweats) are common among breast and prostatecancer patients resulting from treatment options that induce hormone deprivation. 60%–70% of women treated with
antiestrogenic agents (tamoxifen) have hot flashes, while 80% of men on androgen deprivation therapy (leuprolide)
will experience hot flashes. NK3 receptor (NK3R) antagonists have been shown clinically to significantly reduce
VMS in postmenopausal women without the need for added estrogen. We built a mechanistic model of the
hypothalamus-pituitary-gonadal (HPG) axis to evaluate the use of a NK3R antagonist (ACER-801) to reduce thefrequency and severity of VMS using representative virtual patients undergoing tamoxifen or leuprolide treatment
concomitantly.
Methods: We developed a quantitative systems pharmacology (QSP) neurobiology model of KNDy neurons in thearcuate nucleus based on relevant mechanistic and efficacy data from literature. The model includes detailed NKB,
dynorphin and estradiol effects on KNDy neurons, neuroendocrine feedback, and downstream effects on the HPG
axis and sex hormones. ACER-801, tamoxifen and leuprolide pharmacokinetics and pharmacodynamics were
included. Model parameters such as drug pharmacokinetics, receptor numbers, and hormone production wereidentified from literature data. Published clinical monotherapy data for other NK3R antagonists, tamoxifen, andleuprolide were used for model calibration and testing. Two Virtual Patients (VPs) were developed based on typicalpatients included in clinical trials for tamoxifen (postmenopausal female) and leuprolide (male). VMS were
estimated based on the level of NKB binding to NK3R.
Results: The Platform was developed and qualified, and drug coadministration simulations conducted for a fiveweek clinical trial duration. ACER-801 treatment reduced NKB binding and lowered VMS frequency by 80% frombaseline in the postmenopausal VP. Tamoxifen treatment in this VP increased VMS by 15%. Coadministration of
ACER-801 and tamoxifen in this VP reduced VMS by 75% compared to tamoxifen monotherapy. In the male VP,
leuprolide administration induced three episodes of VMS per day, and coadministration of ACER-801 andleuprolide reduced the frequency and severity of VMS to near 0.
Conclusions: Simulations using the HPG Platform demonstrate the efficacy of the NK3R antagonist ACER-801 to
reduce NKB binding and vasomotor symptoms due to menopause or tamoxifen- or leuprolide-induced hormone
deprivation.

Figure 1. Simulation results showing change in VMS with drug treatment in a female postmenopausal (left) andmale (right) virtual patient. Drugs were dosed at time 0.

WED-058

Application of a Deep Learning Algorithm as a Prescreening Tool to Assign Individualized
Absorption Models in a Population Pharmacokinetic Analysis
Mutaz M. Jaber1, Kyriakie Sarafoglou1,2, Mahmoud Al-Kofahi1, Richard C. Brundage1
Experimental and Clinical Pharmacology, 2Department of Pediatrics University of Minnesota

1

Objectives: A specific model for drug absorption is necessarily assumed in pharmacokinetic (PK) analyses.
However, with frequent sampling, not only may it become evident that a simple absorption model is inadequate, butalso that data from different individuals in a population-based analysis may suggest different absorption processes.
An inappropriate absorption model may force other model parameters to be poorly estimated. The aim of this study
is to demonstrate that a Deep Neural Network (DNN) algorithm can be used to prescreen data and assign anindividualized absorption model consistent with either a first-order, Erlang, or split-peak/shoulder process.
Methods: In a recent cortisol population PK analysis, 12 concentrations were obtained over 6 hours in 48 children
with congenital adrenal hyperplasia, a form of adrenal insufficiency. Three distinct absorption shapes wereidentified and classified visually by two independent researchers as either a first-order, Erlang, or splitpeak/shoulder process1. Using the results from this analysis, 10,000 profiles were simulated for each of the three
aforementioned shapes and used for training the DNN algorithm (TensorFlow/Keras engine in R). The DNN
algorithm consisted of input, a single hidden, and output layers. Categorical cross-entropy was used as a loss
function with an RMSprop optimizer and a 0.01 learning rate. 30% of the 30,000 profiles were randomly selected asvalidation data during training. Once the training phase was completed, the 48 observed cortisol data sets from the
PK study were used to evaluate the algorithm classification performance as a prescreening tool. The percentages ofcorrect classifications were calculated by comparing the algorithm classification to the original visual inspection
classification. We compared the accuracy rate with uninformative rate (correct classification due to chance).
Results: No benefit was noted when increasing the number of hidden layers beyond one. A 99% accuracy rate wasfound using the validation data during the training phase. In testing the algorithm classification performance withobserved patient data, a 92% overall accuracy was attained. Table 1 presents the confusion matrix. A comparison
between accuracy rate of 92% and the uninformative rate of 45% was significant (p<0.001).
Table1. Confusion matrix for classification results.
Reference/Visual Classification
Algorithm
Classification
First order
ErlangShoulderFirst order
94.4%
0.0%
5.6%

Erlang
4.0%
96.0%
0.0%

Shoulder
11.6%
0.0%
88.4%

Conclusion: A DNN algorithm was developed to classify absorption shape and was found to have a higher accuracy
rate in classifying shape compared to the uninformed rate. This algorithm is envisioned to be used as a prescreening
tool to select individualized absorption models prior to a population modeling analysis and has the potential toreduce the time needed to perform a manual visual assignment and eliminate inter-assessor variability and bias inassigning the shapes. This approach may be useful in other situations with multiple sub-models observed.
Reference:
1.

Jaber MM, Al-Kofahi M, Sarafoglou K, Brundage RC. Individualized absorption models in population
pharmacokinetic analyses.CPT PSP. 2020:1-3. DOI:10.1002/psp4.12513

WED-060

Population Pharmacokinetics of Heptanoate in Healthy Volunteers and Patients with Long-Chain Fatty
Acid Oxidation Disorders (LC-FAOD) Treated with Triheptanoin
Authors: Nathalie H Gosselin1, Sun Ku Lee2, Claudia Jomphe1, and Jack Shi2
Institutions: 1Certara Strategic Consulting, Princeton, NJ, USA, 2Ultragenyx Pharmaceutical Inc., Novato,
CA, USAOBJECTIVE: Long-chain fatty acid oxidation disorders (LC-FAODs) are a group of rare, autosomal recessive genetic
disorders that impair the conversion of long-chain fatty acids into energy. Triheptanoin (UX007) is an oddcarbon, medium-chain triglyceride consisting of three 7-carbon fatty acids on a glycerol backbone, and is being
investigated for LC-FAOD treatment. Heptanoate is the most abundant and pharmacologically-active metabolite oftriheptanoin in human plasma. The objective of this study was to characterize the pharmacokinetics (PK) of
heptanoate in patients with LC-FAOD and evaluate covariates that potentially affect the exposure of heptanoate.

METHODS: A non-linear mixed effect model (NLME) in PhoenixTM NLME® v8.1 was developed using thepooled concentration-time data of heptanoate from adult healthy subjects (N = 13) and adult andpediatric subjects with LC-FAOD (N = 30). A total of 528 plasma concentration data points for heptanoate
was included in the analysis, with the majority of data from healthy subjects (~83%). Multiple structuralmodels were evaluated to describe the unique PK characteristics of heptanoate such as double peaks, andan apparent accumulation after multiple dosing. The final structural model was selected based on Akaike
information criterion, goodness-of-fits plots and visual predictive check plots.
Following visual assessments, selected covariates (i.e., healthy volunteers vs LC-FAOD, creatinine
clearance and bilirubin) were further tested in the final structural PK model using a forward inclusion(p<0.05) and backward elimination (p<0.01) approach. The influence of patient characteristics (age,hepatic function, race, weight and sex) was evaluated based on heptanoate exposure derived with theposterior Bayes parameters.
RESULTS:The observed plasma concentration-time profiles of heptanoate were adequately described by a
1-compartmental, linear-elimination model with the dual first-order absorption across a wide range ofdose levels (0.3 – 2.2 g/kg/day) tested in both healthy subjects and subjects with LC-FAOD. Notably, theobserved double peaks of heptanoate in human plasma (~45 min and ~4 h) were adequately described byincorporating dual-absorption compartment. The apparent clearance (CL/F) and apparent volume (V/F)
were allometrically scaled with body weight to describe PK data across a wide range of body weights inadult and pediatric subjects with LC-FAOD. Time-varying CL/F described the apparent accumulation of
heptanoate after multiple doses of triheptanoin. The typical CL/F in adult subjects with LC-FAOD at steadystate with a body weight of 58 kg is 236 L/hr, which is ~22% lower in comparison to the one estimated inhealthy subjects with the same body weight. No other covariates influenced the absorption anddisposition of heptanoate significantly.
CONCLUSION: Population PK analysis successfully described the complex absorption and disposition of
heptanoate, and demonstrated that the kinetic nature of heptanoate is similar between healthy subjects
and patients with LC-FAOD.

WED-061
Semi-mechanistic PK/RO modeling of anti-CD47 therapeutic agents
Elena Vasileva1,2, Oleg Demin Jr2
1

Lomonosov Moscow State University, Moscow, Russia

2

InSysBio, Moscow, Russia

Objectives:
Antibodies and fusion proteins targeting CD47 are the new type of therapy to treat both solid and liquid tumors. Aimof this work is to develop semi-mechanistic PK/RO model of anti-CD47 therapeutic agents and to predict CD47
occupancy in tumor tissues and periphery during treatment with ALX148, a CD47 blocker.
Methods:
A semi-mechanistic PK/RO model was developed. It describes PK of ALX148 in blood plasma, distribution to thelymph nodes, spleen and bone marrow, non-linear clearance of ALX148 due to the binding with CD47 and its
further internalization. CD47 occupancy was described on the red blood cells and tumor cells. The model takes into
account CD47 expression (molecules per cell) and a number of cells expressing CD47. Parameters were fitted
against clinical PK data. Clinical data on RO in the periphery was used for model validation.
Results:
The model successfully described ALX148 clinical data on PK and RO in blood plasma. Concentrations in lymph
nodes, spleen and bone marrow were predicted to be similar and in approximately 3 times lower than in bloodplasma. Such high concentration in tumor tissues is caused by relatively small size of ALX148 (molecular weight is
78 kDa). High doses (10 mg/kg QW and 30 mg/kg Q2W) of ALX148 resulted in 95% median trough steady-state
RO in tumor tissues, whereas even lower 95% confidence band is above 90%. The model predicted that troughsteady-state CD47 occupancy in lymph nodes, spleen and bone marrow was around 85- 90% after administration of
3 mg/kg QW.
Conclusions:
Developed semi-mechanistic PK/RO model predicted that at least 10 mg/kg of ALX148 should be administrated
weekly to achieve more than 90% of CD47 occupancy in tumor tissues. Similar doses were chosen for further phase2 clinical trials of ALX148.

WED-063
Prediction of overall survival with atezolizumab-containing treatments in solid tumors using tumor growth
inhibition-overall survival (TGI-OS) modeling framework
Phyllis Chan1, Mathilde Marchand2, Kenta Yoshida1, Benjamin Wu1, Yachi Chen1, Kari Morrissey1, Sandhya Girish1,
Jin Y. Jin1, René Bruno3
1

Genentech, Inc., South San Francisco, CA, USA; 2Certara, Marseille, France; 3Genentech/Roche, Marseille, France.

Objectives: To utilize multivariate tumor growth inhibition-overall survival (TGI-OS) models to predict the OS
distribution and benefit of atezolizumab-containing treatments across 10 clinical studies with different solid tumor
types.
Methods: Longitudinal tumor size data from 10 Ph2 and Ph3 studies (Figure 1) were independently fitted to estimate
TGI metrics, using a bi-exponential model [1] and implemented as a nonlinear mixed effect model in NONMEM [2].
TGI-evaluable patients had to have at least one post-baseline tumor assessment (N=6699 of 7367 treated patients,
90.9%). The impact of TGI metrics and baseline prognostic factors from a pre-defined list for each study on OS wasevaluated using Kaplan-Meier and Cox regression analyses. Parametric survival regression models were developed todescribe the OS distribution. Full models were built by including all significant (p < 0.05) covariates from the Cox
univariate analysis, followed by a backward elimination step (p < 0.01). The model performance for each trial was
evaluated by simulating 1000 times OS distributions and hazard ratio (HR) of the atezolizumab-containing arms vs.
respective controls. Baseline prognostic factors as well as the tumor growth rate constant, KG, were resampled fromindividual patients for each study.
Results: KG was the most significant predictor of OS across all studies. In the final multivariate models, several
baseline prognostic factors were highly significant across multiple studies, such as inflammatory status (C-reactive
protein, albumin, and/or neutrophil-to-lymphocyte ratio, all studies), tumor burden (sum of longest diameters, numberof metastatic sites, and/or presence of liver metastases, 9 studies), Eastern Cooperative Oncology Group performancestatus (6 studies), lactate dehydrogenase and race (3 studies). The model predicted HR indicates good model
performance across the 10 studies, with observed HR within the 95% prediction intervals for all study arms except for
IMpower131 Arm B (atezolizumab+carboplatin+nab-paclitaxel vs. carboplatin+nab-paclitaxel), where the modelpredicted HRs were numerically lower than observed (Figure 1). The discrepancy between the model-predicted andobserved HR in IMpower131 may be attributed to a high proportion of patients who received 2nd-line therapy withother immunotherapies in the control arm of the study.
Conclusions: TGI-OS models adequately described the Kaplan-Meier OS curves and showed that KG, baseline
inflammatory status, and tumor burden were the top predictors of OS. Multivariate TGI-OS models developed for
different solid tumor types were able to predict treatment effect with various atezolizumab monotherapy or
combination regimens. These models are being used to help support decisions based on early tumor dynamic data for
novel combinations in these diseases [3].
Acknowledgements: Xiaobin Li, Shweta Vadhavkar, Alyse Lin, Nina Wang for data preparation.
References:[1] Stein et al. Clin Cancer Res. 2011;17:907-917.

[2] Claret et al.. Clin Cancer Res. 2018;24:3292-3298.
[3] Bruno et al. Clin Cancer Res. OnlineFirst December 23, 2019; DOI: 10.1158/1078-0432.CCR-19-0287.
Figure 1: Evaluation of overall survival models by hazard ratio (HR) across 10 clinical trials

Note: N=number of TGI-evaluable patients with non-missing covariates in atezolizumab-containing arm/control arm
in each trial; Observed=observed HR; Predicted=predicted HR;circles=model-predicted HRs; squares= observed HRs;
horizontal bars=95% prediction intervals (1000 replicates); vertical dashed line=HR of 1; 1L=chemotherapy-naïve
patients; 2+L= patients who have progressed during or following platinum-containing regimen; NSCLC=non-small
cell lung cancer; TNBC=triple negative breast cancer; mUC=metastatic urothelial carcinoma; RCC=renal cell
carcinoma. IMpassion130 evaluation performed in the ITT population.

WED-064
Population Pharmacokinetic (PopPK) Modeling of SAR440340 in Asthma Patients and Healthy Volunteers
Pratik Bhagunde1, Christine Xu1, Xia Pu2, Matthew P. Kosloski2, Vanaja Kanamaluru1, Qiang Lu1
1
Sanofi, Bridgewater, NJ; USA 2Regeneron Pharmaceuticals, Inc., Tarrytown, NY; USA
Objectives:
SAR440340 (REGN3500), a fully human anti-interleukin (IL)-33 monoclonal antibody, binds to circulating IL-33,
inhibiting signaling of IL-33 and potentially attenuating downstream Type-1 and (or) Type-2 inflammatory pathways.
This analysis aimed to develop a PopPK model to characterize the SAR440340 PK model structure, variability andidentify intrinsic/extrinsic factors significantly contributing to PK variability.
Methods:
The SAR440340 PopPK model was developed using data pooled from two phase 1 studies (phase 1a in healthy
volunteers, NCT02958436 and phase 1b in asthma patients, NCT02999711) and one phase 2a study (NCT03387852)
in asthma patients. In the phase 1a study, SAR440340 doses were 0.3, 1, 3, or 10 mg/kg as a single intravenous (IV)dose or 150 mg as a single subcutaneous (SC) dose. In the phase 1b study, patients received SAR440340 at doses of
75 or 150 mg SC every week (QW) for 4 weeks. In the phase 2 study, patients received SAR440340 at 300 mg SC
every two weeks (Q2W) alone or in combination with 300 mg Q2W SC dupilumab, for 12 weeks. Demographic
variables, baseline disease characteristics (e.g. baseline eosinophil level) and disease status (healthy volunteer or
patient) were tested as covariates by forward selection and backward elimination. The model was validated with visual
predictive check and bootstrap.
Results:
The PopPK dataset included 1426 PK observations from 193 subjects (SAR440340 alone in N = 30 healthy volunteers
and N=163 patients; SAR440340 and dupilumab combination in N=74 patients). A 2-compartment PK model with
first order absorption and linear elimination was used to characterize SAR440340 PK. SAR440340 PK exhibited linear
elimination kinetics. Baseline body weight (WT) on clearance (CL), central (VC) and peripheral (VP) volumes, age on
VP and disease status on CL were identified as statistically significant covariates. Typical bioavailability estimate was
56.5%, with 27.1 day terminal half-life in patients. The CL was 29% lower and 51% higher for a 57 kg (5th percentile
WT) & 109 kg (95th percentile WT) patient respectively, relative to 76 kg (median WT) patient, and CL was 18%
lower in patients relative to healthy volunteers. For 300 mg Q2W dosing regimen, steady state area under theconcentration time curve (AUCss) was 38% higher and 33% lower for a 57 kg and 109 kg patient respectively, relative
to a 76 kg patient. The disease status and age did not show clinically relevant impact on AUCss. Other demographic
factors, baseline disease characteristics, and dupilumab co-administration showed no significant influence on
SAR440340 PK.
Conclusions:
The SAR440340 PK in healthy volunteers and asthma patients was well characterized by a 2-compartment model with
first order absorption and linear elimination. Body weight was determined to be the primary source of SAR440340
PK variability.

WED-065
Vadadustat Population Pharmacokinetic Modeling to Support Dosing Recommendation for the Treatment of
Anemia in Japanese Patients with Chronic Kidney Disease
Huub Jan Kleijn1, Shinsuke Inoue2, Megumi Furukawa2, Rishikesh Sawant3, Atsuhiro Kawaguchi2, Shabnam Sani3,
Ajit Chavan3
1

Certara Inc., Oss, the Netherlands; 2Mitsubishi Tanabe Pharma Corp., Tokyo, Japan; 3Akebia Therapeutics, Inc.,
Cambridge, MA, USA.
Objectives: Vadadustat, an oral hypoxia-inducible factor prolyl hydroxylase inhibitor (HIF-PHI), is in late-stage
development for the treatment of anemia in chronic kidney disease (CKD). Vadadustat has been studied in patientswith anemia due to CKD at a starting daily dose of 300 mg and dosing was adjusted between 150 and 600 mg daily
based on hemoglobin response. A moderate variability in pharmacokinetic (PK) exposures was observed. The aim of
population PK (POPPK) modeling was to quantify the impact of intrinsic and extrinsic factors affecting theexposures of vadadustat in the patient population.
Methods: Vadadustat concentration data available from multiple studies with either intensive or sparse bloodcollection were included in the analysis. A structural model was built with intensive PK data collected from both
healthy subjects after multiple dosing and patients after single dosing of vadadustat. The structural model
incorporated body weight, estimated glomerular filtration rate (eGFR), and food intake as covariates. Upon inclusionof sparse PK data in CKD patients from Phase 2 and Japanese Phase 3 studies, covariate effects were investigated.
Nonlinear mixed-effect modeling (NONMEM 7.3) was applied to the PK data. The post hoc estimations were used
to predict the vadadustat exposures in CKD patients in the Phase 2 and Japanese Phase 3 studies and to further
characterize and quantify the covariate effects.
Results: The POPPK analysis data set consisted of 678 subjects and was composed of 60 healthy volunteers, 327
non–dialysis-dependent (NDD) CKD patients, and 291 dialysis-dependent (DD) CKD patients. A model with a lagtime and first-order oral absorption accurately described the pharmacokinetics of vadadustat. In the NDD-CKD and
DD-CKD patient populations, geometric mean oral clearance (CL/F) values were 0.652 L/h (47.1% coefficient ofvariation [CV]) and 0.505 L/h (42.9% CV), respectively. The apparent steady-state distribution volume wasestimated as 11.6 L (13.8% CV). PK parameters were allometrically scaled by body weight. Furthermore, lower
eGFR was associated with a decrease in CL/F, and higher bilirubin was associated with decreased CL/F. Food
impacted absorption of vadadustat, decreasing the absorption rate constant (ka) and increasing lag time, but did notaffect bioavailability. Concomitant oral iron or non-iron phosphate binder usage marginally reduced thebioavailability of vadadustat. No effects of age, sex, race, ethnicity, liver enzymes (ALT, AST, albumin), orJapanese descent on CL/F were detected.
Conclusions: This is a first reported analysis to characterize the POPPK of the HIF-PHI class of molecules.
Vadadustat oral PK were well described by POPPK modeling and estimation of individual exposures following post
hoc analyses stratified by covariate subgroups further illustrated that body weight is the most important covariate forvadadustat exposure. No substantial differences were observed in clearance of vadadustat in NDD-CKD and DDCKD patient populations.
Funding: Akebia Therapeutics, Inc.

WED-066
Population RTTE analysis of exacerbations in asthma patients: a novel approach for predicting asthma
exacerbations based on biomarkers, spirometry and diaries/questionnaires
Robin J Svensson1*, Jakob Ribbing1*, Naoki Kotani2, Michael Dolton2, Shweta Vadhavkar2, Dorothy Cheung2, Tracy
Staton2, David F Choy2, Wendy Putnam2, Jin Jin2, Nageshwar Budha2, Mats O Karlsson1,3, Angelica Quartino2, Rui
Zhu2
1

Pharmetheus, Sweden 2Genentech, South San Francisco, USA 3Department of Pharmaceutical Biosciences, Uppsala

University, Sweden *Contributed equally
Objectives: Early-stage asthma trials focus on changes in biomarkers, spirometry, or diaries/questionnaires (herereferred to as covariates), instead of asthma exacerbations (registrational endpoint). Identification of relevant
covariates that predict asthma exacerbations could inform decision-making for subsequent clinical trials.
Furthermore, if the relationship between relevant predictors and asthma exacerbations were to be quantified, itwould allow predictions of asthma exacerbations from early-stage data. The objective of this work was to describe
longitudinal asthma exacerbations in patients, as a function of baseline and time-varying covariates.
Methods: A repeated time-to-event (RTTE) model for asthma exacerbations was developed in NONMEM 7.3
assisted by PsN[1], using 502 severe asthma patients randomized to subcutaneous placebo or 70, 210, or 490 mg
MSTT1041A (mAb targeting the IL-33 receptor, ST2) every 4 weeks for 52 weeks (NCT02918019). Asthma
exacerbations were defined as new or increased asthma symptoms that resulted in treatment a) with systemic
corticosteroids for ≥3 days and/or b) requiring hospitalization/emergency department visit. Covariates were basedon data collected daily (diary-based) or during scheduled visits, including patient demographics, and MSTT1041A
exposure.
Different hazard distributions were explored for the base model. Full random effects modeling (FREM)[2] was used
to explore 20 baseline covariates, followed by stepwise covariate modeling (SCM), exploring 9 time-varying
covariates. Following SCM, MSTT1041A exposure on hazard was explored. Models were selected based on
difference in OFV (dOFV), RSEs and diagnostic plots.
Results: The final model described the observed data well (Figure 1). A Weibull distribution described the baseline
hazard. Exacerbation history and symptom score were the most influential baseline covariates. Diary-based
symptom score (dOFV=83.7), diary-based short-acting rescue medication use (dOFV=33.5) and forced expiratory
volume in 1 second (FEV1, dOFV=14.9) were selected as time-varying covariates. MSTT1041A exposure was not
significant at p=0.05 (dOFV=2.1) but was included as a conservative measure. Exposure was not regarded asirrelevant given the effect size (on top of its effect on the time-varying covariates) of 28.3% lower hazard as a step
function.

Conclusions: The longitudinal asthma exacerbation data were well described by the final model where relevant
covariates for describing asthma exacerbations were identified. An MSTT1041A treatment effect was included,
which should be considered when using this model to predict asthma exacerbations for other drugs. This work
demonstrated the utility of a population-RTTE approach to characterize exacerbation hazard in severe asthma
patients.

Figure 1 Posterior-predictive check for difference in mean-weighted exacerbation rate (ER) for active arms versus
placebo. Black line=observed change in ER. Red bars=distribution of difference in ER for 1000 simulated studies.
Dashed, red lines=95% confidence interval of difference in ER (simulations). Solid red line=50th percentile
difference in ER (simulations).
References:[1] Keizer RJ, Karlsson MO & Hooker A. CPT:PSP 2013;2:e50;doi:10.1038/psp.2013.24[2] Karlsson MO. PAGE 21 (2012) Abstr 2455 [www.page-meeting.org/?abstract=2455]

WED-067
Application of Quantitative Systems Pharmacology (QSP) platform for Immuno-Oncology for evaluating
OX40 agonist, and anti-PD1+OX40 agonist therapies for advanced NSCLCRoy Song1, Loveleena Bansal1, Niranjan Yanamandra2, Herbert Struemper3, Valeriu Damian1
1
Systems Modeling and Translational Biology, GSK, Collegeville, PA; 2Immuno-Oncology and Combinations
Research Unit, GSK, Collegeville, PA; 3Clinical Pharmacology Modeling and Simulation, GSK, Durham, NC
Objective: A comprehensive immuno-oncology (IO) QSP platform was developed using a modular approach for
NSCLC (1). A key step prior to application of the QSP platform to novel IO therapies, is to validate against existing
therapies. For this purpose, the model results were compared with the clinically observed efficacy of OX40 agonist(OX40a), anti-PD1 and OX40a+anti-PD1 therapies.
Methods: Receptor expression and interaction dynamics of PD1 and OX40 was implemented on immune andtumor cells in the IO platform. The effect of PD1-induced CD8 exhaustion and OX40-induced enhanced
proliferation were implemented as the primary effects of receptor engagement. A virtual population of NSCLC
patients was generated by varying biological parameters in the model including tumor growth rate, tumor PD-L1
expression, immune cell proliferation rate, cytokine synthesis, and immune cell receptor expression of OX40 andPD1. A final virtual cohort was assembled to match the variability and distribution of surface molecule expression
and different immune cell-types measured in real NSCLC patient population. Lastly, model response predictions toanti-PD1 and OX40a were compared against results from clinical trials (2-5).
Results: Model prediction of overall response rates was evaluated for anti-PD1, OX40a and anti-PD1+OX40a.
The model exhibited overall response trend consistent to first-line pembrolizumab clinical studies, where thegreatest response was observed in patients with high PD-L1 expression (2-4). In addition to PD-L1, the model
identified CD8 exhaustion in the tumor microenvironment as potential biomarker for responders to anti-PD1
monotherapy. The model also showed minimal response with OX40a monotherapy for NSCLC and marginal
improvement in OX40a+anti-PD1 therapy over anti-PD1 alone. The modeling results based on several virtual
patients predicted an inverse relationship between OX40 expression on Tregs to OX40a monotherapy response.
Conclusion: The model correctly identified high PD-L1 virtual patient cohort as best candidate for first-line antiPD1 monotherapy treatment. The model also correctly predicted OX40a monotherapy clinical outcome forunselected advanced NSCLC in line with clinical trial results (5). Further, the model has provided insights into themechanism of action and has been used for exploring biomarkers for OX40a and OX40a+anti-PD1 therapies. The
QSP model will be further applied for predicting the response distributions in anti-PD1+OX40a clinical trials by
calibrating virtual patient prevalence to real patients with a goal of using the fully validated model to assist in theprioritization of novel combinations at GSK.
References:
1. Song et al. (2018) ACoP9 Poster presentation T-095
2. Garon et al. (2015) doi.org/10.1056/NEJMoa1501824 (KEYNOTE-001)
3. Reck et al. (2016) doi:10.1056/NEJMoa1606774 (KEYNOTE-024)
4. Mok et al. (2019) doi.org/10.1016/S0140-6736(18)32409-7 (KEYNOTE-042)
5. Postel-Vinay et al. (2020) AACR Abstract CT150 (ENGAGE-1)

WED-068

Predicting drug efficacy and cytokine release with a versatile Quantitative Systems Pharmacology(QSP) modeling strategy for multispecific T-cell engagers
Abrams, R.E.1, Pierre, K. 1, El-Murr, N.2, Li, A.3, Fretland J.3, Li, J.1, Wu, L.4, Seung, E.4, Yang Z-Y.4, van de
Velde, H.5, Pelekanou, V.5, Stamatelos, S.K. 1
1 Sanofi, Bridgewater, 55 Corporate Dr., Bridgewater, NJ, 08807 USA
2 Sanofi, 13 quai Jules Guesde 94403 Cedex,VITRY-SUR-SEINE, Vitry/Alforville France
3 Sanofi, 153 Second Avenue, Waltham, MA 02451, USA
4 Sanofi, 270 Albany St., Cambridge, MA 02139, USA
5 Sanofi, 50 Binney St., Cambridge, MA 02142, USAA CD38-CD28-CD3 trispecific antibody targeting tumor cells (e.g. multiple myeloma) by engaging both Tcells and tumor cells is in clinical development. This compound differs from CD3-CD38 T-cell engagers bybinding CD28 antigen on either tumor or T-cells, which improves activation, proliferation, and cytolytic
activity of T-cells.
In order to increase our understanding of the relevant biology and to support the clinical development
program of this trispecific antibody we have established a Quantitative Systems Pharmacology (QSP)
that covers CD28+ and CD38+ tumor cells, CD38+ PBMCs, and six T-cell subtypes. The model describes
synapse-based T cell activation, cytokine production, and tumor killing. To create variations of the model
we developed a rule-based algorithm of cell behavior and synapse formation. We used global
optimization to calibrate parameter sets to preclinical data.
The applied code produced several model versions with minimal effort or time. We calibrated these tocytotoxicity (Fig. 1), proliferation, and cytokine release data. Simulations suggest that tumors with arange of CD38 expression can be treated safely and effectively.
The calibrated model captures a complex system involving multiple cells, antigens, interactions, andprocesses, as shown by its ability to match experimental results. We can easily adapt our model to manysettings, by our code building methodology. We use this platform to predict how differences in levels
and distribution of antigen, tumor, and T-cells will impact drug safety and response.

Figure 1. Model calibration to tumor cytotoxicity data

The QSP model was optimized to in vitro data on cytotoxicity of multiple myeloma cells (KMS-11 cellline) incubated with pre-activated CD8 T-cells and an E/T ratio of 10. The range of experimental data
produced from 3 replicates is plotted here in light blue versus the simulated population distribution inred.

WED-069
Linking Mobile Sensor Technology Measurements to Clinically Meaningful Outcomes: Case Studies in TypeI Diabetes (T1D) and Parkinson’s Disease (PD)
Sakshi Sardar1, Varun Aggarwal1, Roopal Bhartnagar1, Diane Stephenson1, Inish O'Doherty1, Klaus Romero1, Jackson
Burton1
1

Critical Path Institute, Tucson, Arizona, USA

Objectives: The objective of this work is to quantitatively link measurements from mobile sensing technologies toclinically meaningful outcomes in type 1 diabetes (T1D) and Parkinson’s disease (PD). In both diseases, Critical Path
Institute’s public-private partnerships Critical Path for Parkinson’s (CPP) and the Type-1 Diabetes Consortium(T1DC) have specific efforts focused on understanding the voice of the patient (VOP) to identify clinically meaningful
aspects of the disease as well as established clinical outcome assessments (COAs). The utility of mobile sensing
technology has then been reviewed to more sensitively capture existing measures and address gaps in clinicallymeaningful aspects of the disease not currently being captured (Figure 1a).

Methods: Regarding PD, a comprehensive catalogue of three core areas: VOP, COAs, and existing digital studies in
PD was developed. Gait was identified to be one of important aspects of PD and normative digital time-series gait
data were analyzed to extract features that can be used to establish reference standards for PD associated gait
abnormalities. The data were generated in-house using STEVAL-BCN002V1B for a single subject with three repeats.
For T1D, continuous glucose monitoring (CGM) was identified as a technology to sensitively measures blood glucose
and identify clinically relevant diabetic events. CGM and HbA1c (an indirect measure of glycemic control) from the
JAEB study with 70 patients was used. Daily CGM parameters were extracted from the data, followed by data cleanup leading to usable data from 19 patients. Subsequently, feature engineering was carried out and linear regression
machine learning model was trained for HbA1c prediction.

Results: Figure 1 b, c and d show the raw accelerometer data for a walk test, resultant magnitude derived from it andthe filtered signal using a low pass filter with a cutoff frequency of 2 Hz, respectively. The filtered signal was used toderive quantitative measures of steps, strides, times associated with different sections of the gait and their variation
with time. For T1D, the trained model showed good predictive power with an R2 score of 0.69 and mean squared error
was 0.259.

Conclusions: These quantitative methodologies can be emulated across different disease areas to provide informed
guidance on the use of relevant technologies that provide more frequent and objective measure of aspects of thedisease. The case studies highlight the linking of mobile/digital technologies to clinically meaningful outcomes basedon the patient’s perspective. This process is critical in light of the rapid emergence of mobile sensing technology forengaging in regulatory bodies for advancing such technology in drug development.

Figure 1: a) Strategy for identifying clinically important aspects of the disease based on patient input, COAs and,assessment of aspects of disease being captured or have potential to be captured using technology. b, c and d) Case
example from PD showing, raw accelerometer data and subsequent processing to extract quantitative features. e) Daily
CGM measurement example from the data, and f) the prediction capability of using CGM data metrics for HbA1c.

WED-070

Model-Based Assessment of Hemoglobin Response to Treatment with Vadadustat inPatients With Chronic Kidney Disease
Floris Fauchet1, Huub Jan Kleijn1, Shinsuke Inoue2, Megumi Furukawa2, Shabnam Sani3, Atsuhiro Kawaguchi2,
Rishikesh Sawant3, Ajit Chavan3
1

Certara Inc., Oss, the Netherlands; 2Mitsubishi Tanabe Pharma Corp., Tokyo, Japan; 3Akebia Therapeutics, Inc.,
Cambridge, MA, USA.
Objectives: Vadadustat is an orally administered hypoxia-inducible factor prolyl hydroxylase inhibitor (HIF-PHI) in
the late stages of clinical development for the treatment of anemia due to chronic kidney disease (CKD). This analysis
describes the use of an empirical pharmacokinetic/pharmacodynamic (PK/PD) model to explore and quantify the
exposure–response relationship for hemoglobin (Hb) modulation after vadadustat treatment in the CKD population
based on longitudinal Hb data. The analysis takes into account prior use of erythropoiesis-stimulating agents (ESAs)
and other patient characteristics affecting exposure and Hb modulation in CKD patients.
Methods: Long-term Hb concentration data (16-24 weeks) for vadadustat were obtained from 4 completed phase 2
studies (US, 2; Japan, 2) and 2 Japanese phase 3 studies conducted in both dialysis-dependent and non–dialysisdependent CKD populations. Dose titration based on Hb response was applied, aimed to maintain the desired
therapeutic Hb target range in patients. The data set contained longitudinal Hb observations (typically every 2 weeks),
actual dosing information, patient demographics, prior ESA status and dose level, disease state, estimated glomerular
filtration rate, and concomitant medications. The model included components that described the residual effects of theprior ESA treatment, baseline endogenous Hb, and vadadustat-induced Hb response. Vadadustat exposures wereobtained from a previously developed population pharmacokinetics model. The analysis was performed using
nonlinear mixed-effects modeling implemented in NONMEM 7.3. The predictive performance of the final model wasassessed by conducting a posterior predictive check.
Results: A total of 726 patients (7298 observations) treated with vadadustat (150–600 mg daily) were included in themodel-based analysis. Hb versus time profiles were characterized by an initial Hb decline, which reflected thedecreasing impact of the prior ESA treatment, followed by a gradual vadadustat-induced Hb increase, with stable Hblevels achieved after approximately 10 weeks of treatment. Structural model components consisted of (1) baseline Hblevel, (2) a first-order rate process to account for the washout of residual Hb effects from prior ESA use, and (3) a setof transit compartments to account for the gradual response to vadadustat modulation, with a linear function translating
exposure to modulation of the Hb production rate. The baseline Hb level, prior ESA effect, associated washout rate,
Hb production rate, and slope of the exposure–response relation were accurately estimated, and predictive checks
confirmed the model’s performance.
Conclusions: This is a first-reported analysis to characterize the PK/PD of the HIF-PHI class of molecules. The
PK/PD model adequately described the longitudinal Hb–time data in CKD patients. Further understanding of the
exposure–response relationship for Hb could be used to optimize vadadustat dosing in CKD patients.
Disclosure: This study was funded by Akebia Therapeutics, Inc.

WED-071
Genotype-Dependent Changes in Warfarin Clearance upon Co-administration of an Inhibitor (fluconazole)
and an inducer (rifampin): A Model-based Analysis
Shen Cheng1, Darcy R. Flora1, Timothy S. Tracy2, Allan E. Rettie3, Richard C. Brundage1
1. Department of Experimental and Clinical Pharmacology, College of Pharmacy, University of Minnesota, Twin
Cities; 2. Tracy Consultants; 3. Department of Medicinal Chemistry, School of Pharmacy, University ofWashington, Seattle
Objective: Warfarin is one of the most commonly prescribed oral anticoagulant drugs for preventing long-term
thromboembolic events worldwide. Warfarin is administered as a mixture of two enantiomers, S-warfarin and Rwarfarin, in a 1:1 molar ratio. CYP2C9 is highly associated with the metabolic clearance of S-warfarin but not Rwarfarin. Since S-warfarin is 7-fold more potent, patients’ CYP2C9 metabolic status impacts exposure andtherapeutic response. CYP2C9 is subject to genetic polymorphisms as well as susceptible to enzyme induction andinhibition, all of which complicate the dose-exposure relationship of warfarin. The objective of this research is toinvestigate the impact of CYP2C9 genetic polymorphisms on warfarin drug-drug interactions using a nonlinear
mixed-effects modeling approach.

Methods: From our pharmacogenomics registry of 1950 subjects, 29 subjects with five CYP2C9 genotypes wererecruited and administered single doses of warfarin alone, and together with fluconazole (inhibitor) and rifampin(inducer) in a crossover fashion (Figure 1). S- and R-warfarin were quantitated using LC/MS/MS in plasma andurine collected out to 15 days. A linear compartmental modeling approach was first explored for S-warfarin and Rwarfarin but significant model misspecification was noted. A target-mediated drug disposition (TMDD) model was
found to adequately describe both S-warfarin and R-warfarin plasma and urine concentrations after administration of
warfarin alone as well as when fluconazole and rifampin were co-administered. The TMDD model was
implemented within NONMEM using the IMP estimation method.

Results: Our results confirm S-warfarin exhibits CYP2C9 genotype-dependent clearance whereas R-warfarin doesnot. S-warfarin baseline clearance and the percent of baseline clearance when administered with fluconazole orrifampin are presented in Table 1. R-warfarin clearance decreased to 52% of baseline when warfarin was taken withfluconazole and increased to 270% of baseline when taken with rifampin. No significant differences in R-warfarin
clearance were noted among subject genotypes.

Conclusions: S-warfarin is the major active compound in warfarin and patients with different CYP2C9 genotypes
will likely require different warfarin doses to attain a similar exposure, and moreover, require different magnitudesof dose adjustments when adding CYP inhibitors and CYP inducers.
Reference:
1. Flora, D. R., Rettie, A. E., Brundage, R. C., & Tracy, T. S. (2017). CYP2C9 Genotype-Dependent Warfarin
Pharmacokinetics: Impact of CYP2C9 Genotype on R- and S-Warfarin and Their Oxidative Metabolites. J Clin
Pharmacol, 57(3), 382-393. doi:10.1002/jcph.813
2.

Levy, G., Mager, D. E., Cheung, W. K., & Jusko, W. J. (2003). Comparative pharmacokinetics of coumarin
anticoagulants L: Physiologic modeling of S-warfarin in rats and pharmacologic target-mediated warfarindisposition in man. J Pharm Sci, 92(5), 985-994. doi:10.1002/jps.10345

Figure 1. Study Design Diagram. Each subject went through 3 study periods (upper dark-blue section). Period 1(red box), single 10mg dose of warfarin. Periods 2 and 3 in crossover (yellow box), each subject was pretreated witheither 400mg fluconazole or 300mg rifampin once daily for 7 consecutive days, followed by a single 10mg dose of
warfarin and continuous treatment with either 400mg fluconazole or 300mg rifampin once daily through thesampling phase.

CYP2C9
Genotypes% of baseline clearance when administered with an inhibitor or inducer
Baseline CL (L/hr)(%RSE)

Fluconazole (%RSE)

Rifampin (%RSE)*1/*1 (n=8)

0.260 (8%)

30.3% (4%)

211% (4%)*1B/*1B (n=5)

0.230 (13%)

NA

207% (5%)*1/*3 (n=9)

0.157 (11%)

34.9% (4%)

215% (4%)*2/*3 (n=3)

0.071 (16%)

40.5% (7%)

295% (9%)*3/*3 (n=4)

0.056 (14%)

50.3% (7%)

291% (6%)

BSV

23.2% (36%)

11.8% (85%)

11.4% (55%)

Table 1. Genotype-Dependent Changes in Warfarin Clearance upon Co-administration fluconazole andrifampin. (BSV: Between Subject Variability, expressed as %CV)

WED-073
Population pharmacokinetics and pharmacodynamics of tranexamic acid in
women undergoing cesarean delivery
Shuhui Li1, Homa Ahmadzia2, Dong Guo1, Adam Miszta3,4, Naomi Luban5, Jeffrey Berger6, Andra H. James7, Alisa
S. Wolberg3, Johannes Van den Anker8,9, Jogarao Gobburu13.

1

School of Pharmacy, University of Maryland, Baltimore, M.D., USA; 2Division of Maternal-Fetal Medicine,

Department of Obstetrics & Gynecology, The George Washington University, Washington D.C., USA; 3Departmentof Pathology and Laboratory Medicine and UNC Blood Research Center, University of North Carolina, Chapel Hill
N.C.,USA; 4Synapse Research Institute, Maastricht, the Netherlands; 5Division of Hematology, Children’s National
Hospital, Washington D.C., USA; 6Department of Anesthesiology and Critical Care Medicine, The George
Washington University, Washington D.C., USA; 7Division of Maternal-Fetal Medicine, Department of Obstetrics &Gynecology, Duke University, Durham N.C., USA; 8Division of Clinical Pharmacology, Children’s National
Hospital, Washington D.C., USA; 9Division of Pediatric Pharmacology and Pharmacometrics, University of BaselChildren’s Hospital, Basel, Switzerland.

Objectives: Few pharmacokinetics (PK) and pharmacodynamics (PD) studies have been conducted for tranexamic
acid (TXA) in preventing postpartum hemorrhage (PPH). As a result, there is a lack of information regarding which
dose of TXA to administer for PPH prevention. This study characterized the PK and PD of TXA in pregnant women
who underwent cesarean delivery, with a goal of projecting the prophylactic dose for future studies.

Methods: Thirty patients scheduled for cesarean delivery entered a prospective, single-center, open-label, doseranging study. Ten patients each were sequentially given 5 mg/kg, 10 mg/kg, or 15 mg/kg of TXA intravenously after
the umbilical cord clamping. Seven pre- and postpartum specimens were collected up to 24 hours postpartum. The
maximum lysis of tissue plasminogen activator spiked whole blood sample using rotational thromboelastometry
served as the PD response. Non-linear mixed effects models were adopted to characterize the 174 observations, using
Pumas v0.10.0 (Pumas-AI Inc., Baltimore, MD, USA). The in-vitro PK threshold of 10 mg/L TXA and the normal

3

Gobburu is a co-founder of Pumas-AI that commercializes Pumas software.

reference value of maximum lysis of 17% were selected as the therapeutic targets to come up with the prophylactic
dose of TXA. Serial thrombin generation and D-dimer data were evaluated as safety outcomes.

