bluuebunny/crossref_metadata_2025 huggingface dataset:
Optimized Noncompartmental Half-Life Estimation with Tobit Regression
Authors: Denney William S
Published: February 2025

Objectives: When calculating half-life (t1/2) in noncompartmental analysis, concentrations below the limit of quantification (BLQ) are typically omitted prior to estimating the elimination rate (kel). The omission of BLQ concentrations removes information from the concentration-time profile as the fact that a concentration is BLQ indicates that it is censored below the limit of quantification.Methods: Tobit regression is a method to estimate linear models with censored data;[1] it is equivalent to Beal's M3 method of handling BLQ concentrations with nonlinear mixed-effect modeling.[2] We describe a method for applying Tobit regression to estimate the half-life in data with or without BLQ concentrations, and using simulated pharmacokinetic (PK) data, we summarize the improvement of Tobit regression compared to least-squares regression.Results: Via simulation-estimation using 1-, 2-, and 3-compartment simulated PK profiles, the method has been optimized such that it provides more accurate half-life estimates compared to the commonly-used curve-stripping linear regression method used by all software estimating noncompartmental half-lives.Conclusions: Tobit regression provides an improved method for estimating noncompartmental half-life compared to current methods.Citations: [1] Tobin J. Estimation of Relationships for Limited Dependent Variables. Econometrica. 1958;26(1):24-36. doi:10.2307/1907382[2] Beal SL. Ways to Fit a PK Model with Some Data Below the Quantification Limit. J Pharmacokinet Pharmacodyn. 2001;28(5):481-504. doi:10.1023/A:1012299115260
Population Pharmacokinetic-Pharmacodynamic Modeling for Triapine to Optimize Dosing Regimen
Authors: Lee Heekyung , Taylor Sarah , Chauhan Aman , Ivy S. Percy , Gobburu Jogarao , Beumer Jan , Dunn Allison
Published: February 2025

OBJECTIVES: Triapine is a ribonucleotide reductase inhibitor used in combination with radiation and/or chemotherapy for the treatment of various types of advanced stage solid cancers. While the clinical activity of intravenous (IV) triapine appears promising in Phase I and Phase 2 studies, oral dosing may be a more feasible option, and oral triapine in Phase I studies showed a comparable safety profile to that of IV triapine. The objective of this study was to develop a population pharmacokinetic-pharmacodynamic (PK/PD) model to characterize the disposition of triapine, the effect of smoking on exposure, and the association of methemoglobin levels with exposure for triapine.METHODS: A total of 36 patients with advanced stage cervical or neuroendocrine cancers from NCI9892 and NCI10388, two phase 1 studies (NCT02595879, NCT04234568) were included for population PK/PD model building. Triapine plasma concentrations and methemoglobin levels were sampled over a 24-hour period after the 1st, 6th, or 9th dose. The model qualification step was implemented using a visual predictive check. Using the parameters of the final model, plasma concentrations were simulated and the 5th, median, and 95th quantiles were calculated. Simulations will be performed to explore once versus twice daily dosing regimen for triapine to predict the percentage of patients above the methemoglobin level considered to be unsafe/symptomatic (~10%). Data were analyzed by a nonlinear mixed-effects modeling approach using Pumas software (www.pumas.ai). RESULTS: A two compartment PK model with two-compartment Erlang transit absorption and first-order elimination adequately described the concentration time-profile of triapine. The final PK model incorporated covariates of weight and smoking status in relation to their effect on both clearance and volume of distribution. The mean (%CV) estimates of apparent clearance and central volume of distribution were 55.15 L/hr (28.4%) and 59.37 L (26.9%), respectively. In smoking patients, a 37% increase in clearance was observed. An effect compartment PD model adequately described the methemoglobin levels. CONCLUSIONS: Differences in PK behavior and methemoglobin levels with triapine exposure have been identified among smoking and non-smoking patients. The population PK/PD model can be effectively leveraged to inform optimal dosing regimen for triapine.Citations: N/A
PMXModelStructuR: A Tool for Pharmacometrics Model Visualization in R
Authors: Soufsaf Sara , Tremblay Pierre-Olivier , Kheibarshekan Leila
Published: February 2025

