The 5th
Annual Meeting of Asian
Pharmacometrics Networkin Tokyo2023

full on-line (Zoom) conference

29 - 30, November

Plenary Session for
ICH-M15 by Japan
Topic Leaders
Panelists:
Registration:
https://www.apn-tokyo.jp

Dr. Iwata Daisuke
PMDA, Japan

Dr. Norisuke Kawai
Pfizer, Japan

Kenya Nakai

APN Oﬃcial Website:
http://www.go-apn.org/

Esai, Japan

Chair: Dr. Jenny Chien
Eli Lilly, U.S.A.

Opening Remarks

1

APN President
Kyungsoo Park, PhD, MD
Professor
Department of Pharmacology
Yonsei University College of Medicine
Korea

Hello everyone.
Welcome to the 2023 APN Annual Symposium. We are delighted to have you here. Thissymposium marks the fifth since the inaugural event held in 2019, and we extend ourheartfelt gratitude to all of you who have supported the symposium's growth over the years.
As the Chair of APN, I have consistently emphasized our organization's primary mission in
every symposium's opening address since our inaugural event. As you are aware, thismission is to promote the advancement and expansion of pharmacometrics in the Asian
region while fostering networking opportunities.
Despite the challenges faced by emerging organizations, APN has consistently invited
speakers from over ten countries each year, starting with our inaugural conference. As the
Chair, I take great pride in our efforts over the years, and this year, we continued thistradition by inviting 15 speakers from more than ten countries.
I am very pleased to see that, through the participation of speakers from various Asian
countries, the APN Symposium is increasingly establishing itself as a well-deserved panAsian conference in the field of pharmacometrics.
This year, following the end of the COVID pandemic, we had initially planned to host the
symposium in an offline format. However, due to a proposal from our host country, Japan,
this year's event will take place online, and we plan to return to an offline format in Japannext year.
We extend our gratitude to all those who have been involved in preparing for this event,including the APN board members. A special thanks to the members of the Japaneseorganizing committee, including Professor Yasuhiro Tsuji and Dr. Atsunori Kaibara, for theirinvaluable contributions.
Furthermore, this year, in collaboration with Professor Surulivelrajan Mallayasamy from
Manipal College inIndia, I co-developed a graduate-level course titled "Special Lectures inPharmacometrics." This course ran from March to June, spanning 16 weeks, with 3 hours
of real-time lectures every Thursday afternoon.
2

A total of 26 students enrolled in the course, with 21 instructors participating, including 14
from Asia. This collective effort allowed us to fulfill APN's second mission, which is "To
provide training opportunities to future pharmacometricians."
APN will continue to develop such educational programs in the future to contribute to thetraining of future pharmacometricians in the Asian region.
In conclusion, we hope that this symposium has been both informative and enjoyable forall of you who attended. We sincerely ask for your ongoing support and commitment to
APN in the future.
Thank you.
APN Chair
Kyungsoo Park

3

Local organizer
Yasuhiro Tsuji, PhD
Professor
Laboratory of Clinical Pharmacometrics
Nihon University
Japan

Dear Colleagues,
On behalf of the local organizing committee of the 5th Asian Pharmacometrics Network(APN) Symposium 2023, I extend a warm welcome to all of you to this online symposium.
First, I would like to express my gratitude to the APN Council for orchestrating this virtual
symposium, our Japanese advisor members, fellow APN council members for theirinvaluable support in various capacities, and our generous sponsors.
The challenges in the realm of pharmacometrics, particularly those pertaining toindividualized administration, are undeniably formidable. To thrive in this pursuit, it isimperative to harness cutting-edge technologies that transcend the conventionalboundaries of pharmaceutical science. These include artificial intelligence, real-world data,
innovative antibody technologies, liquid biopsies, and multi-omics approaches. APN2023
represents a distinctive event that not only presents a vision of the future ofpharmacometrics and individualized dosing, but also illustrates how these innovations canbe pragmatically applied in drug development and clinical practice. We will also delve into
the potential of these advancements to revolutionize the field of pharmacometrics.
We eagerly anticipate your virtual presence at the symposium scheduled for November 29
and 30th, and 2023. Furthermore, we anticipate an "in-person" gathering the following
year in Tokyo.

4Session Program

5

6

7

8

Plenary Session

9

ICH M15 Model-Informed Drug Development (MIDD) Guidelines

Daisuke Iwata

Kenya Nakai

Norisuke Kawai

Jenny Chien

Biography
Daisuke Iwata, Ph.D.
Dr. Iwata started his career as a Reviewer at PMDA in 2008. Currently, he serves as the
Review Director in the Office of New Drug I, Pharmaceuticals and Medical Devices Agency(PMDA), and engages in the reviews and consultations of new drugs in PMDA. He is the
WHLW/PMDA Topic Leader on ICH M15.
Kenya Nakai, MS
Mr. Nakai is a Senior Director of the Clinical Pharmacology group in Eisai. He joined
Eisai in 2006. He has 17-years of clinical pharmacology experiences leading multiple
projects, especially early clinical pharmacology studies including First in Human study.
He has been involved in JPMA since 2017. He has served as the JPMA Topic Leader on
ICH M15 since July 2023.
Norisuke Kawai, Ph.D.
Dr. Kawai is the Head of Clinical Research in Pfizer R&D in Japan. He joined Pfizer in2003 after working at Yamanouchi Pharmaceutical for 6 years. He has over 25 years ofexperience in clinical development. He earned his PhD in Statistics from Osaka
University in 2001. He serves as a vice-pesident of the Japanese Society ofComputational Statistics. He was the JPMA Topic Leader on ICH M15 EWG prior to July
2023 and currently serves as additional support staff on the EWG.
Jenny Chien, Ph.D.
Dr. Chien is an associate VP and a scientific lead in the Global PK/PD andPharmacometrics Department at Eli Lilly and Company. She is recognized for her
expertise in MIDD strategy, applying drug-disease models in trial design optimization anddose-finding in Drug Discovery and Development. She received her Ph.D. degree in
Pharmaceutical Sciences from the University of Washington, Seattle in 1997. She
currently serves as the PhRMA Topic Leader on ICH M15.