Results: A 2 -compartment model with proportional residual error model best fitted the data with following
parameters: 9.6 L/hr (clearance), 23.8 L (central compartment volume), 16.5 L (peripheral compartment volume), and
3.4 L/hr (intercompartmental clearance). Between-subject and residual variability were less than 25% (CV%) and nocovariates were found to further explain the variability. A fractional sigmoid Emax model with combined error model
best linked the TXA concentration to the effect on the maximum lysis, with the baseline lysis at 98%, maximum
inhibition of TXA at 89% of the baseline, 5.7 mg/L as the IC50 and hill factor at 7.7. The variability of the PD model
parameters was about 88% (CV%). Simulation results showed that 550 mg and 650 mg of TXA maintained theconcentrations and maximum lysis levels above the therapeutic targets for 30 minutes and 1 hour, respectively, formore than 90% of the patients. Currently pregnant women receive 1 gram of TXA. There was no significant differencein thrombin generation data or D-dimer data across different dosing cohorts, supporting the safety of TXA use.

Conclusions: To the best of our knowledge, the PK and PD profiles of TXA in pregnant women who underwent
cesarean delivery are described for the first time. The optimal single dose providing sufficient PPH prophylaxis up to
1 hour after delivery was 650 mg.

WED-074
Population Pharmacokinetic Analysis of N-acetylcysteine in Pediatric Patients with Inherited Metabolic
Disorders Undergoing Hematopoietic Stem Cell Transplant
Authors: Sahasrabudhe SA1,2, Kartha RV1,2, Ng M3, Cloyd JC1,2, Orchard PJ3, Brundage RC1,2, Coles LD1,2
Affiliations:1Center for Orphan Drug Research 2Department of Experimental and Clinical Pharmacology at University of
Minnesota 3Department of Pediatric Blood and Marrow Transplantation at University of Minnesota
Objectives
Inherited metabolic disorders (IMDs) include peroxisomal and lysosomal diseases. Adrenoleukodystrophy (ALD) isan X-linked, rare, peroxisomal disorder caused by mutations in the ABCD1 gene, encoding a peroxisomal
membrane-bound protein necessary for transport of very long chain fatty acids (VLCFAs) into the peroxisome. In
ALD, the VLCFAs accumulate in tissues leading to adrenal insufficiency and, in approximately 40% of cases, aneuroinflammatory condition termed cerebral ALD (cALD). If untreated, cALD is associated with rapid
neurological decline and death. Hematopoietic stem cell transplantation (HSCT) is an effective therapy for selected
IMDs, particularly when performed early in the disease. The preparatory regimen for HSCT involves myeloablation
using radiation and chemotherapeutic drugs. Given the severe neuroinflammation and increased oxidative stress in
IMDs, an antioxidant, N-acetylcysteine (NAC), is used in association with HSCT. The objective of this study was tocharacterize the pharmacokinetics (PK) of NAC in patients with cALD and other IMDs before and after HSCT.
Methods
The 18 boys (median age 8 years) included in this study received NAC (70mg/kg) infused over 1h and repeatedevery 6h starting approximately 2 weeks before HSCT and until approximately 6 weeks thereafter. Fifteen patients
had cALD, while 3 patients were diagnosed with other IMDs. Intensive PK blood sampling was done on up to threeoccasions (OCC; -7 days, +7 days and +21 days relative to HSCT (day 0)). Plasma NAC was measured using avalidated HPLC-MS method. NAC concentrations were modeled in NONMEM v7.4.3 using FOCE-I. Allometricscaling of body size on PK parameters was assumed using standard coefficients. Various structural andpharmacostatistical models were evaluated. OCC was evaluated as a fixed effect and as random effect (BOV) on CL
and/or V1. Model selection was based on change in objective function value (dOFV) and diagnostic plots.
Significance of covariate additions was set at 0.01 (Chi-square).
Results
NAC PK was best described by a two-compartment model with first order elimination. The estimates for CL, V1,V2 and Q were 13.4 L/h, 16.1 L, 16.1 L, 4.6 L/h, respectively for a 70kg person. HSCT did not change V1 and CL
significantly and analysis across OCC did not reveal any trend. The data only supported BSV in CL (19%) and V1(53%), and BOV in CL (22%). RUV was estimated to be 17% in this setting.
Conclusions
This is the first characterization of NAC PK in children with IMDs undergoing HSCT. A nonlinear mixed-effects
model was used to describe concentration-time profiles of NAC before and following HSCT. These results suggestthat dosing of NAC does not need to be altered following HSCT. This study has limitations associated with a small
sample size that are common with rare diseases.

WED-075
Longitudinal Model Based Meta-Analysis of EASI %Change from Baseline Score, and IGA Responders ofClear (0) or almost Clear (1) for Topical Drugs in Atopic Dermatitis Clinical Trials
Sima Ahadieh1, Steve Martin2, Ye Tan3, Gerald Fetterly3
1
Global Product Development, Pfizer Inc., Groton CT, 2Global Product Development, Pfizer Inc., Cambridge, MA,
3Worldwide Research, Development and Medical, Pfizer Inc., Cambridge, MA, U.S.A
Objectives: To characterize the efficacy and safety profiles of vehicle/placebo and selected topical comparators indevelopment or marketed treatments for atopic dermatitis (AD). In addition, determining the probability of attaining
an EASI and IGA responder target value at specific time points for selected topical agents.
Methods: Meta-Analysis included data obtained from searching Ovid SP and various clinical trials websites. The
search targeted mild to moderate AD patients receiving topically administered ruxolitinib, delgocitinib, difamilast,crisaborole, pimecrolimus, and tacrolimus in randomized vehicle controlled trials measuring endpoints of EASI
(Eczema Area Severity Index) % change from baseline (%CFB), and IGA (Investigator Global Assessment)
responders of clear (0) or almost clear (1) score. The literature was reviewed, and the final selection was extracted andmodeled using nlme package in R Version 3.6.1. An exponential model for EASI %CFB vs. time for vehicle and drug,with an Emax dose-response function describing drug effects was developed. The final model for IGA responder wasa logit scale of exponential versus time for vehicle and drug, with a linear dose response.
Results: A total of 20 studies were included in the analysis. For EASI %CFB, the drugs reached maximal effects atweeks 4 to 6 and for IGA responders of clear or almost clear (0 or 1) this time was 8 weeks. Table 1 summarizes key
results.
There was a 90% probability that most drugs could achieve -40 %CFB in EASI score by week 4 and 70% probability
that some of these drugs could achieve 30% IGA responder rates at week 8.
Conclusions:This model was adequate to predict efficacy of topical drugs for atopic dermatitis. Furthermore, the model provides
information about the minimum treatment duration needed that would be predictive of the time at which maximum
benefit occurs.
Table 1: Predicted EASI %CFB and IGA Responders of 0 or 1

Drug

Predicted EASI %CFB
[95% CI] at 6 weeks

Predicted IGA Responders[95% CI] at 8 weeks

ruxolitinib 1.5% BID-62.9% [-72.1%, -53.8%]

33.5% [21.3%, 42.8%]

delgocitinib 0.5% BID-63.3% [-74.3%, -45.2%]

30.3% [23.3%, 37.6%]

difamilast 1% BID-42.2% [-49.3%, -35.0%]

33.1% [22.2%, 41.9%]

pimecrolimus 1% BID-44.3% [-51.6%, -37.0%]

41.1% [34.1%, 48.5%]

tacrolimus 0.1% BID-57.0% [-65.9%, -46.6%]

51.2% [42.9%, 59.2%]

crisaborole 2% BID

NA

42.9% [20.7%, 56.7%]

vehicle-10.5% [-16.5%, -4.49%]

18.1% [13.9%, 23.1%]

WED-077
Characterization of Clinical Pharmacokinetics and Exposure-Response Relationships of AMG 330, a CD33
Bispecific T-Cell Engager Antibody Construct, in Patients with Relapsed/Refractory AML
Authors: Agarwal SK1, Bhagwat S1, Mehta K1, Hindoyan A2, Stein A3, Ravandi F4, Subklewe M5, Walter RB6, JongenLavrencic M7, Ossenkoppele GJ8, Paschka P9, Khaldoyanidi S10, Dutta S11, Upreti V1
1

Clinical Pharmacology, Modeling & Simulation, Amgen, Inc., South San Francisco, CA, USA; 2Clinical Biomarkers& Diagnostics, Amgen, Inc., Thousand Oaks, CA, USA; 3Gehr Family Center for Leukemia Research, City of Hope,
Duarte, CA, USA; 4Department of Leukemia, The University of Texas MD Anderson Cancer Center, Houston, TX,
USA; 5Department of Medicine III, University Hospital, LMU Munich, Munich, Germany; 6Clinical Research
Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA; 7Erasmus University Medical Center,
Rotterdam, Netherlands; 8VU University Medical Center, Amsterdam, Netherlands; 9Ulm University Medical Center,
Ulm, Germany; 10Amgen, Inc., Early Development, Thousand Oaks, CA, USA; 11Clinical Pharmacology, Modeling& Simulation, Amgen, Inc., Thousand Oaks, CA, USA
Objectives: AMG 330 binds both CD33 and CD3 and redirects T cells toward CD33+ cells leading to T-cell‒mediatedcytotoxicity against AML blasts. An ongoing open label phase I dose-escalation study (NCT02520427) has shown
preliminary activity and acceptable safety in relapsed or refractory (R/R) acute myeloid leukemia (AML) patients (pts)(Ravandi et al. ASH 2018). To characterize the pharmacokinetics and exposure-response (E-R) relationships of AMG
330 in patients with R/R AML.
Methods: A continuous IV infusion of AMG 330 was evaluated at escalating target doses (range from 0.5 to 720
μg/day) using a 3+3 design with pts receiving step dose/s prior to reaching target doses of ≥ 30 μg/day. Population
pharmacokinetics (popPK) using non-linear mixed effects modeling and E-R analyses were conducted to characterize
relationships between AMG 330 exposure (steady-state concentration [Css]) at target dose, the baseline tumor burden,
clinical response per revised IWG criteria and incidence of cytokine release syndrome (CRS).
Results*: As of Dec 10, 2019, 55 patients (males, 56.4%; median age, 58.0 [18.0–80.0] years) were enrolled in 16
cohorts. AMG 330 PK was best described by a one-compartment linear PK model. Dose dependent increases wereobserved in AMG 330 Css exposures. Responders typically showed higher AMG 330 Css than non-responders.
Preliminary exploratory analysis indicated that higher AMG 330 exposures, lower baseline leukemic burden in bonemarrow and CD33+ AML cells in peripheral blood, and higher baseline Effector:Target cell ratio were associated withclinical response. Additionally, a positive relationship was observed for AMG 330 exposures and baseline leukemic
burden (p<0.05) with probability of CRS occurrence and severity. Based on the model, at a baseline leukemic burdenof 20%, a 240 µg/day target dose is predicted to result in 28% and 4% probabilities of developing CRS of grade ≥ 2and ≥ 3, respectively.
Conclusions: Clinical pharmacokinetic profile and E-R relationships of AMG 330 were characterized to identify
optimal AMG 330 step dosing regimens that minimize the risk for CRS in ongoing and planned clinical investigations.
*The results in this abstract have been previously presented in part at American Society of Clinical Oncology 2020
Virtual Annual Meeting and published in the conference proceedings as abstract 7536".

WED-078

Innate and Adaptive Immune Response to SARS-CoV-2
Infection Play Major Role in Disease Severity

Tarunendu Mapder1*, Sara K. Quinney1,2#, Richard F. Bergstrom1§, Robert E. Stratford1¤
1

Division of Clinical Pharmacology, Department of Medicine, Indiana University
School of Medicine, Indianapolis, IN 46202, USA
2
Department of Obstetrics and Gynecology, Indiana University School of
Medicine, Indianapolis, IN 46202, USA

Objective: The rapid transmission of SARS-CoV-2 in a community is manifested by a wide range of symptoms ofcoronavirus disease 2019 (COVID-19) including fever, pneumonia, acute respiratory distress syndrome andlymphopenia. The manifestations of CoV-2 infection can be classified into three different timescales: (i) asymptomatic
incubation, (ii) mild symptoms and (iii) severe respiratory and immune distress, while the variability between
individuals is very high. Unlike other coronaviruses such as SARS-CoV and MERS viruses, SARS-CoV-2 infection
exhibits faster virus shedding and a longer incubation period. Hence, community transmission is faster than the disease
manifestation and detection of infection. We believe the emergence of a “cytokine storm” as the major regulator ofthe disease severity. Our aim is to study the role of nonspecific immune response and cytokine release syndrome indisease severity and hence propose different treatments that seek to reduce the symptoms of COVID-19.

Methods: We calibrated a population of models (POMs) with clinical data including viral load, spike protein
receptor binding domain (RBD) and nucleoprotein (NP) -specific IgM and IgG antibody levels for 23 patients reported
across various days of illness1. The calibration of the POMs is performed by matching the multivariate distributionsof the observed data and model outputs. Optimal distribution matching was used to unlock the underlying variability
in the data for the patient population and predict the regime of intervention. We use a computationally less expensive
Latin Hypercube Sampling based probability distance (symmetric Kullback-Leibler divergence) minimization in
MATLAB 2019b.

Results: Our model indicates that differential feedback regulation among the antigen presenting cells (dendritic cells(DC) and macrophages (MՓ)), helper T-cells (Th1, Th2) and cytokines (IL2, IL4, γ-IFN) contribute to the accumulationof cytokine pool and T-cell exhaustion. When the adaptive immune response is triggered, the activated B-cells (B)start to differentiate into short and long living plasma cells (PS, PL) that produce the IgM and IgG, simultaneously.
Although conventional immunopathology of RNA virus infection suggests that the production of IgG is lagged by
IgM, the clinical data does not exhibit any delay in the case of CoV-2 infection. A decision switch between the twokinds of plasma cells and their conversion was able to model this phenomenon. The switch is controlled by theconcentration of available IgM and IgG levels, viral load and the copies of infected epithelial cells.

Conclusions: We were effectively able to model the time course of viral and immune response following CoV-2
infection. This model may inform development of intervention strategies through monoclonal antibodies, cytotoxic
T-cell gene therapy and inhibitors for cytokine receptors.

References:[1] To KK, Tsang OT, Leung WS, Tam AR, Wu TC, Lung DC, Yip CC, Cai JP, Chan JM, Chik TS, Lau DP. Temporal
pro_les of viral load in posterior oropharyngeal saliva samples and serum antibody responses during infection by
SARS-CoV-2: an observational cohort study. The Lancet Infectious Diseases. 2020 Mar 23.

WED-079
Population Pharmacokinetic- Pharmacodynamic Model of Eribulin Associated Neutropenia in Children with
Refractory or Recurrent Solid Tumors
Thomas R. Larson1, Eric S. Schafer2, Rachel E. Rau2, Charles G. Minard2, Elizabeth Fox3, Brenda J. Weigel4, Joel
M. Reid1
1

Mayo Clinic, Rochester, MN, USA; 2Baylor College of Medicine, Houston, TX, USA; 3Children’s Hospital ofPhiladelphia, Philadelphia, PA, USA; 4University of Minnesota, Minneapolis, MN USA

Objectives: Eribulin mesylate, a synthetic analog of halichondrin B, inhibits microtubule growth without effects on
microtubule shortening and promotes nonproductive tubulin aggregate formation. It has been approved for thetreatment of metastatic breast cancer, for patients who have received at least two prior therapies, and inoperable
liposarcoma, for patients who have received a previous therapy. A phase 1 trial of eribulin was conducted inchildren and adolescents with relapsed or refractory solid tumors1. In support of this study, a population
pharmacokinetic-pharmacodynamic (PK-PD) model for eribulin mesylate associated neutropenia was developed inpediatric patients.
Methods: Eribulin was administered intravenously on days 1 and 8 of a 21 day cycle. Three dose levels (1.1, 1.4,
and 1.8 mg/m2) were evaluated using the rolling six design. Additional patients were enrolled into a PK expansion
cohort at the maximally tolerated dose. A total of 256 eribulin plasma concentrations and 108 absolute neutrophil
counts (ANC) from 22 patients ages 3-17 years and body surface area (BSA) 0.57-2.19 m2 were analyzed using
nonlinear mixed-effects modeling with NONMEM® v7.4 to develop a semi-mechanistic population PK-PD modelof eribulin associated decrease in ANC 2. No patients received myeloid growth factors during treatment.
Results: A dose-independent three-compartment PK model with BSA scaling adequately described thepharmacokinetic data, and was linked to a semi mechanistic PD model with the proliferating cell compartment(PROL) estimated separately (Figure 1). Population typical values (between subject variability) of CL (AGE<12)3.64 L/h (0.355), CL (AGE≥12) 1.79 L/h (0.35), V1 3.15 L (0.12), Q2 1.62 L/h (0), V2 1.77 L (0.22), Q3 5.45 L/h(0), V3 95L (0.16), ANC C0 1310 /ml (0), PROL 1570/ ml (0.17), MTT 190 h (0), Gamma 0.237 (1.08), and Slope
0.0099 µg/ml (0). Consistent with our previous report, children less than 12 years of age had a substantially higher
CL compared to those 12 years and older and no other covariates were found to be significant1. The PK and PK-PD
models sufficiently describe the data as evaluated by goodness-of-fit plots, visual predictive checks, and bootstrap
analysis.
Conclusion: A semi-mechanistic PKPD model was used to describe eribulin mesylate associated neutropenia inchildren. Body surface area and age of the children were found to be significant covariates in the PK of eribulin, andno pharmacodynamic covariates were identified in the model. This model reports similar results to previous models,
but required the addition of separately estimated PROL to capture both trough and rebound ANC in these patients3.
References: [1] Schafer ES, et al. Pediatr. Blood Cancer. 2018 Feb; 65:e27066. [2] Friberg, LE, et al. J. Clin. Oncol.
2002 Dec;20(24): 4713-4721. [3] Coen van Hasselt JG, et al. Br. J. Clin. Pharmacol. 2013 Sep; 76(3):412-24.

Figure 1.

WED-080
The platform physiologically-based quantitative computational model for different size of bispecific T cell
engagers for cancer immunotherapy
Tomoki Yoneyama, Mi-Sook Kim, Konstantin Piatkov, Haiqing Wang and Andy Z.X. Zhu
Takeda Pharmaceutical International, Cambridge, MA, USA.
Objective: Bispecific T-cell engager (TCE) is an emerging anti-cancer modality which redirects cytotoxic T-cells totumor expressing tumor-associated antigen (TAA) thereby forming the immune synapses to exerts anti-tumor effects.
Considering the complexity of bispecific intercellular bridging and associated challenge in molecular design
optimization, a more physiologically relevant computational framework is of crucial importance in TCE discovery
and development. In this study, we developed a quantitative, physiologically-based computational framework to
predict immune synapse formation in tumor considering the biodistribution of different size of TCEs and shed antigen
as well as T-cells extra-vascularization in multiple myeloma.
Methods: The biodistribution of different size of TCEs and shed antigen was estimated using an adapted
physiologically-based two-pore theory model. The developed platform model consists of blood, biodistribution tobone marrow (tumor site), lymph nodes and lumped other tissues, and further integrated with extra-vascularization of
T cells and tumor cells followed by mechanistic bispecific binding of TCE in the presence of shed antigen. The model
was further calibrated using data from clinical stage BCMA-targeting TCEs: AMG420 in BiTE format and PF06863135 in IgG format. All the physiological and TCE related parameters were collected from public domain except
for binding affinities to shed antigen which were estimated against in vitro tumor killing assay in presence of shed
BCMA.
Results: The biodistribution of TCE was verified by positron emission tomography (PET) imaging data for[89Zr]AMG211 (CEA-targeting BiTE). The developed model reasonably reproduced the observed biodistribution of
PET tracer-labelled TCE into bone marrow in patients. The model was further calibrated for AMG420 in terms ofbinding affinities, membrane-associated and shed BCMA expression. The simulation results suggested the number of
immune synapses in tumor site is low (~20/tumor cell) at the clinical efficacious dose of AMG420. The parameter
sensitivity analysis indicated that immune synapse formation was highly sensitive to TAA expression, degree of
antigen shedding and selectivity to cell surface TAA over shed antigen. Interestingly, the model suggested a “sweetspot” for TCE’s CD3 binding affinity which balanced the trapping of TCE in T cell rich organs. The developed
framework was applied to TCE with IgG format in order to determine the dose and dose schedule to achieve the samelevel of immune synapse formation with AMG420 and critical parameters to maximize efficacy.
Conclusions: A quantitative computational framework for TCEs was developed by integrating the biodistribution ofdifferent size proteins based on two-pore theory followed by mechanistic bispecific binding to form immune synapses
in presence of shed antigen. This framework can be applied to other targets beyond multiple myeloma to provide aquantitative mean of molecular design and clinical benchmarking in order to facilitate the model-informed best inclass TCE discovery and development.

WED-082

A unifying approach to sampling NLME model parameters
Venelin Mitov , Daniel Lill1, Mohammed Cherkaoui-Rbati2, Anne Kümmel1
1

1

IntiQuan GmbH, Elisabethenstrasse 23, 4051 Basel, Switzerland

2

Medicines for Malaria Venture, Route de Pré-Bois 20, Post Box 1826, CH-1215 Geneva 15, Switzerland

Objectives: Simulation of models is a key element of model diagnostics as well as a popularmethod for model-based prospective decision making. In the context of NLME models, samplingof the model parameters before simulation is a key prerequisite ensuring that the simulation isconsistent with the estimated parameter uncertainty, individual random variability, covariate
effects and residual error of the model. Despite its importance, to date there is neither acommonly accepted NLME parameter sampling tool, nor a specification for how NLME
parameter sampling should be done. Instead, the parameter sampling is done in a rather adhoc
way, differing between modeler and modeling project; the sampling procedures are scarcely
reported and hard or impossible to reproduce. We present a unifying software approach for
NLME parameter sampling.
Methods: Our proposed solution is based upon the following components:
1. IQRnlmeParamXLS: a new Excel file format providing a clear description of the NLME
model estimation results and all additional information needed for sampling theparameters. Importantly, this format has been designed to be both, programmable aswell as readable by humans.
2. A set of exporter R functions implementing the conversion of model estimation resultsfrom existing tools (e.g. NONMEM, MONOLIX) to IQRnlmeParamXLS.
3. A set of sampler and calculator R functions implementing all steps from sampling
population parameter values from the estimate’s uncertainty distribution to obtaining
individual parameter values for a user specified dataset of individual covariates.
4. A set of validator R functions checking the consistency of the parameter samples with
the IQRnlmeParamXLS file.
Results: In this talk, we present the above approach based on real life PKPD modelingexamples. For a well behaved NLME example, we show that our implementation provides
parameter samples such that the sampled distributions are in line with the distributions defined.
Using other examples, we will highlight an important caveat concerning the sampling of positivedefinite variance covariance matrix parameters. As the uncertainty of the elements of such
matrix-parameters usually is reported as a multivariate normal distribution, there is an inherent
risk of sampling invalid parameter sets, namely, negative-definite matrices. We will present ourcurrent implemented approach to tackle this problem. Finally, we will show how the validator
functions can be used to detect and notify the modeler about mismatches between the sampled
and required parameter distributions.
Conclusions: IQRnlmeParamXLS is a powerful yet human readable format for specifying
NLME model parameters enabling easy sharing of NLME parameter estimation results
independent of the used modeling platform. The implementation of a generic and transparent
parameter sampling tool presents an important step towards improving the quality andreproducibility of NLME model simulations.

WED-083

A Quantitative Systems Pharmacology Model of Alzheimer’s Disease Pathology and Treatment Modalities
Authors: Vidya Ramakrishnan1, *, Christina Friedrich2, *, Colleen Witt2, Meghan Pryor2, Michael Dolton1, Jasi
Atwal1, Kristin Wildsmith1, Katherine Kudrycki2, Robert Sheehan2, Seung-Hye Lee1, Hans Peter Grimm3, Roxana
Aldea3, Norman Mazer3, Carsten Hofmann3, Ronald Gieschke3, Reina Fuji1, Jin Yan Jin1, Saroja Ramanujan1,
Angelica Quartino1*VR and CF are co-first authors

Affiliations: 1Genentech, Inc., South San Francisco, CA, USA; 2Rosa & Co., LLC., San Carlos, CA, USA;3

Hoffmann-La Roche Ltd., Basel, Switzerland.

Objectives:
A quantitative systems pharmacology (QSP) model of Alzheimer’s Disease (AD) pathologies was developed andutilized to assess the impact of investigational Aβ and tau targeting treatments in support of drug development
strategies.

Methods:
The inclusion of the mechanisms of the two defining features of AD pathology: Aβ and tau in the comprehensive QSP
model allows for the benchmarking-based evaluation of therapies targeting the Aβ and tau pathways. The model tracks
the production, aggregation, transport dynamics and clearance of soluble biomarkers involved in these mechanisms inthe brain, CSF, and peripheral plasma, as well as the impact of investigational treatments on these processes.
SimBiology, a MATLAB® based application was used for the implementation of the model. Initial conditions andreaction rate parameters were calibrated based on literature and in-house informed data. The model scope enables
scientific hypothesis assessments of biomarker target engagement in brain/CSF, biomarker transport dynamics, patientphenotype evaluation, therapy benchmarking, and treatment strategies.

Results:
The developed model is a comprehensive platform model describing the currently known key disease mechanisms in
AD. The detailed mechanisms built into the Aβ and tau modules, namely: production, aggregation (monomer—
oligomer—fibril/plaque), and transport allow assessment of diverse therapeutic agents targeting the two pathways.
The model is calibrated to pharmacokinetic and pharmacodynamic/biomarker data in plasma and CSF from publishedand in-house clinical studies evaluating diverse mechanisms of action targeting different aggregated species. For Aβ,
model simulations allowed quantitative assessment of target engagement in brain and CSF, enabling a comparison
between therapies intervening in the Aβ pathway and the effects of different doses, durations, and dosing regimens(flat/titration doses). The model is adequately equipped for a similar assessment within the tau module. The model isalso calibrated to available imaging-based Aβ PET SUVR data directly assessing disease burden. The calibration
provided insights into the possible mechanisms of plaque clearance mediated by plaque targeting antibodies. The
overall long duration of disease progression facilitates the model utility to test several patient phenotypes based onstage of disease progression (prodromal/moderate/severe AD) and evaluate effect of investigational therapies on these
phenotypes.

Conclusions: A calibrated QSP model incorporating a detailed representation of Aβ and tau production, aggregation,transport and clearance was developed. The model facilitated a quantitative assessment of target engagement by
disease-modifying therapies directed towards Aβ and tau pathologies in the brain and CSF. The disease platform
model is a comprehensive tool to test biomarker and therapy-based strategies and scientific hypothesis.

WED-084
Optimization of FIH (First-in-Human) Starting Dose for an HLE-BiTE® (Half-life Extended Bispecific T-cell
Engager Molecule for Patients with Gastric Cancer Using Translational PKPD Modeling
Michael Z. Liao1, Sergey Ermakov1, Roman Kischel2, Sabine Stienen2, Alex Sternjak2, Kathrin Locher2, Michael
Lutteropp2, John Harrold3, Julie Bailis3, Benno Rattel2, Matthias Friedrich2, Khamir Mehta1, Sandeep Dutta4, Vijay
V. Upreti1
1
Clinical Pharmacology Modeling & Simulation, Amgen Inc., South San Francisco, CA, USA; 2Amgen (Munich)GmbH, Munich, Germany; 3Research, Amgen Inc., South San Francisco, CA, USA; 4Clinical Pharmacology
Modeling & Simulation, Amgen Inc., Thousand Oaks, CA, USA
Objectives:
HLE BiTE® antibody construct is a targeted immuno-oncology platform designed to engage patients’ own T cells tokill cancer cells. This work is to provide an innovative approach for selecting a more rational FIH starting dose.
Herein we provide an example of optimizing the starting dose for an HLE BiTE® molecule, integrating preclinicaldata and the specific BiTE mechanism of action for gastric cancer patients that enabled a 10-fold higher starting
dose through translational PK/PD modeling.
Methods:
The selection of starting dose was based on the recommended approach for T cell recruiting constructs using
Minimum Anticipated Biological Effect Level (MABEL). MABEL assays with different effector T cell to tumor cell(E:T) ratios were assessed and an assay that is expected to recapitulate patients with solid tumors was selected.
Based on human PK modeling and simulation, predicted first dose Cmax approximated the optimized MABEL. A two
compartmental human PK model was derived by allometrically scaling PK parameters of the model developed basedon nonhuman primate data. The minimally efficacious exposure range was estimated using both in vitro and in vivo
data. The in vitro prediction was based on the EC90 of the cytotoxicity assay. The in vivo prediction used a mouse
xenograft tumor growth inhibition (TGI) translational PK/PD model that incorporated the time course of tumor
01"#$%&

volume change [0#], as below. The model assumed tumor growth as exponential [Kgrow], drug effect on tumor

described by concentration [C], maximal inhibition [Imax], tumor volume [Vtumor], and half-maximal inhibitory effect[IC50].
𝑑𝑉#2%34
𝐼%"7 𝐶 ∙ 𝑉#2%34= 𝑘5436 𝑉#2%34 −
𝑑𝑡
𝐼𝐶89 + 𝐶
The model estimated the in vivo IC50 for TGI. Exposure - tumor response curves in patients were simulated to
predict the dose range for target trough coverage and tumor tissue distribution.
Results:
The starting dose based on MABEL using typical assay conditions (E:T ratio of 10:1) resulted in a very low dose.
Using an E:T ratio of 1:5 to better mimic physiological conditions resulted in a 10-fold higher starting dose forbased on PK modeling and simulation. This starting dose was supported by favorable nonclinical safety data.
Human PK modeling was used to predict drug concentrations in the tumor tissue, assuming approximately 10% of
the serum exposure. Minimal efficacious dose was set to be in the range between the in vitro EC90 and in vivo IC50
values estimated using the translational PK/PD mouse tumor xenograft model.

Conclusion:
We present an approach that uses translational PKPD modeling to provide a more rational FIH starting dose for
HLE-BiTE® antibody constructs. It enabled a 10-fold higher starting dose compared to previously used methods inpatients with gastric tumors. The clinical starting dose was deemed safe and well tolerated. The current model
adequately predicted the drug exposures observed in patients.

WED-085

Preclinical-to-clinical translation of GalNAc-siRNA using multiscale PK-PD modeling:a case study with fitusiran
Vivaswath S. Ayyar, Dawei Song, Songmao Zheng, Thomas Carpenter, Donald L. Heald
Discovery Translational Research, Biologics Development Sciences, Janssen BioTherapeutics, Janssen Researchand Development, Spring House, PA, USA

Objectives: Recent advances in gene therapy for both rare and prevalent diseases are transforming
precision medicine. Conjugation of small interfering ribonucleic acid (siRNA) to tris-Nacetylgalactosamine (tris-GalNAc) enable highly selective, potent, and durable knockdown of targeted
proteins in liver. However, there is limited mechanistic understanding on how preclinical PK/PD studies
using in vitro systems, mice, and nonhuman primates can be translated to predict clinical responses
apriori as well as guide first-in-human (FIH) dose selection. Many intermediary steps along the PK-PD
cascade are nonlinear and time-dependent, complicating dose-exposure-response relationships for thesedrugs. A multiscale minimal physiologically based pharmacokinetic-pharmacodynamic (mPBPK-PD)
model for GalNAc-siRNA was developed using published data for fitusiran (ALN-AT3SC), an
investigational compound targeting liver antithrombin (AT) to delineate putative determinants governing
their whole-body-to-cellular PK-PD and to facilitate preclinical-to-clinical translation.
Methods: The mPBPK-PD model mathematically linked key PK-PD mechanisms: i) hepatic
biodistribution, ii) tris-GalNAc binding to asialoglycoprotein (ASGPR) receptors on hepatocytes, iii)
ASGPR endocytosis and recycling, iv) endosomal transport and escape of siRNA, v) cytoplasmic RISC
loading, vi) cleavage of target mRNA, and vii) knockdown of protein. Physiological ranges for 38 out of
48 model parameters (including species-dependent volumes and flow rates) were obtained from theliterature. Kinetic parameters for GalNAc-ASGPR binding and internalization were obtained by modelingin vitro uptake experiments in hepatocytes. The data were extracted from published graphs by computer
digitization (WebPlotDigitizer, 4.1). The PK-PD analysis was performed using ADAPT 5 (BMSR). A
global sensitivity analysis (GSA) using the Partial Rank Correlation Coefficient-based method was
conducted in R to deduce the impact of parameter uncertainty on select model outputs.
Results: The proposed model characterized reported PK, RISC loading, and knockdown of AT mRNAand protein upon ALN-AT3SC dosing in wild-type and Asgpr2-/- mice reasonably well. Model
simulations bridged multiple PK-PD datasets in preclinical species (mice, rat, monkey) and successfully
predicted plasma PK and serum AT knockdown in a Phase-I ascending dose study. Estimation of selectedmodel parameters from the mice PK-PD datasets (e.g. liver uptake clearance, cytoplasmic siRNAdegradation rate constant, siRNA-RISC association rate constant) were obtained with acceptable precision(< 30% CV) and these estimates were fixed during model scale-up. The estimated in vivo drug potency
parameters varied within two-fold across species. Subcutaneous drug absorption and serum AT
degradation rate constants were identified retrospectively to scale across species by body weight withallometric exponents of 0.3 and 0.2.

Conclusion: The developed platform mPBPK-PD model successfully predicted clinical PK-PD of siRNAbased on model-based analyses of preclinical in vitro and in vivo data. The proposed modeling framework
can accelerate the development of future siRNA therapies.

Figure 1. Schematic of the platform mPBPK-PD model for GalNAc-siRNA mediated gene knockdown(top left and right) and model-based characterization of plasma PK, liver biodistribution, RISC
occupancy, and mRNA knockdown following a single dose of ALN-AT3SC in mice (serum protein
knockdown not shown here).

WED-086
Pharmacokinetic analysis of Ziltivekimab, anti-Interleukin-6 monoclonal antibody inchronic kidney disease patients
Wansu Park1, Joga Gobburu1, Larry Lo2, Michel Chonchol3 and Matt Devalaraja2
1

Center for Translational Medicine, University of Maryland School of Pharmacy, Baltimore, MD, USA;2Corvidia
Therapeutics, Waltham, MA, USA
2 Corvidia Therapeutics, Waltham, MA 02451
3 Division of Renal Diseases and Hypertension, Anschutz Medical Campus, University of Colorado, Denver CO 80220

Objectives: Ziltivekimab is a human IgG1κ monoclonal antibody directed against interleukin-6 (IL-6) which is being
developed for the treatment of inflammatory sequalae in patients with advanced chronic kidney disease (CKD). The
objective was to describe the pharmacokinetic (PK) profile of ziltivekimab in a Phase I trial of ziltivekimab in CKD
patients using non-compartmental analysis and population PK modeling.
Methods: PK analysis was performed using serum samples from 9 patients following a subcutaneous administrationof single dose of 5, 15, and 50 mg of ziltivekimab. Assessment of the PK included the elimination half-life (t1/2), area
under the curve (AUC), maximum concentration (Cmax), and time to reach Cmax (Tmax) by non-compartmental
analysis (NCA). A compartment PK model was tested to describe the data by nonlinear mixed effects (NLME)
modeling using first-order conditional estimation with interaction as an estimation method. NCA and NLME
modeling were conducted using Pumas version 0.10.0 (www.pumas.ai)1
Results: A total of 135 ziltivekimab concentrations were used for NCA and model development. Ziltivekimab was
slowly absorbed from the injection site reaching the Cmax in approximately 6 days in 5 mg dose group. Mean AUClastwas 21.26, 76.93 and 320.82 day*µg/mL for ziltivekimab 5 mg, 15 mg and 50 mg cohorts, respectively. Mean Cmax
was 0.34, 0.82 and 3.92 µg/mL for ziltivekimab 5 mg, 15 mg and 50 mg cohorts, respectively. Cmax and AUC ofziltivekimab increased with dose in an approximately dose-proportional manner. Mean apparent clearance was 0.23,
0.18 and 0.19 L/day for ziltivekimab 5 mg, 15 mg and 50 mg groups, respectively. Apparent clearance (CL/F) obtainedby NCA were generally similar across the dose groups indicating no evidence of nonlinear PK in the tested dose
range. Ziltivekimab PK was adequately characterized by a one-compartment model with first-order absorption. The
final population parameters by NLME were CL/F, 0.19 L/day; VC/F, 16.5 L; KA, 0.37 1/day. Exploration of therelationship between covariates interested and empirical Bayesian estimates of individual parameters showed that
there was a lack of influence of body weight, body mass index, height, age, serum creatinine, estimated glomerular
filtration rate on clearance, volume of distribution and absorption rate constant of ziltivekimab.
Conclusions: The population PK model adequately described the ziltivekimab PK in CKD patients.
Reference:
1.
Chris Rackauckas, Andreas Noack, Vaibhav Dixit, Yingbo Ma, Patrick Kofod Mogensen, Shubham
Maddhashiya, Simon Byrne, Joga Gobburu, Joakim Nyberg, Vijay Ivaturi. Pumas: High Performance Pharmaceutical
Modeling and Simulation. (In preparation)

WED-087
A Whole-Body Circulatory Model of Neutrophil Dynamics
Wenbo Chen1, Britton Boras2, Mary E. Spilker2, David Z. D’Argenio1
1

Department of Biomedical Engineering, University of Southern California, Los Angeles, CA, USA; 2Pfizer, Inc., San
Diego, CA, USA.
Objectives: To develop a physiological circulatory model characterizing the whole-body distribution of neutrophils
in humans. The model incorporates our current understanding of neutrophil trafficking in organs, includingtransendothelial migration and tissue clearance.
Methods: A circulatory neutrophil model was constructed using previously published 111Indium (111In)-labeledneutrophil tracer kinetic measurements in blood, lung, liver, spleen, and bone marrow (BM) in healthy subjects [1-3].
The circulatory model includes the organs that are primarily responsible for neutrophil hemostasis in health anddisease (venous and arterial blood, lung, liver, spleen, lymph node, BM). Each organ is composed of vascular andextravascular spaces, with neutrophils entering the vascular space of each organ through the arterial blood pool,
followed by transendothelial migration to the extravascular space, where they are then phagocytosed by macrophagesor recycled via the lymphatics to the venous blood pool. The model for the margination process in the blood also
includes the kinetics of circulating and marginated neutrophils in the venous blood. As with our previous model [4],the BM subsystem in this circulatory model includes a cell cycle model of the progenitor cells (PC), which allows fora mechanistic representation of the action of relevant anticancer drugs derived from in vitro BM toxicity studies. The
model also includes granulocyte-colony stimulating factor (G-CSF) kinetics and its regulation, which allows the actionof administered filgrastim or pegfilgrastim support therapies to be modeled [4].
Results: The circulatory neutrophil model adequately described the 111In-labeled neutrophil kinetic data in the bloodand sampled organs over 48 hours reported in [1-3], with R2 ranging between 0.6 and 0.9. The estimatedtransendothelial migration rates and tissue clearance values were comparable to those reported in humans, which
provide confidence in the fitted results. The model was further evaluated by predicting the absolute neutrophil count(ANC), band cell, and segmented neutrophil time course results in healthy volunteers following four dose levels of
pegfilgrastim reported in [5], as well as ANC time course responses following both intravenous and subcutaneousadministration of filgrastim [6]. Additionally, the model-predicted baseline values of G-CSF and various cell types in
the BM (PC, metamyelocyte, band cell, segmented neutrophil) and in the blood (band cell, segmented neutrophil,marginated neutrophil, ANC, G-CSF concentration) are similar to the literature reported values.
Conclusions: The circulatory neutrophil model presented may provide a mechanistic framework for predicting
neutrophil homeostasis and neutropenia following drug treatment in patients with cardiovascular, hepatic, or renal
impairment.
References:[1] Peters et al., Scand J Haematol 34:111-120,1985[2] Saverymuttu et al., Br J Haematol 61:675-685,1985[3] Szczepura et al., Eur J Clin Invest 41:77-83,2011[4] Chen et al., J Pharmacokinet Pharmacodyn 47:163-182,2020[5] Roskos et al., J Clin Pharmacol 46:747-757,2006[6] Krzyzanski et al., J Clin Pharmacol 50:101S-112S,2010

WED-088
Pharmacometric Evaluation of TAK-954 Effects on Gastric Half-Emptying and Colonic Transit in Patientswith Diabetic or Idiopathic Gastroparesis
Chunlin Chen1, Victor Chedid2, George Dukes1, William S. Denney3, Jenifer Siegelman1, Jatinder Muker1, Cristina
Almansa1, Michael Camilleri2
1

Takeda Pharmaceuticals International Co., Cambridge, MA, USA

2

Mayo Clinic, Rochester, MN, USA

3

Human Predictions Cambridge, MA, USA.

Objectives: TAK-954 is a highly selective and potent 5-HT4 receptor agonist with pharmacologic activity ingastrointestinal tract in vitro and in vivo. The objective of this analysis was to characterize the relationship between
TAK-954, gastric half-emptying time, and colonic transit velocity as measured by colonic geometric center (CGC) inpatients with diabetic or idiopathic gastroparesis.

Methods: In a single-center, randomized, parallel group, double-blind, placebo-controlled study, 36 participantsreceived placebo (n=10), 0.1 (n=10), 0.3 (n=9), or 1.0 mg (n=7) of TAK-954 as a 60-minute intravenous infusion oncea day for 3 days. Enrolment into the 1.0 mg arm was stopped after prespecified interim analysis. At baseline, eachparticipant completed 4-hour gastric emptying scintigraphy test. Following infusion on Day 2, scintigraphic
assessment of gastric, small bowel, and colonic transit of solids over 48-hour time period were obtained (same mealplus 111In-charcoal delivered in methacrylate-coated capsule that dissolves in the ileocolonic region) to study effectsof TAK-954. Infusion on Day 3 to maintain plasma levels of TAK-954 while assessing colonic transit. CGC is
measured on 0 to 5 scale based on the weighted proportion of counts in each of 4 colonic regions (ascending,transverse, descending and rectosigmoid) and stool where 0 is in small bowel and 5 is 100% isotope excreted in stool.

Exposure-gastric half-emptying time models were estimated using ‘nlme’ package (version 3.1-140) in R (version
3.6.1). Best model was selected by Akaike’s Information Criterion. CGC models were estimated using 'Stan' package(version 2.10.0) in R (version 3.6.1). Best model was selected by widely accepted information criterion. Final models
were evaluated by visual predictive check (VPC).

Results: TAK-954/gastric half-emptying model was a mixed-effects Emax model with baseline effect. Estimatedconcentration at end of infusion (Ceoi) causing half-maximal decrease of gastric half-emptying time (EC50) is 1262
pg/mL

For CGC model, TAK-954 dose rather than exposure was selected based on model parsimony and the fact that
exposure metrics did not improve the model. TAK-954/CGC model was a zero-inflated beta-regression model withdose- and time-responsive effect on zero inflation, time effect on the beta distribution shape and scale, and Emax effectof dose on beta distribution scale. TAK-954 decreased time in small bowel (decreasing zero inflation metric by a logit
function of dose) and increases colonic transit velocity as measured by CGC (by an Emax function of dose where ED50
was <0.1 mg). In general, the results of the VPC supported predictive ability of the two models.

Conclusions: TAK-954 enhances gastric emptying with an Emax trend, suggesting that maximal receptor agonism wasachieved over this concentration range. All studied doses are at near-maximal effect for colonic transit. These results
support further evaluation of TAK-954 for the treatment of gastric and intestinal motility disorders.

WED-089

Pharmacodynamic modeling of SOD1 reduction in the cynomolgus monkey following
repeat-dose intrathecal injection of antisense oligonucleotide (ASO) tofersen
Xiao Tong1, Ivan Nestorov1
1.

Biogen, Cambridge, MA, USA.

