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  • Dynamic Free Fraction: Concept, Methodology and Utility in DMPK
    Contains 3 Component(s)

    This webinar will introduce dynamic free fraction (fd) as a new concept characterizing drug protein binding and demonstrate the utility of fd in hepatic clearance mediated by CYP and OATP transporter.

    As dictated by the free drug theory (FDT), fraction of unbound drug in plasma (fu,p) is routinely measured to rationalize pharmacological readouts such as drug potency and hepatic clearance. There is an increasing body of evidence contradicting the FDT when fu,p is applied, and one prominent example is the systematic under-prediction of hepatic clearance for highly bound compounds. We reason that fu,p is a static measure of drug binding extent and it does not capture drug protein binding dynamics. As a result, we have introduced the "dynamic free fraction" (fD) as a new binding parameter describing drug protein binding affinity/dynamics that can be indirectly determined by coupling the drug binding assay with a reporter enzyme in combination with high-resolution mass spectrometry, circumventing a long-standing challenge inherent in determining drug binding kinetics constants such as kon and koff. Using a large group of diverse drugs representing both CYPs and OATP transporter substrates, we demonstrated that the well-stirred model incorporating with fD correctly predicted both hepatic clearance and liver extraction ratio without apparent systematic bias, which is markedly better than those predicted with fu,p. The results suggest that dynamic free fraction (fD) as a measure of protein binding affinity is a key determinant in hepatic clearance, which is contrary to the currently held view.

    Zhengyin Yan

    Zhengyin received his BS/MS degrees in Chemistry from Wuhan University in China and his PhD in Biochemistry from Georgetown University in US. After a brief tenure as a postdoc at the National Cancer Institute, NIH, Zhengyin joined Johnson & Johnson Pharmaceutical Research Institute as a Senior Scientist in 1998 where he used to lead both in vitro ADME and discovery bioanalytical groups. In 2015, Zhengyin moved to Genentech, and currently he is a Distinguished Scientist leading the Discovery in vitro DMPK group. His scientific interest includes drug metabolism and bioactivation, ADME-guided lead optimization utilizing both in vitro assays and in vivo studies to assess and/or predict drug-like DMPK properties such as drug clearance and DDI potential. He has published more than 60 research articles and a number of book chapters, and co-edited three books published by Springer. Additionally, Zhengyin served on the US NSF Grant Review Panel and the editorial board of scientific journals such as Drug Metabolism Letter and Pharmaceutical Analysis.

  • From Kidney and Liver Interaction in Patients with CKD to the Drug-Drug-Severe CKD Disease Interaction Predicted by PBPK
    Contains 3 Component(s)

    This webinar is intended to raise the awareness of complicated scenario where drug-drug-disease interaction occurs and how to evaluate the PK of drugs using PBPK to inform the optimal use of drugs in such patient group.

    Chronic kidney disease (CKD) is more than renal malfunction alone. It may affect drug disposition by multiple pathophysiological changes including the changes in abundancy and activity of hepatic drug transporters. Also, the disease factors may interplay with drug-drug interaction (DDI) in patients with CKD causing a different DDI scenario from the one in healthy volunteers. The translation of DDI magnitude between healthy subjects and patients with CKD may not be straightforward due to the complex drug-drug-disease interaction (DDDI) scenarios, whereas physiologically based pharmacokinetic (PBPK) model-based approach may serve as a valuable quantitative tool to predict the complex DDDI. Herein, we illustrate how to use PBPK modelling to predict the DDI between statins and roxadustat mediated by hepatic transporters in patients with severe CKD, and answer the following questions: how much does severe CKD affect the abundancy and activity of hepatic transporters? Which pathways(s) are the major one(s) responsible for the observed alteration of statins PK in patients with severe CKD? Is the magnitude of statin-roxadustat DDI in severe CKD patients similar to that in HV? What are the optimal dose regimens when statins and roxadustat are co-administered in patients with severe CKD?

  • Bioanalysis of Today and Tomorrow: Current Best Practices and Emerging Technologies Enabling Diverse Therapeutic Conjugate Evaluation | An ISSX 2024 Virtual Workshop
    Contains 2 Product(s)

    This workshop focuses on bioanalytical techniques, including biotransformation (and subsequent bioanalysis) of macromolecules. This workshop provides both content focusing on fundamentals and new and emerging technologies.

    This workshop is organized through the efforts of the ISSX Bioanalysis in ADME Science Focus Group and is organized by Drs. Matthew Albertolle, Bingming Chen, Joe Cannon, Mei Han, Thomas Kraft, John Tran, Jim Shen, Faye Vazvaei, and Brian Booth.