Objectives: "PMXModelStructuR" is an R shiny tool designed to streamline the representation of pharmacometrics models in R. This tool aims to provide a standardized and user-friendly platform for model visualization, facilitating collaboration and communication among team members. Methods: "PMXModelStructuR" leverages the capabilities of the "grViz"[1] package and a text-based DOT [2] language to create visually appealing and informative diagrams of pharmacokinetic and pharmacodynamic models. The "grViz" package is a tool used in the R programming language for creating interactive visualizations of graph data structures. DOT provides a simple way to specify nodes, edges, and their attributes, allowing users to create sophisticated visualizations with relatively little effort. In our experience "PKModelStructuR" simplifies the process of drawing models compared to manual methods, enhancing efficiency and accuracy. This flexibility extends to graphical elements such as automatic numbering of compartments and rate constants. With "PKModelStructuR", users can easily modify various model features, including compartment labels, connecting compartments, grouping compartments (e.g. for different sample matrices), and intercompartmental labels. Aesthetic elements can also be modified for further customization, such as colors, shapes and sizes. Results: The implementation of "PMXModelStructuR" allows for the representation of both simple and complex pharmacometrics models. It includes predefined templates for common models features, such as 1 to 3 compartments, first-order absorption, parallel fast and slow absorption, and first-order elimination. These templates enable users to quickly generate model diagrams with standardized layouts and annotations. The output is a figure in the form of a "grViz" object representing the pharmacometrics model. Its implementation in a Shiny app enhances user interaction, enabling seamless modification of model attributes, code copying, and diagram export. Conclusions: "PMXModelStructuR" offers a valuable tool for pharmacometricians, researchers, and practitioners involved in pharmacokinetic and pharmacodynamic modeling. Its integration with R provides a seamless environment for model visualization, promoting consistency, clarity, and efficiency in model representation and communication.Citations: [1] Iannone, R. & Roy, O. _DiagrammeR: Graph/Network Visualization_. R package version 1.0.11. (2024).[2] DOT Language. https://graphviz.org/doc/info/lang.html.
Population Pharmacokinetic/Pharmacodynamic Analysis of Cemdisiran (ALN-CC5) in Healthy Subjects and Patients with Immunoglobulin A Nephropathy
Authors: Park Wansu , Melch Megan , Lee Jongtae , Badri Prajakta , Bhan Ishir , Mehrotra Nitin , Robbie Gabriel
Published: February 2025

Objectives: Cemdisiran is a subcutaneously administered novel synthetic RNA interference (RNAi) therapeutic being evaluated for the treatment of immunoglobulin A nephropathy (IgAN). Cemdisiran acts by reducing complement component 5 (C5) concentrations. The purpose of this analysis was to develop a population pharmacokinetic/pharmacodynamic (PK/PD) model to characterize changes in serum C5 concentrations, to evaluate the impact of covariates on PK/PD parameters and to simulate alternative dosing regimens. Methods: A population PK/PD model was developed using nonlinear mixed-effects modeling approach using NONMEM 7.5. The effect of allometrically scaled cemdisiran liver concentrations on serum C5 was modeled as an inhibitory function through an intermediary RNA-induced silencing complex (RISC) effect compartment. Serum C5 concentration data from phase 1 in healthy subjects and phase 2 in patients with IgAN studies were analyzed. Categorical and continuous measures for demographics and other relevant factors were evaluated for their effect on serum C5 reduction. Standard model evaluation techniques were used to assess adequacy and robustness of the PK/PD model. Results: An indirect response model adequately described serum C5 profiles following single and multiple subcutaneous administration across a wide dose range. The covariate effect of patients with IgAN (compared to healthy subjects) on IC50 and body weight on RISC clearance (CLRISC) were found to be statistically significant in the PK/PD model. Patients with IgAN had a 46% lower IC50 compared to healthy subjects. CLRISC increased with increase in body weight. However, the range of C5 reductions greatly overlapped thus effects of these covariates were not considered clinically meaningful. Other covariates, age, sex, baseline C5 levels, race and estimated glomerular filtration rate (eGFR; range of 26.0~139 mL/min/1.73m2) were not found to be significant. Steady state C5 suppression of 99% is expected with a dosing regimen of 600 mg q4W. Conclusions: The cemdisiran population PK/PD model provided adequate characterization of observed serum C5 concentrations in patients with IgAN. There were no clinically meaningful differences in serum C5 reduction across the covariates evaluated and hence no dose adjustment was deemed necessary.Citations: NA
Longitudinal Population Pharmacokinetic/Pharmacodynamic Modeling of Continuous Efficacy Endpoint and Biomarker for a Novel Compound
Authors: Bozenhardt Emily , Xiong Yuan , Kannan Arun , Zhou Wangda , Samtani Mahesh
Published: February 2025

Objectives: The objective of this work was to demonstrate the utility of including and modeling biomarker data in a longitudinal population PK/PD model for a novel compound.Methods: To determine whether a relationship existed between a biomarker and a continuous efficacy endpoint for a novel compound, a longitudinal population PK/PD model was fitted sequentially, with population PK first and then simultaneous modeling of efficacy endpoint and the biomarker with PK fixed. The population PK was described using a one-compartment model with first-order absorption, linear elimination, and standard allometric relationship for the effect of body weight on apparent volume and clearance. Indirect response models involving a simultaneous fit of the biomarker and efficacy data were used to describe the longitudinal population PD of the efficacy and biomarker time courses, having a common EC50. The same drug concentration was shared for both indirect response models. The model for the drug effect on the continuous efficacy endpoint had a placebo effect taken into account as placebo data were included in the dataset. Each indirect response model had its own baseline and maximum effects, as well as different values for kout. Each response had its own residual error term. Random effects for both baseline responses and the maximum effect for the biomarker were included. The population under consideration consisted of healthy adults and patients. Modeling and simulation of n=1000 replicates for the visual predictive check (VPC) were performed using NONMEM (version 7.4) and Pirana (Certara). Model diagnostics (including binning and plotting of VPC) were generated using an internal R package. Results: Sequential population PK/PD modeling with indirect response models for the continuous efficacy endpoint and the biomarker with the common EC50 and the same drug concentrations as a driver fit the data well. The precision of the parameter estimate for EC50 was slightly improved by inclusion of biomarker data in the model, compared with a similar model for efficacy alone without the biomarker. Both the efficacy endpoint and the biomarker appeared to respond similarly to drug concentrations. The efficacy endpoint and the biomarker followed a similar time course. Conclusions: The model with the common EC50 and the same drug concentrations as a driver for the drug effect on the efficacy endpoint and the biomarker fit the data well and hence demonstrated that the biomarker may in part explain the amelioration of the disease by the novel compound.Citations: NA
Mechanistic Translational Modeling Versus Minimal Anticipated Biological Effect Level Approach to Predict First-in-human Dose For T-cell Engagers: HPN536 Case Study.
Authors: Demin Oleg , Kolesova Galina , Shchelokov Dmitry
Published: February 2025