Objective: Tofersen (BIIB067) is an antisense oligonucleotide (ASO) RNase H1-mediated inhibitor ofsuperoxide dismutase 1 (SOD1) messenger ribonucleic acid (mRNA), in development for the treatment of
amyotrophic lateral sclerosis (ALS) with SOD1 mutations. The therapy has been originally designed by
Ionis Pharmaceuticals and is now being developed clinically by Biogen [1, 2]. Tofersen ispharmacologically active in the CNS of cynomolgus monkey following intrathecal injection. The objectiveof the current analysis was to build a population pharmacodynamic (PD) model to investigate the PD
relationship of tofersen in inhibiting SOD1 protein production measured in cerebrospinal fluid (CSF) in
nonhuman primates (NHPs).
Method: Data from a 9-month toxicology study with 36 NHPs following intrathecal doses of placebo, 4
mg 12 mg and 35 mg was first used for the model development. An indirect response model was built
described by the inhibitory effect of tofersen CSF concentration (Ctrough) on SOD1 CSF protein production(Kin). Baseline SOD1 CSF protein was tested as covariate on EC50 and Emax. The model was further
evaluated by including data from a 13-week toxicology study of the same dosing range (N=42).
Results: SOD1 protein reduction measured in CSF was reasonably described by the indirect responsemodel, where tofersen Ctrough acted as inhibition on the SOD1 protein production (Kin). The estimatedturn-over rate (Kout) based on the 9-month toxicity data was 0.033 day-1 corresponding to a SOD1 protein
half-life of 21 days, which was consistent with the previously published value in SILK study[3]. The
estimated EC50 and Emax were 7.45 ng/mL and 0.458, respectively, corresponding to approximately 50%
maximum reduction of SOD1 protein from baseline in monkey CSF. The estimation of EC50 and Emax are
within the observed range from data with reasonable precision,. The baseline SOD1 protein level was nota significant covariate on either EC50 or Emax. The parameter estimates remained consistent whencombining the 9-month NHP toxicity data with the 13-week NHP data, with improved precision on Kout.
Conclusion: The indirect response model developed reasonably described the PD relationship between
tofersen and SOD1 protein reduction in CSF of NHP. This model can be extended to study the PD
relationship in humans and provide guidance to the design of future clinical trials of tofersen.
References
1.
2.
3.

Bennett, C.F., A.R. Krainer, and D.W. Cleveland, Antisense Oligonucleotide Therapies for
Neurodegenerative Diseases. Annu Rev Neurosci, 2019. 42: p. 385-406.
McCampbell, A., et al., Antisense oligonucleotides extend survival and reverse decrement in muscle
response in ALS models. J Clin Invest, 2018. 128(8): p. 3558-3567.
Crisp, M.J., et al., In vivo kinetic approach reveals slow SOD1 turnover in the CNS. J Clin Invest, 2015.
125(7): p. 2772-80.

WED-090
Lymphatic Distribution of Etanercept Following Intravenous and Subcutaneous Delivery to Rats
Xizhe Gao1, Gregory O. Voronin1, Claudia Generaux2, Anne Rose2, Alexander Kozhich2, Gerard Dalglish2, Leonid
Kagan11

Ernest Mario School of Pharmacy, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA

2

Bristol-Myers Squibb, New Brunswick, NJ, USA

Objectives: The goal of this work was to investigate systemic pharmacokinetics and lymphatic recovery of etanercept
following intravenous (IV) and subcutaneous (SC) delivery in the thoracic lymph duct cannulated rat model.

Methods: Rats were divided into 4 groups: (1) IV lymph-cannulated group, (2) IV control group, (3) SC lymphcannulated group, and (4) SC control group (n=3 each). Thoracic lymph duct was cannulated, which allowed for lymph
collection for up to 6 days after surgery. Sham operated rats were used as control. Etanercept pharmacokinetics wasstudied after a single IV or SC (hind leg) dose of 1 mg/kg. Lymph samples were collected continuously (in 30 - 60
minutes intervals) on day 1 and then once daily. Systemic blood was sampled from jugular vein cannula or saphenous
vein. Samples were analyzed by a Gyros-based method. Mechanistic pharmacokinetic model with lymph and bloodabsorption pathways and redistribution of systemically available drug into lymph was developed to capture etanercept
disposition.

Results: In lymph-cannulated rats following IV dosing, etanercept concentration in the lymph was consistently lower
than in serum (e.g., 4-fold at 24 hours). On the other hand, following SC administration, the concentration in the lymph
was significantly higher than in serum (e.g., 11-fold at 24 hours). Lymphatic cannulation reduced the serum AUC (upto 120 hours) by 28% in IV group and by 91% in SC group. SC bioavailability of etanercept in control rats was 65 %.
About 53.9% of the dose was absorbed via lymph pathway and 11.3 % of dose was absorbed directly into the bloodstream. Pharmacokinetic model successfully captured serum profiles and lymph collection data in all groups and allparameters were estimated with sufficient precision.

Conclusions: Lymphatic system plays a major role in absorption of etanercept after SC administration. Furthermore,a significant redistribution of systemically available etanercept into lymph was observed.

WED-091
Pharmacokinetics of Buccal versus Vaginal Misoprostol for Labor Induction at Term
Yana Vorontsova1, David M. Haas1, Kathleen Flannery1, Andrea R. Masters1, David Guise1, Michael Heathman2,
Sara Quinney11

Indiana University School of Medicine, Indianapolis, IN, USA; 2Metrum Research Group, Tariffville, CT, USA

Objectives: The IMPROVE study (NCT02408315) compared the efficacy and safety of vaginal and buccal
administration of misoprostol for full-term uncomplicated labor induction. The current study compares thepharmacokinetics (PK) of misoprostol between vaginal and buccal routes.
Methods: Women ≥ 14 years of age undergoing induction of labor ≥ 37 weeks gestation without significant
complications were randomized to vaginal or buccal administration of misoprostol (25 µg first dose, 50 µg
subsequent) every 4 hrs. Plasma concentrations were collected for the first 8 hours. A population PK model wasdeveloped using NONMEM®.
Results: Data included 469 plasma concentrations from 47 subjects. Considerable differences were observed
between the two routes of administration. A 1-compartment non-linear CL model with IOV on F, IIV on CL, V, Ka,with the covariates of dose on Ka, and route on F and Ka adequately described the data.
Conclusions: This is the first study to compare PK of buccal versus vaginal misoprostol for labor induction at termusing doses recommended in clinical practice. The rate of buccal absorption was 2 times faster for 25mcg and 2.6
times faster for 50 mcg dose than that of vaginal, while bioavailability of vaginal administration was 2.4 times
higher than that of buccal. Differences in bioavailability between routes of administration may explain why vaginaladministration of misoprostol was more effective than buccal administration.
The results in this abstract have been previously submitted to ASCPT Annual Meeting, Marriott Marquis, Houston,
TX, March 18-21, 2020, and published in the conference proceedings as abstract ID:741634.

WED-093

Speed up population Bayesian inference by combining cross-chain warmup andwithin- chain parallelism
Authors: Yi Zhang(1), William R. Gillespie(2), Ben Bales(2), Aki Vehtari(3)Institutions: (1) Metrum Research Group, USA (2) Columbia University, USA (3) Aalto University, FinlandObjectives:
The probabilistic programming language and Bayesian inference engine Stan supports general model specification
and uses dynamic HMC for Bayesian analysis [1]. Torsten is a Stan functions library that simplifies pharmacometricmodeling and extends the range of models that may be implemented [2]. To improve the performance of Bayesianinference of population models, we designed a multilevel parallel scheme combining a cross-chain warmup
algorithm with within-chain parallelisation and demonstrated that this approach significantly improves large PKPD
model simulation efficiency.
Methods:
Torsten provides ODE integrator functions and pharmacometrics solution functions [2]. For population modeling,these functions solve each ODE system corresponding to each individual in parallel. This is augmented with a crosschain warmup scheme that performs dynamic warmup adaptation by chain aggregation. During warmup, the joint
log posteriors sampled from parallel chains are collected and used to calculate potential scale reduction coefficients(Rhat) and effective sample sizes (ESS) [3]. When they surpass predetermined target values, warmup is terminatedto avoid unnecessary computation. We also aggregated samples from chains and used them to calculate and update
the HMC stepsize and mass matrix. The proposed algorithm was implemented using MPI for both inter-chaincommunication during cross-chain warmup and within-chain communication for population model calculations,enabling us to perform large-scale simulations using a cluster and distribute the computation load evenly acrosschains.
Results:
Models that would benefit most from the proposed scheme are those that involve heavy computation in each HMC
leap-frog step. We tested two such models: a hierarchical model with ~400 normally distributed parameters (Model1) and an ODE-based model that involves long integration times (Model 2). The models are fit using default Stansettings of 4 chains, with 1000 post-warmup samples/chain, using either the proposed or the regular Stan approach(1000 warmup iterations). The proposed scheme gives similar performance in terms of ESS-per-iteration, while
reducing the number of warmup iterations and thus wall time significantly (Tables 1).

Table 1: Compare sampling performance of regular run and cross-chain-warmup runs
Performance measure*

Model 1
regular run

Model 1cross-chain run

Model 2
regular run

Model 2cross-chain run

MIN(ESS_bulk /iter)

0.29

0.29

0.23

0.28

MIN(ESS_tail /iter)

0.40

0.41

0.30

0.30

MIN(ESS_bulk /sec)

83

103

10

18

MIN(ESS_bulk /sec)

118

144

13

20

Number Of warmup item

1000

350

1000

350

Warmup wall time(sec)

9.9

6.5

48.0

21.2

Total wall time(sec)

14.1

11.1

91.5

61.9

Conclusions:
Multi-level parallelism using in Stan/Torsten significantly improves computational efficiency and extends the rangeof models that may be practically implemented.4
References:
1. Carpenter et al. Stan: A probabilistic programming language. Journal of Statistical Software, 76(1):1–32, 2017.
2. https://github.com/metrumresearchgroup/Torsten
3. A. Vehtari et al. Rank-normalization, folding, and localization: An improved Rhat for assessing convergence of
MCMC. http://arxiv.org/abs/1903.08008.

WED-094
Safety and Pharmacokinetics of Bintrafusp Alfa With Q3W Dosing: Confirmation of
the Model-Informed Dose Selection
Yulia Vugmeyster1, Lena Klopp-Schulze2, Peter Rueckert2, Akash Khandelwal2, Isabell Speit2, Motonobu Osada3,
Italia Grenga1
1

EMD Serono Research & Development Institute, Inc., Billerica, MA, USA a; business of Merck KGaA, Darmstadt,
Germany; 2Merck KGaA, Darmstadt, Germany; 3Merck Biopharma, Tokyo, Japan; an affiliate of Merck KGaA,
Darmstadt, Germany

Objectives: Bintrafusp alfa, a first-in-class bifunctional fusion protein composed of the extracellular domain of the
TGF-βRII receptor (a TGF-β “trap”) fused to a human IgG1 mAb blocking PD-L1, showed a manageable safety
profile and clinical activity in patients with heavily pretreated advanced solid tumors in phase 1 studies of 1200-mg
every-2-weeks dosing. For combination studies with chemotherapies, which follow an every-3-weeks (Q3W) dosing
cycle, a 2400-mg Q3W bintrafusp alfa dosing regimen was selected using the previously described, model-informed
approach [1]. The objective of this study was to evaluate preliminary safety and pharmacokinetic (PK) data from the2 ongoing studies with a 2400-mg Q3W dose to confirm the model-informed dose selection.
Methods: First-cycle PK data from studies NCT03840915 (n=23; phase 1b/2, stage IV non-small cell lung cancer)
and NCT04066491 (n=6; phase 2/3, first-line biliary tract cancer) with a 2400-mg Q3W regimen were analyzed bynoncompartmental analysis (NCA), exploratory statistical data, and graphical analysis. Graphical comparison ofobserved concentrations/NCA-derived PK parameters vs simulated values from a previously published bintrafusp
alfa population PK model [2] was performed. Safety (NCT03840915, n=8; NCT04066491, n=6) was evaluated by
the Safety Monitoring Committee.
Results: The safety profile in patients treated at a dose of 2400 mg Q3W to date appears to be consistent with that at
lower doses. The observed PK in patients treated with 2400-mg Q3W dosing is in line with the population PK
simulation of a 2400-mg Q3W regimen, confirming model-informed dose selection.
Conclusions: Although experience to date is limited, the observed safety and PK profile in patients treated withbintrafusp alfa 2400-mg Q3W supports model-informed dose selection.
References:1.
2.

Vugmeyster Y, et al. Clin Pharmacol Ther. 2020 Jan 18. [Epub ahead of print].
Wilkins JJ, et al. Adv Ther. 2019;36(9):2414-33.

WED-095

A time to event model to predict overall survival in non-human primates with acute radiation syndrome
Zack Jones1, Per Olsson Gisleskog2, Ari Pritchard-Bell1, Sameer Doshi1
1
Amgen, Inc., Thousand Oaks, CA, USA; 2POG Pharmacometrics, UK.
Objectives: Acute radiation syndrome (ARS) is an acute illness caused by exposure to a high dose of penetratingradiation over a short period of time or by intermediate doses of radiation if the exposure occurs over a longer period.
Hematopoietic subsyndrome of ARS (HS-ARS) is characterized by dose dependent bone marrow depression leading
to lymphopenia, neutropenia, thrombocytopenia, and anemia. Death due to HS-ARS from infection or excessive
bleeding usually occurs within 2 to 3 weeks after exposure. The primary objective of this study was to develop a timeto event model that establishes the relationship between hematological response, following acute radiation exposure,and overall survival (OS) in non-human primates (NHP) in the presence and absence of romiplostim treatment whileaccounting for covariates that may be strong predictors of OS.
Methods: Data used to inform OS model development included both romiplostim and control treated male and female
NHPs (N = 218) exposed to a range of radiation doses (500-750 cGy). Romiplostim was administered 24 hours post
irradiation. Rich sampling provided dense platelet counts over time. Model fitting and development was done using
NONMEM 7.4. Decrease in objective function value (OFV) and visual predictive checks (VPC) were used to assess
the model’s ability to predict OS. Kaplan Meier plots stratified by treatment, radiation dose, and covariates (sex andbaseline weight) were used to explore the effect of radiation and romiplostim treatment on OS of irradiated NHPs andguide model development. Commonly used parametric time to event models were evaluated to describe OS in control
animals including testing the effect of covariates, radiation, and platelet counts as predictors of OS. The best model
for control animals based on objective function and VPC was selected and tested on both control and romiplostim
treated animals with reevaluation of covariates.
Results: The best model structure, determined by drop in OFV and VPC, was a log-logistic survival model with atime-varying hazard related to platelet count (Table 1). The addition of covariates did not provide significant
improvement with respect to drop in OFV or VPC, suggesting that platelet count alone is sufficient to capture any
survival differences due to sex or baseline body weight. Furthermore, the model describes OS over a range of radiation
doses in both control and romiplostim treated animals.

Table 1: Parameter estimates for log-logistic OS model, describing survival as a function of platelet count.
Parameter

Estimate

%RSE

Units

g (base hazard)

0.057

4.5

day-1

k (shape)

6.63

26.8

----

b (slope)-0.02328.5

1/109
cell/L

Conclusions: A log-logistic survival model with a time-varying hazard related to platelet count was able to accurately
capture OS in NHPs with HS-ARS across a wide range of radiation doses, treatment, and sex.

WED-096

Quantitatively Modeling Factors that Influence the Therapeutic Doses of Antibodies
Yu Tang 1, Xiaobing Li 1,2, Yanguang Cao 1,3 #1Division of Pharmacotherapy and Experimental Therapeutics, UNC Eshelman School of Pharmacy, University ofNorth Carolina at Chapel Hill, Chapel Hill, North Carolina. 27599, United States. 2Department ofPharmacy, Shengjing Hospital of China Medical University, Shenyang, China. 3Lineberger Comprehensive Cancer
Center, School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA

Objectives:
Dose selection and confirmation are critical tasks in the development of therapeutic antibodies. Although muchknowledge has been acquired in the past decade, it remains uncertain which factors are relevant and how to selectdoses more rationally. In this study, we (1) systematically evaluate the factors that are associated with the doses
and dose selections across antibodies, indications, target properties, and stages of development and (2) identify
the potentially overlooked factors.

Methods: To evaluate the selected therapeutic doses (TDs) and the influencing factors across diverse classesof antibodies, we developed a quantitative metric, Therapeutic Exposure Affinity Ratio (TEAR), TEAR =log([C:: ]/K ; ) to retrospectively evaluate up to 60 antibodies that have been approved by the FDA or the EMA
from 1995 to 2019. TEAR reflects the ratio between antibody plasma concentrations at therapeutic doses and thetarget binding affinity. TEAR has accounted for the variance from antibody bioavailability, systemic clearance, andtarget affinity, which make the remaining difference in TEARs across antibodies as results of the influence of otherfactors on TDs, including target properties (target baselines, turnovers, and location), antibody mechanisms ofaction, and all other factors on the TDs of antibodies.

Results: Most of the surveyed antibodies (55 out of 60) had TEARs > 2, suggesting that most antibodies had almost
completely saturated the target in the plasma at their TDs. Our results challenged the traditional
perception that antibodies should have high TDs when the target has a high baseline, rapid turnover, and deep
anatomical location. Instead, the mechanisms of action of the therapeutic antibodies were found crucial for theselection of TDs. Among all the antibodies applied in cancer treatment, the ones that trigger immune functions tolyse targeted cells have the lowest TEARs compared with the antibodies with the other modes of action.
Mechanism of action also significantly affected the selection of first-in-human doses and the dose selection
patterns in Phase I and II trials.

Conclusions: Our study offered a quantitative method to support systematic evaluations of antibody TDs and dose
selection patterns, provided some insights into the factors that could have an impact on antibody efficacy and theselections of TDs, and highlighted the importance of the mechanisms of action in the selection of TDs (NIH:
GM119661).

THU-001

Practical Lessons in Designing Pediatric PK Studies
Authors: Abhinav Kurumaddali1, Navin Goyal1
Institutions: 1CPMS, GSK, USA
Objectives:
The precise characterization of a drug’s PK in pediatrics while critical is challenging owing to the patient, study andresource related considerations. It is thus important that the PK data collected is apt for such analyses. Several open
source software (such as PFIM, PopED etc.) based on D-optimal design algorithms are being routinely used todetermine the optimal sampling times and sample size required for conducting pediatric PK studies. The objective of
this work is to highlight practical issues when employing such approaches and to propose solutions that address them.
Methods:
Trial simulations were conducted for a Drug X that follows two compartment PK model (Q/F and V3/F fixed) with
500mg QD dosing regimen studied in a virtual pediatric population of age 2-18 years and six weight bands. The PK
parameters were scaled allometrically and the dose was adjusted by body weight. Both between subject variability(30%CV on KA, CL/F and V2/F) and residual variability (proportional error: 15%CV) were incorporated in thesimulations. A virtual population of 30,000 individuals (5000 per weight band) was simulated to represent the true
population. A thousand trials were generated by sampling N subjects (N/6 per each weight band) per study followedby the estimation of post-hoc steady-state individual PK parameters including KA, CL/F, V2/F, AUC0-24 and Cmax.
Eight study designs (D1-D8) with different sampling times and sample size were evaluated (Table 1) by estimating
precision for key PK parameters. These designs ranged from a PK rich sampling scheme (D1) to practically feasible
designs (D2-D6) based on study site logistical constraints to D7 and D8 where PFIM suggested PK sample timepoints
were included.
Results:
The arbitrarily chosen practically feasible designs (D3 & D4) demonstrated better performance (higher precision,
Table 1) compared to the designs (D7 & D8) suggested by the optimal sampling software tool. Six samples (from 36
subjects, D4) or eight samples (from 24 subjects, D3) provide precision that is highly comparable to that observedwith an extensive PK design (Table 1) and seem sufficient for precise characterization of the drug’s PK in pediatrics.
The analysis also demonstrates that increasing the number of subjects or PK samples may not always result in
substantial improvement in precision i.e. – diminishing returns. In fact, a smaller study with a sparse PK sampling
scheme would provide adequate data to characterize exposures.

Conclusions:
The results highlight that there are several approaches that may provide better performance in PK characterization
than solely relying on use of optimal design tools to assess and recommend pediatric study designs. The proposed
approach allows designing an efficient study that prevents unwarranted drug exposure and minimizes patient burden
while adequately characterizing the PK in pediatric population.

Design

D1
D2
D3
D4
D5
D6
D7
D8

Table 1. Relative bias (RB) and root mean square error (RMSE) of AUC0-t and Cmax for designs D1-D8
Design type
SampleNumberSampling
RB (%)RMSE (%)
sizeoftimepoints (hMedian (90% PI)
Median (90% PI)
sampling
post dose)
AUC0-24
Cmax
AUC0-24
Cmax
timepoints
Extensive PK (for36
150,0.1,0.25,0.5,0.7
13.6
19.4
17.0
24.6
comparison)
5,1,2,3,4,5,6,8,(10.6,(15.4, 24.0) (13.3, 21.9) (19.3, 31.3)
10,18,24
16.8)
Practically feasible
36
8
0.5,1,2,3,5,8,10,2
14.4
20.1
18.2
25.3
arbitrarily chosen
4(11.5,(16.1, 25.2) (14.5, 23.1) (20.2, 32.4)
18.3)
Practically feasible
24
8
0.5,1,2,3,5,8,10,2
14.5
19.9
18.0
25.1
arbitrarily chosen
4(10.7,(15.1, 26.0) (13.6, 23.9) (19.1, 33.4)
18.9)
Practically feasible
36
6
1,3,5,8,10,24
15.4
21.7
19.2
27.5
arbitrarily chosen(12.2,(17.1, 26.8) (15.4, 24.0) (21.5, 36.1)
18.6)
Practically feasible
24
6
1,3,5,8,10,24
15.4
21.7
19.1
27.5
arbitrarily chosen(11.4,(15.8, 28.6) (14.2, 25.5) (19.8, 37.2)
20.0)
Practically feasible
36
4
1,3,8,24
17.3
22.1
21.6
27.9
arbitrarily chosen(13.7,(17.4, 28.0) (17.0, 27.5) (21.7, 36.8)
21.5)
PFIM FW
36
60.25,0.5,0.75,6,
18.1
23.9
22.6
30.3
algorithm
7,24(14.4,(18.8, 29.9) (18.2, 28.4) (23.6, 39.4)
22.6)
PFIM Simplex
36
60.25,0.27,5.98,
17.9
24.1
22.6
30.7
algorithm
6.23,6.35,23.89(14.3,(19.1, 30.0) (17.9, 28.2) (24.1, 39.7)
22.2)

THU-002
A quantitatitive systems pharmacology (QSP) model to evaluate valproic acid induced hyperammonemia andthe benefit of carnitine supplementation
Authors: Alejandra Schiavo (1), Cecilia Maldonado (1), Marta Vázquez (1), Pietro Fagiolino (1), Iñaki F. Trocóniz(2,3), Manuel Ibarra (1)Institution: (1) Department of Pharmaceutical Sciences. Faculty of Chemistry. Universidad de la República.
Montevideo, Uruguay. (2) Pharmacometrics and Systems Pharmacology Research Unit, Department ofPharmaceutical Technology and Chemistry, School of Pharmacy and Nutrition, University of Navarra. Pamplona,
Spain. (3) IdiSNA; Navarra Institute for Health Research, Pamplona, Spain.
Objective: To develop a QSP model for the understanding of the mechanisms behind the valproic acid (VPA) induced
hyperammonemia and the assessment of the benefits related to carnitine supplementation (CS). This adverse effectcorrelates with VPA dosage and has an overall incidence of ~40%, being associated to CNS damage in symptomaticpatients.
Methods: A population pharmacokinetic model for VPA developed in NONMEM 7.4 (Icon plc.) with data from anaverage bioequivalence study was expanded integrating literature data for the kinetics of VPA metabolites (4-en-VPA,
VPA glucuronide, VPA-Carnitine and 2-en-VPA), Carnitine, Ammonia and fatty acids to develop a mechanistic QSPmodel using mlxR 4.1.0 package (Lixoft) in R 3.5.3 (R-Project). Model validation was performed using reported andclinically available data for different VPA dosage regimens, evaluating model accuracy for VPA, 4-en-VPA, ammonia
and carnitine at steady state. For other elements integrating the model for which experimental observations were notavailable, a baseline of 1 was assumed and relative changes were modeled. A local sensitivity analysis was conducted
for critical parameters evaluating the impact of a +/- 10% change on VPA, 4en-VPA, Ammonia and Carnitine mean
steady state concentrations. Effect of VPA dosage over ammonia levels and CS antidote efficacy (2g/12h PO) was
assessed simulating from the final model.
Results: A QSP model comprising 17 ordinary differential equations relating, through 38 reactions, the elevenelements of the system, was built. The prediction errors were: VPA (-6.6% single dose, -3.0% multiple dose), 4enVPA (+7.0%), Carnitine (+2.5%) and Ammonia (-12%). The model resulted robust to changes in parameter values.
Assuming a normal range of 15-95 uM for Ammonia in plasma, the hyperammonemia predicted incidence for threedifferent VPA dosage regimens was in accordance with previous reports: 14% (500q12), 32% (500q8) and 69%(1000q12). To reproduce clinical observations, a mechanism involving a Carnitine induced Glutamate formation
through Acetyl-CoA was included. Secondary to CS, a decrease in VPA and 4-en-VPA levels is predicted, with asubsequent fast decrease in Ammonia and Glutamate levels.
Conclusions: A mechanistic QSP model describing the VPA-Ammonia pathway was developed. Simulations support
the use of CS as an antidote for VPA-induced hyperammonemia.
References[1] M. Vázquez et al., “Hyperammonemia associated with valproic acid concentrations”, BioMed ResearchInternational, vol. 2014, Article ID 217269, 7 pages, 2014.
[2] P. Harper et. al., “Pharmacokinetics of Intravenous and Oral Bolous Doses of L-Carnitine in Healthy Subjects”,
Eur J Clin Pharmacol., 35:555-562, 1988.
[3] R. S. Addison et. al., “Steady State dispositions of valproate and diflunisal alone and administrated to healthy
volunteers”, Eur J Clin Pharmacol., 56:715-721, 2000.

THU-003

Modeling across scales: Application of biophysical, PBPK, and mechanistic models for dose prediction of
inhaled anti-infective therapies to inhibit SARS-CoV-2 viral kinetics
Allison Claas, Domenico Bullara, Thomas Dimke, Xiaojun Ren, Monish Jain,
Suzanne Gaudet, Jeff Kearns, Andreas Kuttler, Birgit Schoeberl

Novartis Institutes for BioMedical Research. Cambridge, MA USA

Objectives: In response to the pandemic caused by the SARS-CoV-2 outbreak, there has been a rapid uptick in thenumber of clinical trials aiming to combat the virus. As viral pneumonia is among the severe responses to SARSCoV-2 infection, inhaled delivery is an attractive avenue for delivering anti-infective treatments directly to the lung.
In the absence of deep biological understanding and clinical experience treating the virus, mathematical modeling
becomes critical for ensuring that anticipated safety and efficacy thresholds are met in patients. Here, we develop an
integrative modeling framework for predicting anti-viral response after inhaled anti-infective drug delivery.

Methods: To model the delivery to the lung after drug inhalation, we employ a biophysical model describing theunderlying architecture and properties of the lung. Combined with a physiologically based pharmacokinetic (PBPK)
pulmonary compartment and transit model validated with systemic clinical pharmacokinetic (PK) data, we predictthe lung and systemic drug kinetics. To model the anti-infective effect, we combine the pharmacokinetic predictions
with models for the in-host viral kinetics of SARS-CoV-2, parameterized by recently published data. To understandthe impact of model selection on anti-viral treatment prediction, we employ a family of model structures
representing varying complexity and distinct mechanisms of viral kinetics.

Results: Systemic PK reported in the literature was well described by the integrated biophysical and PBPK model.
Combined with the family of viral kinetics models, we find that the timing of dose-initiation is critical to reducing
viral load to significant levels compared to untreated, while there are subtleties in the predictive behavior of thedifferent model structures. Further, we find concordance between viral inhibition and a PK based time above EC90
metric. Because of this, frequent dosing was predicted to be required to maintain the anti-viral effects.

Conclusion: Through an innovative integration of biophysical, PBPK, and viral kinetics models, we leveraged themulti-scale knowledge about drug and virus behavior to generate highly mechanistic predictions. This enabled amodel-based dose-regimen selection optimized for anti-viral effect after inhaled drug delivery.

THU-004

Translational modelling of innate immune response following lipopolysaccharide challenge:
from piglets to humans
Anders Thorsted1, Elisabet I. Nielsen1, Sven Benson2, Miklos Lipcsey3, Peter Matzneller4, Markus Zeitlinger4,
Matthijs Kox5, Peter Pickkers5, Lena E. Friberg1
1

Department of Pharmaceutical Biosciences, Uppsala University, Uppsala, Sweden

2

Institute of Medical Psychology & Behavioural Immunobiology, University Hospital Essen, Essen, Germany
Department of Surgical Sciences, Uppsala University Hospital, Uppsala, Sweden

3
4
5

Department of Clinical Pharmacology, Medical University of Vienna, Vienna, Austria
Department of Intensive Care Medicine, Radboud University Medical Center, Nijmegen, The Netherlands

Objectives: Lipopolysaccharide (LPS), a component of the plasma-membrane of Gram-negative bacteria, induces the
mammalian immune response through toll-like receptor 4 stimulation. Consequently, LPS administration is utilized
in studies of innate immune response both in vitro and in vivo, including in healthy volunteers [1]. The objective wasto assess if and how pharmacometric models developed from piglet studies could be translated to predict time-courses
observed in human LPS challenge studies.
Methods: A pharmacometric framework [2,3] based on data from 116 piglets, infused with E. coli LPS O111:B4 in
rates from 63 to 16,000 ng/kg/h for up to 30 hours, was the basis for translation. Human data for tumor necrosis factor
alpha (TNFα), interleukin 6 (IL6), heart rate (HR) and mean arterial pressure (MAP) was available from 230
volunteers. Observations were made after intravenous LPS bolus (0.4, 0.8, 1.0 and 2.0 ng/kg) or continuous infusion(1.0 ng/kg bolus followed by 1.0 ng/kg/h for three hours).
Time-related variables (e.g. turn-over rates) were scaled allometrically by weight (fixed exponents) while speciesdependent variables, such as cytokine and physiological baselines, were re-estimated. As humans are known to be
LPS sensitive, estimation of species-differences in key exposure-response parameters were explored (e.g. the LPSTNFα relation).
Results: Allometric scaling of time-related variables and refit of baseline parameters adequately predicted MAP, but
underpredicted the time-courses of TNFα, IL6 and HR. As refinements, the TNFα potency parameter (EC50) was reestimated, resulting in a human estimate of 44.3 EU/L (15% of piglet estimate). For prediction of IL6, the stimulationfrom TNFα was re-parameterized to describe a surge, with the surge amplitude governed by the TNFα increase relative
to baseline. For HR and systemic vascular resistance (SVR), EC50’s of 5.27 and 0.598 EU/L (2% of piglet estimates)
and an inhibitory Emax for SVR of 0.778 (2.73 times piglet estimate) were estimated. These changes improved theperformance of the model to predict the time-courses of TNFα, IL6, HR and MAP across all the doses studied inhealthy volunteers.
Conclusions: The experimental model structure worked well to predict the observed median time-courses of TNFα,IL6, HR and MAP following LPS administration in healthy volunteers. LPS was more potent at producing an immune
response in humans, compared to piglets, and quantification of differences in exposure-response relationships between
species – three potencies, one maximum effect and one slope – were required. The work illustrates how anexperimental model can be used to predict human time-courses, exemplified with biomarkers relevant for sepsis andanti-inflammatory research. The developed translational model can potentially be used to inform future study designs
and for bridging experimental results to humans.

References[1] Suffredini AF. Clin Pharmacol Ther. 2014;96(4):418-422.
[2] Thorsted A. PLoS ONE. 2019;14(2):e0211981.
[3] Thorsted A. Uppsala: Acta Universitatis Upsaliensis. 2020.

Figure: Final human model schematic, scaled from piglet studies. Administered LPS (CETX) is linked to
predict time-courses of two cytokines (TNFα, IL6) and two cardiovascular variables (HR, MAP), withrefinements indicated by parameters: TNFαf,hu, IL6f,hu, SVRf,hu, CVf,hu, IL6T0, and IL6SW.

THU-006
Estimating Impact of Acute Radiation Exposure on PKPD of Romiplostim in Rhesus Monkeys
Ari Pritchard-Bell1, Zack Jones1, Sameer Doshi1
1

Amgen, Inc., Thousand Oaks, CA, USA

Objectives: Acute radiation syndrome (ARS) is caused by exposure to a high dose of penetrating radiation over ashort time or by intermediate doses of radiation if the exposure occurs over a longer period. Hematopoietic
subsyndrome of ARS (HS-ARS) is characterized by dose dependent bone marrow depression leading to lymphopenia,
neutropenia, thrombocytopenia, and anemia. Death due to HS-ARS from infection or excessive bleeding usuallyoccurs within 2 to 3 weeks after exposure. Duration of thrombocytopenia is a predictor of mortality in lethally
irradiated animal models, suggesting that a treatment for thrombocytopenia may increase survival in patients with HSARS. Our objectives are to integrate time course PK and platelet data from healthy/irradiated nonhuman primates(NHP) dosed with romiplostim and characterize the effects of radiation on the hematopoietic system using amechanistic PKPD model. This work will be linked with a survival model to predict survival benefit of romiplostim
in humans with HS-ARS.
Methods: We use mathematical modeling to bridge romiplostim PK and platelet dynamics from healthy NHP with
irradiated NHP with ARS receiving romiplostim rescue. Data from four irradiated NHP studies were combined withdata from healthy NHPs who received a single dose of romiplostim. Previously published models of romiplostim
PKPD in NHP and humans were adapted to bridge the data. Modifications to the model were made to characterize theeffects of acute radiation on romiplostim PKPD.
Results: A mechanistic PKPD model describes the effects of radiation on romiplostim PK and PD in NHP. The effectsof acute radiation on platelet dynamics were estimated using data from irradiated NHP across a range of acute radiation
exposure from 500 to 750 cGy. Subsequently, the effects of romiplostim dosing on platelet dynamics following
radiation exposure were estimated. Age, body weight, and sex were investigated as possible covariates and as potential
prognostic or predictive factors for hematopoietic response following irradiation.
Conclusions: The mechanistic PKPD model describing the effects of radiation on romiplostim PK and PD in NHP
can be used to predict acute radiation effects on platelets and survival in humans. The typical values of the radiationspecific model parameters obtained from NHP will be scaled to describe the thrombopoiesis and survival effects ofacute radiation in humans. Given the lack of human platelet data following acute radiation exposure, the acute radiation
model parameters derived from NHP can be calibrated to describe the historical mortality data from humans exposedto acute radiation (in absence of romiplostim) reported in the literature. This work combined with a model linking
survival to platelets in NHP (see abstract by Jones et al. for additional detail) can be used to predict overall survival
benefit in irradiated humans receiving romiplostim treatment.

THU-007

Nonlinear Dynamics Analysis within the Context of Cancer Combination Therapy
Aymen Balti1, Didier Zugaj2, Frédérique Fenneteau1, Guillaume Bonnefois2, Julie Desrochers2, Pierre-Olivier
Tremblay2, Fahima Nekka1,3,4
1

Faculté de Pharmacie, Université de Montréal, Montréal, Québec, Canada; 2Clinical Pharmacology, Syneos Health;3

Centre de recherches mathématiques, Université de Montréal, Québec, Canada; 4Centre for Applied Mathematics in

Bioscience and Medicine (CAMBAM), McGill University Montréal, Québec, Canada.

Objectives: Therapies combining immunotherapy with more traditional therapeutic approaches,
including chemotherapy and radiation, are increasingly being used. These combinations are
purposed to amplify the immune response against the tumor cells and modulate the suppressive
tumor microenvironment (1). In order to get the best performance from these combinatorial
approaches and derive rational regimen strategies, a better understanding of the interaction of thetumor with the host immune system is needed. Quantitative systems pharmacology (QSP) proved
to be a powerful tool to elucidate the underlying pathophysiological complexity that is intensified
by biological variability and overlapped by the level of sophistication of dosing regimens in termsof dosing and combinations. The objective of the current work is to provide new insights for the
dynamics of immune-mediated tumor environment and its modulation by combination therapy that
includes radiation and anti-PD-1/L1 treatment.
Methods: Based on a recent QSP model by Kosinsky et al. (1) to reproduce tumor size dynamics
upon administration of radiation therapy and immune-oncology treatment, we here revisit thismodel in order to perform an in-depth mathematical analysis of its representative components,
expressed as ordinary differential equations. A bifurcation analysis of the model is performed andthe model stability is analysed.
Results: The bifurcation analysis led to the identification of equilibria points, stability properties
and basins of attraction. These system characteristics are informative on the fate of therapy indifferent pathological conditions. The dynamical behavior of model outcomes, including volume
growth with time, is investigated with respect to model parameters. The ability of T cells to
infiltrate tumor tissue, originally identified by Kosinsky et al. as responsible for individual
therapeutic variability, is shown here to be a saddle-node bifurcation point for which the dynamicalsystem oscillates between two states: tumor free, or maximum growth tumor, depending on theinitial tumor size (or onset of therapy).

Conclusion: Adopting a dynamical system analysis, we showed that bifurcation theory can bringcomplementary information to the understanding of complex systems of immunotherapy. The
proposed analysis is appealing for its mathematical foundation and interpretation and can beapplicable to rationalize therapy combination. The involvement of more traditional
biomathematical scientists in quantitative pharmacology will lead to a synergetic collaboration
between QSP-based and dynamical systems approaches.
Reference:
1- Kosinsky et al. Radiation and PD-(L)1 treatment combinations: immune response anddose optimization via a predictive systems model Journal for lmmunoTherapy of Cancer(2018) 6:17. https://doi.org/10.1186/s40425-018-0327-9
Figure: Tumor volume with time, for different values of T cell infiltration that determine thetumor evolution towards a tumor free or a maximum growth tumor state.

THU-008
Magrolimab Treatment Does Not Alter Rituximab Pharmacokinetics in Patients with Non-Hodgkin Lymphoma
Bing Wang1, Denise Jin2, Mwe Mwe Chao2, Chris Takimoto2, Mark Chao2, and Balaji Agoram2
1

Amador Bioscience Inc, Pleasanton, CA

2Forty Seven Inc, Menlo Park, CA

Objectives: Magrolimab is an investigational anti-CD47 antibody in Phase 2 trials for treating relapsed/refractory(R/R) non-Hodgkin lymphoma (NHL) patients in combination with rituximab. The objective of this analysis was toevaluate the population pharmacokinetics (PK) of rituximab in combination with magrolimab and test for presenceof drug-drug interaction between magrolimab and rituximab.
Methods: In a Phase 1b/2 study (5F9003; NCT02953509), NHL patients, including diffuse large B-cell and follicularlymphoma, received magrolimab in combination with rituximab. Sparse PK of rituximab were collected before firstdose and at pre-designated timepoints during the study. The PK data of rituximab was analyzed using a population
approach. Approximately 50% of patients in the study had quantifiable rituximab concentrations at baseline due toprior treatment with rituximab.
A previously published model of rituximab population PK [1] was used to simulate rituximab concentrations in
5F9003 study. In this model, rituximab was eliminated via parallel first-order and nonlinear clearances and thenonlinear clearance decreased after start of rituximab treatment due to loss of CD20-bearing B-cells. To accountfor time-dependent increase in rituximab clearance after end of previous treatment prior to study 5F9003, a
cumulative Weibull function was used to describe recovery of nonlinear rituximab clearance during treatment
holiday prior to start of 5F9003. The disposition parameters of rituximab were fixed to values previously reported[1]. The observed rituximab concentrations in study 5F9003 were compared with the model simulations.
Results: The PK dataset contains 518 quantifiable rituximab concentrations from 56 NHL patients. The serum
concentrations of rituximab in NHL patients with quantifiable baseline values were underpredicted when priorrituximab treatment was not accounted for. The inclusion of a cumulative Weibull function to describe theunderlying recovery of nonlinear clearance during the rituximab holiday substantially improved the overall model
fitting. The observed rituximab concentrations during the Phase 1b/2 study of magrolimab were well predicted bya rituximab PK model previously developed for NHL [1].
Conclusions: A novel approach was developed by incorporating prior rituximab treatment history in a model todescribe the observed rituximab PK in a Phase 1b/2 study of magrolimab in patients with NHL. The observed
rituximab concentrations were well predicted by a model with disposition parameter values fixed to literature
values, suggesting the PK of rituximab was unaltered by the concomitantly administered magrolimab.
References:[1] Levi, M., et al, Characterization of the time-varying clearance of rituximab in Non-Hodgkin’s lymphoma patientsusing a population pharmacokinetic analysis, ACoP Annual Conference, 2008.

THU-009
Mechanistic PK Modeling to Characterize Relationship of Acetaminophen Exposure Between
Blood and Sweat
Authors: Bradley Miyagawa1,2, Tomoyuki Mizuno1,3, Bill Buggele4, Gavi Begtrup4, Alexander A. Vinks1,3
1

Division of Clinical Pharmacology, Cincinnati Children’s Hospital Medical Center, 2Division of Pharmaceutical
Sciences, James L. Winkle College of Pharmacy, University of Cincinnati, 3Department of Pediatrics, University ofCincinnati College of Medicine, 4Eccrine Systems, Inc., Cincinnati, OH, USA

Objectives: Drug exposure monitoring is an integral part of therapeutic drug monitoring (TDM), but the burden ofcollecting blood samples is a major hurdle, especially in children. Recently, innovative wearable biosensors havebeen developed to quantitate substances in sweat that potentially serve as surrogates for circulating analyte
concentrations in blood [1]. This technology has the potential to provide clinicians personal PK data for select small
molecule drugs via an immediate reading of drug concentration in secreted eccrine sweat. Acetaminophen has been
proven as detectable in sweat secretions. The aim of this investigation was to develop a mechanistic PK model toestablish a relationship between acetaminophen concentration and exposure in sweat and in blood.
Methods: Time-matched blood and sweat samples were collected from 4 subjects. Each subject took 2 separate
single doses (500 mg or 1000 mg) of oral acetaminophen tablets, resulting in 8 profiles for each of blood and sweat
concentrations. Blood samples were obtained via a finger prick. Sweat samples were obtained using a sweat
collection device developed by Eccrine Systems, Inc. ®. Blood and sweat concentrations were determined by
ELISA. Edsim++ software (Mediware, Prague, Czech Republic) was used to develop a structural PK model todescribe both blood and sweat concentration profiles and estimate PK parameters.
Results: The structural model that best captured the clinical data had the following properties: two oral absorption
routes described by transit compartments reflecting the observed delay and bi-phasic absorption, twocompartmental-distribution, and inclusion of a virtual “sweat” bio-compartment linked from the centralcompartment with a first-order rate constant. Substantial inter-individual variability was observed in each PK
parameter estimate. Despite large inter-individual variability in blood and sweat concentration profiles, theacetaminophen concentrations and AUC estimates in sweat were correlated with those in blood, respectively(R2=0.65 and 0.84). The mean AUC ratio of sweat to blood was 20% (range: 16-26%).
Conclusions: A population PK modeling approach was utilized to characterize the relationship between sweat andblood concentration profile of orally-administered acetaminophen. A strong correlation was discovered between theacetaminophen AUC estimates in blood and sweat, indicating a promising future for sweat-monitoring technology incombination with PK model-based prediction as a non-invasive method of drug exposure monitoring for precision
dosing.
Reference: 1. Heikenfeld et al. Nat Biotechnol. 2019 Apr;37(4):407-419.