    The scope of this workshop is to provide content for attendees from early career/grad school who want to learn fundamentals (Day 1) to senior career leaders in the field who want to learn about what is the new and emerging technologies (Day 2). Workshop topics can include bioanalytical techniques, including biotransformation (and subsequent bioanalysis) of macromolecules. Each session of the workshop consists of presentations from several expert speakers. In addition, there will be an interactive virtual poster session which will be selected from the pool of abstracts from attendees. 

  • An African Hepatic Modeling Platform: Can We Get There?
    Contains 3 Component(s)

    In this webinar we will discuss efforts for improving the global underrepresentation of African genetics in science. We will further explore the current landscape and challenges in realizing an African hepatic modeling platform - with a specific focus on the development of hiPSC models that can better recapitulate hepatic function.

    The African continent harbors unparalleled genetic diversity yet remains largely underrepresented in  pharmaceutical  research  and  development.  Pharmacogenomics  is  fundamental  to  precision medicine strategies, and although to date largely absent from implementation, representing the genetic diversity of the African population within the laboratory is critical to the democratization of stratified  and  effective  healthcare  in  Africa.  Afrocentric  preclinical  resources  could  support precision  medicine on  the  continent  and  reshape  the  global  underrepresentation  of  African genetics  in  science. Models  which  could  begin  to  address  this  include  primary  human hepatocytes,  immortalized  hepatic  cell  lines,  and human induced  pluripotent  stem  cell  (hiPSC)derived hepatocyte-like cells – derived from individuals of African genetic ancestry. The feasibility of an African hepatic modeling platform is slowly being realized due to the convergence of several technologies and methodologies which have traditionally been siloed on the continent.

  • How To Build Robust Predictive Human Organ Models To Improve the Prediction of Drug ADME Parameters
    Contains 3 Component(s)

    This symposium/webinar is intended to educate ADME scientists on MPS technology and its potential to generate clinically translatable data.

    Despite billions in annual investment, most drugs never reach the market because preclinical experiments fail to predict human effects. In this webinar, we will cover why in vivo models only deliver an approximation of what is likely to happen in the human body and why building more predictive, human-relevant models is key to reducing drug attrition. During the webinar we will introduce ADME scientists to the field of microphysiological systems (MPS), also known as organ-on-a-chip (OOC).

    We will explore how MPS technology can be implemented to create human-specific tissue models such as the gut and liver and interconnect them into systems that generate clinically translatable data. We will demonstrate how a deeper and earlier knowledge of human drug ADME profiles enables issues to be addressed prior to costly preclinical studies.

    The webinar will conclude by highlighting how the complementary use of MPS bridges the human-relevance gap, facilitating the confident selection of safer and more effective drug candidates, whilst also reducing costs.

    This webinar is sponsored by CN Bio Innovations.

    Marco Ortiz

    CN Bio

    Marco Ortiz has experience in developing cell types as commercial products and clinical therapeutics. In his most recent role, he performed preclinical experiments of iPSCs derived hepatocytes for acute liver diseases as a potential indication.  Marco Ortiz completed a bachelors in genomic sciences working at the University of Wisconsin-Madison and completed a UCL Wellcome Trust PhD programme at the Francis Crick Institute. 

  • Pharmacogenomics from a Protein Structure Perspective
    Contains 3 Component(s)

    The human genome comprises approximately 20,000 protein-coding genes and over 900 million variants according to dbSNP. Systematic understanding of the impact of genomic alterations in humans is critical for the development of effective medicines. However, it is simply not feasible to study every single variant in detail. This challenge extends to the analysis of how pharmacogenes are affected by genetic polymorphisms, as it is impossible to study the impact of every individual single nucleotide polymorphisms/variations (SNPs/SNVs) of pharmacogenes in human clinical trials. Yet, understanding drug metabolism and pharmacokinetics is crucial for assessing drug efficacy and safety. To minimize harmful side effects from drugs while maximizing their therapeutic effectiveness in each patient or group of patients, we would need to understand the effects of population specific SNPs in pharmacogenes and drug-enzyme interactions. To date the effect of non-synonymous SNPs, more specifically missense mutations, at the protein level is poorly studied in pharmacogenomics research. We previously proposed a post-hoc analysis approach of molecular dynamics (MD) simulations using dynamic residue network (DRN) analysis to consider the dynamic nature of functional proteins and protein-drug complexes and to probe the impact of mutations and their allosteric effects. This talk will discuss the computational approaches and tools that we have developed over the years with applications to pharmacogenomics.