Objectives: The minimal anticipated biological effect level (MABEL) approach remains a preferred strategy for establishing the safe clinical starting dose of T-cell engagers (TCE). However, it can occasionally yield conservative minimal recommended starting doses (MRSD), resulting in multiple dose escalations and sub-therapeutic doses for patients [1]. Our aim was to evaluate the MRSD predictions for HPN536, a trispecific TCE which binds to mesothelin (MSLN), CD3 and albumin, employing mechanistic translational pharmacokinetic (PK), receptor occupancy (RO), pharmacological activity (PA) modeling, and the MABEL PK-driven approach.Methods: In vitro model was developed to describe published data on T-cell dependent cytotoxicity (TDCC), cytokine secretion (IFNg, TNFa), and T-cell activation (% of CD25+ cells) in presence of various HPN536 concentrations [2]. PK/RO/PA model was developed to fit PK in cynomolgus monkey and translate it to human using standard allometric scaling exponents without fitting. The model for cancer patients includes distribution of TCE into the tumor (ovarian cancer was considered) and PA (cytotoxicity, T-cells activation, and cytokine secretion) based on EC50 values identified in the in vitro model. PA depends on a number of timers of HPN536 bound with CD3 and MSLN in immunological synapse between T-cell and cancer cell. MABEL PK-driven approach was also used to estimate MRSD.Results: PK data in cancer patients were successfully predicted via translation from cynomolgus monkey data. EC50 values of cytotoxicity, T-cells activation and cytokine secretion were in the range of 500-900 trimers. MRSD was identified using 20%-50% PA as a target level of activity for both approaches. MRSD calculated by MABEL PK-driven approach (1 – 23 ng/kg) and starting dose used in phase 1 clinical trial of HPN536 (6 ng/kg) were lower than MRSD predicted by the model (35 – 228 ng/kg). If binding of soluble MSLN with HPN536 was turned off in the model, predicted MRSD range was lower than in presence of soluble MSLN (18 – 114 ng/kg). Model prediction was validated by the fact that lowest dose resulted in CRS grade 3 without premedication with dexamethasone was 54 ng/kg [3]. Conclusions: The conventional allometric scaling method efficiently aids in translating PK from cynomolgus monkeys to humans for therapeutic antibody targeting albumin, thereby extending PK profile. The model demonstrated the influence of soluble MSLN concentration within the tumor on the predicted MRSD. Application of mechanistic modeling to predict MRSD in addition to the MABEL PK-driven approach might help to reduce the number of dose escalation steps exposing patients to sub-therapeutic doses in first-in-human trials.Citations: [1] Saber et al. An Regul Toxicol Pharmacol. 2017 Nov;90:144–152. [2] Molloy et al. Clin Cancer Res. 2021 Mar 1;27(5):1452–1462.[3] Harpoon Therapeutics Investor Presentation February 2022.
Characterizing Pharmacokinetics Effects on Measures of Overall Drug Exposure during Pregnancy
Authors: Lyu Xintian , Long Yuhan , Barry Jessica , Sherwin Catherine , Birnbaum Angela
Published: February 2025

Objectives: Pregnancy is a complex time when drug pharmacokinetics change rapidly. Therapeutic drug monitoring during pregnancy can help determine exposure despite sparse sampling. This study investigated the differences in pharmacokinetic parameters when calculated via multiple methods to characterize drug exposure in pregnant patients to inform the design of pregnancy outcomes studies.Methods: A generic one-compartment model with first order absorption and 1000 mg twice daily dosing was used to simulate predicted concentrations (Julia v.1.9.2). Volume and clearance were set to 32 L and 4 L/h preconception increasing linearly by 0.12 L/wk and 0.032 L/h/wk, respectively, for 40-week gestation. Mid-gestational month trough samples (~10 samples/patient from 2-38 weeks) were simulated to determine 4 exposure metrics: 1) lowest concentration, 2) average concentration, 3) highest concentration, 4) trapezoidal AUC0-40weeks. To reduce duration bias, methods 1, 2, and 3 were time-weighted by multiplying by gestational age. For method 4, the first and last simulated trough concentrations were used as surrogates for 0 and 40 weeks before estimation with trapezoidal rule. Sensitivity analyses of gestational week, preconception volume and clearance, and rate of volume and clearance increase were performed.Results: For sensitivity on gestational weeks, monthly samples introduced with additional months of gestation led to drop in exposure estimates for methods 1 and 2. Otherwise, all exposures increased proportionally with gestational age. For higher preconception clearances, exposure measures converged to 0 mg/L*wk due to complete elimination of drug. For higher preconception volumes, the exposure measures increased but started decreasing at nonphysiologically relevant volumes (37,000 L for highest, 17,000 L for lowest). Increasing the rate of clearance change decreased all exposures, converging to 0 mg/L*wk at high rates of clearance change with method 3 taking the longest to converge. Increasing the rate of change in volume increased all exposure estimates. The rate of volume increase during pregnancy affected which sample is the lowest concentration for calculation with method 1 due to its relation with changing clearance. At rates below 0.7 L/wk, the last sample was the minimum, but the first sample was the lower at rates above.Conclusions: Although additional samples provide more data, additional sparse sampling from longer pregnancies can bias exposure estimates for child outcomes. Differences in exposure measures need to be considered across and within methods due to biases arising from changes in pharmacokinetics during pregnancy. Therefore, choice of exposure method and sampling strategy are important considerations for pregnancy outcome studies.Citations: none
popPK & ER analysis of amivantamab in combination with chemotherapy for the treatment of patients with EGFR-mutated locally advanced or metastatic non-small cell lung cancer after osimertinib failure
Authors: Bishop Corey , Zhou Jie , Yu Alex , Yuan Dongfen , Mehta Jaydeep , Clemens Pamela , Haddish-Berhane Nahor , Su Yaming , Bauml Joshua , Man Julie , Samtani Mahesh
Published: February 2025