10

Presentation Summary
M15 is a new multidisciplinary guideline that was endorsed by ICH in November 2022. It isan over-arching general principles and good-practices guideline for MIDD that aims toharmonize expectations on regulatory review, documentation standards, and goodpractices. It introduces the concept of a risk-based assessment, and advocates forcross-functional strategic planning and multidisciplinary communications within andbetween regulatory agencies and industry scientists to improve efficiency of drug
development and regulatory decision-making.
Expectations for the Audience:
Be introduced to the newly proposed ICH M15 guideline and its objectives andexpectations.

Gain a better understanding of the current approaches, best practices, andopportunities to enhance MIDD and regulatory decision-making globally.

As a scientific community, has an opportunity to provide feedback to ICH working
group members prior to issuance draft guideline through the Q&A and panel discussion

11General Session

12Unlocking Insights from Real-World Data in Pharmacometrics
Philippe Auvaro
Executive Director
EBM Overseas Business and New Product Development
Medical Data Vision Co., LtdJapan

Biography
Philippe Auvaro joined MDV in January 2022. MDV is a leader in Japan for the provision ofhealth data generated by one of the largest hospital database in the country. His role is todevelop MDV data business overseas. Prior to MDV, Philippe has been President ofOrphanPacific, Inc. from 2016 to 2021, leading the joint venture between CMIC Group and
MEDIPAL Group specialized in rare diseases. Prior to joining CMIC Group, he establishedand was President of the Japan-based affiliate of Aspen Pharmacare, now part of Sandoz
Japan KK. He spent the 25 year period prior to joining Aspen holding various senior
management positions at companies including GlaxoSmithKline in Japan, the Aventis
joint-venture in Korea, Handok Pharmaceuticals in Korea, Aventis in Taiwan, and Roussel
in various countries in the Asia-Pacific region. He received a bachelor’s degree inManagement & Finance from ESLSCA Business School in France by completing themaster’s of art exchange program with Sophia University (Japan) in Corporate Finance.

Presentation Summary
Following a brief introduction of MDV and its role in healthcare data analytics, I will
highlight how MDV's expertise in collecting and analyzing real-world patient data could
bring significant support and discuss the significance of real-world evidence inpharmacometrics. Based on practical cases, I will show examples showcasing how MDV's
real-world data solutions have contributed to improved drug development strategies andpatient outcomes. In a second part, I intend to discuss the challenges faced in working
with real-world data in pharmacometrics and present the innovative approaches andmethodologies developed by MDV to address these challenges.
In conclusion, I wish to explore the emerging trends and future directions in the use ofreal-world data in pharmacometrics, such as data sharing and collaboration.

13

Drug Development Support Applied Pharmacometrics Approach
Ayako Mori & Kaoru Suzuki
EPS Corporation
Japan

Ayako Mori

Kaoru Suzuki

Biography
Ayako Mori is in charge of clinical pharmacology at EPS Corporation. I have a backgroundin the pharmaceutical company, where I conducted research on dose optimization of
antibiotic. Since 2021, I have been actively involved in pharmacometrics analyses andconsultations in EPS Corporation.
Kaoru Suzuki is an engineer at EPS. I have 14 years of experience in Clinical Research
Organization. In the initial stage, I was involved in EDC system development projects for 9
years. Since 2020, I have been leading several projects to support pharmaceuticalcompanies in submitting electronic study data.

Presentation Summary
EPS Corporation is a clinical research organization founded in 1991, with headquarters inJapan. EPS has been providing pharmacometrics related services since 2０12. We would
like to introduce our pharmacometrics related services in two parts. Part1 covers EPS’s
pharmacometrics analysis services, featuring a brief introduction to EPS corporation, thepharmacometrics analysis performance, the standard workflow of the analysis, the casestudies, and the ongoing challenge using model analysis. Part 2 covers support for
submitting electronic study data of population pharmacokinetic analysis to PMDA which
includes electronic study data that requires submission, the overall workflow for supporting
electronic data submission, and the timeline from preparation to the approval application.
We would like to introduce the comprehensive support services which we provide forpharmacometrics over part1 and part2.

14

Population pharmacokinetic model of intravenous immunoglobulin G
in patients treated for various immune system disorders
Shamin Mohd SaffianUniversiti Kebangsaan Malaysia

Biography
Dr Shamin Mohd Saffian is a senior lecturer at the Faculty of Pharmacy, Universiti
Kebangsaan Malaysia. He specializes in the field of clinical pharmacology with an interestin pharmacometrics. Graduating with a BPharm from the University of Otago in 2009, he
practiced as a pharmacist with the Ministry of Health, Malaysia, for three years. In 2018,
Shamin earned his PhD from the University of Otago, New Zealand, focusing on the dose
individualization of oral anticoagulants. Since 2018, as a senior lecturer, he has led
research in clinical pharmacology, focusing on improving the dosing and monitoring ofpharmacotherapy. He has published extensively, with a particular emphasis on
anticoagulants, showcasing a keen interest in advancing and optimizing treatment
strategies. Additionally, he holds a strong interest in the application of pharmacometrics inoncology, infectious diseases, and immunology.