THU-010
Population Pharmacokinetics of Olanzapine and Samidorphan When Administered in Combination in
Healthy Subjects and Patients With Schizophrenia
Lei Sun,1 Richard Mills,2 Brian M. Sadler,3 Bhaskar Rege1
1
Alkermes, Inc., Waltham, MA, USA; 2ICON, Marlow, UK; 3ICON, Gaithersburg, MD, USA
Objectives:
A combination of the antipsychotic olanzapine and opioid receptor antagonist samidorphan (OLZ/SAM) is indevelopment for the treatment of schizophrenia and bipolar I disorder and is intended to provide the establishedantipsychotic efficacy of olanzapine while mitigating olanzapine-associated weight gain. Population
pharmacokinetics (PopPK) analyses were conducted to determine olanzapine and samidorphan PopPK parameters,
their associated variability, and covariate effects.
Methods:
PopPK models were developed for olanzapine and samidorphan using nonlinear mixed-effects modeling based ondata from 10 studies of OLZ/SAM and 1 study of samidorphan in healthy subjects and patients with schizophrenia.
Results:
PK of both olanzapine and samidorphan were linear across the 5- to 30-mg dose range and best described by a 2compartment disposition model with first-order absorption and elimination and a lag time for absorption. Population
estimates of CL/F and Vc/F were 15.5 L/h and 656 L for olanzapine and 35.4 L/h and 297 L for samidorphan,respectively. Inter-individual variability estimates of CL/F and Vc/F were 43.2% and 35.6% for olanzapine and29.4% and 23.3% for samidorphan, respectively. The effects of age, sex, race, smoking, and body weight onolanzapine PK based on PopPK analysis were consistent with the reported findings for olanzapine. Samidorphan PK
was not affected by age, sex, race, or smoking; the impact of body weight on samidorphan exposure was predicted
to be small and not considered clinically meaningful. The effects of hepatic and renal impairment on the PK ofolanzapine and samidorphan, estimated by PopPK analyses, were consistent with the findings from clinical studies
in subjects with hepatic or renal impairment.
Conclusion:
PopPK for olanzapine and samidorphan was characterized with covariates identified that explain a portion of theinter-subject PK variability.
This abstract was published in Clin Pharmacol Ther. 2020;107(Suppl 1):PIII 070 following acceptance to theAmerican Society for Clinical Pharmacology & Therapeutics (ASCPT) 2020 Annual Meeting, but it was notpresented at ASCPT 2020 due to a COVID-19−related conference cancellation.

THU-011
Population PK and exposure-response analyses supported the first approval of rituximab in pediatrics with
Granulomatosis with Polyangiitis (GPA) and Microscopic Polyangiitis (MPA).
Authors Candice Jamois1, Jacques Gaudreault2, Clarisse Chavanne1, Melissa Cheu3, and Leonid Gibiansky4.
Institution(1) Clinical Pharmacology, Pharmaceutical Sciences, Pharma Research and Early Development, Roche
Innovation Center Basel.
(2) JJG Pharma Consulting, Basel, Switzerland(3) BioAnalytical Sciences, Genentech Inc., South San Francisco, CA(4) QuantPharm LLC, North Potomac, MD, United States

Introduction: Rituximab (Rituxan/MabThera®, RTX), an anti-CD20 monoclonal antibody (mAb), is approved fortreating adults with GPA and MPA (systemic autoimmune conditions affecting blood vessels) based on the results of
RAVE (Induction) and Mainritsan (Maintenance) trials. This work reports the modeling work that supported a full
extrapolation strategy from adults to pediatrics1.
Methods: A population PK model developed in 122 adults from RAVE was updated with data from 25 pediatricpatients from Study PePRS who received at least 4 weekly RTX IV doses of 375 mg/m2.
Cumulative area under the concentration-time curve over the six-months Induction (AUC0-180) estimatedusing actual dosing history and individual PK parameters was used to assess (1) similarity in exposure between
children and adults; (2) similarity in exposure, -B-cells response, -probability of Month 6 remission, and probabilities of occurrence of adverse events relationships.
Finally, simulated exposure was compared between adults and pediatrics to support the extrapolation tochildren from 2 to < 6 years old (yo), age group not included in PePRS, and a proposal for a standardized
maintenance regimen in pediatrics.
Results: A linear 2-compartment model described RTX PK. Model parameters were in the typical range for mAbs
(Table 1).
In both adults and pediatrics, RTX induced a rapid and prolonged B-cell depletion across the whole rangeof exposure with B-cell depletion lasting longer in patients with higher exposure. Similar to adults, no association
was found between variability in exposure and clinical efficacy and safety endpoints in pediatrics, suggesting theInduction regimen resulted in exposure at the plateau of the ER relationship.
Known features of FcRn and IgG biology in children, the strong relationship between RTX CL and BSA,
with similar AUC0-180 in children compared to adults across the entire range of BSA and supportive PK simulations
suggested that RTX disposition in ≥ 2 to< 6 yo pts is expected to be similar to the one assessed in children >6 yo.
In addition, simulations showed that a 250 mg/m2 dosing regimen in children is expected to result in comparable
exposure to adults treated with the approved follow-up dose of twice 500 mg administered 2 weeks apart.
Conclusion: In summary, this work supported the regulatory approval of the first pediatric indication for
MabThera/Rituxan. An induction regimen of 4 weekly IV infusions of 375 mg/m2 is recommended in children >2
years of age with GPA or MPA, in US and Europe. A follow-up dosing regimen consisting of two 250 mg/m2 IV
infusions separately by two weeks, followed by a 250 mg/m2 IV infusion every 6 months, is also approved in theUS.

References1.

Mulugeta Y, Barrett JS, Nelson R, et al. Exposure matching for extrapolation of efficacy in pediatric drug
development. J Clin Pharm 2016; 56(11):1326−34.

THU-012
A Bayesian Approach to PK/PD Modeling – A Comparison of NONMEM, Stan, Python, and Julia
Casey Davis, Sandeep Dutta, Khamir Mehta
Clinical Pharmacology, Modeling and Simulation, Amgen Inc, South San Francisco, CA
Objectives:
From the design of clinical trials to the analysis of data, Bayesian methods are becoming more common and more
important in the drug-development cycle. They provide an infrastructure to coherently incorporate information fromprevious experiments and/or expert knowledge and provide a natural probabilistic interpretation of uncertainty.
Traditionally, computational difficulties have limited the use of Bayesian methods in PK and PK/PD modeling.
However, the increase in computational power and the implementation of the No-U-Turn Sampler (NUTS) to
perform Markov Chain Monte Carlo (MCMC) sampling on various software platforms has allowed for estimation
with models from simple compartmental PK models to PopPK models to complex TMDD models where Bayesianestimation with older software implementing a Gibbs sampler and Metropolis-Hastings algorithm would have been
unreliable or impossible. The goal here is to compare the results and efficiency of the NUTS implementation in
Stan1, NONMEM2, Python with PyMC33, and Julia with Pumas4.
Methods:
Population data sets from a two-compartment model with first-order absorption and varying levels of between
subject variability and residual variability are simulated, and identical models are fit with each of the software
platforms. The accuracy and precision of the estimates are analyzed to compare the ability to recover the true
parameters, and analysis of convergence of the MCMC chains is used to compare robustness of each platform.
Results:
Our results show that Stan, Python, and Julia perform similarly and slightly better than NONMEM at recovering thetrue parameters as measured by root mean squared errors (RMSEs). The robustness of all platforms are similar, witheach of them occasionally, but rarely, failing to properly explore the posterior parameter space.
Conclusions:
Stan, NONMEM, Python (with PyMC3) and Julia (with Pumas) are solid platforms to perform Bayesian inference
on PopPK models. Stan and Python are more flexible environments, with more distributions available to specify thepriors and the ability to write a model and all of its components in any way desired, and both have large user
communities, while NONMEM and Julia with Pumas are more tailored to PK/PD-specific modeling.
References:
1. https://mc-stan.org
2. NONMEM Users Guides
3. https://docs.pymc.io/
4. https://pumas.ai/

THU-013
Development of a Minimal Physiologically-Based Pharmacokinetic-Pharmacodynamic Model to UnderstandCellular Kinetics of CAR-T Cells following Local Regional Deliveries
Chia-Hung Tsai, Cindy Xia, Haiqing Wang
Takeda Pharmaceutical Company Limited, Cambridge, MA, USA

Objectives:
CAR-T cell therapies have revolutionized the treatment of hematologic malignancies and are striving for solid tumor
treatment. To overcome limited distribution of CAR-T cells to solid tumor, local CAR-T deliveries have been tested
and shown promising therapeutic outcome in some cases. It is, therefore, of great interest to understand the impactof dosing routes on cellular kinetics, distribution, and subsequent proliferation in a quantitative fashion, which may
provide insights into CAR-T efficacy and safety profile, and facilitate design of next generation CAR-T to overcome
immunosuppressive tumor microenvironment to improve its efficacy.
Methods:
A minimal physiologically-based pharmacokinetic (mPBPK) model describing CAR-T cell distribution was
combined with pharmacodynamic (PD) components to delineate CAR-T cell proliferation, tumor growth, and tumor
cell killing after initial distribution. This model was used to investigate the differential responses resulted fromsystemic intravenous administration, intrapleural delivery and local liver delivery where a pleural tumor and a liver
tumor were present, respectively.
Results:
The developed mPBPK-PD model reasonably captured published anti-mesothelin CAR-T cellular profiles in
response to intravenous and intrapleural administrations to mice with a pleural tumor, where intrapleuraladministration led to earlier CAR-T cell proliferation and faster tumor growth inhibition. The established model
structure was adapted to describe hepatic artery injection to treat liver metastatic tumor. Local regional delivery toliver tumor resulted in a substantial increase in initial tumor infiltration, followed by an earlier CAR-T cellproliferation phase. Blood flow rate in the tumor was found to be a sensitive parameter for CAR-T cell proliferationand tumor growth inhibition, indicating a potential role of tumor vascularization in the treatment efficacy of CAR-T
cell therapies.
Conclusions:
A mPBPK-PD model was established to support local regional deliveries to the tumor site as an effective way toenrich CAR-T cell in the tumor microenvironment in order to improve the efficacy of CAR-T cell therapies.

THU-014
How does mechanistic quantitative systems pharmacology (QSP) modeling reduce R&D risk in data-poor
disease areas such as central nervous system (CNS) diseases despite biological uncertainty?
Authors: Christina Friedrich (1), Colleen Witt (1), Meghan Pryor (1), Katherine Kudrycki (1), Rebecca Baillie (1),Robert Sheehan (1), Mike Reed (1)Institutions: 1. Rosa & Co., LLC, San Carlos, CA
Objectives: Neurodegenerative CNS diseases present unique challenges for drug development, including
significant uncertainty about pathophysiology, slow disease progression, and limited data and biomarkers. We
examine the conditions under which mechanistic QSP modeling of CNS diseases can be useful when data are sparse,
describe methodologies that are most relevant under these conditions, and highlight the impact that QSP modelingcan have in this context.
Methods: Mechanistic QSP models were developed in CNS indications with significant biological uncertainty:
Parkinson’s disease (PD), frontotemporal dementia (FTD), schizophrenia, and Alzheimer’s disease (AD). The
contexts, methods, and results of these projects were identified and common themes were analyzed. The results of
the analysis were summarized and illustrated in case studies from these disease areas.
Results: In a PD example, QSP research contextualized evidence of α-synuclein’s role in vesicular trafficking tosuggest incisive experiments to resolve uncertainties. In FTD, modeling of progranulin (PGRN) showed a mismatch
between data and hypothesized mechanisms and led to recommendations for specific experiments. In schizophrenia,
QSP modeling strengthened the rationale for moving a program forward by illustrating likely superiority to an
existing compound with related mechanism of action, despite remaining uncertainties. In AD, QSP modelingsupported integrated exploration of Aβ and tau pathologies. In all cases, precise quantitative data about the role ofthe drug’s targets were lacking, and clinical data were of limited utility for extrapolation to novel mechanisms.
Despite these challenges, QSP modeling clarified the current state of knowledge, enabled extrapolation, identified
key uncertainties, and supported recommendations for incisive experiments to advance the programs. Methods
crucial to success included identification and documentation of uncertainties, and systematic exploration of
uncertainties using sensitivity analysis, virtual patients, and what-if scenarios. Detailed discussions with all membersof the cross-functional project team about biological interpretations, assumptions, and results were also critical.
Modeling advanced the state of information about each disease and supported development decisions.
Conclusion: In disease contexts with little data and significant biological uncertainty, QSP modeling supported arange of activities that moved each program forward. Mechanistic QSP modeling in CNS should be undertaken withthe understanding that precise quantitative predictions of drug effects may not be possible, but that modeling isvaluable to improve understanding, direct research, and mitigate program risk. QSP modeling is valuable for
improving biological understanding and reducing uncertainty and risk in disease areas with sparse data.

Figure 1. A graphical depiction of the biological scope of a schizophrenia QSP model focused on
evaluating D-serine-modulating therapies. Despite significant uncertainties, modeling helped clarify thecase for superiority of a new drug and supported dose selection.

THU-015
Extrapolation Strategy in Pediatric Patients with Polyarticular-Course Juvenile Idiopathic Arthritis (pcJIA):
Pharmacokinetics-Pharmacodynamics (PK-PD) Modeling and Clinical Trial Simulation
Mei Zhang1, Tao Sheng1, Ying Liu1, Lydie Baret-Cormel2, Zhaoling Meng1, and Christine Xu1
1
2

Sanofi, Bridgewater, NJ, USA
Sanofi, Paris, France

Objectives: Sarilumab blocks interleukin-6 (IL-6) from binding to membrane and soluble IL-6 receptor-α.
Sarilumab is approved for adults with rheumatoid arthritis (RA). A 12-week dose-finding study was performed to
identify an appropriate sarilumab dose for use in 2–17-year-old patients with polyarticular-course juvenile idiopathic
arthritis (pcJIA). Exposure-response (E-R) analyses of selected efficacy and safety endpoints were conducted toevaluate PK-PD relationships.
Methods: Patients were divided by body weight into 2 groups, A (30–60 kg) and B (10–<30 kg), and received
sequential ascending doses of sarilumab, Dose 1 (Group A/B): 2.0/2.5 mg/kg q2w; Dose 2 (Group A/B):
3.0/4.0 mg/kg q2w; Dose 3 (Group A/B): 2.0/2.5 mg/kg qw. Treatment effects of different dose regimens were
evaluated through the empirical exposure-responses models established for efficacy endpoints (JIA ACR30/50/70
responses) and safety endpoint (percent changes from baseline in absolute neutrophil count [ANC]) at Week 12. For
each endpoint, empirical linear, log-linear, and Emax PK-PD relationships were evaluated to select the model thatbest fitted the data. The E-R relationship curve for pediatric patients with pcJIA and adult patients with RA werecompared to assess the E-R relationship consistency between pediatric and adult patients. Model-based simulationsof 1000 clinical trials were performed to assess the impact of the different sample sizes (n=60 vs. 100 patients) on
the precision of efficacy endpoints and robustness of exposure-response relationships of JIA ACR responses.
Results: A log-linear logistic regression model adequately described the E-R relationships between the efficacy
endpoints JIA ACR responses and sarilumab trough serum concentration (Ctrough) at Week 12. JIA ACR30 response
rates increased with increasing Ctrough, but a nearly flat dose-response for concentration range corresponding to
Dose 2 and Dose 3. Percent change from baseline in ANC at Week 12 were reasonably well described by a loglinear model, with a trend toward greater ANC reduction with increasing trough concentration. Thus, PK-PD
analyses supported the optimal dose selection in pediatric patients with pcJIA. A comparison of empirical models in
adult patients with RA and in pediatric patients with pcJIA demonstrated a similar E-R relationship of JIA ACR30.
Clinical trial simulations indicated that a total sample size of 60 patients is adequate to assess JIA ACR30 responses.
Conclusions: The E-R relationship of key efficacy and safety endpoints were characterized in pediatric patients with
pcJIA. Modeling and simulation supported the extrapolation of efficacy from adults to pediatric patients,
streamlining the pcJIA development program and reducing the total number of pediatric patients needed in the trial.
Disclosures: Mei Zhang, Tao Sheng, Ying Liu, Lydie Baret-Cormel, Zhaoling Meng, and Christine Xu areemployees of Sanofi and may hold stock and/or stock options in the company.

THU-016

Population Pharmacokinetic, Exposure-Response and Probability of Target Attainment Analyses for
Tedizolid in Adolescent Patients with Complicated Skin and Soft Tissue Infection
1
1
1

Dan Li, 1Philip E. Sabato, 1Hwa-Ping (Ed) Feng, 2Benjamin Guiastrennec, 2Vincent Duval 1Pamela Sears,
Margaret Chou, 1Catherine Hardalo, 1Natalya Broyde, 1Matthew L. Rizk
MRL, Merck & Co., Inc., Kenilworth, NJ; 2Certara, Basel, Switzerland

Backgrounds:
Tedizolid phosphate (SIVEXTRO, MK-1986, TZP) 200 mg once daily IV/PO is approved for treatment of
acute bacterial skin/skin structure infections (ABSSSI) in adults. A Phase 3 study (MK1986-012) inadolescents with ABSSSI has been completed. This abstract reports the population PK analysis,
probability of target attainment (PTA), and exposure-response analyses results for tedizolid, which
support selection of a 200 mg once daily IV or Oral dose for adolescent patients.
Methods:
MK-1986-012 is a randomized (3:1 ratio), single-blind, multicenter, global Phase 3 study to assess thesafety and efficacy of intravenous (IV) and/or oral (PO) TZP 200 mg once daily for 6 days versus that ofcomparators, for 10 days, for the treatment of ABSSSI in participants aged 12 to < 18 years of age. Five
PK samples were collected for each subject. A previously developed population PK model for tedizolid
was updated with additional adolescent data to estimate exposure in patients in this study forcomparison with adults and conduct PTA and exposure-response analyses.
Results:
Similar exposure was observed between adolescent and adult patients given the same 200 mg daily
dose, with AUC and Cmax distribution largely overlapping as shown in Table 1. PTA analysis predicted a
100% PK/PD target (fAUC/MIC) attainment at the proposed dose up to the susceptibility breakpoint of
0.5 µg/mL. A high rate of clinical success was observed in adolescents, and no new safety concerns wereidentified. No exposure-efficacy or exposure-safety relationship was identified at the 200 mg daily dose.
Conclusions:
The proposed 200 mg once daily dose, administered IV or PO, provides sufficient exposure to beefficacious in adolescent patients. There was an exposure-safety profile with 200mg QD in adolescentssimilar to adults. These results support the selection of a 200 mg dose for the adolescent population.

Table 1

Population PK Model Predicted Exposure After First and Last Dose in Adolescent (MK1986-012) and Adult ABSSSI Subjects (Previous Phase 2 and 3 Studies)
Mean ± SD

Population

AUC0-24h_day1(µg·h/mL)

AUC0-24h_last(µg·h/mL)

Cmax_day1(µg/mL)

Cmax_last(µg/mL)

Adult(N = 830)

23.6 ± 7.03

22.1 ± 6.54

2.11 ± 0.915

2.15 ± 0.737

Adolescent
Study P012 (N = 91)

28.0 ± 9.95

30.8 ± 15.5

3.08 ± 1.44

3.37 ± 1.42

Abbreviations: ABSSSI = acute bacterial skin and skin structure infection, AUC0-24h_day1 = area under the tedizolid concentrationtime curve from 0 to 24 h on the first dosing day, AUC0-24h_last = area under the tedizolid concentration-time curve from 0 to
24 h on the last dosing day, Cmax_day1 = maximum tedizolid plasma concentration on the first dosing day, Cmax_last = maximum
tedizolid plasma concentration on the last dosing day, N = group size, PK = pharmacokinetic.
Note: The adult population includes ABSSSI subjects of age >18 years old from previous Phase 2 and 3 studies receiving 200 mg
of tedizolid phosphate。

THU-018

Practical conclusions from practically unidentifiable models: a case study in semi-mechanistic PK
modeling of an enzyme replacement therapy
David Flowers1, Hoa Q. Nguyen2, Diana Marcantonio1, Helen Moore1,
Fei Hua1, Shivakumar Patil2, Nancy Chen2
1

Applied BioMath, LLC, Concord, MA, USA; 2Takeda Pharmaceutical Company, Cambridge, MA, USA.

Objectives: Mechanistic systems biology models combined with curve-fitting to data can be a useful toolfor enabling mechanistic interpretations of data trends. When the data are insufficient to fully informparameter values, the model predictions can be uncertain and unreliable. In this work, we show how
models with practically unidentifiable parameters can still be used to make predictions and identify key
sources of uncertainty.
Methods: We built a semi-mechanistic model of the pharmacokinetics of an enzyme replacement
therapy (recombinant arylsulfatase A, ASA) for the treatment of metachromatic leukodystrophy (MLD)
to predict how the enzyme distributes into the white matter of the brain following intrathecal dosing.
The model tracks changes in exposure over time in various central nervous system tissues and thecirculation. Nominal values for model parameters were estimated from literature sources and fit totissue exposure data from cynomolgus monkeys. Based on these nominal parameter values, confidence
intervals on the parameters and intracellular white matter exposure were calculated using profile
likelihood methods. These methods for investigating identifiability are global but do not depend on
expensive sampling procedures.
Results: While many of the model transport rates are not completely identifiable, model predictions of
the key output of interest, area under the curve (AUC) of intracellular white matter ASA concentrations,
are well-constrained by the data. Ninety-five percent confidence intervals for transport parameters
between CNS compartments span two to eight orders of magnitude, but the confidence interval of theintracellular white matter ASA concentration AUC spans no more than a three-fold interval. Additionally,
uncertainty in the sensitivity of the AUC to the ASA-M6PR binding affinity is found to result largely fromuncertainty in the level of M6PR in the brain.
Conclusions: The results suggest that the significant uncertainty in the model parameters does notpropagate to the key model predictions of interest. Specifically, in this case, the model provided
predictions of intracellular white matter ASA concentrations with acceptable uncertainty, despite highuncertainty in parameter values. More generally, parameter uncertainty does not imply prediction
uncertainty when the prediction is not sensitive to the uncertain parameters. The result of this work wasthat this model was able to provide mechanistic explanations for data trends, despite practicallyunidentified parameters.
Acknowledgements: This work was financially supported by Takeda Pharmaceutical Company

Figure 1. Confidence intervals for model parameters and white matter exposure predictions. (a) 95%
confidence intervals on the model parameters. The light shaded lines indicate the lower and upperbounds used for the parameter estimation, and the dark shaded lines indicate the calculated confidence
interval. The points indicate the best-fit parameter values. (b) 95% confidence intervals on single-dose
intracellular white matter ASA exposure, as represented by the AUC calculated over seven days. The
dose is indicated on the x axis. Lines indicates the confidence interval, and the points indicate the bestfit model prediction. Abbreviations: P: partition coefficient of transport, T: half-time of transport
equilibration, CSF: cerebrospinal fluid, CSF1: injection-site-proximal CSF subcompartment, CSF2: brainproximal CSF subcompartment, GM: gray matter, WM: white matter, ASA: recombinant arylsulfatase A,
AUC: area under the curve.

THU-020
Semi-Mechanistic Model of Thrombocytopenia Post-Autologous Stem Cell Transplant in Multiple Myeloma
Donald J. Irby1, Janet Guo1, Francesca Cottini2, Craig C. Hofmeister3, Mitch A. Phelps1
1

Division of Pharmaceutics and Pharmacology, College of Pharmacy, The Ohio State University, Columbus, OH
Department of Hematology/Oncology, College of Medicine, The Ohio State University, Columbus, OH
3
Department of Hematology and Medical Oncology, Emory University School of Medicine, Atlanta, GA
2

Objectives: A rapid recovery of platelet counts has been predictive of superior survival following different stem celltransplant regimens in several bone marrow malignancies, including multiple myeloma (MM)1, 2. We aimed tocharacterize the platelet kinetics in MM patients post melphalan conditioning and transplant using novel
modifications to the semi-mechanistic myelosuppression model first proposed by Friberg and colleagues.
Methods: Melphalan plasma concentrations (PK) and platelet counts (PD) from 118 MM patients were used to
inform the PK/PD model parameters describing post-transplant thrombocytopenia. The FOCEI procedure in
NONMEM 7.33 was used for the parameter estimation, and NONMEM post-processing was completed in R 3.4.34.
Results: A 2-compartment PK model for melphalan combined with a transit PD model for platelets was developed.
The final platelet model included several modifications to the Friberg model, including additional empirical
feedback functions on megakaryocyte maturation and platelet egress from circulation. The effect of the stem celltransplant was modeled as a linear function on the proliferation rate of progenitor cells in the bone marrow, and it
was modified by the stem cell dose. An additional platelet maturation chain was also included in the model to mimic
the terminal proplatelet fragmentation that occurs in the peripheral circulation and lung. Platelet transfusions weremodeled as bolus IV infusions into circulation. Finally, a published PK model for Granulocyte-colony stimulating
factor (G-CSF) was used to incorporate a previously-reported G-CSF induced effect on platelet turnover5.
Conclusions: A semi-mechanistic model of melphalan-induced thrombocytopenia was developed on MM patient
data in the transplant setting. The final estimates of the model parameters with physiological interpretation were
well-aligned with prior expectations. The model also performed well at replicating the observed platelet kinetics inour dataset, and we are currently using the model to discover covariates relevant to platelet recovery in this setting.
Ultimately, the model will be explored as a tool to adjust therapy towards more favorable predicted platelet profiles.
References
1.

Ninan MJ, Flowers CR, Roback JD, Arellano ML, Waller EK. Posttransplant thrombopoiesis predicts
survival in patients undergoing autologous hematopoietic progenitor cell transplantation. Biology of bloodand marrow transplantation : journal of the American Society for Blood and Marrow Transplantation 13
895-904. (2007)2.
Ramirez P, Brunstein CG, Miller B, Defor T, Weisdorf D. Delayed platelet recovery after allogeneictransplantation: a predictor of increased treatment-related mortality and poorer survival. Bone marrowtransplantation 46 981-986. (2011)
3.
Beal SL, Sheiner, L.B., Boeckmann, A.J. & Bauer, R.J. NONMEM 7.4 users guides. (1989–2018).
4.
Team RC. R: A language and environment for statistical computing. Vienna, Austria.: R Foundation for
Statistical Computing; 2017.
5.
Krzyzanski W, et al. Population modeling of filgrastim PK-PD in healthy adults following intravenous andsubcutaneous administrations. Journal of clinical pharmacology 50 101S-112S. (2010)

THU-021
Development of a semi-mechanistic pharmacokinetic model to describe gemcitabine triphosphate formationin murine peripheral blood mononuclear cells
Elizabeth G. Gibson1, Olivia Campagne1, Abigail Davis1, Bo Zhong1, Martine F. Roussel2, Clinton F. Stewart1
1
Pharmaceutical Sciences Department, St. Jude Children’s Research Hospital, Memphis, TN
2
Department of Tumor Cell Biology, St. Jude Children's Research Hospital, Memphis, TN
Objectives: Gemcitabine (dFdC), a cytosine analog, is currently being evaluated in combination with otherchemotherapeutics to treat children and young adults with CNS tumors in institutional clinical trials (NCT01878617,
NCT03434262, and NCT04023669). dFdC undergoes a complex and saturable cellular transformation into its primaryactive metabolite dFdCTP. Our ultimate goal is to determine dFdCTP exposure in pediatric brain tumor tissue and use
a translational pharmacokinetic/pharmacodynamic modeling approach to adjust dFdC dosage. As a first step, theobjective of this study was to characterize dFdCTP formation in murine peripheral blood mononuclear cells (PBMCs)
a surrogate matrix to study the intracellular pharmacokinetics of nucleoside analogs.
Methods: Non-tumor bearing CD-1 nude mice (N=4/ time point) were dosed with 10, 20, 40, 60, and 120 mg/kg dFdC
via IV bolus tail vein injection. Each mouse was sacrificed at different time-points (0.25, 1, 2, and 4 h post-dose) via
cardiac puncture to collect adequate blood to obtain plasma and process for PBMCs. Plasma dFdC and dFdU, aputatively inactive dFdC metabolite, and PBMC dFdCTP concentrations were measured using validated LC-MS/MS
methods. In a stepwise approach, murine plasma dFdC and dFdU, and PBMC dFdCTP data were integrated into asemi-mechanistic pharmacokinetic model using a nonlinear mixed-effects approach with Monolix v2019R2.
Results: The murine plasma dFdC and dFdU and PBMC dFdCTP were adequately described by the pharmacokinetic
model depicted in Figure 1. The 10, 20, 40, 60, and 120 mg/kg dosages resulted in plasma dFdC area under the curve(AUC) of 5.8±0.5, 11±1.1, 20±2.3 , 35±4.6 and 63±9.6 µM·h, which were within the range of dFdC exposuresdetermined in the institutional clinical trials (14 to 56 µM·h). dFdU formation was best characterized as non-linear
and modeled with the Michaelis-Menten equation. dFdU exposure was significantly higher than dFdC exposure at all
dosages [p < 0.001]. Higher PBMC dFdCTP exposures were observed after 20 mg/kg (143±13 µM·h) and 40 mg/kg(130±31 µM·h) compared with 60 mg/kg (44±13 µM·h) or 120 mg/kg (42±12 µM·h) [p<0.0001], which suggested
an inhibition more than a saturation of dFdCTP formation at high dFdC dosages. This phenomenon was best captured
by including transit compartments for dFdCTP formation and an inhibitory effect on the dFdCTP formation rate
constant driven by dFdU concentrations using a Hill function.

Conclusions: An inhibition of dFdCTP formation in PBMCs at higher dFdC dosages and linked to plasma dFdU
concentrations was characterized in mice using pharmacokinetic modeling. To obtain the highest dFdCTP exposure
in PBMCs for murine studies with dFdC, dosages between 20-40 mg/kg would be recommended. Future work will
evaluate dFdCTP formation in brain tumor tissue in a CNS tumor-bearing murine model and the effect of drug-druginteractions on dFdCTP formation to predict dFdCTP formation in children with CNS tumors.

THU-022

A novel physiologically-based pharmacokinetic (PBPK) model to quantify antibody tissue concentration
gradients in primary tumors and tumor-draining lymph nodes (TDLNs)
Eric Salgado1, Yanguang Cao1
1

Division of Pharmacotherapy and Experimental Therapeutics, UNC Eshelman School of Pharmacy, University ofNorth Carolina at Chapel Hill, NC, USA.
Objectives: Emerging evidence across various cancer-specific clinical trials has demonstrated clear efficacy for
antibody immunotherapy prescribed in neoadjuvant (pre-surgical) settings. The primary tumor-TDLN signaling axis
has recently been highlighted as a focal point for its effectiveness [1]. Moreover, organ-specific “immunostats,” which
includes TDLNs, have recently been observed to contribute to lesion-level heterogeneous responses and the efficacy
of antibody immunotherapy in neoadjuvant settings [2]. Thus, the goal of the current work was to construct the first
to-date physiologically-based pharmacokinetic (PBPK) model to quantify antibody distribution gradients from organspecific primary tumors to their tumor-draining lymph nodes (TDLNs). The model would be valuable for predicting
antibody concentration gradients in different clinically relevant scenarios, including surgery.
Methods: A PBPK model was constructed to define the micro-anatomical structures of organ-specific primary tumors
and their associated TDLNs. Physiologically relevant parameters such as tumor-specific lymph flow were considered.
Antibody-antigen binding in both the primary tumor and TDLNs were described by a quasi-steady state targetmediated drug disposition (QSS-TMDD) model. FcRn-mediated antibody salvage in the draining lymphatic vessels
was also considered. The model was further calibrated using data from eight immuno-PET mAb-tumor imaging
studies in humans (brain, head/neck, breast, lung, esophagogastric, renal, ovarian, pancreatic).
Results: The developed PBPK model was able to adequately capture the antibody PK profiles across all eight cancer
types in three anatomical locations (blood, primary tumor, TDLNs) (Fig. 1). Antibody concentration gradients fromprimary tumors to the TDLNs were also well recapitulated, exhibiting high heterogeneity across tumor types andanatomical locations. The model was used to investigate antibody distribution in all primary tumors and TDLNs bothpre- and post- tumor resection, as well as in the absence or presence of TDLN metastases, highlighting the importanceof the intact lymphatic system (i.e. prior to tumor resection) for antibody distribution and immune activation in TDLNs
in neoadjuvant settings.
Conclusions: With this PBPK model, we have provided the first quantitative, proof-of-principle framework that can
predict antibody distribution gradients in primary tumors and their TDLNs in various clinical situations (NIH:
GM119661).
References:
1. Topalian SL, Taube JM, Pardoll DM (2020) Neoadjuvant checkpoint blockade for cancer immunotherapy.
Science 367 (6477):eaax0182. doi:10.1126/science.aax0182
2. Osorio JC, Arbour KC, Le DT, Durham JN, Plodkowski AJ, Halpenny DF, Ginsberg MS, Sawan P, Crompton
JG, Yu HA, Namakydoust A, Nabet BY, Chaft JE, Riely GJ, Rizvi H, Diaz LA, Jr., Hellmann MD (2019) LesionLevel Response Dynamics to Programmed Cell Death Protein (PD-1) Blockade. J Clin Oncol 37 (36):3546-3555.
doi:10.1200/jco.19.00709

Fig. 1. PBPK model adequately captures antibody concentration gradients from organ-specific primary tumors
to the TDLNs. PK data from eight published immuno-PET mAb-tumor biodistribution studies were calibrated into
the model. Tissues are color-coded and defined as depicted above; shapes represent observed data. Each plot is a single
study and incorporated the following cancer type-immunoPET mAb pairs: (a) brain, 89Zr-Fresolimumab; (b)head/neck, 99mTc-mAb E48 IgG; (c) NSCLC, 89Zr-Bevacizumab; (d) breast, 64Cu-DOTA-Trastuzumab; (e)esophagogastric, 89Zr-Trastuzumab; (f) renal, 89Zr-Bevacizumab; (g) pancreatic, 89Zr-anti-Mesothelin; (h) ovarian,89

Zr-anti-Mesothelin. SUV = unitless standard uptake value of immuno-PET mAb into tissue.

THU-023“Heta compiler” is a framework for the development and management of
Quantitative Systems Pharmacology modeling platforms
Authors: Evgeny Metelkin
Affiliations: InSysBio LLC, Moscow
Objectives
Today the pharmacology modeling is evolving towards complex mechanism-based dynamical modeling andinvolves a large amount of biological and clinical data [1]. A typical QSP project may include thousands ofmolecular components, several drugs and usually is done by a research group involving people with different
expertise. The data management, workflow and co-operation in such projects are the challenges. In many cases,
the critical step also is the translation of the modeling results to other modeling formats and frameworks.
This study is an effort to resolve the typical problems in a QSP project by creating the software infrastructure
based on Heta formats and develop a shared and controllable working environment. The pre-formulated
requirements are: (i) storing the QSP models and data in integrated infrastructure, (ii) support iterative platform
updates, (iii) support of models written in human-readable text and table formats, (iv) export models and data todifferent popular formats on the fly.
Methods
Heta language is a Domain Specific Language (DSL) designed for the development and annotation of dynamical
models in QSP projects. Heta standard of version 0.2 is fully supported in the Heta compiler. The core of the Heta
compiler was developed in JavaScript language and can be used in Node environment, can be a part of a webbased application or potentially integrated with simulation software.
Results
Heta compiler is developed and tested based on the series of QSP models and projects. Currently Heta compiler
supports the export to the following formats: DBSolve, Simbiology, mrgsolve, Matlab, SBML, etc. It was used inopen and commercial QSP projects developed by InSysBio: Alzheimer-consortium platform, Immune Response
Template, PK/RO simulator for anti-PD-1 mAbs.
Discussion
Heta compiler will be developing as free open-source software on GitHub and is expected to be a communitydriven framework (https://hetalang.github.io). A series of additional tools to support Heta and Heta compiler isdeveloped: highlighting syntax in Atom and VSCode, SbmlViewer, slv-utils.
Conclusions
Heta compiler can be used as the framework for a QSP modeling project of any size and complexity. It allows
integrate the dynamical models and data as modules and transform the modeling code to different popularformats. It can be easily integrated with existed infrastructure, workflows or used as a part of the CI/CD strategy.
References1.

Knight-Schrijver et al. Computational and Structural Biotechnology Journal (2016) 14, 363-370

THU-024

Mechanistic modeling of Levodopa pharmacodynamics through Parkinson’s disease progression

Florence Véronneau-Veilleux1, Mauro Ursino2, Philippe Robaey3, Fahima Nekka1,4,5
1Faculté de Pharmacie, Université de Montréal, Montréal, Québec, Canada; 2Department of Electrical, Electronic andInformation Engineering Guglielmo Marconi, University of Bologna, 40136 Bologna, Italy; 3Faculty of Medicine,
University of Ottawa, Ottawa, Ontario, Canada; 4Centre de recherches mathématiques, Université de Montréal,
Québec, Canada; 5Centre for Applied Mathematics in Bioscience and Medicine (CAMBAM), McGill University
Montréal, Québec, Canada.

Objectives: Levodopa is one of the most efficient drugs to alleviate Parkinson's disease symptoms. Although very
effective in the first years after diagnosis, its effect is altered as the disease progresses3. Indeed, its chronic use
combined with the progression of denervation in the substantia nigra leads to motor complications and decrease ineffect duration. It results in difficulties to optimize the regimen which calls for a better understanding of thepharmacodynamics through disease progression. The main objective of this project is to understand the role of thedifferent processes involved in the alteration of Levodopa pharmacodynamics as the disease progresses throughmathematical mechanistic modeling. To quantify the effect of Levodopa, the frequency in the finger tapping task isused as a measure of bradykinesia.

Methods: We developed an integrative mechanistic model to investigate the modifications of Levodopa’s
pharmacodynamics.

This model is composed of three sub-models that represent respectively levodopa

pharmacokinetics1, dopamine dynamics2, interaction with D1 and D2 receptors2 and neurotransmission in the basal
ganglia with the three main neurotransmission pathways1. The neurocomputational model of basal ganglia isrepresented in Figure 1. The impact of denervation on all the mechanisms is included in the model. Each sub-model
was built in line with up-to-date knowledge and data of the pathophysiology and pharmacology.

Figure 1 Schematic representation of the neurocomputational model of basal ganglia adapted from [1]Results: Using our model, we simulated Levodopa plasma concentration, dopamine concentration in the brain,neuronal activity in each nuclei of the basal ganglia as well as frequency of tapping. According to different degrees
of denervation and Levodopa intake, we were able to reproduce the tapping frequency across disease progression. A
reduction in the effect duration as well as the delay of the effect is reproduced by the model. The model also highlights
the role of D2 dopaminergic receptors compared to D1.

Conclusions: This Quantitative Systems Pharmacology approach is a promising step towards regimen optimizationand identification and understanding of important mechanisms involved in Parkinson's disease and its therapy.

References[1] Baston, C., Contin, M., Buonaura, G.C., Cortelli, P., Ursino, M.: A mathematical model of levodopa medication
effect on basal ganglia in parkinson's disease: An application to the alternate finger tapping task. Frontiers in Human
Neuroscience 10 (2016). DOI10.3389/fnhum.2016.00280[2] Dreyer, J.K.: Three mechanisms by which striatal denervation causes breakdown of dopamine signaling. Journalof Neuroscience 34(37), 12444{12456 (2014). DOI10.1523/jneurosci.1458-14.2014[3] Holford, N., Nutt, J.G.: Disease progression, drug action and parkinson's disease: why time cannot be ignored.
European journal of clinical pharmacology 64, 207{216 (2008). DOI 10.1007/s00228-007-0427-9

THU-025

Well-Tempered MCMC Simulations for Population Pharmacokinetic Models
Frederic Y. Bois1, Nan-Hung Hsieh2, Wang Gao3, Weihsueh A. Chiu2, Brad Reisfeld4.
1

Certara, Simcyp division, Sheffield, UK.
Department of Veterinary Integrative Biosciences, College of Veterinary Medicine and Biomedical
Sciences, Texas A&M University, USA.
3
INERIS, METO unit, Verneuil en Halatte, France.
4
Department Chemical and Biological Engineering and School of Biomedical Engineering, Colorado
State University, USA.
2

Objectives:
A full Bayesian statistical treatment of complex pharmacokinetic or pharmacodynamic models, inparticular in a population context, gives access to powerful inference, including on model structure. We
present here the results obtained with an improved implementation of the simulated tempering Markov
Chain Monte Carlo (TMCMC) algorithm of Geyer and Thompson (1995). TMCMC has a number ofadvantages over usual MCMC algorithms: it can sample efficiently from sharp multi-modal posteriors; it
provides insight into identifiability issues useful for model simplification; it has assured convergence witha single simulated chain if infinite temperature can be reached; it can be used to compute accurate Bayes
factors for model comparison and choice.
Methods:
The main challenge to implementing this approach has been setting an adequate scale of auxiliary
temperatures, which we solved by stochastically optimizing a grid of inverse-temperatures, or “perks,”
through a Robbins-Munro process and adaptive optimization. The resulting grid of perks and associatedpseudo-priors was used to perform thermodynamic integration, bridging the joint prior and the posterior
distributions, for two stylized case studies and two realistic population pharmacokinetic inference
problems: One for theophylline, with a small pharmacokinetic model; the other one for acetaminophenwith a large PBPK model.
Results:
In all the cases investigated here, perk zero (infinite temperature) was reached by the automatic perks’setting algorithm. In the case of a sharply bimodal regression model, the two high posterior density region(in a three dimensional space) were accurately identified, where other algorithms, such as MCMC or
Hamiltonian MCMC failed. In the simple case of the identification of a Gaussian mean, we show how tocompute a close approximation of the integration constant needed to estimate Bayes factors. Thetheophylline population PK modelling case shows that a model with inter- and intra-individual variability
is clearly favored compared to a model without intra-individual variability, based on Bayes factors. The
posterior parameter estimates are close to those obtained with standard MCMC, except that TMCMC
identify a secondary mode of the posterior. The posterior distributions of the most sensitive parameters of
the acetaminophen PBPK are close than those obtained with MCMC, but obtained 20 times faster due tothe improved mixing of the chain mixing and assured convergence.
Conclusion:
TMCMC has a number of advantages over other MCMC algorithms, if it is implanted efficiently. Ourcase studies show a full range of its capabilities, which are useful for pharmacokinetic or
pharmacodynamic modeling, but also extend well beyond it.
References:Geyer CJ and Thompson EA, 1995, Annealing Markov chain Monte Carlo with applications to ancestral
inference, Journal of the American Statistical Association. 90:909–920.
Acknowledgements:
Funding for this work was made possible, in part, by the U.S Food and Drug Administration throughGrant 1U01FD005838. Views expressed in written materials or publications do not necessarily reflect theofficial policies of the Department of Health and Human Services; nor does any mention of trade names,
commercial practices, or organization imply endorsement by the United States Government.

THU-029

Hereditary angioedema (HAE) prophylaxis with plasma kallikrein inhibitors: Role of target binding kinetics,
pharmacokinetics and treatment adherence
Hoa Q. Nguyen1, Rangaraj Narayanan2, Haobin Luo3, Zhiwei Zhang3, Paul Jasper3, Peng Lu1, Salomé Juethner1,
Dan Sexton1
1

Shire, a Takeda company, Cambridge, MA; 2 Grünenthal Group, Cambridge, MA; 3 RES Group Inc., Needham,

MA
Objectives: Unopposed activation of the contact system leads to excessive production of plasma kallikrein (pKAL)
and, subsequently, increased release of bradykinin, a key mediator of HAE attacks pKAL inhibitors block contact
system activation; the resulting disease control can be driven by various drug-related factors. We sought to better
understand the impact of these factors via a quantitative systems pharmacology (QSP) approach.
Methods: A mechanistic biological model of HAE was developed using QSP modeling that incorporated criticalcomponents of the contact system, and a virtual HAE patient population was established. Cleaved high molecularweight kininogen pharmacodynamic biomarker data, and clinical outcomes from lanadelumab clinical studies wereused to verify the model. The verified model framework was used to evaluate impact of various drug-related factors
on HAE disease outcome, including target binding potency, PK characteristics and treatment adherence in patients(assessed via novel virtual scenarios) of orally available, small molecule pKAL inhibitors.
Results: Target binding potency has a direct correlation with improved disease outcome; however, a critical factor
for improved efficacy is that sufficient amounts of “free” drug are available to bind to pKAL. Impact of drug halflife was explored, and simulations showed that drugs with long half-lives (≥2 weeks) had ≥2-fold improved efficacy
versus those with shorter half-lives (approximately 1–2 days). Simulations showed that with short acting pKAL
inhibitors (half-lives <2 days) 1–2 days of missed doses per week could translate to an approximately 2-fold efficacy
decline, thereby increasing risk for HAE attacks.
Conclusions: Results from an in-silico QSP approach were consistent with attributing improved efficacy of
lanadelumab in HAE prophylaxis to high availability of “free” pKAL inhibitor and long half-life (≥2 weeks).
Disclaimer: The results in this abstract have been previously presented as Poster 327 online at the American
Academy of Allergy Asthma and Immunology (AAAAI) Annual Meeting, Philadelphia, PA, March 13–16, 2020,
which was cancelled due to COVID-19, and published in the conference proceedings as abstract #327 in J Allergy
Clin Immunology 2019;145(2):AB105.