Objectives: The objectives of the popPK and ER analyses of the Mariposa-2 (M2) study were to: 1) characterize the popPK of amivantamab (ami) for non-small cell lung cancer (NSCLC) patients who received ami, carboplatin and pemetrexed (ACP) based on a prior model; 2) evaluate the impact of potential covariates including demographic characteristics and chemotherapy on ami PK; 3) explore and quantify the relationship between the efficacy and safety endpoints and ami exposure in NSCLC patients who received ACP. Methods: The popPK analysis included 1,106 ami PK concentrations from 123 participants in M2. The prior model (2-compartment (V1 and V2) with parallel linear (CL) and Michaelis-Menten clearance (Vmax and Km)) was used for external validation (MAXEVAL=0 in NONMEM v7.4.3). Exposure subgroup analyses were accomplished via univariate and multivariate forest plots. The ER analysis for efficacy focused on progression-free survival (PFS) and were evaluated via stratified Kaplan-Meier plots. Covariates were explored graphically or using Cox proportional hazard modeling. ER analyses for safety endpoints were evaluated using quartiles (R >3.6.2).Results: Model diagnostic plots and VPCs demonstrated that the model adequately described the PK data collected in M2. Baseline body weight (BW), sex, albumin, and age were covariates on CL, sex on V1, and BW was a covariate for V1 and V2. According to forest plots, chemo had no impact on ami PK exposures. No apparent ER relationship for PFS was observed across exposure tertiles for all exposure metrics. There were no apparent relationships between ami exposure and paronychia, rash, nausea, nor constipation. Incidence rates of hypoalbuminemia slightly increased with PK exposures, but were generally limited to grade 1 or 2.Conclusions: Based on individual post hoc PK parameters, ami steady-state exposures following the BW tiered regimen were comparable in subgroups by age, albumin, race, renal function, hepatic impairment, EGFR mutation type, brain metastases, or ECOG performance status. Although females appeared to have ~30% higher PK exposures than males, PFS was similar between males and females. Findings suggest optimal exposures have been achieved at the selected regimen and no dose adjustment is warranted.Citations: Haddish-Berhane, et al. Determination and Confirmation of Recommended Ph2 Dose of Amivantamab in Epidermal Growth Factor Receptor Exon 20 Insertion Non-Small Cell Lung Cancer; 2023.
The PK-PD model of dexamethasone for fetal lung maturation and developmental neurotoxicity: A dose optimization study
Authors: Du Ruihu , Liu Dongyang , Song Ling , Wang Hui
Published: February 2025