Presentation Summary
Background: Immunoglobulin G (IgG) is frequently administered as a treatment forindividuals with primary immunodeficiency (PID), a condition marked by a weakened orabsent immune system. Its application extends to various autoimmune and inflammatory
disorders, where the complexity of its pharmacokinetics is not fully understood. This study
aims to assess the population pharmacokinetic parameters of IgG across different immune
system disorders and investigate the impact of the FcRn gene.
Methods: Patients were enrolled from four Malaysian hospitals. Their medical histories
were documented, and blood samples were obtained for genetic and pharmacokinetic
research. Monolix® was used for model fitting and to estimate the parameters. Potential
covariates included age, weight, baseline IgG concentration, ethnicity, sex, genotype,illness type, and comorbidity were tested. Models were assessed using measures of thedifference in objective function value, goodness-of-fit plots, visual predictive checks, andbootstrap analysis.
Results: A total of 292 blood samples from 79 patients were analyzed. A two-compartment
model with first-order elimination best characterized the IgG concentration data. Weight
was identified as a significant covariate for Vc, Vp, and CL, and disease type was found to
be a significant covariate for Vp. Goodness-of-fit plots indicated that the model effectively
15

represented the data. Importantly, this study found that the FcRn gene polymorphism did
not influence the pharmacokinetics of IgG.
Conclusion: This study reinforces the current approach of using body weight-based
dosing for IgG. The impact of disease type on the Vp emphasizes the diverse mechanisms
through which IgG exerts its effect in different diseases, possibly associated with thevarying levels of inflammation in the body.

16Topic on Clinical Trial Simulation
Xiaoduo Guan
Senior Manager, Pharmacometrician
Pfizer China

Biography
Dr. Guan got her doctoral degree at Peking Union Medical College Hospital in 2021. She
participated in more than 10 Chinese innovative drugs dose regimen design for the first-inhuman studies. After graduation, she joined Pfizer Development China as apharmacometrician to support the development of oncology, immune and inflammation(I&I) and rare disease projects in China.

Presentation Summary.

17

ClinChoice-as an Asia-centric CRO, overview and experience in Asia
Sanae Fujita, EMBA
ClinChoice K.K.,
Japan

Biography
Sanae has a robust background spanning multinational pharmaceutical firms, software
vendors, and CROs. In 2015, Sanae made a pivotal move by joining ClinChoice, aleading player in the clinical research field. Recognizing the potential of the Japanesemarket, she was instrumental in establishing the company's Japan subsidiary, further
cementing ClinChoice's presence in the Asia-Pacific region. Drawing upon her deepindustry expertise, she has been pivotal in aligning ClinChoice's capabilities to support
NDA for PMDA submissions. Beyond PMDA, she also contributes to NMPA and othercountries submissions, encompassing both regulatory and biometrics aspects for products
already launched in Japan. Sanae holds an Executive MBA and is committed to driving
excellence in the ever-evolving healthcare landscape..

Presentation Summary
ClinChoice Japan has been providing services to domestic and foreign healthcarecompanies for eight years as the Japanese base of a global CRO headquartered in both
China and the USA.
We are characterized by the fact that we do not only target and provide services for clinical
trials conducted in Japan, but also support Japanese companies in their overseas
expansion.
In the past, we have assisted Japanese companies to enter Vietnam, Chinese companies
to enter Japan and Japanese government agencies to conduct research in Thailand andIndonesia.
As a model case in this context, we would like to introduce our strategy for Japanesecompanies conducting trials in Vietnam and the services we provided to overseasorganization expanding into Japan.

18

Bounded Integer Modeling of Abnormal Involuntary Movement Scale(AIMS) Score in Patients with Tardive Dyskinesia
Dongwoo Kang, PhD
Pharmacometrics, TEVA Pharmaceuticals
USA

Biography
Education
Electrical Engineering, BS, Seoul National University, Seoul, South Korea



Biomedical Engineering, MS,University of Southern California, Los Angeles, California, USA

Biomedical Engineering, PhD, University of Southern California, Los Angeles, California, USA

Work Experience

ALZA Corporation, a Johnson and Johnson Company, Mountain View, California, USA

Pfizer, San Diego, California, USA

Daiich Sankyo, Basking Ridge, New Jersey, USA

Teva, West Chester, Pennsylvania, USA

Main Areas of Work

OncologyDiabetes



AML



Neuroscience

Areas of Interest

Population Pharmacokinetics



Exposure Response Analysis

Bayesian Methods

Sampling-Based Analysis

Presentation Summary
Objectives: Austedo, a deuterated form of tetrabenazine, is indicated for Tardive
dyskinesia (TD), a serious movement disorder that appears as a complication of the
prolonged use of dopamine receptor antagonists (e.g., antipsychotic agents). In patientswith TD, the Abnormal Involuntary Movement Scale (AIMS) measures the severity ofabnormal movements. The AIMS Total Motor Score is a composite score (range 0-28)
representing the observed movements, with higher scores indicative of more severe
dyskinesia. For a scale with such a large number of discrete levels, an ordered categorical19

modeling approach or treating the total score as a continuous variable on a logit scale is
not appropriate. The novel bounded integer (BI) modeling is evaluated as an alternative
approach to predict changes in AIMS scores in response to twice daily administration ofAustedo.
Methods: Data from 2 Phase 2/3 studies comparing Austedo to placebo were used for the
analysis. Pharmacokinetic (PK) exposures parameters (i.e., average concentration (Cavg),
maximal concentration (Cmax)) for the Austedo metabolites (i.e., (α+β)-HTBZ) werepredicted for each patient using previously developed and qualified population PK models.
The individual total motor AIMS score model was developed with the BI modelingapproach in 2 steps. At first, the model development focused on the description of thelongitudinal placebo data was described with the evaluation of covariate effects on thebaseline and the assessment of a Markovian component. Then, data from the treatment
arm was introduced, and the effect of the different PK exposure parameters wassequentially evaluated using linear and Michaelis-Menten kinetics on the mean of thelatent variable distribution.
Results: In total, the modeling dataset included 1,964 total motor AIMS scores collectedfrom 410 TD patients over the course of 12 weeks. The developed BI model wascharacterized by a linear effect of time (i.e., placebo) and a linear effect of (α+β)-HTBZ
Cmax on the baseline parameter of the latent variable distribution. No covariate effect was
included in the selected model. A Markovian component implying that the current score
depends on the value of the previous score was not supported by the model. The
predicted median change from baseline in total motor AIMS score was -1 point at week 12
in the placebo arm as caused by the time dependent effect. In the treatment arm thepredicted median change from baseline in total motor AIMS score ranged from -1.6 to -3.4
points at 12 weeks for the first (0 - 24 ng/mL) and fourth (71 - 203 ng/mL) quartiles of(α+β)-HTBZ Cmax exposure, respectively.
Conclusions: The BI model was developed for the AIMS score in TD patients following
Austedo administration. The developed exposure-response model could be utilized to
explore the changes in the AIMS score under various dosing regimens.