THU-030
A Mechanism-based Pharmacokinetic-Pharmacodynamic Model for Hemodynamic Effectsof Propofol in Healthy Volunteers
H Su1, M.M.R.F.Struys1,2, P.J.Colin1
Department of Anesthesiology, University Medical Center Groningen, University of Groningen, Groningen, The
Netherlands 2Department of Basic and Applied Medical Sciences, Ghent University, Ghent, Belgium

1

Objectives:
Propofol, a γ-aminobutyric acid receptor agonist, is used for sedation and anesthesia in clinical practice. One of theadverse effects of propofol is hypotension. Propofol reduces sympathetic tone, leading to vasodilation and potentiallydecreases blood pressure [1]. This study aims to develop a mechanism-based pharmacokinetic-pharmacodynamic(PKPD) model that can describe the relationship between cardiovascular variables such as mean arterial pressure(MAP), heart rate (HR), pulse pressure (PP) and plasma concentration for propofol.
Methods:
36 healthy volunteers were enrolled in this study and received propofol in a staircase fashion by target-controlled
infusion using the Schnider model [2, 3]. PK of propofol was described by a three compartment model developed by
Eleveld et al [4]. First, data-driven models for MAP, HR and PP were developed. Then, based on the model by Snelder
et al [5], a mechanistic model was explored. PP was used as a surrogate for stroke volume (SV) [6].
Results:
Our data-driven models and the mechanism-based model describe the effect of propofol on MAP, HR and PP. In thedata-driven models, changes in MAP, HR and PP were described by indirect response models. The relationshipbetween plasma concentration (Cp) and MAP and PP were described by an Emax model. For HR, a sigmoid Emax
model fitted the data best. Age was a covariate on baseline PP. The estimated propofol C50 for MAP, HR and PP were
12.7, 2.32 and 1.34 μg/ml, respectively. In the mechanistic model, we assume that propofol inhibits total peripheral
resistance (TPR) and SV, the resulting change in MAP was calculated from these variables. TPR, HR and SV wereregulated by a negative feedback via MAP. In addition, a direct inverse relationship between SV and HR was also
included. Higher MAP and HR were found before propofol infusion, this effect was explained by an anxiety term in
the HR. Estimated propofol C50 for TPR and SV were 3.37 and 0.52 μg/ml, respectively. The mechanistic model could
describe the propofol-induced cardiovascular side effects. Compare to data-driven models, the goodness of fit plot
showed that the mechanistic model was slightly less accurate.
Conclusions:
We developed three data-driven models for propofol effects on MAP, HR and PP as well as a mechanistic model inhealthy volunteers. Both data-driven models and mechanistic model could describe the cardiovascular effects well.
Further research is focusing on the optimizing and evaluation of the performance the mechanistic model.
References:1.
Sahinovic, M.M., M. Struys, and A.R. Absalom. Clin Pharmacokinet. 2018;57(12):1539-1558.
2.
Schnider, T.W., et al. Anesthesiology. 1998;88(5):1170-82.
3.
Schnider, T.W., et al. Anesthesiology. 1999;90(6):1502-16.
4.
Eleveld, D.J., et al. Br J Anaesth. 2018;120(5):942-959.
5.
Snelder, N., et al. Br J Pharmacol. 2014;171(22):5076-92.
6.
de Simone, G., et al. Hypertension. 1999;33(3):800-5.

THU-032

Exposure-Response Analysis of Insulin-like Growth Factor-1 (IGF-1) and Bronchopulmonary Dysplasia
Following Administration of Recombinant IGF-1 and Binding Protein-3 in Extremely Premature Infants
Authors: Claudia Jomphe1, Nathalie H Gosselin1, JF Marier1 , Linda Han2 and Indranil Bhattacharya2
Affiliations: 1Certara Strategic Consulting, Princeton, NJ, USA, 2Takeda, Cambridge, MA, USA
OBJECTIVE: Extremely premature infants are at very high risk of developing morbidities such asbronchopulmonary dysplasia (BPD) and often experience delayed mortality or long-term disabilities.
Insulin-like growth factor-1 (IGF-1) promotes organ development and drives growth in extremely preterm
infants. A human recombinant form of the naturally occurring protein complex of IGF-1 and insulin-likegrowth factor binding protein-3 (rhIGF-1/rhIGFBP-3, TAK-607) is being developed for the prevention ofcomplications associated with extreme prematurity. The purpose of this project is to assess the exposureresponse relationship between IGF-1 and the prevention of BPD in order to support an optimal dosing
regimen in premature infants.
METHODS: A total of 96 premature infants received daily intravenous (IV) infusion of rhIGF-1/rhIGFBP-3
or standard neonatal care (SOC). BPD was assessed by oxygen challenge testing at postmenstrual age(PMA) 36 weeks. Median (range) gestational age (GA) and body weight at birth were 25.9 weeks (23.1 –27.9) and 0.790 kg (0.425 – 1.22), respectively. Logistic regression models were constructed to assess therelationship between IGF-1 exposure metrics and the probability of BPD (No/Mild vs. Moderate/Severe).
Sources of variability in BPD response were explored (GA and birth weight, sex, oxygen saturation on Day
1, APGAR score at 1 min, and study arm). Based on the exposure-response relationship, clinical trial
simulations were performed to identify the dosing regimen that would result in IGF-1 levels that optimize
BPD response.
RESULTS: A statistically significant exposure-response relationship was observed, whereby higher
observed average IGF-1 levels at day 7 (CavD7) were associated with a greater probability of No/Mild BPD.
In addition, statistically significant effects of weight at birth and sex were observed, whereby larger
females are expected to have a higher probability of No/Mild BPD. Neither oxygen saturation at birth nor
APGAR score at 1 min were predictors of No/Mild BPD. A total of 500 trials of 75 subjects treated with
250 and 400 µg/kg/day rhIGF-1/rhIGFBP-3 or no dose (SOC) in a 1:1:1 ratio were simulated based on a
population PK model. The probability of No/Mild or Moderate/Severe BPD was determined based on
simulated CavD7 in each trial according to the above exposure-response model. Simulations suggested
that both rhIGF-1/rhIGFBP-3 treatments are superior to SOC for BPD. In addition, the 400 µg/kg/day
regimen was associated with a greater probability of No/Mild BPD relative to the 250 µg/kg/day regimen.
CONCLUSION: A statistically significant exposure-response relationship was observed, whereby higher
levels of IGF-1 were associated with a greater probability of No/Mild BPD. Based on simulations, a 400µg/kg/day dose of rhIGF-1/rhIGFBP-3 is expected to result in a greater probability of No/Mild BPD in
extremely premature infants.
WorTHU-033
Unveiling determinants of nonalcoholic fatty liver disease (NAFLD) activity and nonalcoholic steatohepatitis(NASH) using item response modeling
Iris K. Minichmayr (1), Elodie Plan (1), Benjamin Weber (2), Sebastian Ueckert (1)(1) Department of Pharmaceutical Biosciences, Uppsala University, Uppsala, Sweden(2) Translational Medicine and Clinical Pharmacology, Boehringer Ingelheim Pharmaceuticals, Inc., Ridgefield,
Connecticut, USA
Objectives: NAFLD–including its progressive subtype NASH–has emerged as a leading indication for livertransplantation and, fueled by the obesity epidemic, an alarmingly increasing threat to public health. There are still
no approved drugs for NAFLD/NASH owing to challenges in drug development, e.g. the complex, ‘multi-hit’
pathophysiology of NAFLD or inconsistent diagnostic criteria and treatment endpoints [1,2]. Currently, NASH
diagnosis and clinical trial endpoints heavily rely on liver biopsies and histological scoring metrics like the NAFLD
activity score (NAS)–considering steatosis, inflammation and hepatocellular ballooning–and fibrosis [3]. Our
objective was to enhance the understanding of disease processes underlying NAFLD and to assess the role ofdifferent histological and non-invasive markers in assessing NAFLD severity by using item response theory (IRT)
modeling.
Methods: The study population (nID=914) originated from the public NIDDK NAFLD Adult Database [4] andspanned the full spectrum of NAFLD (NAS 0-8; 52.3% patients with NASH). An IRT model was developed (using
R3.6.1/mirt), relating the probability of the outcome of each item (i.e. histological feature) to latent variables, which
represent unobserved disease processes underlying the item responses. Biomarkers predicting NAFLD activity wereidentified using full random effects modeling.
Results: Model development initially focused solely on the NAS items steatosis, inflammation and ballooning andwas subsequently extended to 13 histological features, including fibrosis [3]. The 13-item model, comprising 5
graded-response and 8 two-parameter logit models, allowed for a better distinction between NASH and Non-NASH
patients. The disease processes underlying hepatocellular ballooning and fibrosis appeared to best inform NASH
diagnosis, followed by inflammation and steatosis. The IRT model suggested different disease processes (i.e.
separate latent variables) for the four cardinal features of NAFLD, i.e. the three NAS components and fibrosis.
Highest correlation (70%) was found between processes reflected by hepatocellular ballooning and fibrosis.
Identified non-invasive predictors of NAFLD severity scores inter alia included liver enzymes and partly differed
between the NAS score and fibrosis score.
Conclusions: An item response theory model based on hepatic histological scoring allowed to jointly characterizedisparate disease processes underlying NAFLD–including more rapidly changing aspects like steatosis and theslowly progressing lesion fibrosis–as well as potential non-invasive biomarkers reflecting NAFLD activity. Our
analysis suggests that NASH could be inferred better from a broad panel of histological lesions than from the NAS
alone. The model lays the basis for future investigations on the sensitivity of the NAS to changes of different disease
processes, e.g. as response to a therapeutic intervention, with the ultimate goal to support model-informed drug
development.
References[1]
Wong et al. Lancet Gastroenterol Hepatol. 2016;1(1):56–67[2]
Rinella et al. J Hepatol. 2019;71(4):823–833[3]
Kleiner et al. Hepatology. 2005;41(6):1313–1321[4]
Neuschwander-Tetri et al. Hepatology. 2010;52(3):913–924

THU-034

Population Pharmacokinetics of Pegilodecakin and Exposure-Safety (Anemia)
Assessment in Patients with Solid Tumors

Lisa O’Brien1, David Ferry1, Ivelina Gueorguieva2
1

Lilly Research Laboratories, Indianapolis, Indiana, USA; 2Lilly Research Laboratories, Windlesham, Surrey, UK.

Objectives: Clinical benefit of an IL-10 receptor agonist, pegilodecakin (pegylated recombinant human IL-10) wasinvestigated in solid tumors. Aims included evaluation of the pharmacokinetics (PK) of pegilodecakin,identification of patient factors impacting disposition, and understanding mechanisms for hemoglobin decrease frombaseline during pegilodecakin treatment.
Methods: A total of 3925 serum concentrations from 579 cancer patients, enrolled in three Phase I/II/III studies
were included in the PK analysis. Patients self-administered pegilodecakin subcutaneously at a variety of doselevels (range: 0.02-4 mg) and dosing schedules. Pharmacodynamic (PD) data (~19,000 measurements), including
reticulocyte counts, red blood cells, and hemoglobin levels (Hb), were collected weekly from patients enrolled inthese studies.
Previously, -unpegylated IL-10 caused anemia with concentration-dependent decrease in Hb in patients with
Crohn’s disease associated with increased ferritin and no evidence of hemolysis [1]. A novel RBC life span modelwith pegilodecakin-dependent inhibition on RBC maturation rate was developed using the Phase I/III data. The
model evaluated treatment impact on patient RBC life span which resulted in change in hemoglobin and anemia
severity grade. Dose modifications for treatment emergent adverse events and blood transfusion information weretaken into account. Developmental stages of erythroid cell were modelled related to the hypothesis that
pegilodecakin affects erythropoiesis. Simulations were performed to evaluate the impact of various dosing regimenson pegilodecakin exposure and safety.
Results: The pharmacokinetics of pegilodecakin were well characterized by a linear one-compartment model withabsorption lag and allometric scaling. Apparent clearance was lower in patients treated with high pegilodecakindoses. Mean (CV%) PK parameters derived from post-hoc parameters were: Ka 0.113 h-1 (64%), CL/F 3.86 L/h(61%), V/F 149 L (84%). Between-patient variability on CL/F was high (58%). No other covariates (patient
demographics, disease characteristics) were identified as statistically significant or clinically meaningful. Based on
the PK/PD model, RBC life span was decreased by more than half for pegilodecakin-treated patients, compared toliterature reported healthy subjects (120 days). Estimates of baseline reticulocytes, RBC count, and mean
corpuscular hemoglobin were comparable to those reported for healthy subjects. The mean time for a precursor cellto enter the blood stream for each cell population was reduced in active arm patients (pegilodecakin plus standard ofcare) with mean concentration 0.6 ng/mL (4.51 %SEE) for half-maximum response. Model predictions
demonstrated decreased reticulocyte count and subsequent increased Hb with treatment rest periods. Return tobaseline (with no infusions) was only achieved with a prolonged off-treatment period.
Conclusions: A modelling framework was established to quantitatively describe on-target toxicity (anemia) with
pegylated IL-10 treatment. Simulations to evaluate appropriate doses and schedules will enable future trial design tooptimize exposure-response.
References:1.

Tilg H, H. Ulmer, A. Kaser and G. Weiss. J Immunol 2002, 169: 2204-2209.

THU-036
Leveraging Real-world data for EMA Qualification of a Model-Based Biomarker Tool to
Optimize Type-1 Diabetes Prevention Studies
Jagdeep Podichetty1, Patrick Lang1, Inish O’Doherty1, Sarah David1, Rhoda Muse1, Laura Song1, Klaus Romero1
Jackson Burton1; on behalf of the Type-1 Diabetes Consortium (T1DC)
1

Critical Path Institute, Tucson, AZ

Objectives: Currently, development of therapies to prevent or delay the onset of T1D remains challenging and there
is a lack of qualified biomarkers to identify individuals and quantify risk of conversion to a diagnosis of T1D. It is
widely accepted that four distinct islet auto-antibodies (AAs) namely GAD65, IAA, ZnT8 and IA-2, are biomarkers
associated with risk of T1D diagnosis. The goal of this work was to leverage existing data sources that captured islet
AA measurements and glycemic markers in a population likely to participate in T1D prevention trials, and utilize thedata to develop a time-to-event model for predicting the time-varying probability of T1D diagnosis. Based on thismodel, on 25 March 2020, the European Medicines Agency (EMA) released a public Letter of Support for “Islet
autoantibodies as enrichment biomarkers for type 1 diabetes prevention studies, through a quantitative diseaseprogression model” to address the need for optimized designs of T1D prevention trials. The finalized model will be
submitted to EMA for qualification opinion and endorsement of the model-based biomarkers on behalf of the T1D
consortium founded by the Critical Path Institute.

Methods: Patient-level data from the TN01, TEDDY and DAISY studies was acquired, curated, and aggregated to
conduct analyses. An analysis set was extracted with 2022 individuals and corresponding covariates predictive ofT1D, which included the number and type of two or more AAs, blood glucose measurements, HbA1c measurements,
HLA subtypes and patient demographics. An Accelerated Failure Time (AFT) model was used to quantify the effectof baseline covariates on the time to T1D diagnosis. Internal validation was performed using K-fold cross validationand external validation using DAISY dataset.

Results: Results from the AFT model analysis indicated that GAD65_IAA and GAD65_ ZnT8 combinations havethe least relative risk compared to all other combinations, while the IA-2_IAA_ZnT8 has the highest relative risk.
The use of 120-minute OGTT and HbA1c values provided significant ability to further stratify risk of T1D diagnosis.
A 5-fold cross validation was performed, which showed high overall concordance across all subjects (c-index ≈ 0.81)
and external validation with the DAISY study showed a high concordance as well (c-index ≈ 0.85), suggesting thatthe tool is robust to inform trial design in these islet AA positive populations (Figure 1).

Conclusions: We demonstrated that analysis of integrated data from independent observational data sources can beused as evidence for identifying populations for recruitment into T1D prevention studies of reasonable duration. The
model-based approach provides a basis to quantitatively link independent sources of risk measured by islet AAs andglycemic measures. The EMA qualification opinion will serve to encourage widespread adoption of the proposed
quantitative tool for enrichment strategies and stratification in ongoing and future T1D prevention trials.

Figure 1. Survival plots for k-fold cross validation with 5 folds and external validation with DAISY data. (Dottedcurve represents Kaplan–Meier estimate and solid curve represent model prediction)

THU-039

Spatiotemporal Response Heterogeneity across Metastases in Metastatic Colorectal Cancer
Jiawei Zhou1, Yanguang Cao1,2*
1School of Pharmacy, University of North Carolina at Chapel Hill, NC, 27599, USA; 2Lineberger Comprehensive

Cancer Center, School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA*corresponding author: yanguang@email.unc.edu
Background
The response pattern of overall tumor burden is broadly used to evaluate patient objective response to interventions.
The response patterns of individual lesions and their association with patient survival in metastatic colorectal cancer(mCRC) are still largely uncharacterized. A detailed assessment of the response heterogeneity across metastatic
lesions is valuable, but still lacking.
Objectives
The objectives were to evaluate the response dynamics across individual metastases in mCRC, and characterize theirassociations with treatment types, anatomical sites, and patient survival.
Methods
In this study, we analyzed the lesion-level response dynamics in 2,802 mCRC patients collected in five randomized
clinical trials. An algorithm was developed to characterize the response dynamics in individual metastasis byconsidering three phases: response, stable, and relapse. The response heterogeneity across metastases was quantified
by Gower Distances. The association between Gower Distances and patient overall survival was illustrated using an
adjusted Cox proportional model. Dunn’s multiple comparison test was performed to evaluate the lesion-level
response heterogeneity across anatomical sites and treatment types.

Results
High inter-lesion heterogeneity was observed in patient response patterns, and more than 60% of patients have atleast one lesion responding differently from the total tumor size. The patient overall survival (OS) is closely
associated with the inter-lesion response heterogeneity (HR=1.145, p < 0.001). Patients with high inter-lesion
heterogeneity had significantly shorter survival (422 days) compared to patients with medium (OS 550 days) or low(OS 630 days) heterogeneity (p < 0.0001). Targeted therapies, including bevacizumab or panitumumab in
combination with chemotherapy, showed marked reductions in inter-lesion heterogeneity (p < 0.05), along withimprovements of patient survival compared to chemotherapy alone (p < 0.001). The metastatic lesions in the liver
have significantly higher shrinkage fractions (p < 0.05) and longer response durations (p < 0.05) than the lesions inlymph nodes and the lungs in targeted therapies. In contrast, no apparent improvement was observed in the liverlesions under chemotherapy alone.
Conclusions
Due to the high inter-lesion heterogeneity, the response dynamics of total tumor sizes could not fully reflect theindividual lesions in mCRC patients. This lesion-level response heterogeneity is associated with patient survival.
Targeted therapies showed better effects than chemotherapy on metastatic lesions in the liver. (NIH GM119661)

Figure 1. Response heterogeneity across metastases is associated with treatment efficacy and patient survival.

THU-040
Predictions of Systemic, Intracellular, and Lung Concentrations of Azithromycin with Different Dosing
Regimens used in COVID-19 Clinical Trials
Jim H. Hughes1, Kevin Sweeney1, Sima Ahadieh1, Daniele Ouellet1
1

Global Product Development, Pfizer Inc., Groton CT and Collegeville PA, USA

Objectives: Azithromycin (AZ) is currently being investigated for its effectiveness against SARS-CoV-2 in patientswith coronavirus disease 2019 (COVID-19). It is hypothesized that the activity of AZ may be linked to its extensive
tissue distribution and accumulation in cells. A hybrid of existing published population pharmacokinetic (PK) models
was developed to assess AZ exposure in different tissues including lung and intracellularly in alveolar macrophages(AMs).
Methods: A hybrid model utilizing parameters of tissue (1) and systemic PK (2) from two separate models wasdeveloped in R using the mrgsolve package. This model was extended to predict concentrations in AMs and lungtissue. Model predictions were compared to digitized aggregate tissue data from 8 publications and individual
concentrations from two clinical trials (n = 64) in plasma/serum, lung tissue, leukocytes and AMs. The model wasused to simulate concentrations (n = 1000 subjects) based on AZ dosing regimens used in clinical trials of COVID 19
and compared to published in vitro EC90 against SARS-CoV-2 (3). Dosing regimens included: 500 mg QD for 3 days(A); 500 mg single dose followed by 250 mg QD on days 2 through 5 (B); 1000 mg single dose (C); and 500 mg QD
for 10 days (D). Finally, a web application for model simulation using additional dosing regimens was developed in
R using the shiny package.
Results: The final model consisted of a base three-compartment PK model with first-order absorption and elimination,
three tissue compartments representing subcutis, muscle and lung tissue, and two intracellular compartments forleukocytes (WBCs) and AMs, each associated with a separate deep-tissue compartment. The final model adequately
described aggregate and individual concentration data across multiple dosing regimens. Simulated median AZ
concentrations and 90% prediction intervals (Figure) showed that concentrations in lung tissue were above the in vitro
EC90 (dashed line) when using the highest AZ dosing regimen (D), while AM concentrations were above the in vitro
EC90 for an extended period with all tested dosing regimens (A-D).

Conclusions: This model was adequate to predict tissue concentrations and will better inform the evaluation andoptimization of dosing regimens used in ongoing and future AZ clinical trials.

References:1.

Zheng, S., et al., Development of a population pharmacokinetic model characterizing the tissue distributionof azithromycin in healthy subjects. Antimicrob Agents Chemother, 2014. 58(11): 6675-6684.
2.

Zhao, Q., et al., Population pharmacokinetics of azithromycin and chloroquine in healthy adults andpaediatric malaria subjects following oral administration of fixed-dose azithromycin and chloroquine combinationtablets. Malar J, 2014. 13: 36-36.
3.

Touret, F., et al., In Vitro Screening of a FDA Approved Chemical Library Reveals Potential Inhibitors of

SARS-CoV-2 Replication. bioRxiv, 2020. 2020.04.03.023846.
Figure: AZ Concentration-Time Profiles in Different Tissues after Different Dosing Regimens

THU-041
Population Pharmacokinetic-Pharmacodynamic Analysis of a Complement Factor B Antisense
Oligonucleotide in Healthy Volunteers to Support Phase 2 Dose Selection
Authors: Joao A. Abrantes1, Daniel A. Norris2, Jayashree Sahni1, Michael McCaleb2, Yanfeng Wang2, Nicolas Frey1
Institutions: 1Roche Innovation Center, Basel, Switzerland; 2Ionis Pharmaceuticals, Carlsbad, CA, USA

Objectives:
IONIS-FB-LRx is a triantennary N-acetyl galactosamine (GalNAc)-conjugated antisense oligonucleotide (ASO) indevelopment as a potential treatment for geographic atrophy associated with age-related macular degeneration (AMD).
The ASO portion of IONIS-FB-LRx targets complement factor B (FB) mRNA thus preventing the production of FB
protein, which increased levels are associated with AMD [1,2]. The aim was to characterize the relationship between
the PK of IONIS-FB-LRx and the FB as well as the alternative complement pathway hemolytic activity (AH50) levels
using a population model-based approach.
Methods:
Data from 41 healthy volunteers (HV) were available from two Phase 1 clinical studies (single ascending dose (SAD),
N=17; multiple ascending dose (MAD), N=24). The drug was administered subcutaneously: 10, 20 and 40 mg singledose (SAD), or 10 and 20 mg administered in 8 doses during a 6-week treatment period (2x/week during the first 2
weeks and then weekly for 4 weeks; MAD). In total, 796 plasma concentrations, 437 FB observations and 288 AH50
observations (measured with the AH50 hemolytic assay) were included in the analysis. The median subject was a 45year 84-kg male. The PK-PD model was estimated simultaneously using NONMEM 7.4.1.
Results:
A two-compartment PK model with first-order absorption and linear elimination and two parallel indirect responsemodels were found to describe well the longitudinal PK, FB and AH50 data. Body weight was implemented on the
PK disposition parameters. The estimated ka was 1.3 h-1, and the derived disposition half-lives were t1/2 alpha=4.7 hours
and t1/2 beta=35 days (inter-individual variability in estimated primary parameters 15%-50%CV, residual error
27%CV). The post-dose decreases in FB and AH50 levels were described using two structurally independent indirect
response models with an Imax drug effect affecting the input. A body weight effect was identified on the estimatedbaseline FB and AH50 (p<0.001; positive relation). For FB, the IC50 was 0.28 ng/mL and the kout 0.0024 h-1, and for
AH50 the IC50 was 0.50 ng/mL and the kout 0.0016 h-1. The residual error for FB was 14%CV, and for AH50 was 8.4
IU/mL. All model parameters were estimated with reasonable precision. Simulations using the PK-PD model indicatedthat to reach an appropriate level of inhibition in FB or AH50 in the median subject (Q2Wx2 + Q4W dosing at steadystate), at least 20 mg or 40 mg, respectively, would have to be administered.
Conclusions:
A population PK-PD model to characterize the PK of IONIS-FB-LRx, and its inhibitory effect on FB production and
AH50 in HV was successfully developed. This model was used to inform the selection of doses and dosing frequency
in the clinical development studies of IONIS-FB-LRx in AMD patients.
References:[1] Boyer et al. Retina. 2017 May;37(5):819-835. [2] Reynolds et al. Invest Ophthalmol Vis Sci. 2009
Dec;50(12):5818-27.

THU-042

Simulx-GUI: a flexible, fast and user-friendly application for simulations

Authors: Géraldine Ayral (1), Monika Twarogowska (1), Pauline Traynard (1), Jonathan Chauvin (1)Affiliations: (1) Lixoft, Antony, France

Introduction:
PK/PD models can be used to simulate the expected outcome in new situations such as future clinical trials. We
present Simulx-GUI, a tool for advanced simulations integrated within the MonolixSuite. Models estimated with
Monolix (population PK/PD) or PKanalix (compartmental analysis) can be exported to Simulx in a single step. Not
only will the model itself be exported, but also the estimated population parameters, the EBEs, the treatments andcovariates defined in the Monolix data set. These elements can be easily reused for the simulations. The clear
interface allows for the definition of new simulations inputs, the setup of the simulation itself and its postprocessing. Simulx-GUI uses the same calculation engine in efficient C++ code and same model language as theother applications of the MonolixSuite.
Methods:
The functionalities of Simulx-GUI are illustrated using a PK/PD model for Remifentanil. The goal is to simulate aclinical trial with 2 arms corresponding to two different infusion rates. The Monolix run is first exported to SimulxGUI. This creates the following elements: the model, the population parameters, the design (treatments andmeasurement times) of the original data set and the covariates. To setup the simulation, new elements are created.
We create two treatment elements by giving the start time, the dose amount and the infusion duration. The model
includes covariates, which can be resampled from the original data set, sampled from an external file or defined via
a distribution. Two model outputs are defined: the smooth PD prediction over a fine grid and PD measurements(with residual error) at a few time points. The simulation is setup by setting the numbers of arms and individuals andassigning the desired elements to each arm. After running the simulation, plots are automatically generated for all
outputs.
We also define an endpoint: the percentage of individuals reaching a target PD interval after 20 minutes. The
uncertainty of the endpoint is then assessed via simulation replicates.

Results:
The simulation of the predicted PD shows that 68% of the individuals are within the target interval for the higher
infusion rate and 53% for the lower infusion rate.
Conclusion:
Simulx-GUI is a flexible, fast and integrated application for simulations. It can be used to simulate a wide variety ofmodels: population PK/PD (including count, categorical and time-to-event), QSP and PBPK with or without interindividual and inter-occasion variability. Clinical trial simulations can be easily setup via the definition of arms,
outcomes and endpoints. Replicates of the simulations allow to assess the uncertainty of the endpoints and can also
incorporate the uncertainty of the population parameters.

THU-043
Modeling Longitudinal Glucose Dynamics to Inform Type 1 Diabetes Prevention Clinical Trial Studies
Juan Francisco Morales1, Karthik Lingineni1, Melissa Thomas1, Patrick Lang2, Jagdeep Podichetty2, Jackson
Burton2, Sarah David2, Jessica Dunne3, Mark A. Atkinson4, Michael J. Haller5, Klaus Romero2, Inish O'Doherty2,
Stephan Schmidt1, Sarah Kim1,*
1

Center for Pharmacometrics and Systems Pharmacology, Department of Pharmaceutics, College of Pharmacy,
University of Florida, Orlando, FL, USA
2
Critical Path Institute, Tucson, AZ, USA
3
JDRF, New York, NY, USA
4
Department of Pathology and Pediatrics, College of Medicine, University of Florida, Gainesville, FL, USA
5
Department of Pediatrics, College of Medicine, University of Florida, Gainesville, FL, USA*
Corresponding author’s email: sarahkim@cop.ufl.edu
Objectives: Type 1 Diabetes (T1D) is a chronic autoimmune disease that results from the destruction of insulinproducing beta cells of the pancreas. Currently, there are no approved therapies to prevent or delay the onset of T1D
and limitations exist for biomarkers that identify disease progression as well as predict the onset probability of aformal T1D diagnosis. The objective of this analysis is to identify subgroups of the population that differ in diseasestate and progression through a covariate analysis using longitudinal data following a 2-hour Oral Glucose Tolerance
Test (OGTT) value.
Methods: A longitudinal glucose dynamics model was developed based on an extensive individual patient-level datafrom the NIH TEDDY (0-15 years) and TrialNet TN01 (3-45 years) natural history studies. Following baseline
screening, these studies followed volunteers with a high risk of developing T1D by monitoring a series of metabolic,genetic and immunologic tests. After data curation, the final dataset included 2,754 individuals with a total of 15,120
scores. Using a non-linear mixed effects (NLME) modeling approach, the longitudinal progression of the 2-hour
OGTT values was quantitatively captured, enabling assessment of inter- and intra-individual variabilities. Candidate
factors contributing to the heterogeneity included various quantities and types of autoantibodies, race, family relatives
with T1D, ethnicity, C-peptide level, body mass index, age, and the change in glucose level during the 2-hour OGTT
test at first visit. The NLME model with stepwise covariate modeling search was developed in Monolix software(version 2019R2).
Results: A linear model structure with a proportional residual error model best captured the time course of the 2-hour
OGTT scores. The number of autoantibodies was found to be a significant covariate on the slope parameter which
was incorporated using a linear function. The estimated slope parameter was -0.739 mg/(dL·year) with a coefficientof 1.93 for the number of autoantibodies. The slope was steeper as the number of autoantibodies increased. The
estimated intercept, i.e. 2-hour OGTT score at time zero, was 112 mg/dL. The 2-hour OGTT value at first visit andthe presence of the IA2A autoantibody were incorporated as covariates on intercept using a power function withestimated exponents of 0.673 and 0.0714, respectively. The intercept was higher as the covariates increased.
Conclusions: The number of autoantibodies, in particular IA2A, as well as the 2-hour OGTT values at first visit wereidentified as clinically relevant covariates for developing T1D. We will now expand this covariate model to a joint
model that includes a time-to-event component in order to characterize the transition from healthy to T1D state. Oncedeveloped and qualified, this joint model will serve as a tool to optimally design clinical T1D prevention trials.

THU-044

Quantitative Systems Pharmacology (QSP) Model Describing the Local and Systemic Antitumor Response ofa Cytokine mRNA Immunotherapy Across Multiple Solid Tumors
Kamau Pierre1, Lin Lin3, Lore Gruenbaum3, Carissa Young3, Joshua Apgar3, Christian Hotz4, Vasiliki Pelekanou2,
Nicolas Acquavella2, Tim Wagenaar2, and Spyros K Stamatelos1
1Sanofi US Services Inc., Bridgewater, NJ; 2 Sanofi US Services Inc., Cambridge, MA; 3Applied BioMath, Concord,

MA, 4Biopharmaceutical New Technologies Corporation (BioNTech), Mainz, Germany.
Objectives: In contrast to conventional approaches, immunotherapy offers a promising alternative for cancer
treatment whereby the therapy engages the immune system instead of directly targeting tumor cells. By various
mechanisms of action, immuno-oncology therapies serve to augment the host’s immune function and promote
effective tumor immune surveillance with various facets of the immune system working in concert to eradicate thetumor. Intratumoral delivery of an mRNA mixture encoding for IL-12sc, IFNα2b, IL-15sushi and GM-CSF cytokines
induced pronounced regression in tumor-bearing mice at both local and distal untreated sites. Additionally, in mousemodels, the antitumor activity was further enhanced by combining the mixture with anti-PD1 and anti-CTLA4 immune
checkpoint inhibitors. Utilizing available data, we have developed a multiscale murine quantitative systems
pharmacology (QSP) model that captures the observed preclinical responses for mono- and combo-therapy settings.
Methods: A 7-compartment model was developed to characterize the pharmacokinetic and pharmacodynamic drug
responses of treated and untreated tumors, and their corresponding draining lymph nodes. The model simulates local
administration of the mRNA therapy at the primary site followed by internalization, translation, and secretion of proinflammatory cytokines within the tumor microenvironment. By binding to their corresponding receptors, the released
cytokines subsequently promote activation and amplification of tumor-infiltrating lymphocytes (TILs), including
CD8+, CD4+ T cells, and natural killer (NK) cells. Primary and abscopal tumor cell killing is modeled to be influenced
either directly or indirectly by these mobilized cellular mediators.
Results: Model simulation findings are in alignment with murine preclinical data as they predict enhanced tumor
growth suppression with increased doses of the mRNA monotherapy. Furthermore, simulations show an augmented
efficacy when combining the mRNA treatment with an anti-PD1 immune checkpoint inhibitor; as observed in the
preclinical mouse model.
Conclusions: Our preclinical QSP modeling efforts provide insights into the complex pathophysiology of solid tumors
and the dynamic interplay between cancer and immune system components following immunotherapy drug
administration. As such, the developed model provides a fundamental platform for probing the therapeutic effects ofa variety of cytokine-based immuno-oncology assets.

THU-045
First in Human (FIH) Pharmacokinetic (PK) Prediction of NKTR-255, an IL-15 Receptor Agonist, Based ona Target Mediated Drug Disposition (TMDD) Model

Kavitha Bhasi1, Palakshi Obalapur1, Janet Cetz1, Muhammad Baluom2, Xue Snow Ge2, Yinyan Tang3, Thomas
Chang3, Peiwen Kuo4, Brian R. Vuillemenot5, Takahiro Miyazaki4, Jacqueline A. Gibbons1,2, and Werner Rubas1
1

Non-Clinical PK, 2Clin Pharm and Quant. Sciences, 3Protein Chemistry, 4Research Biology, 5Safety Assessment,
Nektar Therapeutics, San Francisco, CA
Background: IL-15 is a cytokine that activates and provides expansion benefit to NK and CD8+ T cells and isunder clinical evaluation for cancer treatment. Exploiting the therapeutic value of IL-15 has been challenging due toits unfavorable PK properties requiring high daily doses or continuous IV infusion which result in poor tolerability.
NKTR-255 is an IL-15 receptor agonist exhibiting improved PK vs recombinant IL-15 in animals, thereby providing
sustained pharmacodynamic responses without daily or continuous dosing. A FIH clinical study (NCT04136756)
was initiated to evaluate safety and tolerability of NKTR-255 in patients with select hematologic malignancies.
Previously we presented a TMDD model designed to predict human drug disposition1. Here, we compare thepredictions from that model to available human PK from Cohort 1.
Methods: NKTR-255 exhibited 2-compartmental TMDD in mice, 2-compartmental linear PK in rats, and 1compartmental TMDD in monkeys. In the absence of 2-compartmental disposition in monkeys, allometric scaling
was confined to central volume (V) and clearance (CL). Allometric scaling exponents across species were 0.973 for
V and 1.13 for CL; additionally, a fixed exponent of 0.7 on monkey CL was also explored. For receptor binding
parameters (kon, koff) both monkey in vivo and human in vitro estimates were evaluated. Model-derived monkey invivo internalization rate (kint) was utilized, as human in vitro estimates were not available. Free receptor
concentration Rmax estimates were obtained from monkey PK/PD studies and were also derived from modeling rhIL15 PK2. Modeling was conducted in NONMEM® 7.4.
Results: As of May 14, 2020, PK data were available from the first patient after receiving the first two doses of 1.5µg/kg NKTR-255 (intravenous, Q21D). Preliminary modeling showed that human parameter estimates were withinor close to the simulation estimates. The human receptor binding kon and koff were comparable to human in vitro
estimates of 3.54 nM-1hr-1 and 0.256 hr-1, respectively. Rmax was comparable to model derived value from rhIL-15
human PK of 0.0933 nmol/kg. Internalization rate was comparable to kint derived from monkey studies of 0.0112 hr1. CL was within the range considered for simulations, 0.874-2.90 mL/kg/hr. V was fixed at the projected estimate of
32.1 mL/kg, due to slight underprediction of V. The observed Cmax and AUC of 27.5 ng/mL and 398 ng•hr/mL werecomparable to the predicted estimates of 37.3 ng/mL and 443 ng•hr/mL, respectively.
Conclusion: The pharmacokinetics of NKTR-255 in an initial cancer patient was adequately predicted by a TMDD
model which incorporated in vitro and in vivo animal data. Forthcoming data will further inform the humanparameter estimates.
References:
1) Bhasi K, et al. ACoP 2018.
2) Conlon KC, et al. J Clin Oncol. 2015.

THU-046

Population Pharmacokinetic Modeling of Midazolam to Estimate Efavirenz Inductionof CYP3A4/5 in Healthy Individuals
Authors: Kimberly S. Collins1, Jessica Lu1, Michael Heathman2, Zeruesenay Desta1
Affiliations: 1Indiana University School of Medicine, Indianapolis, IN, 2Metrum ResearchGroup, Tariffville, CT
Objectives: Efavirenz has been shown to interact with many drug metabolizing enzymes,
impacting the pharmacokinetics of numerous drugs. The aim of this study was to develop a
population pharmacokinetic model to determine the extent of efavirenz induction of
CYP3A4/5 and the contribution of genetic and non-genetic factors on midazolam
pharmacokinetics.
Methods: This study was conducted at the Indiana University School of Medicine ClinicalResearch Center. Adult men and women volunteers were given 1 mg oral midazolam after
single dose efavirenz (600 mg) and steady-state efavirenz (600 mg/day for 17 days).
Plasma samples were collected after midazolam dosing. A population pharmacokinetic
model was developed using 1149 midazolam concentrations and 1149 1’hydroxymidazolam concentrations from 70 individuals in NONMEM v7.4 using the SAEM
estimation method followed by IMP for calculation of the objective function. Model
performance was based on IMP objective function value, evaluation of diagnostic plots andvisual predictive checks. Bootstrapping will be performed to determine precision ofparameter estimates.
Results: A two-compartment model described both midazolam (with first order absorption)and 1’-hydroxymidazolam concentration data. Covariates included in the final model were
CYP3A4/5 genotype, sex, and single dose/steady-state efavirenz dosing on the CL/F of
midazolam and weight on the CL/F of 1’-hydroxymidazolam. Steady-state efavirenz
induction in CYP3A4/5 metabolism of midazolam was observed. For example, in individuals
who were CYP3A5 nonexpressors, CYP3A4 normal metabolizers and male, population
predicted apparent oral clearance of midazolam was 32.5 L/hr after single dose efavirenz
and 50.9 L/hr after steady-state efavirenz.
Conclusions: The final pharmacokinetic model including CYP3A4/5 genotype,single/steady-state efavirenz, sex, and weight are consistent with known mechanisms of
midazolam pharmacokinetics. Further analysis is necessary to determine the impact of
CYP2B6 and CYP3A4/5 genotype on efavirenz-mediated induction of CYP3A4/5.
References (if applicable): NA

THU-047
PK and PD modeling and simulation of GX-H9 in the adult patients with growth hormone deficiency
Kwan Cheol Pak1, Yun Jung Choi2, Su Jin Heo3, Hyeong-Seok Lim1
1
Department of Clinical Pharmacology and Therapeutics, Asan Medical Institute of Convergence Science andTechnology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea; 2Genexine
Inc., Seongnam, Republic of Korea; 3Handok Inc., Seoul, Republic of Korea.
Objective:
GX-H9, as long-acting preparation for improved compliance of patients with adult growth hormone deficiency(AGHD), was developed. The major purposes of this study were followed as: 1) To describe the pharmacokinetic(PK) and pharmacodynamic (PD) profile of GX-H9; 2) To explore appropriate starting dose of GX-H9 and thecriteria of dose increase/decrease of GX-H9 adopted in the simulation; 3) To predict the sampling time for IGF-1
SDS (Standardized Insulin-like Growth Factor 1 Score) close to average IGF-1 SDS after dosing of GX-H9 at steadystate for the purpose of design of phase 3 clinical trial.
Methods:
The serum GX-H9 and IGF-1 concentration data used in this modeling analysis were from the phase 1b/2 study [1].
PK-PD modeling and simulation analyses were conducted using NONMEM® (version 7.4.3) [2]. IGF-1 SDS was
simulated by two groups (Group 1 : older male, Group 2 : female who did not take estrogen and younger male)according to the PK-PD model based on the predicted serum IGF-1 level. The starting dose and the degree of dose
adjustment were determined by simulations (1,000 replicates) where the IGF-1 SDS is expected to be within thetarget range (-0.5 SDS ~ 1.5 SDS) [3] in majority of patients. In another simulation study (1,000 replicates),
we decided a single sampling time point for IGF-1 SDS which is possible to predict the average IGF-1 SDS atsteady state.
Results:
Two-compartmental linear model with mixture of structural model where majority of the parameters were describedby mixture models described the PK data well. The PD model was implemented using an indirect response model.
The simulation suggests that the appropriate starting dose was 9.547 mg for group I and 16.46 mg for group II. The
appropriate doses for increase and decrease during dose adjustment were respective 12.412 mg and 4.966 mg forgroup I and 14.322 mg and 5.730 mg for group II. The proper sampling times after the last dosing for the predictionof average IGF-1 SDS at steady state was 112 hour (90% PI, 58 - 169 hour) for group I and 116 hour (90% PI, 47 169 hour) for group II, respectively.
Conclusions:
We successfully constructed the PK-PD model for GX-H9 and IGF-1 using phase 1b/2 study. The sampling time for
IGF-1 SDS will help predict the average IGF-1 SDS to adjust doses, and provide a lot of useful information for
designing the future phase 3 clinical trial for GX-H9.
References[1] Ku CR, Brue T, Schilbach K, et al. Long-acting FC-fusion rhGH (GX-H9) shows potential for up to twicemonthly administration in GH-deficient adults. Eur J Endocrinol. 2018 Sep;179(3):169-179.
[2] Beal SL et al. 1989-2011. NONMEM Users Guides. Icon Development Solutions, Ellicott City, Maryland, USA.
[3] https://clinicaltrials.gov/ct2/show/NCT01225666

THU-048
Pharmacokinetics of buprenorphine in pregnant women with opioid use disorder
Larissa Lachi Silva1, Misty L McDowell1, Mary P Abernathy1, Andrea R Masters1, Christine Bach1, Sara K
Quinney1
1Indiana University School of Medicine, Indianapolis, IN, USA.
Objectives: Buprenorphine (BUP) is approved by US Food and Drug Administration to treat opioid addiction. BUP
is preferred to methadone for opioid maintenance therapy in pregnancy because it has been shown to result in lesssevere neonatal abstinence syndrome. There is a lack of studies about BUP pharmacokinetics in pregnancy. The
current dose recommendation of BUP for pregnant women is based on posology designed for the nonpregnant subject.
However, some pieces of evidence suggest a decrease in BUP exposure due to high clearance induced by pregnancy.
The objective of this study was to assess the population pharmacokinetics of BUP in pregnant women.
Methods: Pregnant women (n = 34) in the third trimester of singleton gestation, using BUP for opioid use disorder,
with total daily doses ranging from 2 to 24 mg, were enrolled following informed consent. Blood was withdrawn prior
to dosing and at 0.25, 0.5, 1, 2 and 4 hours after the morning dose of BUP. Plasma samples were analyzed by LCMS/MS to quantify BUP. Plasma concentration profile data were analyzed using Stochastic Approximation
Expectation Maximization (SAEM) followed by importance sampling methods implemented on Monolix 2019R2.
The effects of estimated gestational age (EGA) and body mass index (BMI) were evaluated as covariates. The model
selection was based on goodness-of-fit plots, precision of estimations and by changes in Bayesian information Criteria(BIC) and log-likelihood ratio (LRT).
Results: The model with one-compartment, first-order absorption, proportional error and exponential inter-individual
variability on clearance (CL/F) and apparent volume of distribution (Vd/F) was the most suitable for this dataset.
Population estimates were absorption rate constant (ka) of 2.77 ± 0.823 h-1, CL/F of 201 ± 32.2 L/h and Vd/F of 8820
± 1760 L. No correlations were identified between CL/F or Vd/F and EGA or BMI.
Conclusions: A better understanding of BUP exposure in pregnancy is a requirement to optimize the treatment of
opioid use disorder in this population to avoid withdrawal symptoms and decrease the frequency of neonatal syndrome
disorder. Studies using NCA have already shown that pregnant women had lower AUC compared with non-pregnant.
The population model showed an increase of BUP clearance in pregnant women and, consequently, lower exposure.
Corroborating with NCA analysis, this population pharmacokinetics model also indicates that doses adjustment of
BUP may be required during pregnancy

THU-049

Population Pharmacokinetic analysis of the next generation oral SERD AZD9833 in women with ER-positive,
HER-2 negative advanced breast cancer

Li Zhang1, Tim Brier2, Richard Mather2, Justin P O Lindemann2, Teresa Klinowska2, Andy Sykes3
1

CPQP, Clinical Pharmacology and Safety Sciences, R&D, AstraZeneca, Boston, USA; 2Research and Early
Development, Oncology R&D, AstraZeneca, Cambridge, UK; 3CPQP, Clinical Pharmacology and Safety Sciences,
R&D, AstraZeneca, Cambridge, UK

Objectives : AZD9833 is a next generation oral selective estrogen receptor (ER) antagonist and degrader (SERD)1
that has shown antitumor efficacy in a range of preclinical xenograft models of breast cancer. The first-in-human study(SERENA-1; NCT03616587) evaluating AZD9833 as monotherapy and in combination with CDK4/6 inhibitor
palbociclib in pre-menopausal and post-menopausal women with advanced ER-positive HER2-negative breast cancer
is ongoing2. This analysis aimed to develop a population pharmacokinetic (Pop PK) model for AZD9833 and assess
the influences of intrinsic and extrinsic factors on AZD9833 PK in patients with ER-positive, HER2-negativeadvanced breast cancer (ABC).
Methods : The Pop PK model was developed using plasma concentrations of AZD9833 in ABC patients after
receiving once daily (QD) oral administration of AZD9833 monotherapy across doses ranging from 25 to 450 mg in
the ongoing Phase 1 study. Forward selection and backward elimination were used to evaluate the following
covariates: demographic (weight, age), lab (renal/hepatic function), disease severity (ECOG) and concomitant
medication (i.e. proton pump inhibitors (PPIs)). Model development was conducted in NONMEM® (version 7.3.0)
and the final model was validated by visual predictive checks and bootstrap.
Results : The final Pop PK model development dataset included 57 ABC patients with 1126 concentrations of
AZD9833. The longitudinal PK profile of AZD9833 after multiple oral QD dosing was adequately described by a
two-compartment model with linear elimination plus first order absorption in ABC patients. The population estimatesof the key PK parameters were precisely estimated and were : mean apparent clearance of 79.4 L/hr (8.11 %RSE)
with moderate inter-individual variability of 41.3 %CV (%22.9% RSE). None of the covariates are statisticallysignificant. Model-derived Bayesian exposure at steady-state (Day 15) was not correlated with demographic (weight[ ranged 44 – 108 kg ] , age [ ranged 39 – 79 years]), lab (renal [eGFR ranged 45 – 120 mL/min/1.73 m2] / hepaticfunction [mild hepatic impairment plus normal hepatic function]), disease severity (ECOG) or concomitant medication(PPIs).
Conclusion : The Pop PK model adequately described AZD9833 PK in ABC patients and enabled robust predictionof individual patient exposure in support of potential dose justification decisions for special populations.
References :1. Jamie Scott. Small-molecule estrogen receptor degraders (SERDs): Chemical exploration and optimisation at
AstraZeneca. ACS National Meeting, San Diego, USA, 2019
2. Hamilton E, Oliveira M, Banerji U, et al. A Phase 1 Dose Escalation and Expansion Study of the Next Generation
Oral SERD AZD9833 in Women With ER-Positive, HER2-Negative Advanced Breast Cancer. 2020 ASCO
Annual meeting, Chicago, USA.