Objective: Dexamethasone (DEX) is an essential medication for preventing infant respiratory distress syndrome (IRDS) in developing countries. However, its long-term neurotoxicity requires optimizing the dosage. Animal studies combined with model-informed precision dosing can provide crucial support.Method: In pharmacodynamic part, pregnant rats (n=18) were equally divided into three groups, and intramuscularly administered DEX bid at gestational day (GD) 18-19, respectively. Fetal lungs were collected at GD 20, and qPCR and ELISA methods were used to measure the concentrations of surfactant protein A and B (SP-A/B) and related mRNA. A physiologically based pharmacokinetic (PBPK-PD) model was established by MATLAB to describe the quantitative relationship between DEX concentrations and SP-A/B by borrow some parameters from literature. In developmental neurotoxicity part, a total of 54 pregnant rats were administered with the same dosing groups as mentioned earlier, and allowed to give birth naturally. The offspring underwent an open field test on postnatal day 84, and the correlation between drug exposure and offspring behavior was analyzed. The therapeutic window of DEX was determined by balance of PBPK-PD model simulation results and neurotoxicity results. Then we recommend the better dosing regimen using predicted pregnant pharmacokinetic characteristics simulated from published population pharmacokinetic (PPK) model of DEX by assuming same therapeutic concentration window between rats and humans.Results: The mechanistic based PBPK-PD model profiled the quantitative characteristics. Validation results showed no systematic error in our model. The estimated PK parameters were Ka (0.76 1/h), CLh,int (437.65 mL/h) and CLint,P-gp (64.4 mL/(h*mL placenta)). Model simulation indicated that the minimal efficacy plasma concentration of DEX in pregnant rats is 4 ng/mL. Systematic analysis showed that plasma DEX concentrations exceeding 26 ng/mL in pregnant rats can lead to behavioral differences. PPK model simulation indicated that for intramuscular administration of DEX, 2.5 mg per dose, administered every 12 hours for 4 doses could reach the therapeutic window.Conclusion: We firstly constructed mechanism based PBPK-PD model of DEX involving placental P-gp transporter, revealing that toxic effect can also occur with clinical administration for 2 days. We established quantitative relationships of dose-efficacy and dose-safety. Based on the obtained therapeutic window and pregnancy PPK simulation, the recommended optimal dosage is: 2.5 mg per dose, administered every 12 hours for 4 doses.Citations: [1] Du R, Zhao X, Song L, Wang H, Liu D, Wang Q. A physiologically based toxicokinetic model of P-glycoprotein transporter-mediated placenta perfusion of dexamethasone in the pregnant rat. Food Chem Toxicol Int J Publ Br Ind Biol Res Assoc. 2024;183:114213. doi:10.1016/j.fct.2023.114213
Overcome the Challenges of NONMEM PopPK Dataset for Late Stage Daily Dose Study
Authors: Xiao Dan , Su Jing , Kazi Mansur , Moreton Richard
Published: February 2025

Abstract: Create PopPK dataset for late stage daily dose study is much more difficult that early stage single or multiple dose clinical trials, because of large number of participants and long term study period requires to collect medication history through paper-based medication diaries. The dosing data of late stage daily dose study is big and most of dose history are collected at home through diary, only the last dose prior to PK samples is collected at clinic. Because of long dose history and dose entries are folded with start and end datetime, that is why ADDL (additional dose) and II (dose interval) come to play an important role to reveal the entire dosing history given NONMEM software requires certain data structure. There is no single formula to derive ADDL, it depends on the study dosing interval and how EX data is collected. Another challenge and important aspect of creating NONMEM PopPK data for daily dose study is to make sure the individual standalone dose record is available and present before PK record. This paper will show you how to correctly derive ADDL and control the quality for daily dose PopPK dataset, as PopPK dataset quality is critical, the inappropriately built dataset can cause proc mixed model not able to converge and the inference draw from the model might not suitable. Introduction: For phase 3 clinical trials with daily dosing regimen, some company collect dose history solely on study medication eCRF form, while majority of company collect dose history on both medication and PK eCRF form. This paper will focus on the design that collect dose history on both medication and PK form.Use example to show how to make standalone dose records and derive ADDL: Step 1: Provide example EX and PC raw dataset;Step 2: Expand interval dosing records in EX domain into individual daily records, and merge with PC by date of daily dose from EX domain and PCRFTDTC from PC domain;Step 3: Collapse dose records to create final PopPK dataset;Step 4: Expand final PopPK dataset by ADDL, derive time since last dose based on dose history on final PopPK dataset and compare with time since last dose derived from collected PCRFTDTC; --- Provide SAS code and example records.Conclusion: Having the standalone dosing records preceding PK sample, and correctly derived ADDL and ii are critical to PopPK data quality. The way of data entry is different between studies, but you can rely on the general rules and SAS code provided above to control NONMEM PopPK dataset quality, regardless of your role as dataset developer or QC'er.Citations: Shuqi Zhao, 2021. “Imputation for Missing Dosing Time in NONMEM PopPK Datasets”, Proceedings of PharmaSUG 2021.
Advancing Drug Development in Relapsed and Refractory Multiple Myeloma (RRMM): Assessing the Safety and Efficacy Landscape Utilizing Model-Based Meta-Analysis
Authors: SHAH ZEEL , Dhamane Amol , Hu Chuanpu , Kondic Anna , Bouillon-Pichault Marion , Zhou Jian
Published: February 2025