20

Bridging from Preclinical to Clinical in Oncology Drug Development
Soyoung Lee, PhD
Department of Clinical Pharmacology and Therapeutics, Seoul
National University Hosptial
Integrated Major in Innovative Medical Science, Seoul National
University Graduate School
Korea

Biography
Dr. Lee specializes in model-informed new drug development and personalized
pharmacotherapy. With over eight years of experience in early phase clinical trials, her
expertise lies in clinical pharmacology and pharmacy. Her research focuses on population
pharmacokinetic (PK)-pharmacodynamic (PD) modeling and physiologically based
pharmacokinetic (PBPK) modeling, which are crucial for applying personalized
pharmacotherapy in special populations and integral to the early stages of drug
development.

Presentation Summary
In general, assessing the exposure-response relationship of new drugs is essential forestablishing effective and safe dosage regimens for patients during drug development. By
applying a pharmacometrics approach, we can provide evidence for selecting
optimal dose regimens in subsequent late-phase clinical trials. However, in oncology drug
development, the maximum tolerated dose (MTD)-based dosing approach has been used
to determine the phase 2 recommended dose, which is further developed into theapproved dose regimen. While this approach may be suitable for cytotoxic agents, it is not
ideal for targeted therapies. Recently developed targeted anticancer drugs, which act on
signaling pathways involved in the growth or death of cancer cells, often exhibit a flat
exposure-response relationship. Therefore, higher doses and exposure may not yieldadditional treatment response but could lead to off-target effects and other adverse events.
This suggests that the highest dose may not always be the most effective. Thus, from theearly stages of drug development, it is crucial to apply pharmacometric modeling methods
based on preclinical data to explore the anticancer effects relative to drug exposure. Thisapproach is important for establishing the first-in-human dose in patients. In thispresentation, I will discuss a study that established an appropriate initial human dose fordrug A, targeting TGF-β2. This was achieved by applying a tumor growth inhibitor modeland allometry methods based on preclinical data. As clinical data becomes available,
combining it with preclinical data in a model will enable more sophisticated predictions ofdrug-exposure responses, potentially accelerating the clinical development of anticancer
drugs.

21

Predictive modeling of linezolid-Induced thrombocytopenia using
population pharmacokinetics-pharmacodynamics and machine learning
Yasuhiro Tsuji, PhD
School of Pharmacy
Nihon University
Japan

Biography
Yasuhiro Tsuji is a professor in the Laboratory of Clinical Pharmacometrics at the Schoolof Pharmacy, Nihon University. I studied clinical pharmacology and pharmacometrics
under the guidance of Professor Nick Holford at the University of Auckland, New Zealand,for one year starting in 2015. My research focuses on the target concentrationinterventions for antimicrobial dose individualization based on infected patient features andresponses to treatment. My research interests include the application of artificial
intelligence to help design dose-individualization.

Presentation Summary
Linezolid is a synthetic antimicrobial agent used to treat infections caused by MRSA. The
authorized dosage for linezolid is 600 mg every 12 hours for 12 years or older, with norequirement for dose adjustment based on body weight and renal or hepatic function. On
the other hand, thrombocytopenia is one of the most important adverse effects of linezolid
therapy. Our study demonstrated that elderly patients who were administered linezolid atthe conventional 600 mg twice-daily dose are at a high risk of being overexposed totreatment, increasing their risk to experience drug-related hematological toxicity.
Therapeutic intervention targets for predicting linezolid-induced thrombocytopenia include
a platelet count reduction to less than 2.3% below baseline at 96 hours post-initial doseand a linezolid concentration greater than or equal to 13.5 mg/L at 96 hours post-initial
dose based on the decision-tree analysis with machine learning. Reduced linezolid dosing
regimens should be potentially considered in elderly and renal dysfunction patients andwith a high risk of drug accumulation. Target concentration intervention should be used to
optimize exposure and reduce linezolid-induced thrombocytopenia.

22

Integration of Pharmacogenomics and Pharmacometrics
Sung Kweon Cho, MD, PhD
Department of Pharmacology
Ajou University School of Medicine
Korea

Biography
Education
Medicine, MD, SungKyunkwan University School of Medicine, Korea



Pharmacology, PhD, Yonsei University College of Medicine, Korea

Business, MOT, Korea Advanced Institute of Science & Technology

Work ExperienceResearch Professor, SungKyunkwan University School of Medicine



Visiting Fellow, National Cancer Institute, NIHAssistant Professor, Ajou University School of Medicine

Presentation Summary
Pharmacogenomics has started for individualizing medicine using genetic information tominimize the adverse drug events and to maximize the efficacy in a sub-population.
Pharmacometrics is the science of quantifying disease, drug and clinical trials to foster the
drug development, regulatory and therapeutic decisions. Pharmacometric analysis has
revolutionized the way dose/dosing regimens or clinical trials. Established exposureresponse relationships made it possible to make dosing recommendations in special
populations (pediatrics and organ function impaired patients), leading to approval ofdoses/dosing regimen that were not studied in registration trials. Until now,
pharmacogenomics is predominantly used retrospectively to understand the between
subject variability of drug’s pharmacokinetic and pharmacodynamic properties. In thislecture, I will discuss how the advanced pharmacometrics can leverage prior
pharmacogenomic information to design the late stage clinical trials. In addition, I will
demonstrate the application of modeling and simulation in supporting dose selection inpatients with allelic variants across different therapeutic areas.