THU-050
A QSP model of neurofilament trafficking in SOD1 Amyotrophic Lateral Sclerosis
Alessio Paris1, Pranami Bora1, Silvia Parolo1, Michael Monine2, Xiao Tong2, Satish Eraly2, Alex McCampbell2, Danielle
Graham2, Toby Ferguson2, Stephanie Fradette2, Enrico Domenici1,3, Ivan Nestorov2, Luca Marchetti1
1

Fondazione The Microsoft Research - University of Trento Centre for Computational and Systems Biology (COSBI);
Biogen, Inc., Cambridge, Massachusetts, USA; 3University of Trento, Department of Cellular, Computational andIntegrative Biology - CIBIO
2

Objectives: Amyotrophic Lateral Sclerosis (ALS) is a neurodegenerative disease characterized by elevated levels ofneurofilament light (NFL) and heavy (NFH) chains in blood and cerebrospinal fluid (CSF) [1]. Mutations in the SOD1
gene are the second most common genetic cause of ALS, with high variability in the rate of disease progression
depending on the causative mutation. The study aims to develop a Quantitative System Pharmacology (QSP) modelof SOD1-ALS for evaluating the potential of NFs as early biomarkers of disease onset and treatment effect.

Methods: The model was implemented as a set of Ordinary Differential Equations (ODEs) describing the trafficking
of NFs from the nervous system to the periphery (see Figure ). In addition to literature data, NF concentrations in CSF
and blood from a randomized, placebo-controlled single and multiple ascending dose Phase I/II study of tofersen(BIIB067), an antisense oligonucleotide (ASO) being investigated for the treatment of SOD1-ALS, were used tocalibrate the model. To consider the difference in rates of disease progression in patients with SOD1-ALS mutations
typically associated with slower or faster disease progression, two patient groups were defined post hoc for the
purposes of analysis. The model was then validated by assessing its capability to simulate a set of treatment scenarios
previously excluded from the calibration. NF concentrations in CSF and blood from longitudinal natural history andbiomarker studies on ALS mutation carriers [1,2] were also used to define and parametrize a function reproducing NF
time-series data of disease progression.

Results: The model was calibrated to fit the experimental data of NFL and NFH from healthy subjects and SOD1ALS patients with different disease progression rates in both untreated and treated conditions. The computed
parameter estimates indicate a 10-fold higher rate of NF release from diseased neurons in patients with faster rates of
disease progression. Moreover, the model accurately predicts the reduction of NF concentration in the periphery after
a multiple dose administration of tofersen (100 mg) in subjects with faster disease progression. Average peripheral
NF concentration in healthy subjects was used as a threshold that may potentially be exceeded in SOD1-ALS patients.

The model enables to predict the time from the moment of exceeding this threshold to the onset of symptoms, thus
suggesting NFs as a possible biomarker for pre-symptomatic diagnosis of ALS.

Conclusions: We present a QSP model of NFL and NFH trafficking in untreated and treated SOD1-ALS patients.
The predictive model performance makes it a valuable tool for investigating NFs as biomarkers of disease progression
and treatment response in SOD1-ALS and for in silico evaluation of novel treatment protocols.

References
1 Benatar et al. Amyotrophic Lateral Sclerosis and Frontotemporal Degeneration 20:538–548, 2019.
2 Benatar et al. Annals of Neurology 84:130–139, 2018.

Figure 1: Model scheme of NF trafficking in SOD1 Amyotrophic Lateral Sclerosis. Red arrows indicate the NF
additional release from neurons due to the disease.

THU-051
A multivariate hybrid approach for exposure-response analysis of oncology treatments using machinelearning classifiers and population pharmacokinetic modeling

Maitreyee Bose1, Alicia Kaestli2, Sandeep Dutta3, Khamir Mehta1
1

Clinical Pharmacology Modeling and Simulation, Amgen Inc., South San Francisco, CA
Digital Health and Innovation, Amgen Inc., Thousand Oaks, CA
3
Clinical Pharmacology Modeling and Simulation, Amgen Inc., Thousand Oaks, CA
2

Background:
Multivariate machine learning approaches offer a powerful way to model and predict discrete pharmacodynamic (PD)events, for example, the use of tree-based classification methods can be used for modeling discrete adverse events orefficacy of cancer treatments as a function of pharmacokinetics (PK) and specific patient attributes. These exposureresponse analyses are critical to the selection of the optimal dosing regimen of oncology drugs. However, direct useof PK exposure metrics can be plagued by the limitations of the study design and may be sparse, noisy, or missing.
Objectives:
In this poster, we develop and demonstrate the utility of a hybrid multivariate analysis methodology incorporating PK
model driven inputs into machine learning models to improve the accuracy of exposure-response relationships
Methods:
The workflow associated with our proposed method is outlined in Figure 1. By inclusion of a population PK model as
the source of the PK predictors, we anticipate significant improvement in the predictive capability of the exposureresponse models.
We demonstrate the performance of our method using several simulated examples and comparing the performance of
our approach (hybrid), traditional approach, and the ground truth. Specifically, we use both binary and ordinal outcome
variables to simulate the PD. For the underlying model governing the PD outcomes, we assume models with increasing
complexity, for example, linear logistic to non-linear and non-monotonic models with correlated patient specificpredictors. The PK model is assumed to be compartmental, while the study design is assumed to reflect a typical phase
3 design. We employ random forest-based methods to model the PD; random forests are powerful tools when there
are multiple interacting predictors whose relationship with the outcome is not explicitly known. The PK model isdeveloped using a population approach with NLME modeling in NONMEM.
We systematically evaluate the performance of each of the above scenarios by varying the PK properties, i.e., thebetween subject variability, the model residual variability and finally the resolution of the observed data.

Results:
Preliminary results indicate that the hybrid method outperforms the traditional approach in scenarios with moderate
to high between subject variability (> 40%). Situations with sparse data and complexity of the exposure-response
relationship also warrant the hybrid approach. We showcase the robustness of the approach by exploring the prediction
accuracy across the scenarios mentioned above.
Conclusions:
We have developed a novel multivariate hybrid approach for improved characterization of exposure-response analysisof oncology treatments, that leverages information across the individuals and efficiently integrates diverse predictor
variables.

Figure 1: (a) Traditional PD prediction using observed PK. (b) Hybrid PD prediction using model-predicted PK.

THU-052

A Method to Estimate TTP from Summary Level OS and PFS Data: An Example of Use of TTP to Compare
Treatments Based on Published Literature for NSCLC Patients Receiving Immune Checkpoint Inhibitors
Mario Nagase1, Sameer Doshi1, Sandeep Dutta1, Chih-Wei Lin1
1

Clinical Pharmacology Modeling and Simulation, Amgen, Thousand Oaks, CA, USA

Objectives: Summary measures such as progression-free survival (PFS), and overall survival (OS) are commonly
reported in literatures for oncology trials, while time to progression (TTP), another key surrogate endpoint, is not
usually reported. Since PFS events include not only disease progression but death, it is difficult to separate TTP
from PFS and PFS is not interchangeable with TTP. Capturing TTP, a direct measure of treatment effects on cancer
growth, would be helpful for researchers to better understand how drugs delay tumor growth. A time-variant
transition hazard model was developed to estimate TTP from summary level PFS and OS and applied to publishedimmune checkpoint inhibitor trials for non-small cell lung cancer (NSCLC).

Methods: Publications on immune checkpoint inhibitors for NSCLC were extracted from PubMed,
www.clinicaltrials.gov, company registries, conference abstracts/posters/presentations, and regulatory reviews from
FDA. The analysis dataset comprised of 19 studies consisting of 32 arms with a total of 6,676 patients, and included
PD-1 (pembrolizumab, nivolumab), PD-L1 (atezolizumab, durvalumab), and CTLA-4 (ipilimumab) inhibitors as
monotherapies or combination therapies. Kaplan-Meier plots from publications were digitized by month, and thenumber of patients at risk over time was compiled. A time-variant transition hazard model assuming the states 1)
INITIAL, 2) PROGRESSION, 3) DEATH, and 4) CENSORED was developed (Figure). Progression hazard (𝑘*' ), death

hazard due to progression (𝑘'+ ), death hazard without progression (𝑘*+ ), censor hazard (𝑘*, ), and censor hazard
after progression (𝑘', ) were estimated. Time-variant rate was set to progression rate, and random effects (between
study and arm) were evaluated. All the parameters and the latent variables for the number of patients at risk at eachmonth were estimated.

Results: The time-variant transition hazard model optimally characterized the survival probability and number ofpatients at risk for both PFS and OS. PD-1 monotherapy provides better OS and PFS, and longer TTP thanchemotherapy alone. The estimated TTP for PD-1 ranges from 4.9 to 11 months, longer than the estimated 3.4-8.2

months for chemotherapy. Within PD-1 monotherapies, nivolumab yielded similar to slightly shorter TTP comparedto pembrolizumab. PD-1 combined with chemotherapy or other targeted therapy generally had longer TTP than PD1 monotherapy .

Conclusions: OS and PFS implicitly conveys information about TTP. This model-based method was able toestimate TTP from summary level OS and PFS data and provided a quantitative approach for understanding thepatterns of disease progression across treatments through the time-variant disease progression rate function andsummarize how different immune checkpoint inhibitor treatments delay TTP. The method can be applied to theother therapeutic areas in oncology.

Figure: Model Schematics

fk12(t): time-variant piecewise hazard function to progression

THU-053

A Quantitative Systems Pharmacology Modeling Platform for Evaluating Triglyceride Profiles
in Patients with High Triglyceride under Evinacumab Therapy

Jim Bosley1, Lyn Powell1,3, Feng Yang2, Xia Pu2, Nidal Huniti2, John D. Davis2, Yi Zhang2 andMasood Khaksar Toroghi2
1
3

Clermont, Bosley LLC, Kennett Square, PA, 2Regeneron Pharmaceuticals, Tarrytown, NY,

Lynx Bioconsulting, Monmouth, OR

Objectives: The objective of this work is to evaluate lipid trafficking, specifically to understand themechanism of action and predict the transient responses of different TG-rich particles to modulation of
lipoprotein lipase (LPL) activity by Evinacumab, a mAB whose cognate ligand is ANGPTL3 using aquantitative systems pharmacology (QSP) modeling approach.

Methods: A QSP lipid modeling platform was created, based on a previously published lipid model 1
containing only two plasma triglyceride (TG) compartments: meal-derived chylomicrons and hepaticallyderived very low-density lipoproteins (VLDL). The published model was augmented and changed toaddress specific questions relevant to the development of Evinacumab. Intermediate and low-density
lipoprotein (IDL and LDL) TG compartments were added to account for TG uptake and particle
progression. The effects on LPL activity of modulating ApoCIII activity with siRNA therapy, and a target
mediated drug disposition (TMDD) model were included in the model. In addition, ANGPTL3
biosynthesis, clearance, and wall bound compartments were also included.

The particle mass balance approach captured the time profiles of fitted clinical plasma TG data well andpredicted the TG profiles for other trials not used in fitting. Initial attempts to use LDL-TG to estimate
LDL-cholesterol were less satisfactory, as these simpler models did not account for known mechanisms ofparticle update. Biological insight lead to improved LDL-cholesterol estimation, by adding receptors toperipheral tissue to represent whole particle TG-rich particle uptake to the model’s existing incremental TG
uptake via LPL. Virtual patients were created to represent several classes of subject phenotypes. The model
was developed using SimBiology® with Matlab scripting.

Results: The developed QSP model allows stable (>1 year) and accurate representation of plasma TG,
glucose and insulin, both in the absence and presence of anti-ANGPTL3 mAB. Model results agreed wellwith the clinical data that were published in the original article describing the base model1, as well as withother published and in-house clinical data. Explicit treatment of multiple receptors and LPL action
supported facile generation of different virtual patients. Anti-ApoCIII siRNA simulation results agreed withpublished data.

Conclusions: Simulations from a TG-driven QSP model with novel accounting for TG accurately matched
TG profiles for a variety of subject types, both for placebo and evinacumab-treated subjects. Appropriate
representation of LPL-driven and particle uptake-related TG uptake allowed estimation of therapy effects
on cholesterol from this TG model. A physiologically based TMDD model allowed representation of andprediction of trial mAb, ANGPTL3, and TG. The TMDD approach also allowed inference of unmeasured
quantities of interest (e.g., free ANGPTL3). Adding alternate therapies (ApoCIII siRNA) replicated clinical
trial data, further enhancing confidence in the model. Finally, a modeling platform was developed that
represented TG physiology and drug effect in a range of patient populations with varying levels of TG
and/or cholesterol, allowing testing of different hypotheses.

References:(1) Pratt, A. C.; Wattis, J. A. D.; Salter, A. M. Mathematical Modelling of Hepatic Lipid Metabolism.
Math. Biosci. 2015, 262, 167–181. https://doi.org/10.1016/j.mbs.2014.12.012.

THU-054

Individual Risk Prediction Using Machine Learning Approaches in
Cardiovascular Outcomes Trials
Matthew C. Pharris1, Lindsay E. Clegg2, Robert J. Mentz3, Rury R. Holman4, William R. Hiatt5, Manesh
Patel6, Robert C. Penland1, David W. Boulton2
1

Clinical Pharmacology & Safety Sciences, R&D, AstraZeneca, Boston, USA;
Clinical Pharmacology & Safety Sciences, R&D, AstraZeneca, Gaithersburg, USA;
3
Duke University and Duke Clinical Research Institute, Duke University School of Medicine, Durham,
NC, USA;
4
Diabetes Trials Unit, University of Oxford, Oxford, UK;
5
University of Colorado School of Medicine, Division of Cardiology and CPC Clinical Research, Aurora,
CO, USA;
6
Division of Cardiology, Duke University Health System, USA
2

Objectives: A risk calculator for predicting cardiovascular (CV) events in individual patients may aid in
personalizing treatments and optimizing patient cohorts for clinical trials. However, existing CV risk
calculators are built routinely on data from less diverse patients and many perform poorly for particularpatient subgroups such as people with type 2 diabetes (T2DM). Machine learning (ML) may improve on
existing CV risk calculators, but it requires extensive training data and predicts risk poorly for low
frequency outcomes of interest, such as major cardiovascular events (MACE). To compile sufficienttraining data, we pooled two CV outcomes trials that included nearly 20,000 multinational T2DM patients.
We used these data to train an ML model to generate event time distributions that predict n-year MACE
risk for individual patients.
Methods: Model development involved covariate selection, trial dataset pooling, data pre-processing, andmodel training using data from the large-scale EXSCEL (NCT01144338) and EUCLID (NCT01732822)
CV outcomes trials. Covariate selection was based on characteristics broadly categorized as baseline,
medical history, biomarker, concomitant medication, or adverse event covariates. Covariates selected fromthe two trials were identical wherever feasible. Thus, for trial dataset pooling, if a covariate appeared inonly one study, then in the other we introduced placeholder values. Placeholder and missing values were
imputed, and complemented with binary, indicator covariates. We pre-processed by regularizing patientobservations to the nearest three-month time-point; standardizing each covariate; and balancing patientswith and without observed MACE, fulfilling the requirements our neural network-based ML algorithm.
We used the pooled data to train a recurrent neural network (RNN) to predict Weibull distributions of timeto-event (WTTE). Specifically, because ML predictions of discrete event times are error-prone, our model
instead generates event time distributions estimating an individual patient’s future risk of MACE.

Results: The model generated individual MACE probabilities over time. The figure shows how successive
observations for the same patient (each trace) yielded sharpening distributions about their true MACE event
time of 5 years (vertical red line). With such predictions, we estimated all patients’ event times and thenconstructed population survival curves. Indeed, for a preliminary model trained on EXSCEL data alone,comparing predicted and true survival curves using the log-rank test (given five observations of eachpatient) yields a p-value of 0.21, suggesting agreement. Moreover, the preliminary model distinguishes
between low and high risk of MACE, predicting 5-year MACE occurrence with a C-index of 90.9%, again
suggesting strong model performance.

Conclusions: Our methods for pooling datasets and predicting event time distributions shows how ML canbe used to predict individual MACE risk from longitudinal clinical covariates. In the future, it may bepossible to use this model to support individualized treatments, optimize clinical trial cohorts, orquantitatively explore alternative disease treatment strategies.

THU-055
Model-Informed Benefit Risk Assessment of Nivolumab 480 mg Every 4 Weeks acrossMultiple Tumor Types in Japanese Patients
Mayu Osawa1, Vijay D. Ivaturi2, Shinji Uemura3, Kazuyuki Yamada3, Mathangi Gopalakrishnan2, Joga V.S.
Gobburu2, Takayo Ueno1, Amit Roy4, Xiaochen Zhao4
1

Bristol-Myers Squibb K.K., Tokyo, Japan; 2University of Maryland, Baltimore, MD, USA; 3Ono Pharmaceutical,
Osaka, Japan; 4Bristol-Myers Squibb, Princeton, NJ, USA

Objectives: Flat dosing regimens of nivolumab 240 mg every 2 weeks (Q2W) and 480 mg every 4 weeks (Q4W) areapproved in the US, EU and several other markets to provide more flexible options than the initially approved 3 mg/kg
Q2W regimen utilizing upon model-based assessment [1,2]. In Japan, nivolumab 3 mg/kg Q2W and 240 mg Q2W areapproved for multiple tumor types. To assess the benefit-risk of nivolumab 480 mg Q4W in Japanese patients, modelbased approach was used by leveraging robust characterization of nivolumab pharmacokinetics and exposure-response(E-R) relationships for efficacy and safety.
Methods: Nivolumab exposures for 3 mg/kg Q2W, 240 mg Q2W, and 480 mg Q4W were simulated for 420 Japanesepatients across tumor types using a previously established population pharmacokinetic model of nivolumab [3]. The
qualified E-R models for efficacy and safety [4] were used to predict the responses for the 3 dosing regimens forJapanese patients across tumor types. The time-averaged concentration over the first 28-days of treatment (Cavgd28)
was selected for the primary analyses of E-R. Sensitivity analyses were conducted using trough concentration at Day28 (Cmind28) for efficacy, and the peak concentration after the first dose (Cmax1) for safety. E-R relationships werecharacterized using a Cox proportional hazard model for efficacy, and a logistic regression model for safety.
Results: Predicted nivolumab Cavgd28 with 480 mg Q4W in Japanese patients was higher than that with 3 mg/kg
Q2W (~72%) and 240 mg Q2W (~21%). Predicted Cmax1 for 480 mg Q4W was higher than 3 mg/kg Q2W (~185%)
and 240 mg Q2W (~100%), while Cmind28 was comparable with 3 mg/kg Q2W (~6%) and lower than 240 mg Q2W(~-25%). The predicted safety profiles with the 3 dosing regimens differed by ≤4% across tumor types for adverse
events leading to discontinuation/death, adverse events of Grade 3 or higher, or immune-mediated adverse events ofGrade 2 or higher. The predicted probability of survival and achieving a response for nivolumab 480 mg Q4W weresimilar to those for 3 mg/kg Q2W or 240 mg Q2W. Results of sensitivity analyses were consistent with the E-R
analyses with Cavgd28.
Conclusions: Given the established flat E-R relationships for nivolumab, safety and efficacy outcomes achieved with
480 mg Q4W are predicted to be similar to 3 mg/kg Q2W and 240 mg Q2W. Based on the quantitative clinicalpharmacology approach, the less frequent dosing regimen of nivolumab Q4W is expected to produce similar benefitrisk profile compared to the approved Q2W regimens, and to be an alternative treatment option for Japanese patients.
References: [1] Zhao et al., Ann Oncol. 2017;28(8):2002-2008. [2] Zhao et al., Ann Oncol. 2020;31(2): 302-309. [3]Bajaj et al., CPT Pharmacometrics Syst Pharmacol. 2017;6(1):58-66. [4] Bei et al., Cancer Sci. 2020;111(2):528-35.

THU-056

Sensitivity analysis framework for assessing drug target probability ofsuccess in early drug discovery
Michael Vilkhovoy1, Jangir Selimkhanov1, Erica L. Bradshaw1
1

Quantitative Solutions, Quantitative Translational Sciences, Takeda Pharmaceuticals Inc., San Diego, CA, USA

Objectives: Identifying a biological target that can modulate a pathway that results in a therapeutic effect is thefoundation for a successful drug discovery program. However, biological pathways are highly interconnected andcomplex. Thus, successfully identifying the appropriate therapeutic target in a pathway is often unintuitive anddifficult to predict. Approaches such as genetics, proteomics, bioinformatics, etc., are often used to identify targets
but, these methods often lack mechanistic descriptions of the pathway [1]. Here, we provide a sensitivity analysis
framework to enable target prioritization and guide future experiments to increase our confidence in target selection.

Methods: A mechanistic model of a prototypical inflammatory pathway was built as a proof-of-concept. The model
structure consisted of 17 parameters and 4 potential targets. We then generated an ensemble of parameter sets using
Sobol sequencing. The ensemble was used to assess the modulation of the inflammatory signal with the inhibition or
activation of each potential target. The magnitude of reduction in the inflammatory signal relative to no treatment was
defined as the therapeutic effect of each target. The targets were scored by their probability of success compared tothe other targets across the ensemble and ranked according to their therapeutic effect. In parallel, the framework
identified the most significant parameter towards target selection by examining the variance of reduction in the
inflammatory signal across targets. A large variance would yield a more conclusive target choice, whereas a small
variance would yield an inconclusive choice (Fig. 1).

Results: The sensitivity analysis framework was used to rank the targets and to determine their theoretical probabilityof success. Thus, we showed that the number of potential targets can be narrowed down to a select few, even with apoorly parameterized system. Furthermore, the framework identified the parameters that need to be determined
experimentally to increase confidence in target selection. Once the identified parameter is measured experimentally,it can be fixed in the model and the process can then be repeated. With a new ensemble of parameters generated, theframework can reassess the target selection with lower uncertainty compared to the first ensemble.

Conclusions: We present a sensitivity framework that can be used in early drug discovery programs to prioritize
biological targets based on a model-predicted therapeutic effect. The framework can be further leveraged to identify
the most critical parameter value necessary to achieve the greatest improvement in target selection confidence. This
process can be applied iteratively to effectively support drug discovery programs.

References:1.

Paanamen, J. & Fortino, V. Briefings in Bioinformatics. (2019).

Figure 1: Sensitivity analysis framework for assessing drug target probability of success for early drug discovery.

THU-057

QSP modeling predicts higher naloxone doses will safely reverse
more opioid overdoses and save lives.
Ronald B. Moss1, Dennis J. Carlo1, Christina Friedrich2, Katherine, Kudrycki2, Rebecca Baillie2, Meghan Pryor2,
Mike Reed2
1

Adamis Pharmaceuticals Co., San Diego, CA, USA; 2Rosa & Co. LLC, San Carlos CA, USA

Objectives:
The number of deaths due to synthetic opioids has increased dramatically in the last 5 years [1]. Current
recommended doses of naloxone for overdose reversal may be inadequate, and redosing is often required. Becauseof naloxone’s use in opioid overdose in a community setting, clinical trials are not possible. The FDA recommended
modelling of displacement of potent opioids with naloxone, as one possible avenue for providing justification to
support approval of a higher clinical dose to address an unmet medical need.
Methods:
We developed a Mu Opioid Receptor Binding quantitative systems pharmacology (QSP) model with competing
drug treatments (naloxone, fentanyl) based on relevant mechanistic data. The model includes detailed mu opioid
receptor dynamics, drug pharmacokinetics, drug effect site concentration, and competitive ligand binding. Modelparameters such as drug pharmacokinetics, receptor numbers, ligand binding kinetics, and receptor trafficking wereidentified from literature data.
Results:
The model was developed and qualified, and competitive binding simulations conducted for the synthetic opioid
fentanyl and naloxone. At a fentanyl level consistent with a 10 mg trans-dermal dose, naloxone treatment at thecurrent approved dose of 2 mg (intra-muscular) reduces mu receptor occupancy (RO) by fentanyl to just below 50%
at 10 minutes post dose. Simulated naloxone treatment at higher doses (5 mg or 10 mg) reduced fentanyl RO to 29%
and 17%, respectively, at 10 minutes past treatment. Higher doses of naloxone resulted in a faster fentanyl RO drop.
Naloxone treatment of 2, 5, and 10 mg reduced RO below 50% in 10, 4, and 3 min, respectively, after naloxone is
dosed. A similar pattern was observed at varying levels of fentanyl concentrations. In all cases, higher-dose
naloxone led to greater displacement in a shorter amount of time, strongly suggesting that lives could be saved by
the availability of higher-dose naloxone devices.
Conclusions:
Simulations using the Opioid Receptor Binding Platform demonstrate the added utility of higher naloxone doses indisplacing fentanyl from the mu receptor. Since a clinical trial is not feasible in this indication, QSP modelingincreases confidence in the added utility of the higher-dose naloxone product.
1. Moss, R.B. and D.J. Carlo, Higher doses of naloxone are needed in the synthetic opioid era. Subst Abuse Treat
Prev Policy, 2019. 14(1): p. 6.

Figure 1. Simulated changes in mu receptor occupancy with fentanyl only or fentanyl with different doses ofnaloxone treatment. Dashed line in graph shows 50% receptor occupancy (RO).

THU-058
Incorporating pharmacological target-mediated drug disposition (TMDD) in a whole-body physiologically
based pharmacokinetic (PBPK) model of linagliptin in rat and scale up to human
Nan Wu1, Guohua An1
1

Division of Pharmaceutics and Translational Therapeutics, College of Pharmacy, University of Iowa, Iowa city,
Iowa.
Objectives: Linagliptin is a potent small molecule dipeptidyl peptide 4 (DPP-4) inhibitor for the treatment of type 2
diabetes mellitus (T2DM). Linagliptin demonstrated substantial and complex nonlinear pharmacokinetics due to its
saturable binding to DPP-4 in plasma and various tissues, a phenomenon known as target-mediated drug disposition(TMDD). Our objective was to build a mechanistic whole-body PBPK-TMDD model characterizing linagliptin
nonlinear pharmacokinetics in rats, scaling up to human and providing a potential modeling framework for othersmall-molecule compounds exhibiting TMDD.
Methods: The data used for model development came from literature reports. All pharmacokinetics and tissue
distribution data for linagliptin following different doses in both wildtype and DPP-4 deficient rats were analyzed
simultaneously using the nonlinear mixed effect modeling approach with NONMEM interfaced with Pirana. A wholebody PBPK-TMDD model containing plasma and 14 tissue compartments with a total of 26 differential equations wasdeveloped. Among 14 tissue compartments, TMDD binding process was incorporated in 9 of them, namely plasma,kidney, liver, spleen, lung, skin, salivary gland, thymus, and reproductive system. The PBPK-TMDD model
developed in rats was extrapolated to human by taking into account the interspecies physiology differences, and thescale up potential of this model was evaluated by comparing model simulated vs literature reported humanpharmacokinetic profiles.
Results: Our final model adequately captured the concentration-time profiles of linagliptin in both plasma and various
tissues in both wildtype rats and DPP4-deficient rats following different dose regimens. The association rate constant(kon) in plasma and tissues were estimated to be 1.53 and 0.0056 nM-1h-1, respectively, and dissociation rate constant(koff), in plasma and tissues were estimated to be 0.0814 and 0.0098 h-1, respectively. The predicted DPP-4
concentration in plasma for a 0.25 kg rat is 1.11 nmol/L, which is close to the experimental value (3.84 nmol/L). The
binding affinity of linagliptin to DPP-4 (Kd) was predicted to be higher in plasma (0.053 nM) than that in tissue (1.75
nM). When scaled up to human, this model accurately predicted the substantial and complex nonlinear
pharmacokinetic behavior of linagliptin in human adults that is characterized by less-than dose proportional increasein plasma exposure, dose-dependent clearance and volume of distribution, as well as long terminal half-life withminimal accumulation after repeated doses.
Conclusions: Our PBPK-TMDD model not only captures linagliptin tissue and plasma concentration data in all rats
but also has excellent scale-up capability. Our modeling work is not only novel but also of high significance as thewhole-body PBPK-TMDD model platform developed using linagliptin as the model compound could be applied toother small-molecule compounds exhibiting TMDD to facilitate their optimal dose selection.

THU-059
Using Population Pharmacokinetics to capture the binding of a Novel Bispecific Fibroblast Activation Protein(FAP) to its 4-1BB Ligand (4-1BBL) and support Phase 2 dose selection in oncology patients
Authors: Sandrine Micallef1; Peter N Morcos2; Christine McIntyre3; Nicolas Frey1; Vincent Buchheit1; Jasna
Canadi2; Christina Claus4; Ernesto Guarin1; Jon Chick3; Oliver Krieter5; Nassim Djebli1

Institutions: All authors worked for Roche Pharmaceutical Research and Early Development, Roche Innovation
Center at the following locations – 1 Basel, Switzerland; 2 New York City, USA; 3 Welwyn, UK; 4 Zurich,
Switzerland; 5 Munich, Germany.

Objectives: Bispecific modalities show promise in cancer immunotherapy. RO7122290, induces potent 4-1BB
mediated T cell co-stimulation when cross-linked to FAP expressed on tumor stroma [Claus et al 2019]. The
objective of this analysis was to characterize the PK of RO7122290 administered either alone or in combination withatezolizumab in patients with solid tumours through population pharmacokinetic modeling to inform doserecommendation for Phase 2.
Methods: The ongoing study consists of 3 parts: Part A - RO7122290 single agent dose escalation; Part B –RO7122290 dose escalation in combination with atezolizumab; Part C – tumor specific expansion cohort(s),RO7122290 in combination with atezolizumab. RO7122290 was administered via intravenous infusions of 5 to 2000
mg QW as a single agent, and at doses of 45 to 2000 mg in combination with atezolizumab. Serial and sparse PK
were used for Population Phamacokinetic model development with NONMEM v7.4.1. Covariate analysis was
performed exploring age, gender, body weight. Impact of atezolizumab on the RO7122290 PK was also
investigated. Model selection was confirmed by diagnostic plots, visual prediction-corrected predictive check,parameter precision and bootstraping techniques.
Results: 3400 PK samples from 108 patients were included in the analysis. RO7122290 was described by a
2-compartment model with linear and non-linear (Michaelis-Menten) clearance (Figure 1). Inter-individual
variability was included on the following parameters: Linear clearance (CL), central and peripheral volume of
distribution (V1, V2), Michaelis-Menten constant (KM) and maximum elimination rate (VM). Covariance between
CL and V1, and between VM and KM random effects improved the model. Residual variability was described by
combined proportional and additive error. Linear CL appeared slightly higher than typical for IgG1 [Bai et. al.
2012]; however this is also reported for other bispecific antibodies [Chen, 2017]. V1 and V2 approximated plasma
volume. Nonlinear CL likely reflects FAP-binding, as ascribed in nonclinical species [Driessen et al 2019].
Conclusions: A popPK model capturing FAP-binding is established for RO7122290 and is being used to derive
exposures for PK/PD investigations in support of the recommended Phase 2 dose selection.

Figure 1: Fitting using linear CL only (left) vs. parallel Michaelis-Menten non-linear and linear clearances (right).
Individual (continuous) and population (dashed) predicted concentrations versus time.

References:Bai, Shuang, et al. "A guide to rational dosing of monoclonal antibodies." Clinical pharmacokinetics 51.2
(2012): 119-135.
Chen, Y., and Yan X. "Pharmacokinetics of bispecific antibody." Current Pharmacology Reports 3.3 (2017):
126-137.
Claus, C., et al. "Tumor-targeted 4-1BB agonists for combination with T cell bispecific antibodies as off-theshelf therapy." Science translational medicine 11.496 (2019): eaav5989.
Driessen et al. Integrated PK/PD Approach to Derive the First-In-Human Starting Dose of RG7827, a Novel
Tumor Targeted Co-Stimulatory Bispecific Fusion Protein Cancer Immunotherapy. Clin Pharmacol Ther 2019;
105(Suppl S1): Abstract PII-045:S55.

THU-060
Progress Towards Regulatory Endorsement of a Clinical Trial Simulation Tool for
Early Motor Parkinson Disease
Nathan J. Hanan1, Varun Aggarwal1, Diane Stephenson1, Klaus Romero1, Babak Boroojerdi2, David Dexter3, Tanya
Simuni4, Donald Grosset5, Kenneth Marek6, Rachael Lawson7, Michelle Hu8, Caroline Williams-Gray9 Jackson
Burton1 on behalf of Critical Path for Parkinson’s Modeling team1

Critical Path Institute, Tucson, AZ, USA; 2 UCB S.A., Brussels, Belgium; 3 Parkinson’s UK, London, UK; 4
Northwestern University, Chicago, IL, USA; 5 University of Glasgow, UK; 6 Institute for Neurodegenerative
Disorders, New Haven, CT, USA 7 New Castle University, UK; 8 Department of Clinical Neurology, John Radcliffe
Hospital, Oxford, UK, 9 Department of Clinical Neurosciences, University of Cambridge, UK.
Objectives: Development of disease-modifying therapeutics targeting early Parkinson’s disease (PD) is a primary
focus in PD drug development. Critical Path Institute’s Critical Path for Parkinson’s (CPP) Consortium houses acomprehensive database containing early PD patient-level observations from 12 clinical trials and five observational
cohorts. A primary objective of CPP is to leverage the database to develop clinical trial simulation tools for both motor
and non-motor aspects of disease using the Movement Disorder Society’s revised Unified Parkinson’s Disease Rating
Scale (MDS-UPDRS). To optimize trial design, simulation tools are being built with disease progression, placebo
response, patient dropout, and drug effect models. Here, we describe preliminary results of a disease progression
model that will serve as the backbone for clinical trial simulation using the MDS-UPDRS Part II endpoint.
Methods: Four observational studies (PPMI, OPDC, ICICLE-PD, Tracking) and one randomized clinical trial(STEADY-PD3, control arm) comprising 3,339 patients (PD diagnosis < five years at baseline) and 13,534 MDSUPDRS Part II observations were utilized in a nonlinear mixed-effects model to describe early PD progression withthree levels of random effects: beta-distributed residual variability, inter-individual variability (IIV) and inter-study
variability (ISV). A preliminary covariate model was developed using a stepwise approach, with significance set at0.01 for forward addition and 0.001 for backward selection. Model performance was evaluated using goodness-of-fit
criteria and visual predictive plots.
Results: MDS-UPDRS Part II progression was best described by the generalized Gompertz function, a 3-parameter
logistic model that allows for a non-symmetric inflection point (estimated at Part II score ~ 23). Baseline severity andprogression for a typical patient aged 67 years with a PD duration of 11 months were 6.70 points and 0.080
points/month (0.96 points/year), respectively (Table 1). The identified covariates for baseline MDS-UPDRS part IIwere sex, age, and PD duration. Baseline MDS-UPDRS part II was about 15 % lower in females than males andincreased with age (0.04 points/year) and PD duration (0.08 points/month). Age was also identified as a covariate on
progression rate, with older patients progressing faster. Both base and covariate models were stable upon perturbationof the initial estimates and had low condition numbers (11.62 and 12.8, respectively).
Conclusions: This preliminary work, which quantifies the effect of patient characteristics on MDS-UPDRS Part II
progression, is under regulatory review with FDA and EMA. Covariate modeling will be extended to assess the effects
of genetics and longitudinal PD medication use. The execution of the final model will also include MDS UPDRS Part
III and will quantify placebo response, patient dropout, and drug effect models. Upon final endorsement from FDA
and EMA, the tool will be publicly available for sponsors to help optimize clinical trial design in early motor PD.
Table 1: Parameter estimates from MDS-UPDRS Part II disease progression model
Base Model
Estimate (% RSE)

Final Model
Estimate (% RSE)

6.48 (1.2)

6.70 (1.5)

NE

0.0118 (10.5)NE-0.143 (14.3)

NE

0.00638 (19.6)

0.00732 (3.6)

0.00721 (3.6)

NE

0.0135 (15.9)

Dispersion factor of Beta Distribution

38.6 (1.7)

38.6 (1.7)

Shape (γ)

0.659 (8.3)

0.723 (7.8)

Parameter
MDS-UPDRS2 Baseline••

PD Duration at Baseline(centered at 11 months)
Female•Age (centered at 67 years)

Intrinsic Progression Rate (per month)•Age (centered at 67 years)

Non-parametric
Bootstrap Mean
(95% CI) n=1000
6.71(6.50, 6,91)
0.0118(0.00945, 0.0143)
0.006413(0.00374, 0.009012)
0.00720(0.00654, 0.00788)
0.0135(0.00811, 0.0189)
38.6 (36.6, 40.7)
0.727(0.551, 0.895)

IIV•

MDS-UPDRS2 Baseline

0.388 (3.1)

0.366 (3.1)

0.365(0.342, 0.390)•

Intrinsic Progression Rate(per month)

0.918 (8.5)

0.905 (8.5)

0.906 (0.759, 1.07)

ISV
0.0288(0.0215, 0.0358)
0.0150• Intrinsic Progression Rate
0.0165 (71.5)
0.0134 (74.0)(0.00163, 0.0289)(per month)
RSE: relative standard error. NE: not evaluated. MDS-UPDRS2 Baseline and the typical progression rate in MDSUPDRS2 score is estimated by: MDS-UPDRS2 Baseline = 6.70*(1 + 0.0118*(Disease duration - 11.01))*
exp(0.00638*(age - 67))* ( 1 -0.143, if female); Intrinsic Progression Rate=0.00721 *exp(0.0135*(age - 67)). The
typical progression rate (points/month) in MDS-UPDRS2 score is estimated by dScorei/dt = ri*Scorei*[ln(max(Scorei)/Scorei)] ^γ.
•

MDS-UPDRS2 Baseline

0.0344 (60.8)

0.0286 (60.5)

THU-061

Mechanistic Modeling of Cyclosporine A-induced Acute Kidney Injury with RENAsym®

Pallavi Bhargavaa, Christina Battistaa, Viera Lukacovab, Jeff Woodheadaa

DILIsym Services, Inc., Research Triangle Park, NC; bSimulations Plus Inc., Lancaster, CA

Objectives:
The use of Cyclosporine A (CsA) can cause tubular damage leading to a decline in renal function as
determined by decreases in serum creatinine levels and glomerular filtration rate (GFR)1. This work uses
RENAsym®, a quantitative systems toxicology (QST) model of acute kidney injury (AKI), to recapitulateclinical outcomes following long-term CsA administration in humans.
Methods:
The effects of CsA on mitochondrial function and reactive oxygen species (ROS) production were assessed
to define the potential for CsA-induced kidney injury. Human renal proximal tubule epithelial cells(RPTECs) were treated with CsA and its effects on mitochondrial respiration as well as ROS production
were measured. Seahorse XFe96 Analyzer was used to measure mitochondrial respiration. High content
screening was used to measure ROS production after RPTECs were exposed to dihydroethidium staining.
These in vitro data were used to define kidney toxicity parameters, and together with PBPK simulations ofclinical CsA exposure created in Gastroplus®, kidney injury was predicted in RENAsym.