Objectives: The primary aim of this study was to understand the comparative safety and efficacy profiles of existing drugs in the RRMM market. Published clinical trial data was analyzed using the model-based meta-analysis (MBMA) technique, a quantitative approach to mine information from summary level data and aid decision making, thus fitting in the model-informed drug development (MIDD) framework. Specifically, the focus was on evaluating Grade 3+ neutropenia as a safety endpoint and Overall Response Rate (ORR) as an efficacy endpoint within the context of the current therapeutic landscape. The impact of potential prognostic variables on these endpoints was investigated alongside using simulations. The MBMA approach, aims to overcome data heterogeneity challenges and enable comparisons between existing treatments, incorporating dose-response relationships and covariate effects.Methods: A literature database comprising of 352 randomized clinical trials in RRMM with 512 treatment arms and 177,692 RRMM patients, was utilized. The database was further refined to only include treatment arms with more than 10 patients and treatments with at least 3 studies to ensure that the data used for modelling is statistically robust. This resulted in a total of 130 trials (154 treatment arms) representing 17,246 patients for Grade 3+ neutropenia. For the ORR endpoint, 192 trials (239 treatment arms) were included, covering 22,227 patients. The probability of Grade 3+ neutropenia and ORR incidence were estimated using Binomial regression. Between-trial and between-treatment-arm variabilities were applied to the model parameters and potential covariates (patient demographics, prior treatments, and clinical trial design) influencing the probability of the endpoints were evaluated. Covariate selection was performed using stepwise covariate modelling approach (∆ BIC).Results: The preliminary model successfully predicted the probability of both endpoints in alignment with observed trial data which was assessed by visual predictive check plots. Key findings indicated a decrease in Grade 3+ neutropenia probability with the progression of study phases, and that the probability of ORR increased with background steroid therapy and higher percentage of patients in stage 1 of the disease but decrease with therapy line advancements. Conclusion: The study highlights the utility of MBMA as a powerful tool in ranking different RRMM interventions with respect to Grade 3+ Neutropenia and ORR endpoints sourcing data from a publicly available clinical trial information. This MBMA model could ultimately aid in comprehensive evaluation of safety and efficacy of RRMM treatments strategies.Citations: NA
Population pharmacokinetics of revumenib in patients with relapsed/refractory acute leukemias
Authors: Sunkaraneni Soujanya , Lin Yu-Wei , Smith Steven , Cobbina Enoch , Cheung S.Y. Amy , McNeer Nicole , Maier Gary , Ordentlich Peter
Published: February 2025

Objectives: Revumenib, a selective, orally bioavailable small molecule inhibitor of the menin-lysine methyltransferase 2A (KMT2A) interaction is under investigation in patients with acute leukemias.1 Clinical pharmacology of revumenib has been determined in clinical, modeling, and simulation studies. Here, we report the results of PopPK analyses assessing the effects of potential covariates on revumenib exposure. Methods: AUGMENT-101 Phase 1/2 [NCT04065399] evaluated revumenib in adult and pediatric populations with acute leukemias.1 Revumenib was administered orally twice daily (BID) in a 28-day cycle via capsule, tablet, or oral solution. A PopPK model was developed using AUGMENT-101 study data. First-order conditional estimation was used with interaction in NONMEM (v7.4.3). Monte-Carlo simulations were conducted in 1000 virtual patients to support dose and regimen justification. Results: A total of 251 patients had evaluable PK data, consisting of 4,869 revumenib plasma concentrations. Exposures were similar between capsules, tablets, and oral solution. PopPK analysis showed, after taking body weight effect into account, there were no clinically significant differences in the PK for age (9 months–82 years), race, gender, ethnicity, and in patients with body weights of 40-146 kg; differences were observed in patients with < 40 kg (~30–58%), thus dosing in these patients is per body surface area (BSA). Markers of hepatic function (ALT, AST, albumin, bilirubin) had no clinically significant effect on PK; thus, no dose adjustment is recommended for patients with mild or moderate hepatic impairment. Markers of renal function (i.e., serum creatinine) had no clinically significant effect on PK, thus no dose adjustment is recommended for patients with mild or moderate renal impairment. Co-administration with a strong CYP3A4 inhibitor (CYP3A4i) increases plasma concentrations, therefore the dose should be reduced when taken with a strong CYP3A4i. There was minimal effect of a low-fat meal, thus revumenib can be taken fasted or with a low-fat meal. Conclusions: The clinical pharmacology profile of revumenib has been well characterized in adult and pediatric populations with R/R acute leukemias. There was no clinically meaningful impact of intrinsic factors on revumenib PK except in patients with body weight < 40 kg, hence a BSA adjusted dosing is proposed. Strong CYP3A4i increases plasma concentrations, hence a dose reduction is proposed. Revumenib can be administered under fasted or with a low-fat meal. Pediatric patients and those who cannot swallow a tablet may substitute for the oral solution, as the PK is equivalent.Citations: [1] Issa GC, et al. Nature. 2023;615:920–24.
Modeling the evolution of activation marker distributions in flow cytometry data representing cell populations in patients dosed with immunomodulating agents
Authors: Bottino Dean , Bellout Hamid , Stroopinsky Dina , Kasar Siddha , Saggu Gurpanna , Gibbs John , Berger Allison , Chudnovsky Aleksander
Published: February 2025