23

Development of MIPD for vancomycin
Vu Dinh Hoa, PhD
National Drug Information and Adverse Drug Reaction Monitoring
CentreHanoi University of Pharmacy
Vietnam

Biography
Vu Dinh Hoa, PhD is Deputy Director of National Drug Information and Adverse Dru
g Event Monitoring Center of Viet Nam. He is also the lecturer in Hanoi Universityof Pharmacy and Adjunct Researcher at Bach Mai Hospital.
He obtained his bachelor of sciences in pharmacy from Hanoi University of Pharmac
y in 2003. He continued with his master degree in clincal pharmacy, Hanoi Unversityof Pharmacy in 2006. In 2013, he received his PhD degree from Department of P
harmacotherapy and Pharmaceutical Care, University of Groningen and Department of Clinical pharmacy and Pharmacology, UMCG.
His fields of interest focus on optimizing the use of of antimicrobial agents (antibiotics, antifungal and antituberculosis drug) based on PK/PD; dried blood spot for antitub
erculosis drugs, and therapeutic drug monitoring. He is supervising TDM teams in numbers of hospitals in developing TDM service for individualize dose of vancomycin
and amikacin. His research team is working on the Model Inform Precision Dosing(SmartDoseAI® ) for vancomycin using a web-based platform. He is also working with
the Intensivists and Pediatricians to support the animicrobial treatment for critically il
l patients, patients on Renal Replacement Therapy, neonates or paediatrics.
In addition, as the deputy Director of DI&ADR centers, he is working to monitor the
drug safety signals from National Pharmacovigilance Database. His research group also published number of papers on this aspect on international and national journals.

Presentation Summary
Vancomycin is an old glycopeptide antibiotic for resistant Gram positive pathogens
including Methicillin-resistant Staphylococcus areus (MRSA). The efficacy and toxicity ofthis antibiotic showed AUC-dependent and the target level of 400 – 600 mg.h.L-1 wasrecommended. For convenience in practice, it was widely accepted that the surogate
parameter, Ctrough, was used for TDM purpose [IDSA 2009 Guideline,
https://doi.org/10.1086/600877]. Nevertheless, studies suggested that Ctrough monitoring
could introduce substantial bias and increase the risk of suppratherapeutic with higher riskof nephrotoxicity. The Bayesian estimation from individual blood sample and a population
model as a priori was recommended for vancomycin exposure monitoring, especially in
critically ill patients [ASHP 2020 Guideline, doi: 10.1093/ajhp/zxaa03]. We develop an
web-base platform (SmartDoseAI® ) to support vancomycin therapy monitoring using
24

Bayesian estimation. Pharmacokinetic models for specific patient population werereviewed and sellected as a. priori in the Bayesian algorithm. We also applied available
data from TDM of vancomycin for external validation the sellected models. In addition, weapplied a flatten Bayesian estimation to reduce the bias in the forcasting concentration in
TDM proecess. Furthermore, tweaked model using avaialble TDM data has been proved
to significantly reduce the bias of a. priori model in predicting vancomycin levels of patients.
The MIPD is used in number hospitals to support therapy of thousands patients in
Vietnam..

25

Cardiovascular safety assessment: Concentration-QTc assessment in
Drug Development
Subramanian Ganesasundararajan, PhD
Director, Clinical Pharmacology and Translational Medicine
Certara Drug Development Solutions
Germany

Biography
Subramanian (goes by Subbu) has over 18 years in Drug Discovery and Development.
Subbu received his Bachelors and Masters degrees in Pharmaceutical Sciences andobtained PhD from Manipal University, India.
He worked in nonclinical pharmacology aspects at Advinus Therapeutics, Bangalore for 2
years. Later moved to Novartis where he spent 9 years working in the ClinicalPharmacology aspects in Neurosciences, Infectious diseases and Immunology on small
molecules. He supported in the early and late-stage Clinical Pharmacology programs in
Novartis. Subbu moved to Sandoz Biopharmaceuticals, Germany where he spent 3 years
in Clinical Pharmacology aspects of Biosimilars development. During his tenure at
Novartis/Sandoz had regulatory interactions with FDA, EMA, BfArm, SwissMedic, ANVISA,
Cofepris and Health Canada. He has authored over 50 peer reviewed publications and abook chapter.
Currently working as Director, Clinical Pharmacology at Certara, GmbH, Germany as asenior consultant to provide clinical pharmacology support to the client to develop thedrugs – both small molecules and Biologics.

Presentation Summary
Concentration-QTc assessment is being evaluated for the new molecular entities in earlyclinical pharmacology studies. Prior to the release of ICH E14 guidelines Questions andanswers on Clinical and nonclinical evaluation of QT/QTc potential of the drugs, thoroughQT studies are conducted to assess the potential of QT prolongation for new molecular
entities as part of cardiovascular safety assessment. The presentation discusses the study
design components of Thorough QT study and the design components in concentrationQT assessment in single ascending dose/multiple ascending dose studies. The two casestudies from literature are used to provide the differences in study designs and theprinciples of model development and evaluation in concentration-QTc assessment of two
chemical entities.

26

A framework for evaluating pediatric population PK models
Swantje Voeller, PhD
Leiden Academic Centre for Drug Research
Leiden University
Netherlands

Biography
Swantje Voeller graduated cum laude from her Pharmacy degree in Germany in 2012.
After that did her PhD with Georg Hempel and Joachim Boos at the University of Muenster
and the University Hospital Muenster studying the PK and PD of doxorubicin in children
with cancer. After finishing her PhD she moved to the Leiden Academic Centre for Drug
Research in the Netherlands as a postdoc focusing on studying pharmacometrics inpreterm and term neonates. Since 2018, she is an assistant professor in Pharmacy in the
Leiden continuing her research with a main focus in neonates.