Results:
CsA inhibited the mitochondrial election transport chain flux (ETC inhibition coefficient=1458.33 μmol/L)and induced ROS production (Vmax=0.049 1/hr, Km =13.075 μmol/L). RENAsym predicted CsA-induced
kidney injury such as a decrease in kidney average ATP as shown in Figure 1. RENAsym was further
utilized to perform a mechanistic analysis to determine the main driver in simulated CsA nephrotoxicity.
The mechanistic analysis indicated that CsA-induced kidney injury is primarily driven by inhibiting
mitochondrial function via inhibition of the electron transport chain.
Conclusions:
Using in vitro data to determine toxicity parameters, RENAsym accurately predicted CsA-induced
nephrotoxicity in humans, consistent with observed in clinical studies.
References:
1. Schaft, J., Zuilen, A., Deinum, J., Bruijnzeel-Koomen, C. & Bruin-Weller, M. Serum Creatinine
Levels During and After Long-term Treatment with Cyclosporine A in Patients with Severe Atopic
Dermatitis. Acta Derm. Venereol. 95, 963–967 (2015).

THU-062
Population Pharmacokinetics and Simulation to Support CC-93538 Dosing in Pediatric Subjects
Peijin Zhang1, Nastya Kassir2, Jonathan Q. Tran1
1

Bristol-Myers Squibb, Summit, NJ, USA; 2Certara, Princeton, NJ, USA

Objectives: CC-93538 (previously known as RPC4046 or ABT-308) is a humanized immunoglobulin G1
monoclonal antibody (mAb) that binds to human IL-13 and blocks its binding to IL13Rα1 and IL13Rα2. CC-93538
has completed Phase 1 and 2 studies and is being developed for treatment of eosinophilic esophagitis (EoE).
Subjects eligible for enrollment in the Phase 2 study had body weight (WT) ≥40 kg. The current analysis was
performed to determine (1) a population pharmacokinetic (PPK) model of CC-93538; (2) if adolescents participatingin the adult Phase 3 study with WT <40 kg can receive the same adult dose; and (3) initial CC-93538 doses forpediatric EoE subjects based on simulation of systemic exposure.
Methods: Pharmacokinetic samples from 118 subjects including Phase 1 (24 healthy subjects) and Phase 2 (94 EoE
subjects) studies were used for the PPK analysis. Several models were examined. A formal covariate analysis was
performed using stepwise covariate analysis. The final model was qualified using goodness-of-fit criteria, bootstrap
resampling, and visual predictive check. CC-93538 exposure (area under the curve at steady state [AUCss]) was
determined for adolescent and pediatric subjects with WT 10 to 40 kg using empirical individual Bayesian estimatesof pharmacokinetic parameters from the final PPK model. Appropriate CC-93538 dose levels for pediatrics by WT
range were determined by matching simulated adult AUCss at 360 mg SC QW (<25% difference). In addition, thepercentage of subjects with simulated AUCss above the 95th percentile of adult exposure was determined withattempts to make it less than 20%.
Results: A two-compartmental model with first-order absorption and first-order elimination was selected. WT andbaseline albumin were identified as statistically significant covariates for clearance (CL). WT was identified as astatistically significant covariate of volume of distribution in the central compartment (Vc). Both CL and Vc
increased with increasing WT. CL decreased with increasing albumin. Sex and age were not significant covariates.
Pharmacokinetic simulation suggested that the use of the adult CC-93538 dose in subjects 30 to <40 kg would resultin at least 58% of AUCss above the adult 95th percentile. For pediatric subjects with WT 10 to 25 kg and 25 to 40 kg,
CC-93538 doses of 150 mg and 200 mg QW SC, respectively, resulted in simulated AUCss similar to adults at 360
mg SC QW.
Conclusion: CC-93538 exhibits pharmacokinetic properties typical of mAbs without target-mediated clearance. The
WT threshold for pediatric subjects to receive the adult dose of CC-93538 is 40 kg. For pediatric subjects with WT
10 to 25 kg and 25 to 40 kg, the anticipated CC-93538 dose to achieve the adult exposure is 150 mg and 200 mg SC
QW, respectively.

THU-064

Comparison of Joint Tumor Dynamic-Overall Survival Models with a Long-Term Surviving Fraction

Ramon I Garcia1, Seth Robey2, Pavan Vaddady2, Lokesh Jain2, and, Jonathan L French1
1
Metrum Research Group, Tariffville, CT, USA; 2Quantitative Pharmacology and Pharmacometrics, Merck,
Kenilworth, NJ, USA
Objectives:
In certain disease and treatment settings, some patients achieve long-term survival. In these cases, the survival
function for the time to event can appear to have a non-zero asymptote. Two approaches which accommodate sucha “cure fraction” are the mixture cure rate model [1] and the promotion time cure rate model [2]. It is of interest tocompare and contrast the assumptions, model fit, and long-term survival predictions of these two models.
Methods:
A joint model for tumor size and survival using a promotion time cure rate framework was developed using datafrom three clinical trials of first- and second-line pembrolizumab monotherapy in non-small cell lung cancer. In thismodel the hazard function is given by:ℎ$ (𝑡) = 𝜆 × 𝛼$ 𝑅𝑇𝑆(𝑡)< × 𝑒𝑥𝑝(−𝜆𝑡)where RTS(t) is the predicted tumor size relative to the baseline value and 𝛼$ is a scale parameter which includes
effects of subject-specific covariates. A key feature of this model is that when RTS(t) is bounded or RTS(t) is
substituted by a tumor metric g(t) which is O(𝑒𝑥𝑝(−𝜆𝑡)) then the limit of the cumulative hazard function is finite
which is a property required of a cure rate model.
The promotion time cure model was compared to a mixture cure rate model:
𝑆$ (𝑡) = 𝑝$ + (1 − 𝑝$ ) × 𝑆1$ (𝑡),where pi is the probability of achieving long-term survival and S1i(t) is a proper survival function.
Results:
The mixture cure rate model directly expresses the cure fraction as a function of baseline covariates but not timevarying predictors. In contrast, the promotion time cure rate model estimates fewer parameters and allows forbaseline and time-varying predictors to influence the cure fraction, but the cure fraction has to be derived using
numerical methods.
Objective function values and visual predictive check plots indicate both models fit the observed data equally well.
However, the models yield different extrapolations to 5-year survival. For example, for patients with PDL1
expression > 50%, the promotion cure rate model estimates a 5-year survival of 0.03 (.01, .06) and .30 (.22,.38) for
chemotherapy and pembrolizumab, respectively, while the mixture cure rate model estimates are 0.09 (.04,.19) and.26 (.18,.34) respectively.

Conclusions:
Mixture and promotion time cure rate models produce similar results over the observed time span yet yield different
predictions of long-term survival. The promotion time cure rate model allows more flexibility in the key drivers oflong-term survival.
References:[1] Law NJ, Taylor JM, Sandler H. The joint modeling of a longitudinal disease progression marker and the failure
time process in the presence of cure. Biostatistics. 2002;3(4):547–563. doi:10.1093/biostatistics/3.4.547[2] Chen M-H, Ibrahim JG, Sinha D. A new Bayesian model for survival data with a surviving fraction. J Am Stat
Assoc. 1999 Sep;94(447):746.

THU-066

A QSP model of B-cell depletion in RA and SLE
Authors: Rohit Rao, Sarita Koride, C.J. Musante, Richard Allen
Affiliations: Pfizer Early Clinical Development Worldwide Research, Development & Medical
Cambridge, MA 02139, USA

Objective
B-cell depletion is a promising therapeutic strategy for the treatment of rheumatoid arthritis (RA) andsystemic lupus erythematosus (SLE). However, the factors determining the extent of B-cell depletion andrepopulation are not well understood leading to concerns around excessive immunosuppression. B-cell
subsets exhibit differential susceptibility to depletion. For example, naïve B-cells exhibit greatest
susceptibility to depletion by anti-CD20 targeting rituximab (RTX), whereas auto-antibody producing
plasma cells are relatively resistant to depletion (1). Moreover, multiple approaches for B-cell depletion
have been proposed with recent evidence suggesting that additional targeting of T-follicular helper (TFH)
cells in the germinal center may be a viable therapeutic strategy (2). A QSP model of the B-cell activationcascade was built to assess potential downstream effects of such therapeutic strategies on B-cell
subpopulations.
Methods
The model accounts for interactions between multiple B-cell subpopulations including naïve, activatedand memory B-cells, plasmablasts, and TFH cell-dependent germinal center reactions [Figure 1].
Redistribution of B-cells between plasma and lymph compartments was implemented to account for theeffects of differential drug exposure between these compartments (3). As an initial step, we focused on
simulating B-cell depletion by RTX. The prospective model response was validated against publisheddata that quantified the effect of two 1000mg RTX doses on naïve B-cells, memory B-cells andplasmablasts (4). A simplified pharmacokinetic model for RTX was implemented using published data(5). Parameter estimates determining B-cell dynamics were obtained from prior in vitro and modelingstudies (6).

Figure 1: Schematic of model of B-cell activation cascade

Results
Model simulations predicted a near total depletion of naïve and memory B-cell subpopulations in both
plasma and lymph compartments in agreement with the clinically reported time-course of B-cell
dynamics. Plasmablasts exhibited >70% depletion; however, were less susceptible to depletion by RTX
than naïve and memory B-cells. In agreement with clinical data, B-cell repopulation occurred within 52
weeks.
Conclusion
The model accurately captured clinically reported measurements on the time-course of depletion andrepopulation for plasma B-cell sub-populations in response to RTX administration. Moreover, it provides
insight into B-cell subpopulations at the site of action (lymph) that are not readily amenable to clinicalmeasurement. The model will be used as a platform to optimize dosing schedules and assess the efficacyof strategies that target additional cell populations in the B-cell activation cascade including thegerminal center-localized TFH cells.

1.

M. J. Leandro Arthritis Res Ther 15 Suppl 1, S3 (2013).

2.
3.
4.
5.
6.

L. Yan et al. Front Immunol 8, 1510 (2017).
P. M. Glassman et al. MAbs 9, 297-306 (2017).
E. M. Vital et al. Arthritis Rheum 63, 3038-3047 (2011).
S. Rozman et al. Br J Clin Pharmacol 83, 1782-1790 (2017).
X. Chen et al. CPT Pharmacometrics Syst Pharmacol 3, e133 (2014).

THU-067
Using an IO QSP Model to Re-Define Efficacy, Discontinuation Criteria, and Biomarker Analysis.

Authors: Madhav Channavazzala* (1), Kannan Thiagarajan* (1), Tamara Ray (1), Rukmini Kumar (1), Kapil
Mayawala (2), Dinesh De Alwis (2), Alex Snyder (2), Brian Topp (2)Institutions: Vantage Research, India (1) Merck & Co., Inc., Kenilworth, NJ, USA (2)*Co-first authors

Objectives: First generation immuno-oncology (IO) therapies have generated unprecedented success in treating
cancer. Several companies are developing IO QSP models to facilitate development of second-generation IO
combination therapies. These models generally focus on immune-mediated tumor killing within a single lesion. Here
we develop an IO QSP model that includes the full complexity of patients with multiple target, non-target, and newmetastatic lesions.
Methods: We developed a basic tumor dynamics model with replication (k1) and death (k2) rates. This model was
expanded to include inter- and intra-patient variability for each parameter. Virtual patients were created with multiple
tumors, randomly assigned as Target and Non-Target. Next the probability of developing a new metastatic lesion
during any given time interval was assumed to be driven by overall tumor burden. The model was calibrated to lesionlevel and patient-level data using a pembrolizumab melanoma study (Keynote-001).
Results: Intra-Patient (tumor-to-tumor) heterogeneity was significant (%CV = 70%) and clinically meaningful in thatthe same patient can have shrinking and growing lesions. While 308 patients developed progressive disease (PD),
only 113 displayed a net increase in tumor burden. Most patients displayed stable (n = 125) or reduced (n = 70) tumor
burden at the time of progression. PD in these patients is defined by growth of a non-target or new metastatic lesions.
Only 6% of patients displayed a loss of efficacy (rebound).
Conclusions: Disease progression is generally considered a surrogate for drug failure. However, this analysis clearlyshows that most melanoma patients with progressive disease display continuing response to pembrolizumab in the
majority, but not all, of their lesions. This suggests that depth of response may be a better index of drug efficacy than
RECIST v1.1. Furthermore, patients with disease progression are often switched to a new therapy. However, patients
who are showing continuing efficacy at the time of progression, may be benefit from continued pembrolizumab (forresponding lesions) and a new therapy (for the non-responding lesions). Finally, the large degree of heterogeneity
observed in patients suggests that biomarkers sampled from a patient with PD could have come from a growing or ashrinking lesion.

THU-069
Trapezoid bioequivalence: the exchange rate between mean and variance of bioavailability
Authors:
Sara Soufsaf, B.Sc.1, Fahima Nekka, Ph.D.1, Jun Li, Ph.D.1
1

Université de Montréal, Montréal, Québec, Canada

Abstract:
Objectives:
In the statistical analysis of bioequivalence, the FDA defines three levels of bioequivalence: average bioequivalence(ABE), population bioequivalence (PBE) and individual bioequivalence [1]. To establish bioequivalence, PBE adds astatistical comparison of the bioavailability metrics’ variance for the reference and generic drug (𝜎=' and 𝜎>' ). Since
PBE was defined in FDA’s 2001 Guide Statistical Approaches to Establishing Bioequivalence [1], it has been
criticized for its overly permissive results [3]. Thus, the objective of our work is to propose an alternate bioequivalencecriterion which overcomes the limitations of PBE and incorporates parameters dependent on therapeutic properties ofa drug.
Methods:
In the assessment of therapeutic equivalence, we define an alternative trapezoid bioequivalence (TBE) which considers
the variability of the drugs’ PK and allows a drug-specific trade-off in the comparison of its average and variance. In
other words, the TBE criterion establishes bioequivalence by balancing a similarity in both drugs’ average (μ) AND
variance (σ2) of bioavailability. The TBE is translated in the equation below, defining the graphical trapezoid in Figure1.
𝝈𝟐 − 𝝈𝟐
𝜎>' − 𝜎='⎧ 𝝀𝟏 ∗ ] 𝑻 𝟐 𝑹 _ + 𝝀𝟐 ,< 𝛼#B4CDB3E0 ⎫
𝜎='
𝝈𝑹
⎪
⎪
⎪
⎪
𝜇> − 𝜇= '𝜎>' − 𝜎='TU ≤ 𝜽𝒕𝒐𝒍𝒆𝒓𝒂𝒏𝒄𝒆 ,𝛼#B4CDB3E0 ≤≤𝛼#B4CDB3E0
𝜇=
𝜎='
⎨
⎬
𝟐
𝟐
'
'
⎪
⎪
𝜎> − 𝜎=⎪−𝝀 ∗ ]𝝈𝑻 − 𝝈𝑹 _ + 𝝀 ,> 𝛼#B4CDB3E0 ⎪
𝟏
𝟐
'
𝟐
𝜎=
𝝈𝑹
⎩
⎭
Where 𝜃#3EC4"N!C is the maximal relative difference of μ; 𝜆* and 𝜆' are a flexible trade-off between μ and σ2; and𝛼#B4CDB3E0 is the therapeutically acceptable relative difference of σ2.
Our methodology is applied to the treatment of Attention Deficit Hyperactivity Disorder with methylphenidate (MPH)
since the individualization of doses is especially difficult [4]. Using a two-linear absorption (immediate-release andextended-release) population pharmacokinetic model with different ranges of absorption parameters, we simulated
plasma concentrations of MPH. We then applied ABE, PBE and TBE to these datasets. In concordance with MPH
literature, we fixed 𝜆* = 0.4, 𝜆' = 0.08, 𝜃#3EC4"N!C = 0.04, and 𝛼#B4CDB3E0 = 0.1.
Results: As expected, TBE slightly more strict than PBE. In fact, 20% of simulations where the absorption constant
was changed between the test and reference formulations did not pass TBE but passed PBE. This number climbs up
to 30% when evaluating changes to the fraction of MPH that is formulated as an extended release.
Conclusion: TBE, contrary to PBE, allows a balance between μ and σ2 to be adapted according to desirable
pharmaceutical characteristics. This is particularly interesting for drugs with high variability between individuals,
where the variance’s importance may be higher to ensure best interchangeability for a patient.

References:
1.
2.
3.
4.

Food and Drug Administration, Guidance for Industry. Statistical Approaches to Establishing
Bioequivalence, H.a.H. Services, Editor. 2001.
Sheiner, L.B., Bioequivalence revisited. Stat Med, 1992. 11(13): p. 1777-88.
Zariffa, N.M. and S.D. Patterson, Population and individual bioequivalence: lessons from real data andsimulation studies. J Clin Pharmacol, 2001. 41(8): p. 811-22.
Soufsaf, S., et al., A Quantitative Comparison Approach for Methylphenidate Drug Regimens in AttentionDeficit/Hyperactivity Disorder Treatment. J Child Adolesc Psychopharmacol, 2019.

THU-070
Development of Quantitative Systems Toxicology Model to Predict Drug Induced Acute Kidney Injury
via mtDNA Depletion Pathway
Shailendra B. Tallapaka, Nader Hamzavi, Yeshitila Gebremichael, Scott Q. Siler, Jeffrey L. Woodhead
DILIsym Services, Inc., a Simulations Plus company, Research Triangle Park, NC, USA
Objective: Treatment of chronic viral infections like HIV, Hepatitis B etc., necessitates long-term administration of
powerful antivirals like nucleoside/nucleotide reverse transcriptase inhibitors (NRTIs). Several NRTI’s are primarily
excreted renally and have been reported to cause nephrotoxicity. The observed toxicity is partially attributed tomitochondrial dysfunction caused by inhibition of mitochondrial DNA (mtDNA) synthesis[1]. We are developing aquantitative systems toxicology (QST) model called RENAsym® that can potentially predict drug-induced acutekidney injury (AKI) in humans and pre-clinical species by leveraging in vitro mechanistic toxicity data. The objectiveof this work is to develop a mechanistic model to represent drug-induced mtDNA depletion in proximal tubular cells
and subsequent downstream toxic effects in the kidney.
Methods: The mtDNA depletion model was built within the mitochondrial dysfunction sub-model of RENAsym®.
The current representation of kidney bioenergetics includes equations that describe mtDNA and electron transport
chain (ETC) protein turnover, and substrate uptake and utilization by mitochondria to produce ATP. The model wasparameterized based on human and rodent kidney bioenergetics data reported in literature. A physiologically based
pharmacokinetic (PBPK) model including kidney exposure of IV administered adefovir was developed in
GastroPlusTM 9.7 as an input for RENAsym. Toxicity parameter inputs into RENAsym for adefovir were derived fromin vitro studies where changes in mtDNA content in primary human renal proximal tubule cells exposed to adefovir
were measured using RT-PCR[2].
Results: Simulations in human at 3 mg/kg QD dosing for 40 weeks predicted a ~10% decrease in ATP level andminimal reduction in the fraction of viable PTCs. Sensitivity analysis indicated that a ~10-25% increase in either
mtDNA destruction rate or kidney concentration of adefovir would predict significant nephrotoxicity (Fig. 1), which
is well within the expected variability in pharmacokinetic and toxicological parameters. This qualitatively agrees withclinical observation of elevated serum creatinine in ~60% of patients treated with 120mg QD adefovir for 24 weeks[3].
Conclusions: A QST model of drug-induced mtDNA depletion and subsequent AKI has been constructed within
RENAsym®. The model reasonably predicts adefovir-induced nephrotoxicity and shows promise in being a predictive
tool for drug-induced AKI.

Figure 1. Sensitivity analysis of toxic and PK parameters demonstrating adefovir induced AKI

References:
1.
2.

3.

Lewis, W., Day, B. & Copeland, W. Mitochondrial toxicity of nrti antiviral drugs: an integrated cellularperspective. Nat Rev Drug Discov 2, 812–822 (2003).
Faye Wang, Oliver P. Flint. BMS-986001, an HIV Nucleoside Reverse Transcriptase Inhibitor, Does Not
Degrade Mitochondrial DNA in Long-Term Primary Cultures of Cells Isolated from Human Kidney,
Muscle, and Adipose Tissue. Antimicrobial Agents and Chemotherapy Nov 2013, 57 (12) 6205-6212
Kahn J, Lagakos S, Wulfsohn M, et al. Efficacy and Safety of Adefovir Dipivoxil With Antiretroviral
Therapy: A Randomized Controlled Trial. JAMA. 1999;282(24):2305–2312.

THU-071
Whole-body physiologically-based modeling to predict monoclonal antibody pharmacokinetics in multiple
species with different administration routes
Shihao Hu and David Z. D’Argenio
Department of Biomedical Engineering, University of Southern California, Los Angeles, CA, USA
Objectives: Monoclonal antibodies (mAbs) are used to treat a broad range of diseases, including cancers,
immunological disorders and infectious diseases. Various methods have been investigated to improve mAb PK,
including Fc engineering and co-formulation strategies [1-4]. Previously, we developed a whole-body physiologicallybased PK (PBPK) model to predict mAb PK after intravenous (IV) and subcutaneous (SC) administration in humans[5]. The goal of this work was to extend the previous model and apply it to investigate the complex relationshipbetween observed mAb PK and underlying properties governing mAb absorption, distribution, metabolism, andexcretion (ADME).
Methods: In this work, we extended the model in [5] as follows. The endosomal space in each organ now includes
vascular membrane and interstitial membrane compartments, where IgG is released from FcRn binding at neutral pH
after processing and recycling in early endosomes. The overall PBPK model includes all major organs, with versions
that incorporate physiological parameter values for different species (mouse, monkey, human). The model was usedwith mAb PK data digitized from published studies [1-4] to investigate the role of model-based ADME properties
related to absorption, transendothelial migration and FcRn-mediated processing.
Results: The resulting PBPK model simulations are consistent with the observed complex relationship between
plasma concentration and FcRn-IgG binding kinetics reported in several mouse studies [1-3]. For example, comparedto the wildtype antibody, the variant with increased binding affinities at both pH 6.0 and pH 7.4 was predicted by themodel to have no significant change in plasma concentration, as reported in [2]. Also, model simulations identified
the FcRn binding affinity threshold at neutral pH that controls IgG recycling, in agreement with [3]. Using the humanversion of the PBPK model with a mechanistic SC site sub-model, hyaluronidase’s influence on the SC absorption ofTocilizumab reported in [4] could be described. Specifically, a two-fold increase in the model-based parameter
representing interstitial transport and lymphatic uptake described the difference observed in Tocilizumab PK alone
and after coadministration with hyaluronidase [4] (pre-systemic catabolism rate constant unchanged in the model).
Thus, the model provides a basis for mechanistically representing the effects of hyaluronidase to digest extracellularmatrix, enhance the interstitial permeation and facilitate SC absorption of biomolecules.
Conclusions: Our study presents a mechanistic, whole-body PBPK model for different species that can be applied toinvestigate the role of ADME properties on observed mAb plasma concentrations.
References:[1] Brian Mackness et al, mAbs (2019)[2] Rong Deng et al, Drug Metabolism and Disposition (2010)[3] Jack Borrok et al, Journal of Biological Chemistry (2015)[4] Peter Morcos et al., Int. Journal of Clinical Pharmacology and Therapeutics (2013)[5] Shihao Hu et al, J Pharmacokinet Pharmacodyn (in press)

THU-072

Implementation of gamma distributed delays through a fast ODE approach

Authors: Shuhua Hu1, Michael Tomashevskiy1, and Wojciech Krzyzanski2
Institutions: 1Certara; 2Department of Pharmaceutical Sciences, University at Buffalo

Objectives: The gamma distributed delay is a natural extension of the widely used transit compartment model(TCM). It was implemented in Phoenix Modeling Language (PML) through the delay function, which allows users
to estimate the shape parameter instead of manually finding a proper one as required by the TCM [1]. However, thedelay function involves direct numerical calculation of the integral with the values of the signal to be delayed
recorded in a table upon every derivative evaluation, and hence it may become very expensive or even prohibitive
for complex population analysis. In Phoenix NLME 8.3, an alternative function, gammaDelay, was implemented
based on the ODE approximation method proposed in [3] wherein it was shown that this approach is much fasterthan the delay function. The goal is to demonstrate the gammaDelay function through an example.

Methods: The example is a gamma distributed delay model in [1], which is a generalization of the TCM in [2] fordescribing the delay process in the platelet production following Lusutrombopag dosing with feedback mechanism.
The data was simulated using the original TCM approach with number of platelets simulated for 78 subjects every
24 hours during the timeframe of 648 hours. To compare the performance of gammaDelay and delay functions, thegamma distributed delay model was implemented using both functions. They were then fitted to the data in Phoenix
NLME 8.3 using the FOCE-ELS engine, where the values of all PK and some PD parameters are fixed to the ones
used to generate the data. To see if the shape parameter can be recovered from the data, the initial value for all theestimated parameters are chosen away from their true values.

Results: The models using gammaDelay and delay functions gave very similar estimation results, and both welldescribed the simulated data. However, the total running time for the model using the gammaDelay function is 9.4
minutes while the one for the delay is 25.5 hours with the same setup running in a Windows desktop machine with
1.8 GHz Intel Core i7-8565U CPU. Table 1 lists the estimation results obtained using the gammaDelay function, andit indicates that the shape parameter can be reliably estimated along with other parameters with its estimate very
close to its true value.

Conclusions: The gammaDelay function in PML provides a flexible and powerful way to model delayed outcomes
without compromising the performance. Even though the ODE approximation approach [3] can be manually
implemented in any PKPD software, the gammaDelay function allows single line code specification, and hence ismore efficient and less error-prone.
References[1] Hu et al (2018) J Pharmacokinet Pharmacodyn 45:285-308.
[2] Katsube et al (2016) Clin Pharmacokinet 55:1423-1433.
[3] Krzyzanski (2019) J Pharmacokinet Pharmacodyn, 46:53-63.

THU-074

Combining Elementary Effects and Sobol Global Sensitivity Analysis to
reduce computational expense for complex models
Authors: Sietse Braakman, Florian Augustin, Ricardo Paxson
Affiliations: The MathWorks, Natick, MA, USA
Objectives: Decision making in pharmacological development relies increasingly on complex, mechanistic models,such as Quantitative Systems Pharmacology (QSP) and Physiologically-based Pharmacokinetic (PBPK) models.
These models often have more parameters than available data can reasonably constrain. Sensitivity analyses canelucidate which parameters most affect the model output. Local sensitivity analysis (LSA) is generally inadequate tocharacterize sensitivities in non-linear models with uncertainty in parameters and global sensitivity analysis (GSA)
is preferred instead. However, GSA methods are generally computationally expensive and scale exponentially withincreasing model complexity. To address this problem, we present a systematic and scalable workflow to perform
GSA on larger, mechanistic models using SimBiology®.
Methods: A mechanistic lesinurad/febuxostat model1 with 14 independent input parameters was used to illustrate
the workflow. Physiologically meaningful bounds on each input parameter were determined and used to span the
input-space. An Elementary Effects GSA2 (multi-point, one-at-a-time LSA) was performed on all input parameters
to prioritize 8 parameters that most affect serum uric acid. The EEGSA was performed to reduce the dimensionality
and make the computational load of the subsequent Sobol GSA3 manageable. A Sobol GSA (all-at-a-time, Sobol
sampling) with 10,000 samples was then performed to rank the remaining parameters. A multiparametric GSA4(MPGSA) was used to investigate whether input effects were significant with respect to serum uric acid nadir. All
methods were implemented using SimBiology and MATLAB® using built-in function for LSA, Sobol GSA and
MPGSA.
Results: The EEGSA was able to reduce the dimensionality of the problem to the 8 most important parameters. The
remaining parameters were ranked using a Sobol GSA with first-order indices. Total-order Sobol indices were used
to identify interactions between input parameters. The MPGSA showed that 6 out of 8 parameters had a significant
effect (p<0.05) on reaching serum uric acid nadir<68mg/L by comparing empirical cumulative distribution functionsof accepted and rejected samples using a Kolmogorov-Smirnov test.
Conclusions: The workflow described above enables the systematic exploration of global input parameter
sensitivities on model outputs of interest using SimBiology. Sobol GSA does not have the underlying assumptionsof monotonicity and linearity that EEGSA has and is therefore preferred for ranking most important parameters.
Meanwhile, the EEGSA step reduces the computational expense of GSA by reducing the number of input
parameters whose input space can be sufficiently sampled for a Sobol GSA or MPGSA analysis. This allows theworkflow to scale better with increasing model size.
References:[1] Aksenov S et al., Physiol Rep. 2018, 6(5): e13614.
[2] Saltelli A et al., Global Sensitivity Analysis, The Primer, Wiley 2007
[2] Saltelli A et al., Computer Physics Communications 2010, 181(2): 259–70.
[4] Tiemann C et al., PLoS Computational Biology 2013, 9(8): e1003166.

THU-075

Longitudinal Placebo Response Modeling in Patients with Ulcerative Colitis
Authors: Sonoko Kawakatsu, Rui Zhu, Wenhui Zhang, Meina T. Tang, Tong Lu, Angelica Quartino, Matts Kågedal
Affiliations: Genentech, Inc., South San Francisco, CA, USA
Objectives: Clinical trial design in the ulcerative colitis (UC) population faces the challenge of historically highplacebo response rates. A better understanding of placebo response rates and factors that influence response may
allow for more informed clinical trial design. A placebo response model was developed to describe the combinedstool frequency and rectal bleeding (SF+RB) subscore of the Mayo Clinical Score over time in placebo arm patientswith UC, and to evaluate the impact of baseline characteristics on placebo response.
Methods: Placebo arm individual level clinical trial data from the TransCelerate Database were pooled for analysis.
A proportional odds (PO) model and two logistic regression models were developed using nonlinear mixed effects
modeling. The PO model was used to describe the time course of the SF+RB subscore, and the two logistic
regression models were used to correlate SF+RB subscore with discontinuation from placebo arm at the end of
induction phase and during the maintenance phase (drop-out model). The combined model was evaluated by visual
predictive check.
Results: Data from 755 adult, placebo arm patients with moderate to severe active UC were available from five
Phase 2/3 trials. Three trials were induction and maintenance studies, and two were induction only studies. A PO
model with linear placebo effect, logistic regression model correlating discontinuation with SF+RB subscore at endof induction phase, and logistic regression model correlating discontinuation during maintenance phase with the last
observed SF+RB subscore adequately described the time course of the SF+RB subscore (Figure 1). Baseline SF+RB
subscore and TNF status were included as covariates on α1 (intercept parameter). Patients with higher baseline
SF+RB subscore and prior anti-TNF therapy had higher SF+RB subscore, and patients with higher SF+RB subscoreat the end of induction phase and during maintenance phase were more likely to discontinue from the placebo arm.
Conclusions: A placebo response model was developed using an ordered categorical approach to describe thelongitudinal SF+RB subscore in placebo arm patients with moderate to severe active UC. Expected placebo

response can be simulated to inform clinical trial design, and simulations can potentially be used as “virtual placeboarms” for trials in which a placebo arm is not feasible to enroll. Future work will include the use of Markov models
to describe the SF+RB subscore.

THU-076

Mechanistic PK/PD Modeling of Canakinumab to evaluate the exposure and target
engagement requirements for Interleukin (IL)-1β antagonists in atherosclerotic patients
Authors: Sumit Basu, Gayathri Swaminath, Paul Rearden, Harish Shankaran
Affiliations: Merck & Co., Inc., Kenilworth, NJ, USA
Objectives:
Interleukin (IL)-1β, an inflammatory cytokine is one of the significant pathogenic mediators of
autoimmune, autoinflammatory, infectious and degenerative diseases. Inflammation in the arterial
walls, driven in part by IL-1β, is a key risk factor for cardiovascular events. In 2017, the CANTOS
trial demonstrated canakinumab, a neutralizing anti-IL 1β antibody, can significantly lower the
rates of cardiovascular events, independent of lipid levels. Importantly, cardiovascular benefits
were observed in patients who achieved on-treatment high sensitivity C-Reactive protein (hsCRP; a downstream biomarker) levels of less than 2 mg/L. Our objective was to develop amechanistic PK/PD model for canakinumab to understand the exposure and target engagement
requirements to achieve meaningful reductions in hs-CRP for driving efficacy in theatherosclerotic patient population.
Methods:
An extensive literature review was performed and two population PK/PD models of canakinumab
were found that described the temporal changes in total canakinumab, IL-1β and hsCRP
concentrations in cryopyrin-associated periodic syndromes (CAPS)1 and rheumatoid arthritis(RA)2 patients. Further, clinical PK data was available in the literature from a dose-escalation
study of single intravenous doses of canakinumab (0.03, 0.1, 0.3, 1.5 or 10 mg/kg) in type 2
diabetes mellitus (T2DM) patients (n=222)3, and this dataset was used to calibrate the model tothis population. Model development, evaluation and simulations were performed in Berkeley
Madonna version 8.3.18.
Results:
A standard two compartment PK model with linear clearance from the central compartment
described the PK of total canakinumab and a quasi-equilibrium model was used to describe thebinding of canakinumab to IL-1β. Simulations of drug exposure and total IL-1β were consistent
with data in T2DM patients. The model was adapted to describe the relationship between
canakinumab exposure and hs-CRP kinetics in T2DM patients. Additional models were built tolink free IL-1β levels to hs-CRP kinetics. Our analysis revealed a closer temporal link between
reductions in free IL-1β in the interstitium and serum hs-CRP levels.
Conclusions:This mechanistic PK/PD model can be utilized to understand the PK/PD requirements for IL-1βantagonists in the atherosclerotic patient population.
References
1. Lachmann et al., 2009, 2. Ait-Oudhia et al., 2012, 3. Noe et al., 2014.

THU-077
Physiologically-based Pharmacokinetic (PBPK) Modeling to Assess the Impact of Cytochrome P450 2D6
Activity on Tramadol and O-Desmethyltramadol Exposure
Tao Long1, Rodrigo Cristofoletti1, Jacques Turgeon2, Veronique Michaud2, Lismaury Galiano1, Nashid Farhan1,
Stephan Schmidt1*
1

Center for Pharmacometrics and Systems Pharmacology, Department of Pharmaceutics, College of Pharmacy,
University of Florida, Orlando, FL, USA

2

Tabula Rasa HealthCare, Precision Pharmacotherapy Research and Development Institute, Orlando, FL, USA.

Objectives: Tramadol is a µ-opioid receptor agonist and a serotonin–norepinephrine reuptake inhibitor, used to treat
moderate to severe pain. O-desmethyltramadol (M1), the major active metabolite, is mainly produced by CYP2D6. It
has a much stronger µ-opioid receptor binding affinity and higher potency for analgesic effects compared to those ofTramadol. Hence, the variability in CYP2D6 activity due to intrinsic and extrinsic factors is expected to modulate
analgesic effects of the drug. The objective of this research was to develop and verify of a PBPK model to evaluate
the impact of CYP2D6 activity on Tramadol and M1 exposure in healthy Caucasian subjects.
Methods: A combination of bottom-up and top-down approaches, leveraging published clinical studies on Tramadoland M1 biotransformation, were used to derive a comprehensive mass balance analysis, mapping out the contributionof different metabolic pathways. PK-Sim V8 was used for model development. A PBPK model was first developed
using the physicochemical properties and total clearance data of Tramadol and M1 as the initial parameters inputs asreported in the literature. Based on the relative contribution of individual clearance pathways, the model was further
refined by optimizing parameters, e.g. renal clearance and retrograding the corresponding unbound intrinsic clearance
for each involved CYP450 isoforms, using well-stirred liver model and in vitro/in vivo extrapolation approaches. The
developed PBPK model was verified by comparing the predicted (PRED) vs. observed (OBS) clinical data fromsubjects receiving a single dose of 100mg Tramadol by i.v. (infusion) and p.o., and from subjects classified as CYP2D6
normal (NMs) and poor metabolizers (PMs).
Results: About 26% of the administered dose of Tramadol is excreted in urine in the unchanged form; CYP3A4 andCYP2B6 contribute 21% to its metabolic clearance forming N-desmethyltramadol, while 53% is transformed to M1(38% by CYP2D6 and 15% by an unknown enzyme). For M1, 20% of the administered dose is excreted in urine while
33% undergoes phase 1&2 metabolism. The ratios of PRED vs. OBS AUC0-inf and Cmax are 0.82 and 0.99 forTramadol; 1.20 and 1.16 for M1. The PRED PM/EM AUC0-inf ratios are 1.52 and 0.44 for Tramadol and M1,
respectively, which are consistent with the OBS. These results suggest that our model adequately captured theconcentration–time profiles of Tramadol and M1 from multiple clinical studies.
Conclusions: A PBPK model was developed and verified using existing clinical pharmacokinetic and gene-druginteraction data. The final model can be prospectively applied to estimate the effects of concomitant administrationof CYP2D6 inhibitors or higher affinity substrates on the PK of Tramadol and M1 and further to determine theoptimized time delay associated with minimized DDI between Tramadol and perpetrator drugs.

THU-078

Monoclonal antibody therapy efficacy can be boosted by combinations with other treatments:
predictions from integrated Alzheimer’s disease platform

Tatiana Karelina, Stepan Lerner
InsysBio, Moscow, Russia
Objectives:
Clearance of pathological accumulations of amyloid beta (Aβ) and tau protein are still considered asimportant requirement for the treatment of Alzheimer’s disease. It is known that pathologies interact,
but the efficacy of amyloid targeting therapies on tau pathology is questionable and pharmacodynamics
of CSF tau markers differ from amyloid markers. Improvement of efficacy could be achieved by
combinations of treatments. Quantitative systems pharmacology model allows for integration ofavailable information from multiple preclinical studies to simulate clinical studies. The goal was to apply
the translational systems pharmacology model of key AD markers to simulate efficacy of potential
combinations of known treatments with perspective immunotherapy.
Methods:Aβ [1] and tau pathology submodels are merged through the model of intracellular protein degradation
systems (proteasome, autophagy) which govern protein accumulation and can be disrupted
progressively in AD. Literature data (concentration baselines and dynamics, SUVR) from mice andhuman, in vitro perturbations of cellular pathways (rapamycin, proteasome inhibitors, vinblastine, ACAT
inhibitors, calpain inhibitors) have been used for calibration. Simplified pharmacokinetic model ofantibodies describes distribution to the brain and target binding, preventing seeding and growth ofaggregates; it was calibrated on the data for non-specific IgG [2], and partially verified of BAN2401 CSF
data. Data from clinical studies for aducanumab and BACE inhibitor (verubecestat) have been used formodel validation, together with mouse data for treatment by rapamycin, calpain inhibitors, proteasome
activation as well as tau targeting antibodies, DC8E8 (tau) [3] and GW-23B7 (anti-β-sheet), in Tau and
APP/Tau mouse AD models.
Result:Model correctly describes progression and interaction the Aβ and tau pathologies. It correctly predicts
effect of rapamycin, calpain inhibitors, proteasome activation on tau or amyloid in transgenic mousemodels, as well as efficacy of antibodies. Predictions for aducanumab efficacy in Tg2576 mouse [4]correspond to the data (about 40-50% for soluble and insoluble amyloid). Translation to clinical trial
predicts correctly amyloid SUVR decrease by 0.2 from baseline within 54 weeks [4]. Tau in cortex andhippocampus is predicted to change by 10-15%. Similar prediction was derived for verubecestat low
efficacy towards tau in CSF. Simulated addition of rapamycin to aducanumab therapy increase efficacy
tremendously, leading to 80% brain tau reduction within one year and acceleration of amyloid
clearance.
Conclusion:
QSP modeling allows for choice of combinations of newly emerging biologics combined with targetingcell metabolism, to lead optimization and increase of treatment efficacy.
References

1. Karelina, T. et al. CPT: Pharmacometrics & Systems Pharmacology (2017) 6(10), 676–
685.
2. Chang, H.-Y. (2019). Journal of Pharmacokinetics and Pharmacodynamics (2019) 46(4),319–338
3. Kontsekova, E. et al. Alzheimers Res Ther (2014) 6(4), 45.
4. Sevigny, J. The antibody aducanumab reduces Aβ plaques in Alzheimer’s disease.
Nature, (2016). 537(7618), 50–56.

THU-079
Identifying a coherent target modulation of ion channel conductances to rescue striatal neuronal excitability
in Huntington’s Disease phenotypes

Sushmita Allam1, Tim Rumbell1, Jaimit Parikh1, Viatcheslav Gurev1, James R. Kozloski1
1

IBM T.J. Watson Research Center., Yorktown Heights, NY, USA

Objective: Huntington’s Disease, an autosomal dominant neurodegenerative disorder mainly affects striatal neurons
by making them hyperexcitable, a biomarker for appearance of first motor symptoms [1]. Prior evidence points todysfunctional potassium channels that cause these changes in neuronal excitability [2]. Population-based modelingis widely used to represent intrinsic variability of electrophysiological features of healthy, disease and drug treatedphenotypes. The objective of this work is to identify a single coherent direction of ion channel target modulation forphenotypic recovery, represented by a modulation of neuronal excitability that transforms the heterogeneous
Huntington’s disease (HD) phenotype towards the Wild-Type (WT) phenotype.

Methods: Our approaches are based on quantitative systems pharmacology principles combining mechanistic
simulations, generation of populations of models using evolutionary algorithms [3] and hybrid
statistical/mechanistic modeling approaches grounded in theoretical frameworks described in related reports of thismethod. We built a large population of neuron models representing both healthy, disease, and drug-treatedphenotypes. We describe several approaches using heuristic and applied machine learning methods to performphenotypic screening and design single target and multiple target perturbations of ionic currents for rescuing aheterogenous disease population to the healthy phenotype. We introduce distance metrics to compare theperformance of the virtual drug perturbations with a known candidate pharmacotherapy, PDE10i in HD.

Results: Using robust linear regression models, we were able to provide mechanistic insights into how the ionic
conductance parameters influenced the varied electrophysiological properties that gave rise to distinct clusters ofphenotypes. We found that coherent modulation of fast inactivating potassium, persistent potassium, and transientsodium conductances identified using heuristic approaches performed better than the single target modulators and
PDE10i to rescue the healthy population in terms of divergence of various excitability features. Using similar
population-based modeling approaches, we also found populations of models adhering to in vivo

electrophysiological recordings of striatal neurons under modulation of the drug papaverine, and similarly identified
underlying coherent parametric changes to ion channels capable of altering the membrane excitability to represent
that context.

Conclusions: We show how our population of models approaches both identify multiple target perturbations to
rescue HD phenotypes, and represent pharmacological perturbations to single neurons, by identifying ion channel
parameter combinations that recreate alterations of membrane excitability. Combining these insights, we introduce a
potential program for ‘reverse phenotypic screening’, in which coherently modeled drug-induced membrane activity
changes are mapped to desired recovery vectors in parameter space for disease states.

References:
1.
Cao Y, Bartolomé-Martín D, Rotem N, Rozas C, Dellal SS, Chacon MA, et al. Rescue of homeostatic
regulation of striatal excitability and locomotor activity in a mouse model of huntington’s disease. Proc Natl
Acad Sci U S A. 2015 Feb 17;112[7]:2239–44.
2.
Ariano MA, Cepeda C, Calvert CR, Flores-Hernández J, Hernández-Echeagaray E, Klapstein GJ, et al.
Striatal potassium channel dysfunction in Huntington’s disease transgenic mice. J Neurophysiol. 2005
May;93[5]:2565–74.
3.
Rumbell T, Kozloski J. Dimensions of control for subthreshold oscillations and spontaneous firing in
dopamine neurons. PLoS Comput Biol. 2019;15[9].