Background: The growing emphasis on immunology in oncology and other therapeutic areas has increased the importance of modeling flow cytometry data. For example, subasumstat (TAK-981) has been shown to stimulate CD69 expression on NK cells both in vitro and in the peripheral blood of cancer patients in a Phase I clinical trial (NCT03648372) . Typically, we would use ordinary differential equations (ODEs) to model the median fluorescence intensity (MFI) of CD69 or the percent CD69 positive (%+) on NK cells. However, flow cytometry data shows the distribution of CD69 fluorescence intensities right-shifting and spreading under subasumstat administration, reflecting a stochastic process, from gene to mRNA to protein expression, that cannot be fully captured by ODE modeling. Methods: Starting with a chemical master equation (CME), we derive a partial differential equation (PDE) governing the evolution of the distribution of CD69 expression on the NK cell population in a patient after a single dose of subasumstat. We then attempt to find necessary conditions for the model to be consistent with three key clinical observations: (a) homeostasis before treatment, (b) response to treatment, and (c) return of the distribution to the original homeostatic CD69 distribution. Results: Theoretical as well as empirical comparison to the observed CD69 expression distributions at pre-dose, 24 hours after first dose, and 4 days after the second BIW dose (day 4) provided a relationship between the functional forms of the coefficients of the PDE required to fit the observed data. Conclusions: The approach used is able to describe the evolution of the distribution of CD69 expression on NK cell populations in individual patients while constraining the parameter ranges that fit the data. This procedure can be generalized to establish conditions and parameter values allowing characterization of pre-treatment homeostasis and on-treatment dynamics of immune cell biomarker distributions as determined by flow cytometry. By capturing the richness of flow cytometry data (as opposed to modeling a scalar quantity such as MFI or %+), this approach can provide better quantification of key immunological processes in vitro, in vivo, and in patients.Citations: [1] A Study of TAK-981 in People With Advanced Solid Tumors or Cancers of the Immune System, NCT03648372, clinicaltrials.gov.
Population pharmacokinetic analysis of anti-interleukin-15 monoclonal antibody TEV-53408 in healthy volunteers
Authors: Dotan Or , Radivojevic Andrijana , Schnir Victoria , Gutman Dikla , Singh Rajendra
Published: February 2025

Objectives: TEV-53408 is a human monoclonal antibody (mAb) that binds to and neutralizes interleukin-15 currently being evaluated in celiac disease. Utilizing data collected during the Phase 1 first-in-human (FIH) study in healthy participants, this presented analysis sought to characterize pharmacokinetics (PK) of TEV-53408 and further anticipate the PK signature in patients for various indications. Methods: Population PK modeling was used to describe the dynamics of the serum TEV-53408 concentrations over time, and several structural models were evaluated. A set of demographic and baseline characteristics were tested to explain the interindividual variability in selected PK parameters (i.e., covariate analysis). During model development, population PK models were assessed using the evaluation of individual and population mean parameter estimates and their precision as measured by the percent standard error of the population mean estimate relative to known priors or physiological values, and by graphical examination of standard diagnostic and population analysis goodness-of-fit plots. Various steps of analysis dataset preparation, exploratory analysis and simulations were performed in R and appropriate R packages. Nonlinear-mixed effect modeling was performed using NONMEM. Results: A two-compartment model with first-order absorption, linear and saturable elimination, body weight at baseline as a covariate on clearance, maximum apparent clearance rate and peripheral volume, and fixed inter-individual variability of the Michaelis-Menton constant, best described the serum TEV-53408 concentration-time data. The model was then used in simulation mode to anticipate outcomes with alternative doses and dosing regimens in a virtual participant population. Conclusions: The resulting population PK model adequately characterizes PK following subcutaneous administration of TEV-53408. This provides a robust quantitative platform for future decision making and informing dose selection for subsequent clinical trials.Citations: n/a
Redefining Multidrug Resistance Quantitatively using Live Cell Functional Data
Authors: Ray Ashley , Douglass Eugene
Published: February 2025

Objectives: P-glycoprotein (Pgp) contributes to drug distribution (oral bioavailability[1], blood-brain barrier[2], stem cells and cancer chemoresistance[3]). Predicting drug access to tissues and cells is important to understand the drug response in-vivo. Unfortunately, Pgp specificity for FDA drugs is unclear due to technical variability in assays that quantify barrier ratios and enzyme kinetics (kcat/Km) using non-cellular experimental models[4]. Our research improves Pgp specificity scores by leveraging new Pgp expression and function datasets including drug screening data, Pgp expression data (cell lines, tissues) and barrier ratio datasets.Methods: Emerging big datasets (gene expression, drug response etc.) provide an opportunity to simultaneously measure Pgp quantity and function across tissues and cell lines. At the same time, physics-informed machine learning infers biophysical kinetic parameters from these datasets. Here, we experimentally and computationally integrate functional dataset information to better understand Pgp specificity. Pgp function is measured through barrier ratios (tissue and cells—Calcein AM[5]) and drug response data in cells. Pgp expression is quantified through RNAseq[6], proteomics and flow cytometry. We take these pre-existing datasets and create an experimental procedure that leverages both data types. We utilize multilinear regression as our machine learning approach. Our rationale is based on underlying Michaelis-Menten physics and enables us to make functional assays for patient-specific diagnostics.Results: We obtained consensus scores for Pgp specificity across 1,500 FDA approved drugs (PRISM[7]) and validated them experimentally in a subset of 76 substrates selected to represent top and bottom drugs for Pgp specificity. These scores can be used to calibrate clinical diagnostics (Pgp expression), and our experimental platform can be used to quantify Pgp function in clinical samples. Conclusions: Overall, we have developed a parallel computational and experimental procedure to estimate Pgp selectivity in live cells. The long term implications for this research include multidrug resistance diagnostics, tissue distribution predictions, drug-drug interaction predictions and transporter kinetics measurement (cellular vs protein) improvements.Citations: [1] Eur J Pharmacol., 2010, 627:92-98. [2] Cell, 1994, 77:491-502.[3] Endocr Relat Cancer., 2003, 10:43-73.[4] J Biol Chem., 2017, 292:15838-15848. [5] Eur J Pharm Sci., 2001, 12:205-214. [6] Nature, 2019, 569:503-508. [7] Nature Cancer, 2020, 1:235-248.
SEMI-PHYSIOLOGIC POPULATION PHARMACOKINETIC MODEL TO EVALUATE THE EFFECT OF HEPATIC-UPTAKE TRANSPORTER GENE SLC22A1 POLYMORPHISM ON PHARMACOKINETICS OF PROGUANIL AND ITS METABOLITE, CYCLOGUANIL
Authors: Khwarg Juyoung , Yang Eunsol , Park Chan Song , Ji Sang Chun , Yu Kyung-Sang , Lee SeungHwan
Published: February 2025