Presentation SummaryChildren show a rapid maturation of different organs and organ functions, especially during
their first year of life. These fast changes in both, the pharmacokinetics and thepharmacodynamics, require us to pay special attention to aspects that may be lessimportant when evaluating adult models.
Additionally, the people applying the knowledge from these models in practice such astreating clinicians are rarely modelers themselves. Therefore, it is essential thatpublications are written in a way that key-users know how to interpret them. Here, we
propose a framework including essential aspects to report for a population pediatricpopulation PK model together with their interpretation. Topics covered include, among
others, stratified goodness-of-fit plots, the use of normalized prediction distribution error
and handling of time-varying covariates in pediatric models.
The proposed framework was be tested by evaluating all published neonatal population
PK model for caffeine. The results show, that most publication did not provide sufficient
diagnostics to convince the reader that covariates have adequately incorporated.

27

Student Session
528

Physiologically Based Mechanistic Insight into Differential Risk of
Valproate Hepatotoxicity between Children and Adults: A Focus on
Ontogeny Impact
Yu-Ting Huang
Department of Medical Genetics
National Taiwan University Hospital

Biography
National Taiwan University (NTU)
MS in Pharmaceutical Sciences
Chang Gung University (CGU)
BS in Biomedical Sciences

Taipei, Taiwan
09/2020 – 06/2022
Taoyuan, Taiwan
09/2016 – 06/2020

Presentation Summary
Objectives: The anticonvulsant valproic acid (VPA) despite complex pharmacokinetics
has been in clinical use for nearly 6 decades. Previous reports indicated neonates, infants,
and toddlers/preschoolers had higher risk of valproate hepatotoxicity than adults. However,dosing recommendations for those less than 10 years of age are lacking.
Methods: To decipher clinical puzzles, physiologically-based pharmacokinetic (PBPK)
models of VPA and its hepatotoxic metabolite 4-ene-VPA were constructed and simulatedwith particularly integrated information of drug-metabolizing enzyme ontogeny. Adult andpediatric PK data of VPA (n = 143 subjects) and 4-ene-VPA (n = 8 subjects) collected fromprevious reports were used for model development and validation. Sensitivity analyses
were performed to characterize ontogeny impacts of CYP2C9 and UGT2B7 on
dispositions of VPA and 4-ene-VPA across age groups. Optimal VPA dosing for each
pediatric age group was also predicted and objectively judged by ensuring VPA efficacy
and avoiding 4-ene-VPA hepatotoxicity.
Results: The study revealed UGT2B7 ontogeny was quite influential on VPA clearance
even in neonates and small children. Intrinsic clearance of CYP2C9 was the mostprominent determinant for areas under the concentration-time curve of VPA and 4-eneVPA in infants, and toddlers/preschoolers, reflecting higher hepatotoxicity risk due tonoxious 4-ene-VPA accumulation in these groups. The ontogeny-based PBPK approachcomplements conventional allometric methods in dosing estimation for the young by
providing more mechanistic insight of the processes changing with age.
Conclusion: The established ontogeny-based PBPK approach for VPA therapy deserves
further corroboration by real-world therapeutic data to affirm its clinical applicability.

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Population PKPD of Metformin in Type 2 Diabetes Mellitus patients:
Analysis of Sparse Sampling Data
Dimas Adhi Pradana
Public Health and Nursing
Universitas Gadjah Mada Yogyakarta
Indonesia

Biography
Dimas Adhi Pradana was born in Sleman, a district in the Special Region of Yogyakarta,
on February 24, 1985. Upon completing his education in elementary and secondary
schools in Yogyakarta, he went on to obtain a Bachelor's degree in Pharmacy from thePharmacy Department at Universitas Islam Indonesia (2007), followed by the Pharmacist
Profession Program at the Faculty of Pharmacy Universitas Gadjah Mada (2008). In 2009,
he earned a Master of Science degree from the Postgraduate Program in the Faculty of
Pharmacy at Universitas Gadjah Mada. After finishing his education, he worked as alecturer in the Pharmacy Department at Universitas Islam Indonesia from 2010 to now. He
is currently pursuing a Doctorate degree in the field of Medical and Health Sciences at theFaculty of Medicine, Public Health, and Nursing, Universitas Gadjah Mada. His researchinterests
encompass
pharmacology,
population
approachpharmacokinetics/
pharmacodynamics modeling, and pharmacogenomics. He has authored and co-authoredworks in reputable journals such as Current Diabetes Review, Indonesian Journal ofChemistry, Indonesian Journal of Pharmacy, Journal of Traditional and Complementary
Medicine, and Research in Pharmaceutical Sciences.

Presentation Summary
Background: Metformin is a drug that is often used to treat Type 2 Diabetes Mellitus(T2DM) in its early stages. Different people react differently to it, which makes it animportant topic of study in the field of pharmacokinetic/pharmacodynamic (PK/PD)
research. The main obstacles faced in PKPD studies involving patients is the limitedaccessibility to frequent blood samples for each individual participant.
Objective: This study aims to develop and evaluate population-based PK/PD metforminmodeling in T2DM patients, with a particular focus on utilizing limited sample data.
Method: The development and validation of a method for determining metformin levels inplasma was carried out using a validated HPLC-UV method. A total of 123 Javanese
T2DM patients on 2x500 mg metformin monotherapy were included in this study. Each
respondent will have blood samples taken twice. The first sample was taken just before
taking metformin. The second sampling was randomly conducted between 0.5, 1, 2, 3, 4, 6,
8, and 10 hours after taking metformin. The population parameters determined include ka,
Vd, k, Cssmax, Cssmin, R0, kout, and IC50 using Monolix software version 2023R1 with amodeling structure assuming an indirect response model. Optimization of the covariate
30

model was analyzed using the COSSAC strategy.
Results: Population-based metformin PK/PD modeling best fits the observation data curvewith the model prediction line for individual and population predictions. Population-based
PK/PD parameter values with relative standard error values are still within reasonableconfidence. In addition, the resulting population parameter values are also within thereference value range. Metformin PK/PD parameter values are: ka 1.2/hour; Vd 228.9 L; k
0.089/hour; Cssmax and Cssmin were 2.73 μg/mL and 0.61 μg/mL; R0 161.91 mg/dL;
kout 0.32/hour; IC50 4.19 μg/mL.
Conclusion: Metformin PK/PD modeling from 123 Javanese T2DM patients with sparse
data showed good curve fitting, fairly confident RSE values, and parameter values within
the reference value range.