    The human genome comprises approximately 20,000 protein-coding genes and over 900 million variants according to dbSNP. Systematic understanding of the impact of genomic alterations in humans is critical for the development of effective medicines. However, it is simply not feasible to study every single variant in detail. This challenge extends to the analysis of how pharmacogenes are affected by genetic polymorphisms, as it is impossible to study the impact of every individual single nucleotide polymorphisms/variations (SNPs/SNVs) of pharmacogenes in human clinical trials. Yet, understanding drug metabolism and pharmacokinetics is crucial for assessing drug efficacy and safety. To minimize harmful side effects from drugs while maximizing their therapeutic effectiveness in each patient or group of patients, we would need to understand the effects of population specific SNPs in pharmacogenes and drug-enzyme interactions.   To date the effect of non-synonymous SNPs, more specifically missense mutations, at the protein level is poorly studied in pharmacogenomics research. We previously proposed a post-hoc analysis approach of molecular dynamics (MD) simulations using dynamic residue network (DRN) analysis to consider the dynamic nature of functional proteins and protein-drug complexes and to probe the impact of mutations and their allosteric effects. This talk will discuss the computational approaches and tools that we have developed over the years with applications to pharmacogenomics.

    Özlem Tastan Bishop

    Özlem is full Professor in structural bioinformatics at Rhodes University, South Africa and distinguished adjunct Professor at Saveetha University, Chennai, India.

    She received her BSc degree in Physics from Boğaziçi University, Istanbul, Turkey. Then she moved to the Department of Molecular Biology and Genetics at the same University for her MSc degree. She obtained her PhD from Max-Planck Institute for Molecular Genetics and Free University, Berlin, Germany in 2003. While doing her PhD, Özlem became interested in structural biology, and during her postdoctoral positions (Texas University, USA; University of Western Cape and University of Pretoria, South Africa) she gained experience in structural bioinformatics as well as structural biology.

    In 2009, Özlem took up an academic position at Rhodes University, South Africa. She established the Research Unit in Bioinformatics (RUBi) in 2013. She has graduated 25 PhD and 38 MSc students since she joined Rhodes University. She received Rhodes University Internationalization award for 2018; Rhodes University Vice Chancellor’s Distinguished Senior Research award for 2020 and South African Society for Bioinformatics (SASBi) Silver Award, 2022.

    She serves on the Editorial Board for PLOS Computational Biology, PLOS One and Frontiers in Molecular Biosciences and Frontiers in Applied Mathematics and Statistics, Biological Modeling and Simulation Section, and she is an Advisory Board member of F1000Research Bioinformatics Gateway. Özlem’s broad research interest is structural bioinformatics and its applications to drug design and development. Her recent interest is in the allosteric mechanisms of proteins and understanding the effects of nonsynonymous single nucleotide variations on protein structure and function in the context of drug resistance and drug metabolism. She has published roughly 100 research articles.

  • Integration of PBPK/PD Models to Inform Compound Design and Prioritization in Early Stages of Drug Discovery
    Contains 3 Component(s)

    Physiology-based pharmacokinetic (PBPK) models are broadly applied in late phase drug discovery/ development. Traditionally, compounds are prioritized based on hierarchical filtering with predefined cut-offs for desirable range of various parameters. PBPK models, when combined with related pharmacodynamic assumptions, offer a valuable platform to integrate multiple parameters driving the in vivo PK profile required for target engagement. They also provide mechanistic interpretation of key drivers for the predicted profile to further help with compound design strategies. As such, these results enable compound prioritization in a holistic manner, focusing on multi-property optimization (MPO). This presentation will provide a brief overview of the structure and application of an internal PBPK model. Examples of successful application of this tool on a small molecule drug discovery program will be shared to illustrate its role in driving decisions to guide compound progression.

    Physiology-based pharmacokinetic (PBPK) models are broadly applied in late phase drug discovery/ development. Traditionally, compounds are prioritized based on hierarchical filtering with predefined cut-offs for desirable range of various parameters. PBPK models, when combined with related pharmacodynamic assumptions, offer a valuable platform to integrate multiple parameters driving the in vivo PK profile required for target engagement. They also provide mechanistic interpretation of key drivers for the predicted profile to further help with compound design strategies. As such, these results enable compound prioritization in a holistic manner, focusing on multi-property optimization (MPO). This presentation will provide a brief overview of the structure and application of an internal PBPK model. Examples of successful application of this tool on a small molecule drug discovery program will be shared to illustrate its role in driving decisions to guide compound progression.