OBJECTIVES: Proguanil, an antimalarial drug, is metabolized into cycloguanil by the cytochrome P450 2C19 (CYP2C19). Interestingly, hepatic uptake via the organic transporter 1 (OCT1), encoded by the polymorphic gene SLC22A1, has been reported to affect the biotransformation of proguanil. Building on this, we developed a semi-physiologic population pharmacokinetic (PK) model of proguanil and cycloguanil to evaluate the effect of the SLC22A1 1022 C>T genetic polymorphism on the metabolism of proguanil to cycloguanil.METHODS: A semi-physiologic population PK model of proguanil and cycloguanil was developed based on a total of 160 plasma concentration-time data each for proguanil and cycloguanil from 16 healthy subjects (NCT04568772). For the analysis of the genotype effect, SLC22A1 genotype data of 13 subjects was used. For the 3 subjects without genotype data, estimated genotype was applied, derived from a mixture model based on known allele frequency. The structure model with 1- and 2- compartment for both proguanil and cycloguanil, with or without the liver compartment was tested. The population PK model was evaluated using goodness-of-fit plots and a visual predictive check. The analysis was performed using NONMEM 7.5. RESULTS: A parent 2-compartment and metabolite 1-compartment population PK model with first-order absorption and elimination including a liver compartment, well described the concentration-time profile of proguanil and cycloguanil. The SLC22A1 genotype was evaluated as a covariate for hepatic extraction, and it was estimated that the hepatic extraction was 0.55-fold lower in subjects with the CT genotype compared with the CC genotype. This aligned well with the prior descriptive analysis for systemic exposure and metabolic ratio of cycloguanil according to genotypes. CONCLUSIONS: A semi-physiologic parent 2-compartment metabolite 1-compartment population PK model incorporating a liver compartment successfully characterized the PK of proguanil and cycloguanil and quantitatively evaluated the degree of its metabolism according to hepatic uptake capabilities of OCT1 by SLC22A1 genotypes.Citations: NA
Pharmacokinetic/Pharmacodynamic Modeling for Dose Selection of S-001, a Novel mRNA-based Therapy for Pancreatic Cancer
Authors: Kim Yoonjin , Lee SeungHwan
Published: February 2025

Objectives: S-001 is a newly developed mRNA-based therapy that binds to the messenger ribonucleic acid of TGF-β in a complementary manner, regulating gene expression and subsequent protein expression. It has been developed as a therapeutic agent for solid tumors that overproduce TGF-β, such as pancreatic cancer. This study aimed to establish a pharmacokinetic (PK)-pharmacodynamic (PD) model to suggest an optimal dosing regimen for a clinical trial by integrating preclinical PK and PD data with human pancreatic cancer data.Methods: The PK model was established based on plasma S-001 concentrations observed in ICR mice following intraperitoneal administration and in Cynomolgus monkeys following intravenous infusion. PK model parameters from two different species were allometrically scaled to humans. The initial PD model was developed using tumor volume-time profiles obtained from human pancreatic cancer xenograft mice. Considering the difference in tumor growth rate between human and mouse, PD model parameters, including tumor growth rate constant, baseline and maximum tumor volume, were adjusted using previously reported human pancreatic cancer data.[1] The PK-PD model was evaluated by comparing the plausibility of parameter estimates, objective function value analysis, examination of goodness of fit plot, and visual predictive checks. Using the final PK-PD model, various dose regimens and corresponding tumor volume-time profiles in humans were simulated.Results: A two-compartment model with first-order absorption and first-order elimination best described the pharmacokinetics of S-001. Tumor growth without treatment followed a logistic growth model, and S-001-mediated tumor growth inhibition was modeled using the Norton-Simon killing model with an Imax function.[2] Simulations based on the final PK-PD model indicated that doses exceeding 1 mg/kg once a week resulted in decelerated tumor growth. Conclusion: The S-001 PK-PD model for human pancreatic cancer was developed to guide the dosing regimen of a clinical trial by combining the data across species.Citations: [1] Furukawa H, Iwata R, Moriyama N. Growth rate of pancreatic adenocarcinoma: initial clinical experience. Pancreas. 2001;22(4):366-9. doi: 10.1097/00006676-200105000-00005. PMID: 11345136.[2] Álvaro G. López, Kelly C. Iarosz, Antonio M. Batista, Jesús M. Seoane, Ricardo L. Viana, Miguel A.F. Sanjuán, Nonlinear cancer chemotherapy: Modelling the Norton-Simon hypothesis, Communications in Nonlinear Science and Numerical Simulation, 2019, https://doi.org/10.1016/j.cnsns.2018.11.006.