31

Translational predictions of Phase 2a first in human trials fortuberculosis drugs using preclinical in vivo and in vitro data
Janice Goh, PhD
Bioinformatics Institute, A*STAR
Singapore

Biography
Janice graduated from NUS Pharmacy and is a registered pharmacist with the Singapore
Pharmacy Council. She recently completed her PhD in the lab of Professor Rada Savic at
the UCSF School of Pharmacy. Her work focuses on applying translational
pharmacometrics tools to by capitalizing on preclinical data to predict clinical outcomes
prior to actual trials.

Presentation Summary
Phase 2a trials in tuberculosis typically use early bactericidal activity (EBA), the decline insputum colony forming units (CFU) over 14 days, as the primary endpoint for testing theefficacy of drugs as monotherapy. However, the cost of phase 2a trials can range from 7 to
19.6 million dollars on average, while more than 30% of drugs fail to progress to phase 3.
Better utilizing preclinical data to predict and prioritize the most likely drugs to succeed willthus help accelerate drug development and reduce costs. In this talk, I will show how we
can predict clinical EBA using preclinical in vitro and in vivo pharmacokineticpharmacodynamic (PKPD) data via a model-based translational pharmacology approach.

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Using Phase 1 Glycemic Data in the Evaluation of Two Semimechanistic PK/PD Models for Predicting Long-term HbA1c in Type 2
Diabetes
Austin Tan
National University of Singapore

Biography
Austin Tan is currently a PhD student from the Department of Pharmacy at the National
University of Singapore (NUS), under the supervision of Professor Eric Chan from NUS
and the co-supervision of Dr Tham Lai San from Eli Lilly and Company. His PhD project
involves the evaluation of semi-mechanistic Pharmacokinetic/Pharmacodynamic (PK/PD)
models for predicting long-term HbA1c in Type 2 Diabetes. Austin graduated with a
Bachelor of Science (Pharmacy) with Honors from NUS, and he is also a registeredpharmacist in Singapore. Austin has a strong interest in the use of population PK/PD
models to aid drug dosing decisions and to optimize patient outcomes.

Presentation Summary33

Model-informed precision dosing in vancomycin treatment
Sukyong Yoon, MD
Department of Pharmacology
Yonsei University College of Medicine
Korea

Biography
Sukyong Yoon is currently fellow in the Department of Clinical Pharmacology, Severance
Hospital, Republic of Korea. He received B.S. in Life Science from Hanyang University in2010, M.D. from Chosun University in 2014, and has been in PhD course in the
Department of Pharmacology at Yonsei University since 2019. He has years of experiencein healthcare facilities and five years of experience conducting early-phase clinical trials,
pharmacometrics, including PK/PD and PBPK modeling, and data science in healthcare.

Presentation Summary
This study focuses on optimizing vancomycin treatment to mitigate ototoxicity andnephrotoxicity risks associated with prolonged and excessive use. Leveraging a modelinformed precision dosing approach, the research establishes a population
pharmacokinetic (PK) and pharmacodynamic (PD) model using data from 542 patients,
including 40 children, collected from electronic medical records. The PK model employs a
two-compartment model with allometry scaling to depict vancomycin concentrations. The
findings reveal that systemic clearance is influenced by factors like creatinine levels, bloodurea nitrogen levels, diabetes history, renal diseases, and is further impacted in women.
Meanwhile, the central volume of distribution increases with age. The PD model links Creactive protein (CRP) plasma concentrations to transit compartments, showing adecrease with pneumonia presence. Simulations indicate that optimized doses keep
concentrations within therapeutic ranges, effectively minimizing the risk of toxicity.
Moreover, higher doses lead to a faster decline in CRP levels from day 10 post-treatment
initiation. The study introduces an R Shiny application for visualizing vancomycin and CRP
concentration time courses, demonstrating its potential for designing optimal treatment
regimens through straightforward visual inspection.

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PopED for optimal sampling points: A case among perioperative
patients
Foong Keng Wah
Universiti Malaya
Malaysia

Biography
Foong Keng Wah is a final year PhD candidate in the Department of Anaesthesiology in
University of Malaya. He obtained his Bachelor’s degree in Pharmacy from IMU in 2019and is now actively investigating the population pharmacokinetics of intravenous lidocaine
to ensure safety and improve postoperative outcomes in abdominal surgical patients. He
has a strong interest in drug development and individualising patient care for optimal
outcomes. He also enjoys sharing his experience and guiding others in the field of
research.

Presentation Summary
An optimally designed population pharmacokinetic (popPK) study is one where all of its
aspects are carefully planned to maximise the information obtained. The main aspects
include the number of subjects, and the number of samples and timing per subject.
Optimisation is crucial as studies are often constrained by time and finances.
Optimisation is performed based on maximising the Fisher Information Matrix (FIM) (Doptimality). This matrix quantifies the amount of information contained within a dataset
about the parameters of a model. The inverse of FIM is proportional to the lower bound of
the variance-covariance matrix of any unbiased estimator. Hence, more precise parameter
estimates are obtained as FIM increases.
In our centre, we recruited adult laparoscopic cholecystectomy patients for a popPK study.
We investigated the use of lignocaine infusion for acute nociceptive pain in the
perioperative period. Infusion was started post-induction of anaesthesia and maintained
until the end of operation.
The initial sampling scheme was based on prior studies, PK profile of lignocaine, andlogistics. Three samples were taken during and after the operation. We noted that postoperative samples were more frequently lost due to a larger than expected variation in thetiming of sample collection. This resulted in sample overlap and omission, and also patient
discomfort due to frequent sampling.
PopED, a R package for optimal design, was successfully utilised to identify a sampling
scheme that would provide more precise estimates and be of lesser burden to our patients.
Significant reduction in the relative standard errors (RSE) of the parameter estimates wereobserved for intercompartmental clearance (-11.2%) and its between subject variability
35

estimate (-85%). The RSE for other parameters were comparable. Crucially, the optimised
sampling scheme only required 2 post-operative samples which are approximately 6 hours
apart. This led to lower discomfort among patients and rates of lost samples.