    The optimal PK necessary for a drug candidate to achieve efficacy is highly dependent on the targeted PD, a relationship often not well characterized during the early stages of drug discovery. Relying on generic assumptions about PK and potency can misguide screening and compound design, leading to suboptimal ADME or molecular properties. This, in turn, may increase attrition rates and extend hit-to-lead and lead optimization timelines. The "PD before PK" approach, detailed in this presentation, can be applied in two ways: forward, to virtually screen compounds for potential effectiveness, and reverse, to identify pharmacology-specific PK drivers and the related potency-ADME space early in discovery. This strategy aims to enhance the probability of success and reduce clinical attrition. Potential applications of this approach will also be discussed.

    Emile Chen

    Dr. Emile Chen has thirty years of industrial experience divided between early discovery involved in lead optimization and candidate selection, and late-stage development, including authoring and reviewing of regulatory documentation and NDA submission.  Until March 2024, he was in the System Modeling and Translational Biology group, using PBPK, Mechanistic PKPD modeling, QSP, and machine learning techniques to solve project questions and thereby enhance scientific productivity.

    Emile received his undergraduate degree from University of California at Los Angeles and his PhD from Northwestern University in the field of Biomedical Engineering, specializing in developing mathematical models for the information processing in the brain.  He began his pharmaceutical carrier at Hoffmann La-Roche in 1993, after completing a postdoctoral fellowship in University of California at San Francisco.  He joined GlaxoSmithKline in1996. Over the years, he has led ADME and PK groups at various times in supporting either early discovery or late development DMPK efforts.  More recently, recognizing the current challenge to improve R&D productivity for the pharmaceutical industry, Emile is focused in leading efforts to use innovative mathematical modeling and simulation methods to help reduce attritions while enhancing ability to predict efficacy and safety in human and support portfolio investment decisions.  For the past 10+ years, he also designed and teaching a series of interactive workshops that promote the use of kinetic thinking and mathematical modeling to integrate preclinical and clinical information to aid decision making during drug discovery and development.  The workshops are offered several times each year both internally and externally.

    Wenyi Wang

    Dr. Wenyi Wang is a Principal Scientist in the In Silico predictive ADME (pADME) group within the DMPK function at Genentech. She received her PhD from Rutgers University in application of machine learning for addressing challenges in ADME-Tox. In her current role at Genentech she actively engages with portfolio teams to build and apply ML and mechanistic PK models to address ADME related issues and inform prioritization for compound synthesis and testing. 

  • Clinical Pharmacology Considerations for Development and Approval of Antibody-Drug Conjugates
    Contains 3 Component(s)

    The purpose of this webinar is to: 1. Provide valuable knowledge exchange between ADC developers andthe Regulatory Agency (FDA) 2. Hear from leading experts in PK/PD, pharmacometrics, & clinical pharmacology, plus clinicians and regulatory minds, fostering collaborations and partnerships that can propel ADC projects seamlessly. 2. Demonstrate the successful integration of pharmacokinetics and pharmacometrics in ADC clinical development, enhancing your ability to apply these principles to your own work

    Antibody-drug conjugates (ADCs) are distinct from both biologics and small molecule drugs. There are special clinical pharmacology considerations for ADC development and approval due to their unique structure and mechanism of action. The FDA has approved 11 ADCs as of Aug. 2024 and published the final guidance: Clinical Pharmacology Considerations for Antibody-Drug Conjugates in March 2024. Using approved ADCs as illustrative examples, this lecture will cover key clinical pharmacology considerations regarding ADC dosing strategies, intrinsic factor evaluation, and drug-drug interaction assessment. It will also cover other topics such as bioanalytical approach, dose/exposure-response analysis, QTc assessment, immunogenicity, and post-marketing requirements/commitments. Finally, examples for new generations of antibody conjugates will be discussed. This presentation will inform more efficient clinical pharmacology development strategies for ADCs and help bring more of these therapies to patients.

    Qin Sun

    Dr. Qin Sun is the Therapeutic Biologics Program (TBP) Biologics Lead in the Office of Clinical Pharmacology (OCP), CDER, FDA. Her job functions include guide and support reviews and policy development for novel biologics and biosimilars. She also serves as vice chair for Biologics Oversight Board in OCP, and chair for several biologics and biosimilar guidance working groups. Qin joined FDA in 2016. Before that, she worked at Pharmaceutical Product Development (PPD) from 2015 to 2016, and at Bristol-Myers Squibb from 2008 to 2014. Qin received her PhD from University of Virginia. Her work experience extends from drug discovery to drug development, and finally to regulatory review, focusing on biologics and biosimilars currently.  