THU-080

Physiologic indirect response model to describe buprenorphine pharmacodynamics in newborns
with neonatal abstinence syndrome
Tomoyuki Mizuno1,4, Brooks T. McPhail1,5, Suyog Kamatkar2,6, Scott L. Wexelblatt2,4, Laura P. Ward2,4,
Uwe Christians3, Henry T. Akinbi2,4, Alexander A. Vinks1,4
1

Division of Clinical Pharmacology, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH,
USA 2Perinatal Institute, Division of Neonatology, Cincinnati Children’s Hospital Medical Center,
Cincinnati, OH, USA, 3iC42 Clinical Research and Development, University of Colorado, Aurora, CO,
USA, 4Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH, USA,
5
University of South Carolina School of Medicine, Greenville, SC, USA, 6Community Hospital East,
Indianapolis, IN, USA

Objectives: Neonatal abstinence syndrome (NAS) is caused by disruption of fetal exposure to opioids or
psychotropic drugs used by the mother during pregnancy. Buprenorphine has been shown to be effectivein treating infants with NAS. However, providing individualized buprenorphine dosing to newborns ischallenging as the buprenorphine exposure-response relationship has not been well characterized. The
aim of this study was to develop an integrated pharmacokinetic and pharmacodynamic (PK/PD) model tofacilitate prediction of buprenorphine treatment outcomes in newborns with NAS.
Methods: Clinical data were obtained from 19 newborns who were enrolled in a pilot PK study ofbuprenorphine in patients with NAS. Sparse blood sampling was performed using heel sticks with dried
blood spot technology. Samples were taken prior to the second buprenorphine dose, 1-2 hours postsecond dose and prior to the third or fourth dose. Finnegan scores were evaluated every 2-5 hours basedon symptoms by nursing staff. Mean Finnegan scores were calculated every 12 hours and used as a PD
marker in the modeling. The concentration-Finnegan score relationship was described using a physiologic
indirect response model with the inclusion of natural disease remission. PK/PD modeling was performed
using NONMEM.
Results: A total of 52 buprenorphine blood concentrations and 780 Finnegan scores were available for
the PK/PD modeling. A one-compartment model with first order absorption adequately described the PK
data. The buprenorphine IC50 for the inhibition of disease progression was 0.77 ng/mL (95%CI: 0.32-1.2).
The inclusion of natural disease remission described as a function of postnatal age significantly improved
the model fit. The R2 for individual prediction of PK and PD were 0.53 and 0.47, respectively.
Conclusions: A buprenorphine PK/PD model was successfully developed. The developed PK/PD model
could facilitate model-informed optimization of the buprenorphine dosing regimen in the treatment ofNAS. The results in this abstract have been published in the conference proceedings of American Society
for Clinical Pharmacology and Therapeutics (ASCPT) 2020 as abstract [abstract# PIII-116]1, but have notbeen presented due to cancellation of the meeting.
Reference: 1. Mizuno et al. Clin Pharmacol Ther. 2020; 107(S1). S110.

THU-081
Item Level Analysis of the MDS-UPDRS clinical outcome measure in Parkinson’s Disease (PD)
Varun Aggarwal1, Nathan Hanan1, Robert Stafford1, Mussie Akalu1, Glenn Stebbins2, Tanya Simuni3, Jesse
Cedarbaum4, Donald Grosset5, Kenneth Marek6, Rachael Lawson7, Michele T. Hu8, Caroline Williams-Gray9, Diane
Stephenson1, Klaus Romero1, Jackson Burton1 on behalf of the CPP Modeling team.
1

Critical Path Institute, Tucson, AZ USA; 2 Rush University Medical Center, Chicago, IL USA; 3 Northwestern

University, Chicago, IL, USA; 4 Coeruleus Clinical Sciences LLC, USA; 5 University of Glasgow, UK; 6 Institute for
Neurodegenerative Disorders, New Haven, CT, USA; 7 New Castle University, UK; 8 Nuffield Department ofClinical Neurosciences, Oxford University, John Radcliffe Hospital, Oxford, UK; 9 University of Cambridge,
Cambridge UK.
Objective: This effort is intended to provide a framework to analyze the individual items of the Movement
Disorder Society’s Unified Parkinson’s Disease Rating Scale (MDS-UPDRS), based on an item response theory(IRT) modeling approach. The MDS-UPDRS is a composite scale consisting of four parts- each made up of several
items related to different aspects of PD. It is imperative to understand the behavior of individual items to best assess
their ability to measure underlying disease state for use in drug development. The Critical Path for Parkinson’s(CPP) is a public-private partnership that has enabled significant data sharing of observational studies and clinical
trials, which this analysis utilizes.
Methods: The standardized patient-level data at baseline from the CPP database was integrated with item-level
MDS-UPDRS scores across 5 studies (n=5380, for MDS UPDRS Part II, results shown here). To assess
assumptions required for latent modeling, exploratory factor analysis (EFA) followed by confirmatory factor
analysis (CFA) was used. Modeling of item-level data was conducted using the mirt program implemented in R. A
graded response model was fitted to the responses to derive discrimination and location parameters and construct
item characteristic curves (ICC). Location parameters and ICC provided estimates of sampling adequacy.
Results(Shown here for MDS UPDRS Part II): The CFA confirmed the unidimensionality of Part II. Discrimination
parameters were adequate for all items except Tremor. Location parameters suggested the 5-point Likert scalingwas adequate for all items (see Table 1). Location and discrimination parameters were estimated for other parts aswell and were found adequate (results not shown). On/Off state was not distinguished for part III and needs further
exploration.
Conclusion: This analysis demonstrates that the individual items of Part II provide adequate discrimination ofunderlying motor functional impairment, with the exception of the item measuring tremor. Additionally, the use ofa 5-point Likert scaling appears appropriate for these items. The indication that tremor provides the least
discriminative input may suggest further examination for its use in the composite MDS-UPDRS part II score could
be considered.
Table 1: Discrimination (a) and location (b0-3) parameters of MDSUPDRS Part II.

THU-082

Unified approach for parameters estimation based on in vitro data

Authors: Veronika Musatova (1), Oleg Demin (1), Svetlana Rubina (1)Affiliations: (1) InSysBio, Moscow, Russian Federation

Objectives:
The main aim of this study is to develop a unified approach to parameters estimation from the in vitro data and its
implementation in an upgrade of the Immune Response Template (IRT) - platform describing the interaction ofdifferent types of immune cells, cytokines and facilitating the development new immunotherapies and their
combinations.

Methods:
Immune Response Template (IRT) is QSP platform of the immune system and a tool for the development of QSP
and mechanistic models related to immune response. The core of IRT is an ordinary differential equations-basedmodel, which includes a description of immune cells cycle processes, cytokines secretion and surface moleculesinteraction. Model parameters may be assessed from the in vitro data, obtained in experiments with typical designfor cells survival, proliferation, migration or secretion studies. We developed 6 mathematical models describing
typical in vitro experiments to derive formulas allowing direct parameters calculation from the in vitro dataassessing apoptosis, proliferation, migration, differentiation and cytokine synthesis by immune cells.

Results:
Typical in vitro experiments were modeled, and processes were described using a mathematical approach to accountfor multiple cytokines regulatory impact on cell processes [1]. Derived formulas allow direct calculation ofparameters from the in vitro data, resulting in a reduction of a model development time. The example of parameter
calculation is shown on the figure 1. Developed formulas were used in parameter identification for IRT version 3
and 3.1, allowing the calculation of nearly 950 parameters directly from the in vitro data without additional models
development and fitting.

Figure 1. Example of the migration parameter calculation performed for the in vitro data from Figure 1A [2]Conclusions:
An approach to processes description, accounting multiple regulators impact [1], together with developed
derivations for typical in vitro experiments studying cell proliferation apoptosis, migration and cytokines secretion
allowed the significant increase of identified parameters number in the IRT platform version 3.1 as compared to the
IRT version 2.1.

References:1.

2.

Mechanistic approach to describe multiple effects of regulatory molecules on cell dynamics process inimmune response. Oleg Demin, Evgeny Metelkin, Galina Lebedeva, Sergey Smirnov, ACoP7, Bellevue,
WA, US.
Dieu-Nosjean MC, Massacrier C, Homey B, et al. Macrophage inflammatory protein 3alpha is expressed at
inflamed epithelial surfaces and is the most potent chemokine known in attracting Langerhans cell
precursors. J Exp Med. 2000;192(5):705‐718. doi:10.1084/jem.192.5.705

THU-084

Model-informed target selection and validation through combining quantitative systems
pharmacology (QSP) with network-based analysis (NBA)Viji Chelliah1 & Piet H van der Graaf1,2
1

Certara UK Limited, Canterbury Innovation Centre, University Road, Canterbury, CT2 7FG, United

Kingdom; 2University of Leiden, The Netherlands
Background/Objectives
The majority of drug development projects fail in Phase 2 and Phase 3 clinical trials, mainly due to thelack of efficacy and unacceptable safety profiles . It has been suggested that poor target selection andvalidation is a main underlying reason for this attrition. Indeed, AstraZeneca reported that theimplementation of their revised R & D strategy to focus decision-making based on the so called 5R
framework (which includes “the right target”) increased the trial success rate from 4% to 19% (1).
It has been proposed that target selection/validation is one of the main areas where quantitative systems
pharmacology (QSP) can impact drug discovery and development (2). However, due to the multi-scale
nature and complexity of typical QSP models and the resource it takes to develop them, the target spacethat can be explored in a timely manner is still significantly constrained. Therefore, we propose to combine
QSP with network-based analysis (NBA) to increase the efficiency and effectiveness of in silico (modelinformed) target selection and validation. In this paradigm, the initial target selection step is driven by
NBA and the subsequent target validation by QSP, arguably in the spirit of the original NIH White Paper
where QSP was defined as Quantitative AND Systems Pharmacology(3). Here, we present a workflow of
NBA-QSP integration with a case study of its application to Covid-19.
Methods
We developed a NBA method that integrates differentially expressed genes from individual-specifictranscriptomic data with disease-specific genes and drug targets mapped on to the large-scale proteinprotein interaction network in order to explore potential targets and treatment scenarios.
Results
The gene expression data of SARS-CoV-2 infected patients was obtained from GEO datasets. The NBA of
disease network generated using gene expression data of virus infected individuals and host-virusinteraction identified key molecular signatures and pathways. This followed by the identification of thelikelihood of response to drugs having effect on the predicted pathways using NBA scores formed thebasis for novel QSP models.
Conclusions
Currently, the biological scope of QSP is largely driven by expert opinion and traditional literature survey.
We propose that the impact and efficiency of QSP in target selection and validation can be significantly
improved through integration with NBA.

References(1)

Morgan, P. et al. Impact of a five-dimensional framework on R&D productivity at AstraZeneca.

Nat Rev Drug Discov 17, 167-81 (2018).
(2)

Bradshaw, E.L. et al. Applications of Quantitative Systems Pharmacology in Model-Informed
Drug Discovery: Perspective on Impact and Opportunities. CPT Pharmacometrics Syst Pharmacol
8, 777-91 (2019).

(3)

Sorger, P.K. et al. Quantitative and Systems Pharmacology in the Post-genomic Era: New
Approaches to Discovering Drugs and Understanding Therapeutic Mechanisms: An NIH White
Paper by the QSP Workshop Group. NIH, Bethesda. 1-48 (2011).

THU-085
CYTOCON as a tool to collect, compare and visualize available data on cells and cytokine
concentrations in humans
Vlad Leonov1, Ekaterina Mogilevskaia1, Elita Gerasimuk1, Nail Gizzatkulov1, Oleg Demin1
1
INSYSBIO, Moscow, Russia
Objectives:
Calibration of QSP models may require comparison of model simulations with in vivo baseline data measured indifferent tissues of patients. The data is often available in scientific literature and represents cytokine and cellconcentrations measured in patients. The data is typically measured in different units and for different patient groups
with various demographic and disease associated characteristics. These features make it difficult direct use of thebaseline in vivo data for calibration of a QSP model. To cope with the difficulties and facilitate access of QSP
modelers to such type of in vivo data we have developed an online database and started to collect baseline in vivo
data available in public domain [http://cytocon.insysbio.com/]. The aim of the poster is to describe the database anddemonstrate its application for the comparison of baseline values of cells and cytokines.
Methods:
CYTOCON DB (Cell and cYTOkine CONcentrations database) is developed as a web application based on the
ASP.NET MVC framework, Microsoft IIS web server, Microsoft SQL Server database, Telerik Kendo UI andBootstrap.
Most part of baseline values found in papers were converted from original units (i.e., mg/ml, number of cells per
mm2 of biopsy surface section) to unified units: “pM” for cytokine and “kcell/l” for cell concentration. Formulas forthe conversion were implemented in the database.
Workflow for papers annotation was designed in such a way to provide satisfactory level of quality control. A groupof annotators add information from papers to the DB and a group of reviewers verify the accuracy and completenessof the data. Cytocon DB is continuously extended with new data.
Results: Key features implemented in CYTOCON DB:••

Search query may include both patient group attributes (age, gender, patient number, etc) and diseasespecific clinically measured characteristics (asthma – FEV1 range, dermatitis – SCORAD range, etc)
Both table and graphic representation of search results•

Export of search results in csv format

CYTOCON DB can be applied for comparison of baseline values of cells and cytokines measured in tissues ofdifferent patient groups. For example, aggregating data from multiple sources on IL6 concentration in serum of
healthy subjects and patients with diseases allows us to see the variability of the measured data. Similar analysis forblood level of eosinophils revealed substantial differences between healthy controls and patients suffering fromdifferent immune and inflammatory diseases (Fig.1).

Fig. 1. Comparison of baseline values of eosinophils and IL6 measured in blood of different patient groups collected
in CYTOCON DB and converted to unified units.
Conclusions:CYTOCON DB is a database of human baseline concentrations of cytokines and cells can be applied to facilitate
calibration of QSP models and to compare baseline data measured in different tissues of patients suffering fromvarious pathologies.

THU-087
A modeling and simulation framework to help rationalize the design of BCMA-CD3 bispecific therapeutics
Wenlian Qiao1, Malgorzata Nocula-Lugowska1, Chao-Pei Betty Chang2, Divya Mathur2, Frank Barletta1
1
Department of BioMedcine Design, Pfizer Inc.
2
Department of Oncology Research, Pfizer Inc.
Objectives: The selective expression profile of B-cell maturation antigen (BCMA) prompted the research of
BCMA-CD3 T cell engagers for treating multiple myeloma. As of March 2020, seven BCMA-CD3 T cell engagers
of various formats are being evaluated in the clinic (clinicaltrials.gov). Herein, we established mechanistic models of
BCMA-CD3 T cell engagers for both in vitro and in vivo systems in an effort to understand the potencies of BCMAantibody-CD3 tri-molecular complexes (trimers) formed by different antibody formats, and focus their design tomaximize T cell and myeloma cell engagement in the bone marrow for efficacy. Furthermore, the models were used
to investigate possible effects of shed BCMA (sBCMA) on unbound drug molecules that distribute to the site ofaction in myeloma patients.
Methods: Parameters such as BCMA shedding rate were measured in vitro. Drug elimination and distribution rates
are functions of their molecular sizes. The concentration of trimers was used as a surrogate for efficacy (1,2), which
was then used to investigate the impact of target biology and drug properties on trimer formation and consequential
efficacy in vitro and in vivo.
Results: Membrane BCMA exhibits rapid shedding in vitro. Utilizing the model, binding of antibodies to sBCMA
explained an observed difference between the dissociation constant measured by Surface Plasmon Resonance andthe EC50 of flow cytometry-based on-cell binding data. The model was then used to compare the potency of trimers
formed by different antibody formats within the context of in vitro cytotoxicity. Finally, using a mechanistic modelof the human system, the impact of sBCMA, drug affinities, molecular weight and corresponding PK / distribution
on trimer formation in the bone marrow were all investigated. We found that tighter target affinity does notnecessarily lead to more trimer formation given the presence of sBCMA. For each antibody format, a window of
BCMA-antibody KD was determined to minimize sBCMA sink effect while maximizing T cell and myeloma cell
engagement.
Conclusions: In summary, mechanistic models of BCMA-CD3 T cell engager were established to capture theinterplay between the antibody, sBCMA and membrane BCMA to form trimers that drive target pharmacology. The
in vitro model revealed the cause of the difference between Surface Plasmon Resonance measured BCMA KD and
the EC50 of on-cell binding and allowed us to compare potencies of trimers formed by different T cell engager
formats. The in vivo model enabled us to rationalize the design of BCMA-CD3 T cell engagers to maximize clinicalresponse.
References:
1. Chen et al. (2016) Clin Pharmacol Ther 100(3):232
2. Betts et al. (2019) AAPS J 21(4):66

THU-088

A mechanistic in vitro model of Antibody-Dependent Cellular Cytotoxicity (ADCC) and translation
into a minimal PBPK framework
William Rittase, Katharine Rogers, Songmao Zheng
Biologics Development Sciences, Janssen Biotherapeutics, Janssen Research & Development, LLC.
Objectives: Despite significant interest in developing antibody therapeutics with Antibody-Dependent Cellular
Cytotoxicity (ADCC) function, the quantitative metrics behind ADCC remain elusive. Recent advances in antibody
engineering have found that production of antibodies in a low fucose (LF) environment improves the Fc:CD16abinding affinity ~10 fold over production in a natural fucose (NF) environment, and significantly increases ADCC
activity. A unifying mechanistic model of the ADCC killing process for both LF and NF antibodies was utilized toquantify relevant biophysical aspects of the ADCC cytotoxic process in vitro and in vivo.
Methods: In vitro data from published internal studies as well as literature sources with varying receptor densities
and different targets were fit to a mechanistic ADCC model, which expanded upon a previously published ADCC
model1 to include aspects of 2-dimensional binding after cell-cell contact, to determine relationships between receptor
density, antibody affinity, killing potency, and killing rate. The in vitro model and parameters combined with aminimal PBPK model and patient specific parameters (eg., receptor density and NK cell distribution) were utilized to
predict ADCC activity in humans.
Results: The average number of Fc:CD16a bonds spanning NK cells and target cells required for 50% maximum
killing rate was estimated to be 1.38 (R.S.E. = 21.0%), 1.59 (R.S.E. = 0.1%), and 1.31 (R.S.E. = 17.6%) fortrastuzumab (anti-HER2), JNJ-61186372(anti-EGFR/cMET), and rituximab(anti-CD20), respectively, regardless offucosylation status. The maximum rate of cell killing was determined to vary significantly in each assay, from 3.2x105
to 1.7x10-4 s-1, and appeared to be dependent largely on the target cell lines and not on the quality of the antibody
itself. Utilizing in vitro data from mogamulizumab, an Anti-CCR4 antibody with enhanced ADCC targeting T cells,
clinical published PK data, and patient-specific parameters, the in vivo model predicted complete killing of all CCR4+
T cells in the peripheral blood at doses of 0.01 mg/kg – 1 mg/kg within 2.5 days in Adult T Cell Lymphoma patients,
consistent with the clinical observations.
Conclusions: A mechanism-based in vitro PD model was developed and fit to data with comparable estimatedparameters across various targets and receptor densities. The model can be used to guide target selection and humandose projections for molecules that utilize an ADCC mechanism as a key mechanism of action.
1

Hoffman, F., et al., A mathematical model of antibody-dependent cellular cytotoxicity (ADCC). J Theor Biol, 2018.
436: p. 39-50.

THU-089

Physiologically-Based Pharmacokinetic Analysis of Prednisolone in Rats
Xiaonan Li, Debra C. DuBois, Richard R. Almon, and William J. Jusko

Department of Pharmaceutical Sciences, School of Pharmacy and Pharmaceutical Sciences (X.L., D.C.D., R.R.A.,
W.J.J.); and Department of Biological Sciences (D.C.D., R.R.A.), State University of New York at Buffalo, Buffalo,
New York

Objectives:
The pharmacokinetics of prednisolone in humans and animals exhibit nonlinearity, which is complicated by nonlinear
plasma protein binding, nonlinear tissue binding, metabolic interconversion with prednisone, and nonlinear renalelimination. The aim of this work was to characterize these diverse complexities in disposition of prednisolone in rats
using physiologically-based pharmacokinetic (PBPK) modeling.

Methods:
Blood and 11 tissue samples were collected from 27 healthy male Wistar rats over 24 hr after a subcutaneous (SC)bolus of 50 mg/kg of prednisolone. Concentrations of prednisolone and its major metabolite prednisone in blood,plasma, plasma ultrafiltrates and tissues were measured by liquid chromatography-tandem mass spectrometry (LCMS/MS). The observed plasma and tissue profiles were described by a PBPK model with prednisone and prednisolone
concentrations found to be in rapid equilibrium in plasma and most tissues. The apparent tissue partition coefficients(Kp) of prednisolone and prednisone calculated from the ratio of area under the curve (AUC) of the PBPK modelpredicted tissue and plasma curves were compared to those obtained from a steady-state (SS) infusion study andpublished in silico methods (GastroPlus).

Results:
The blood-to-plasma ratio of prednisolone in rats was about 0.71; calculated red blood cell concentrations ofprednisolone were close to the unbound plasma concentrations, as also found in humans and rabbits. Nonlinear plasma
protein binding (fraction bound of 60 to 90%) was observed owing to known corticosteroid-binding globulin (CBG)
saturation. The tissue-to-unbound plasma concentration ratios decreased in all tissues with unbound plasma

concentrations, indicating nonlinear tissue distribution. The PBPK model-estimated tissue binding equilibrium
dissociation constant (KD) of prednisolone shared by all tissues was about 0.56 ng/mL with highest binding (Bmax)
observed in liver, heart, intestine, fat, and bone, followed by muscle, skin and spleen, with the lowest in kidney, lungand brain. Clearances of CBG-free prednisolone (CLint) was 3051 mL/hr from liver and (CLk) 589 mL/hr from kidney.
Low brain prednisolone concentrations (Kp=0.06) in contrast to its expected high permeability (logP=1.62) were well
captured by a presumed P-glycoprotein (P-gp) mediated efflux process. The PK profiles of prednisolone in fat andbone were reasonably characterized by assuming only the interstitial space was available for distribution. The apparent
Kp values of prednisolone obtained from the PBPK model fittings were similar to values obtained from the SS infusion
study and in silico methods. The metabolite/drug ratio of prednisone/prednisolone was 0.02 for bone and 0.05 for allother tissues except for kidney where the ratio was 0.19 at high prednisolone concentrations.

Conclusions:A PBPK model incorporating nonlinear plasma and tissue binding reasonably characterized the observed prednisolone
and prednisone PK profiles in plasma, and 11 tissues of rats. As steroids have similar PK properties in various species,
receptors in most tissues, and multiple therapeutic and adverse actions, this PBPK model may provide greater insights
into the pharmacodynamic complexities of corticosteroids.

THU-090

The Impact of SLC22A12 Genotype on Pharmacokinetics and Pharmacodynamics of Allopurinol in
Hmong with Gout and/or Hyperuricemia

Authors: Ya-Feng Wen, Pharm.D.1, Richard Brundage, Pharm.D., Ph.D.1, Robert J. Straka, Pharm.D., FCCP1
Affiliations: 1Department of Experimental and Clinical Pharmacology, College of Pharmacy, University of
Minnesota, Minneapolis, USA
Objectives:
Allopurinol is the most widely used urate-lowering therapy. Genome-wide association studies identified an
association between several key renal transporter genes and urate-lowering (PD) response to allopurinol [1].
However, these genotypes did not significantly impact the PK and PD parameters of oxypurinol, the active
metabolite of allopurinol, in a patient cohort of Caucasian, Maori, and Pacific Island decent [2]. Hmong have ahigher prevalence of gout and the gout treatment in this population is often suboptimal. The goal of this project was
to identify and quantify the sources of variability, including key genotypes in oxypurinol PK and their effect on
plasma urate in Hmong population.
Methods:
Hmong patients (n=34) received 100 mg allopurinol twice daily for 7 days followed by 150 mg allopurinol twice
daily for 7 days. On the last day of therapy, plasma oxypurinol concentrations were obtained at times 0, 2, 4, and 6hours. Please urates were obtained at baseline, and before and after the last dose of allopurinol. Using a sequential
PKPD analysis, non-linear mixed-effects modeling (NONMEM v7.4.2) was used to evaluate model parameters. The
impact of anthropometrics, concomitant medications (9 categories of drugs), and key renal transporter genotype
information (17 genotypes) on oxypurinol PKPD parameters was evaluate using a centered power model. Goodnessof fit diagnostics and visual predictive checks were used to evaluate the predictive performance of the PKPD model.
Results:
A one-compartment pharmacokinetic model with first order absorption and elimination best described theoxypurinol concentration-time data. Inhibitory of plasma urate by oxypurinol was described with an inhibitory Emax
model using the steady-state oxypurinol concentration. Weight, renal function measured by creatinine clearance(CrCL) using ideal body weight and genotype of SLC22A12 rs505802 were found to predict differences in theclearance of oxypurinol. Oxypurinol demonstrated a clear exposure-response relationship on urate reduction. Noneof covariates tested were found to predict differences in baseline plasma urate, Imax and IC50. The population
estimates and between-subject variability of the PK and PD parameters are listed in Table 1. The final models
apparent oxypurinol clearance and plasma urate concentration are described in the end of Table 1.
Conclusions:
In addition to body weight and renal function, SLC22A12 rs505802 genotype significantly influences the apparent
clearance of oxypurinol in Hmong patients with gout. An inhibitory Emax model can describe the urate-lowering
response of oxypurinol.
References:[1] Brackman DJ et al. Clin Pharmacol Ther. 2019;106(3):623-31 [2] Wright DFB et al. Br J Clin Pharmacol.
2018;84(5):937-43

Table 1. Parameter estimates of the covariate model and the 1000 bootstrap runs (median and 95th percentiles)

Parameter

Covariate (final) model(RSE%)

Fixed parameters
CL/fm (L/h)
1.4 (11)
V/fm (L)47.7 (10)Kfm
1.1 (fixed)
BLurate (mg/dL)
9.3 (3)Imax
0.6 (fixed)
IC50 (mg/L)
6.5 (14)
Exponents of covariates on CL/fm
Weight1.34 (26)rs505802 CT or TT within SLC22A12
1.30 (55)
Random parameters
BSV CL/fm (CV%) [shrinkage]
34.6 (13) [0%]
BSV V/fm (CV%) [shrinkage]
39.1 (19) [25%]
BSV Kfm (CV%) [shrinkage]
36.1 (fixed)
BSV BLurate (CV%) [shrinkage]
9.3 (3) [8%]BSV Imax (CV%) [shrinkage]
16.0 (fixed)
BSV IC50 (CV%) [shrinkage]
6.5 (14) [70%]Residual error
Plasma oxypurinol, proportional (CV%) [shrinkage] 5.20 (18) [23%]Plasma urate, additive (mg/dL) [shrinkage]
0.76 (37) [18%]Final Model
𝐶𝑟𝐶𝐿 (𝑚𝐿/𝑚𝑖𝑛)
𝑊𝑡 (𝑘𝑔) *.+,
𝐶𝐿/𝑓% (𝐿/ℎ) = 1.4 × ()×()
100 (𝑚𝐿/𝑚𝑖𝑛)
80 (𝑘𝑔)1000 Bootstrap replicatesmedian (95%CI)
1.4 (1.1, 1.8)
47.5 (40.0, 57.9)
1.1 (fixed)
9.3 (8.8-9.8)
0.6 (fixed)
6.4 (5-8.4)
0.51 (0.08, 1.26)
1.29 (0.98, 1.67)
32.7 (23.9, 40.8)
37.5 (21.6, 52.9)
36.1 (fixed)
13.5 (9.5-17.9)
16.0 (fixed)
33.8 (6.9-62.3)
5.16 (4.17, 6.09)
0.60 (0.27-1.19)

× (𝟏 for 𝑆𝐿𝐶22𝐴12 rs505802 CC, 𝟏. 𝟑 for CT or TT)
P%"7 ∗ (DD%'(

𝑃𝑙𝑎𝑠𝑚𝑎 𝑢𝑟𝑎𝑡𝑒(𝑚𝑔/𝑑𝐿) = 𝐵𝐿24"#C × T1 − P()! S (DD%'(U

fm, fraction of the allopurinol systematically available as oxypurinol; CL/fm, apparent clearance of oxypurinol; V/fm,
apparent volume of distribution of oxypurinol; Kfm, combined absorption and formation rate constant; BLurate,baseline urate; Imax, maximum oxypurinol inhibitory effect; IC50, oxypurinol concentration at ½ maximum
inhibitory effect; CrCL, creatinine clearance calculated using ideal body weight; Wt, actual body weight; Cssoxy,
steady-state plasma oxypurinol concentration

THU-091

Application and validation of a whole body PBPK modeling approach to predict OATP
transporter mediated drug-drug interaction with Rifampicin—case study of Asunaprevir
Authors: Yang Song1,*, Hamed Ghaffari2,*, Sietse Braakman3,*, Xiaomin Liang1,
Bernard Murray1, Yurong Lai1**
Institutions: 1. Gilead Sciences, Foster City, CA, USA
2. Department of Mechanical Engineering, University of California Santa Barbara, Santa
Barbara, CA, 93106, USA.
3. Application Engineering, MathWorks, Natick, MA.
* Authors have equivalently contribution to the publication

Objective: Application of a whole body PBPK modeling approach to predict human pharmacokinetics
profiles and OATP transporter mediated drug-drug interaction between rifampicin and asunaprevir, an
OATP transporter substrate.
Methods: A whole-body PBPK model of asunaprevir was first established using human pharmacokinetic
data of asunaprevir from Single and Multiple Ascending Dose (SAD, MAD) studies1,2. in MATLAB
SimBiology. Based on the mechanism known from in vitro assays, the in vivo parameters including
hepatic active uptake, metabolism3, and passive permeability clearance in the model were estimatedusing a particle swarm optimization (PSO) method. Global sensitivity analysis and practical identifiability
methods were used to guide the optimization process. The calibrated PBPK model of asunaprevir wasthen coupled with a established PBPK model of rifampficin4 to predict clinical drug-drug interactions
between the two drugs.
Results: Single and multiple ascending dose human clinical pharmacokinetic profiles of asunaprevir werereasonably captured by the model in a dose range of 10 mg to 1200 mg (in all cases satisfying two-foldcriteria and GMFE for AUC prediction). The nonlinear pharmacokinetics of asunaprevir at high doses was
explained by the saturable OATP uptake (Km,u,uptake= 0.015 mg/L). The model was externally validated by
performing simulation of drug-drug interaction between rifampicin and asunaprevir for the mechanism
of OATP inhibition: the predicted AUC and Cmax change of asunaprevir was within 1.5-fold of clinicalobservations.

Conclusions: A full PBPK modeling approach can be applied in drug discovery to predict nonlinear
human pharmacokinetic profiles and drug-drug interaction for drugs that are cleared by active OATP
uptake with saturable processes. A modular platform to simulate dynamic and complex drug-druginteractions can be implemented in SimBiology, and can be extended to other types of drug-druginteractions and/or to predict interactions between multiple (>2) drugs.
References:
1. T Eley, 2013. Pharmacokinetics of the NS3 Protease Inhibitor, Asunaprevir (ASV, BMS-650032),
in Phase I Studies in Subjects With or Without Chronic Hepatitis C. Clin Pharmacol Drug Dev.
2013 Oct;2(4):316-27.
2. T Shinozaki, 2015. Single- and multiple-ascending dose studies to evaluate the safety,
tolerability, and pharmacokinetics of daclatasvir and asunaprevir in healthy male Japanese
subjects. Int J Clin Pharmacol Ther. 2015 Apr;53(4):292-302.
3. T Eley, 2014. Organic anion transporting polypeptide-mediated transport of, and inhibition by,asunaprevir, an inhibitor of hepatitis C virus NS3 protease. Clin Pharmacol Ther. 2015
Feb;97(2):159-66.
4. Asaumi R, 2018. Comprehensive PBPK Model of Rifampicin for Quantitative Prediction ofComplex Drug-Drug Interactions: CYP3A/2C9 Induction and OATP Inhibition Effects. CPT
Pharmacometrics Syst Pharmacol. 2018 Mar;7(3):186-196.

THU-092

Population pharmacokinetics (PopPK) and concentration-QTc (C-QTc) analysis of DS-1062, an anti-TROP2
antibody drug conjugate (ADC), in patients with non-small cell lung cancer (NSCLC)
Yasong Lu1, Kashyap Patel2, Helen Kastrissios2, Stefanie Hennig2, Bill Poland2, Tajima Naoyuki3, Ophelia Yin1
1. Quantitative Clinical Pharmacology, Daiichi Sankyo, Inc., Basking Ridge, NJ; 2. Certara Strategic Consulting; 3.
Quantitative Clinical Pharmacology, Daiichi Sankyo Co., Ltd., Tokyo, Japan
Objectives:
DS-1062, an anti-TROP2 ADC with a payload DXd (an exatecan derivative), is being investigated in a Ph1 dose
escalation and expansion study in NSCLC1. The objectives of the current analyses were to characterize the PopPKof DS-1062 and to evaluate the QT prolongation potential based on the Ph1 interim data.
Methods:
PK and standard 12-lead ECG data were collected over the DS-1062 dose range of 0.27 to 10 mg/kg, administered
via intravenous infusion every 3 weeks. A 2-step sequential approach was used for PopPK model development: amodel for DS-1062 was first developed, then with the DS-1062-related population parameters fixed to the estimates,a model for DXd was developed. Model structures were selected based on data exploration and model performance.
Candidate covariates were predefined based on pharmacological plausibility and clinical interest, and evaluatedusing stepwise forward selection (p<0.01) and backward elimination (p<0.001). For C-QTc analysis, mean values of
triplicate QT measures were matched with observed concentrations of DS-1062 and DXd within ±30 minutes fromthe ECG time. QT was corrected using both the Fridericia formula (QTcF) and population-based correction (QTcP).
The baseline-subtracted QTc (ΔQTc, ΔQTcF and/or ΔQTcP) was analyzed using linear mixed-effects modeling.
Results:
The PopPK dataset included 1316 observations for DS-1062 and 1313 observations for DXd from 83 subjects. DS1062 PopPK was described using a 2-compartment model with parallel linear and nonlinear (Michaelis-Menten)
clearance. Body weight was a significant covariate on linear clearance and central volume, and baseline albumin
level was a significant covariate on linear clearance. The PopPK of DXd was described using a 1-compartment
model with linear clearance, and no covariate was identified. Adequate performance of both models was confirmedwith diagnostic plots and prediction-corrected visual predictive check. The C-QTc dataset contained 965 pairs oftime-matched observed records from 92 subjects. The RR interval appeared independent of concentrations, and no
hysteresis was evident for the C-ΔQTc relationship. Slopes of the four linear mixed-effects models (ΔQTcF or
ΔQTcP vs DS-1062 or DXd concentration) were not significant at the 0.01 level. Across the four models, the mean
ΔQTc at the geometric mean Cmax of DS-1062 or DXd at 8 mg/kg, the maximum tolerated dose, was predicted tobe from -0.03 to 1.89 ms with 90% CI upper bounds from 2.28 to 3.96 ms.
Conclusions:
PopPK models were developed for DS-1062 and DXd. Identified significant covariates (body weight and baseline
albumin level) are consistent with current knowledge of the PK of DS-1062. DS-1062 did not demonstrate aclinically meaningful QTc prolongation effect based on the Ph1 interim data.
References:
1.

Heist et al. 2019. Oral presentation at IASLC 2019 World Conference on Lung Cancer (Abstract #3854).
Barcelona, Spain.

THU-093

A virtual population (VPop) development workflow with improved efficiency and scale
applied to immuno-oncology quantitative systems pharmacology models (I-O QSP
platforms)
Authors: Yougan Cheng1, Md Shahinuzzaman1, Ronny Straube1, Abed Alnaif1, Radha Konduri2,
Amirmahdi Ghasemi3, Eric Sison2, Tarek Leil1, Brian J. Schmidt1
Institution:
1
2

Clinical Pharmacology and Pharmacometrics, Bristol Myers Squibb

Scientific Computing, Bristol Myers Squibb

3

Data Capability Management, Bristol Myers Squibb

Objectives: VPop development is an essential component of QSP platform application to a clinical setting,as it is often anticipated that a QSP model quantitatively reproduces observed biomarker and response
variations from patients before it should be applied to provide predictions. We previously reported analgorithm to calibrate a VPop to multiple therapies and clinical trials, and applied it to an I-O QSP platform[1]. We noted potential improvements, including: eliminating the manual step to find missing clinicalphenotypes not returned from initial sampling of the parameter space; ensuring the algorithm can weightand resample virtual patients (VPs) for agreement with clinical data across a broader range of fitting
scenarios automatically; and directly calibrating important joint relationships between endpoints. Our goal
was to improve the previous VPop workflow to address these considerations and successfully scale it torun on a cluster, not just a single server.
Methods: The updated I-O QSP platform includes type 1 conventional dendritic cells in the tumor
microenvironment to reflect additional contributions to immunotherapy responses. The workflow andalgorithm were updated to: automate detection of where VPs do not fill ranges in observed data and direct
resampling for missing clinical phenotypes, inform initial points of a prevalence weight optimization stepwith averaged ensemble solutions from a linearized version of the prevalence weight calibration problem[2]; use modifications of Fisher’s r-to-z transformation and a two-dimensional version of the KolmogorovSmirnov test for comparing relationships between endpoints; and automate clustering of VPs to further
expedite prevalence weight optimization in the expanding VP cohort while maintaining diversity inparameter and phenotype space (Figure 1).
Results: We developed a VPop that captures population variability of clinical biomarkers and response atmultiple time points. In addition to previous results [1], the new calibration includes additional therapies
that target CTLA4, additional doses and endpoints, including changes in absolute lymphocyte counts, andjoint relationships that include tumor immune cell content and relative lesion changes. The resulting VPop
had a composite goodness-of-fit (GOF) to data of greater than 0.9 for calibrated endpoints, accurately
predicted response for second line anti-CTLA4 therapy after progression on anti-PD1 therapy (withheld
from training data), and predicted an anti CTLA4 and anti-PD1 therapy combination response. The
algorithm runs on a cluster using MATLAB Parallel ServerTM. The updated code is available(https://github.com/BMSQSP/QSPToolbox).
Conclusions: As new targets, interventions, and biology are added to QSP platforms, increasingly efficientand scaled workflows are needed for model recalibration while maintaining fit and predictive performance.
We integrated an updated I-O QSP platform into an even more automated, iterative VPop development
workflow and developed a predictive, recalibrated VPop.

References: [1] Cheng Y, et al. ACoP10 [2] Alnaif A, et al. ACoP10

Figure 1. Updated workflow

THU-094

Avelumab dose selection for clinical studies in pediatric patients with solid tumors
Yulia Vugmeyster1, Ana-Marija Grišić2, Brigitte Brockhaus2, Peter Rueckert2, Mary Ruisi1, Haiqing Dai1, Akash
Khandelwal2
1
EMD Serono Research & Development Institute, Inc., Billerica, Massachusetts, USA; a business of Merck KGaA,
Darmstadt, Germany; 2Merck KGaA, Darmstadt, Germany
Objectives: Avelumab is an anti–PD-L1 IgG1 antibody approved in the United States and other countries asmonotherapy for the treatment of adults and children aged ≥12 years with metastatic Merkel cell carcinoma and adults
with advanced platinum-treated urothelial carcinoma, and in combination with axitinib for advanced renal cell
carcinoma. The approved dose is 800 mg every 2 weeks (q2w). Avelumab has been administered to children withrefractory or relapsed malignant solid tumors (NCT03451825). The objective of this study was to select the dose forfurther clinical evaluation of avelumab in children.
Methods: Pharmacokinetics (PK) data from children aged 3-17 years and with body weight (BW) of 13.4-78.7 kg
who received either avelumab 10 (n=6) or 20 mg/kg (n=15) q2w were analyzed by noncompartmental (NCA) andpharmacometric analyses. Safety was evaluated by the Safety Monitoring Committee. A population (pop)PK model
was developed in NONMEM using frequentist prior approach. A pediatric population was simulated using US
Centers for Disease Control weight-for-age tables. Simulated exposure distributions for 10 mg/kg, 15 mg/kg, 20
mg/kg, and 800 mg q2w in 2 pediatric subgroups (≥12 years and ≥40 kg, and <12 years or <40 kg) were comparedto popPK-simulated adult exposures at the approved dose [1].
Results: Similar to its safety profile in adults, avelumab up to 20 mg/kg was considered safe in children, since only
1 pediatric patient (in the 20 mg/kg group) had dose-limiting toxicities (DLTs). 20 mg/kg, but not 10 mg/kg, dosing
in children achieved adult exposures associated with the 800 mg dose regardless of BW. A trend of lower exposurein children with BW of ≤40 kg was observed at both the 10 and 20 mg/kg doses compared with those with higher
BW. A 2-compartment model with time-varying linear clearance and BW as a covariate described the data best. The
optimal exposure overlap between simulated adult and pediatric subgroups was achieved with the 800 mg q2w dosefor children ≥12 years and ≥40 kg and with 15 mg/kg q2w for children <12 years or <40 kg, with geometric mean
exposures of ≥90% of adult ones. The pediatric geometric mean exposures for the selected dose are predicted to be
below both the pediatric and adult exposures achieved with the 20 mg/kg dose.
Conclusions: The recommended doses for further clinical evaluation of avelumab in pediatric patients are 15 mg/kg
q2w for children <12 years or <40 kg and the adult dose of 800 mg q2w for children ≥12 years and ≥40 kg; these are
expected to provide exposures similar to those in adults.

References:1.

Novakovic AM, et al. Clin Pharmacol Ther. 2020 ;107(3) :588-96.

THU-095

Modeling Insights on Species Differences in Response to CD8+ T cell-mediated Liver Injury
Zackary R. Kenza , Christina Battistaa, Lisl K.M. Shodaaa

DILIsym Services, Inc., a Simulations Plus company, Research Triangle Park, NC

Objectives: Idiosyncratic drug-induced liver injury (iDILI), which is typically rare and often severe, has led to blackbox warnings or drug withdrawals from the market, with adverse effects on patients and drug development 1. T cellmediated immune responses have been implicated for many iDILI drugs1. Previous work has expanded an existing
quantitative systems toxicology (QST) model (DILIsym®) to include mouse CD8+ T cell responses to hepatocyteexpressed OVA2 and mouse CD8+ T cell responses to hepatocyte-expressed amodiaquine (AQ) antigen3. This work
aims to reproduce human CD8+ T cell responses to hepatocyte-expressed AQ antigen and explore putative
mechanisms underlying cross-species differences.
Methods: Simulating human responses involved human AQ dosing protocols and species differences in the
represented biology, including numbers of human naïve antigen-specific CD8+ T cells4,5. The simulated mouse wastreated with 250mg/kg/day AQ6. The simulated human was treated with 600mg AQ per week7.
Results: AQ exposure in a simulated mouse resulted in delayed, mild hepatotoxicity, illustrated by alanine
aminotransferase (ALT) elevations >100U/L after two weeks of treatment. There was no progression to severe liver
injury on continued drug treatment through 10 weeks, consistent with published data6. In a simulated human, AQ
exposure resulted in delayed hepatotoxicity, illustrated by ALT elevations 120U/L after roughly 4.5 weeks ofsimulated AQ treatment. With continuing AQ treatment, the simulated human progressed to severe liver injury,
evidenced by ALT >1000U/L and TB >2mg/dL, consistent with AQ DILI7. Relative expansion of CD8+ T cells was
similar between species, as was their cytotoxic potential. A surprising variable that differentiated the severity of
injury was the relative recovery potential of mouse vs. human hepatocytes. Simulations include hepatocyte
proliferation with subsequent liver regeneration. Hepatocyte proliferation rates were previously optimized toavailable acetaminophen recovery data8,9 and were unaltered for these simulations. The parameterization ispermissive for balanced injury and recovery in the simulated mouse but generates progressive injury withinsufficient recovery in the simulated human. These abstract results have been previously presented in part at
Immunology2020, Honolulu, HI, 5/2020 and published in the conference proceedings as abstract 2352.
Conclusions: The results provide proof-of-concept that leveraging data from mouse studies of CD8+ T cell
responses in the liver can translate to a human representation consistent with published data. The results illuminate apotential mechanism for cross-species differences in susceptibility to T cell-mediated injury. Further simulations andexperimental follow-up will be conducted to confirm or refute the model findings.
References:
1. Kullak-Ublick. Gut 66:1154-1164 (2017)
2. Kenz. ACoP9 Poster W-076 (2018)
3. Kenz. Immunology2020 2395 (2020)
4. Alanio. Blood 115:3718-3725 (2010)
5. Bechara. Front. Immunol. 10:1331 (2019)
6. Mak. Chem Res Toxicol. 28:1567-1573 (2015)
7. Larrey. Ann. Intern. Med. 104:801-803 (1986)
8. DeAngelis. Hepatol. 42:1148-1157 (2005)
9. Portmann. J Pathol. 117:169-181 (1975)