36

An In-Depth Examination of Physiologically Based Pharmacokinetic
Modeling in the Context of Chronic Kidney Disease: A Comprehensive
Review of Existing Models
Ammara Zamir, PharmD
Bahauddin Zikarya University
Pakistan

Biography
Ammara Zamir is currently pursuing a Ph.D. in the Department of Pharmacy Practice at
Bahauddin Zakariya University, Multan. She successfully completed her Pharm D in 2020
with distinction, following which she enrolled in the M.Phil Pharmacy Practice program.
During this program, she acquired proficiency in various techniques, including the conductof systematic reviews and, notably, physiologically based pharmacokinetic modeling. Her
research endeavors revolve around an innovative approach that integrates changes inblood flow and pathophysiological alterations to develop drug-disease models, enabling
the prediction of drug pharmacokinetics. Her contributions to the field include thepublication of five articles. Having completed her Ph.D. coursework, Ammara has also
served as a co-supervisor for 14 students, resulting in a total of 10 publications to date.

Presentation Summary
Physiologically based pharmacokinetic (PBPK) modeling represents a transformative
approach in the current era, particularly in elucidating drug exposure in populations suchas pediatrics, geriatrics, and individuals with chronic diseases, where conducting clinical
trials proves challenging. This comprehensive review systematically compiles data
pertaining to published PBPK models specifically focused on chronic kidney disease(CKD). The compilation encompasses detailed information on drug, and system-specificinput model parameters, as well as criteria for model evaluation. A thorough search acrossfour databases was undertaken to identify studies meeting predetermined
inclusion/exclusion criteria. Noteworthy factors, such as alterations in plasma proteins(albumin/alpha-1 acid glycoprotein), gastric emptying time, hematocrit, small intestinal
transit time, the abundance of cytochrome (CYP) 450 enzymes, glomerular filtration rate,
and physicochemical parameters for various drugs, are explicitly elucidated based on
previously published studies. Furthermore, the evaluation of the models revealed that, forthe majority of the included drugs, PBPK models in CKD fell within the acceptable two-folderror range. This review serves as a valuable resource for researchers, offering insights
into the application of PBPK models in tailoring drug therapy for patients with diverselevels of CKD, with the aim of averting undesirable side effects and enhancing treatment
efficacy.

37

Evaluation and prediction of drug transporter-mediated drug-druginteractions of methotrexate using physiologically based
pharmacokinetic modeling
Sejung Hwang, PhD
Seoul National University and Hosptial
Korea

Biography
Sejung Hwang is postdoctoral fellow in Department of Clinical Pharmacology andTherapeutics, Seoul National University College of Medicine and Hospital as a clinicalpharmacologist. She had an experience for managing clinical trials from design to analysis
including pharmacokinetics (PK) and pharmacodynamics (PD). Sejung has actively
engaged in the comprehensive process of clinical trial development and demonstrated
proficiency in utilizing NONMEM to perform PK/PD modeling, particularly contributing to
the dose optimization of clinical trials including oncology. Sejung also holds the role ofprincipal investigator in a project focused on physiologically-based pharmacokinetic(PBPK) modeling.

Presentation Summary
Introduction: Methotrexate is an antifolate agent widely used in the treatment of various
diseases, such as rheumatoid arthritis and cancer. As a substrate of various transporters,
methotrexate should be monitored carefully when coadministered with other drugs. This
study aimed to quantitatively interpret drug-drug interactions (DDIs) of methotrexate
mediated by drug transporters using physiologically based pharmacokinetic (PBPK)
modeling.
Methods: A randomized, open-label, 4-treatment, 6-sequence, 4-period crossover study(NCT05575297) was conducted to evaluate the effect of rifampicin and febuxostat on
methotrexate pharmacokinetics (PK) in healthy volunteers. Subjects received eachtreatment according to the assigned sequence, and 4-treatments included theadministration of a single dose of methotrexate 2.5 mg alone, coadministration of
methotrexate with a single dose of rifampicin 600 mg, with febuxostat 80 mg, or both.
Blood samples for PK analysis were collected up to 24 hours post-dose. The PBPK modelof methotrexate, rifampicin and febuxostat was developed based on the in vitro and in vivo
data, and the performance of the final PBPK model was validated using the clinical study.
The final PBPK model was used to quantitatively interpret the methotrexate DDIs andsimulated the high-dose methotrexate with administered with febuxostat in cancer patients.
Results: In the clinical study, when methotrexate was coadministered with rifampicin or
febuxostat, the systemic exposure of methotrexate increased by 33% and 17%,
respectively, compared to those administered alone. When methotrexate was38

coadministered with both rifampicin and febuxostat, the systemic exposure increased by
52% compared to those administered alone. The final PBPK model showed a goodprediction performance of the observed clinical dataThe PBPK model showed that thepresence of febuxostat resulted in increase of AUC by 30% in virtual cancer patients.
Conclusion: This study investigated the clinical potential activity of febuxostat withrifampicin for the breast cancer resistance protein (BCRP) inhibition. Furthermore, the
PBPK model of methotrexate was well developed in this study and can be used as themechanistic model to predict and evaluate the drug transporter-mediated DDIs of
methotrexate with other drugs and contributed to personalized pharmacotherapy.

39