    Venkatesh Reddy

    Dr. Venkatesh Pilla Reddy is a distinguished Senior Director in the Global PKPD and Pharmacometrics group at Eli Lilly and Company in the UK. His extensive expertise stems from earning his PhD in Pharmacometrics through a collaborative program involving Pfizer, Janssen Pharmaceuticals, and Merck via TI Pharma in the Netherlands. His doctoral research centred on the PKPD M&S of antipsychotic drugs, and he has since made substantial contributions to the Quantitative Pharmacology and Pharmacometrics groups at Merck and AstraZeneca.

     

    At present, Venkatesh provides invaluable Clinical Pharmacology and Model-Informed Drug Development support for a range of impactful projects in oncology, neuroscience, and immunology. Notably, he played a pivotal role as Deputy Topic Leader position ICH shaping ICH DDI guideline. Furthermore, he plays a leading role in influential cross-industry working groups including IQ TALG, ISOP, ASCPT, and ISSX M&S. He also serves as an esteemed Editorial Board Member for the Biopharmaceutics and Drug Disposition Journal and is a respected reviewer for clinical pharmacology/PKPD M&S-related journals.

  • Pharmacogenomics Leveraging “omics” to Improve Treatment Response
    Contains 3 Component(s)

    Pharmacogenomics is relevant worldwide for modern therapeutics and yet needs further uptake in developing countries. There is paucity of studies with a naturalistic design in real-life clinical practice in patients with comorbidities and multiple drug treatments. To evaluate the role and impact of host underlying genetics on treatment response, different approaches are used depending on existing levels of understanding on the functional significance of genetic variants in question. This lecture showcases the work done pharmacogenomics research and how this has occurred in Africa and how “omics” is being leveraged. Our approach focuses on common disease conditions and commonly used medications including herbal medicinal plants. We report on antiretroviral therapy and other treatments alter microRNA expression signatures and expression of drug-metabolizing enzyme genes, in vitro. These data point to several important clinical implications through changes in drug/drug interaction risks and achieving optimal therapeutics. Thus, differential expression of microRNAs after treatment with EFV and RMP adds another layer of complexity that should be incorporated in pharmacogenomic algorithms to render drug response more predictable. The lecture will reflect also on pharmacogenomics of herbal medicines, and interaction with conventional drugs. There is a trend of important genes and their variants coming being prominent biomarkers for responses for commonly used drugs. The use of a wholistic approach in pharmacogenomics research translation that transcends disciplinary boundaries incorporating different “omics” ultimately leading to precision medicine.

    Pharmacogenomics is relevant worldwide for modern therapeutics and yet needs further uptake in developing countries. There is paucity of studies with a naturalistic design in real-life clinical practice in patients with comorbidities and multiple drug treatments. To evaluate the role and impact of host underlying genetics on treatment response, different approaches are used depending on existing levels of understanding on the functional significance of genetic variants in question. This lecture showcases the work done pharmacogenomics research and how this has occurred in Africa and how “omics” is being leveraged. Our approach focuses on common disease conditions and commonly used medications including herbal medicinal plants.

    We report on antiretroviral therapy and other treatments alter microRNA expression signatures and expression of drug-metabolizing enzyme genes, in vitro. These data point to several important clinical implications through changes in drug/drug interaction risks and achieving optimal therapeutics.  Thus, differential expression of microRNAs after treatment with EFV and RMP adds another layer of complexity that should be incorporated in pharmacogenomic algorithms to render drug response more predictable. The lecture will reflect also on pharmacogenomics of herbal medicines, and interaction with conventional drugs.  There is a trend of important genes and their variants coming being prominent biomarkers for responses for commonly used drugs. The use of a wholistic approach in pharmacogenomics research translation that transcends disciplinary boundaries incorporating different “omics” ultimately leading to precision medicine.

    Collet Dandara

    Professor Collet Dandara is currently serving as Deputy Dean of Postgraduate Education in the Faculty of Health Sciences, at the University of Cape Town. He is also a Director of the SAMRC/UCT Platform for Pharmacogenomics Research and Translation Unit (PREMED) and is a professor of human genomics. Collet Dandara is principal investigator of the pharmacogenomics & drug metabolism research group at the University of Cape Town. Collet is on the Executive Committee of the African Society for Human Genetics (AfSHG) as well as the African Pharmacogenomics Network (APN) and was Chair of the Southern African Society for Human Genetics (SASHG) (2021-2023).  Professor Dandara serves on the Board of the Sydney Brenner Institute for Molecular Bioscience and served on the South African Medical Research Council (SAMRC) board from 2019 to 2022. Professor Dandara is a biomedical scientist with extensive experience in pharmacogenomics. Professor Dandara’s research covers the understanding of the pharmacogenomics of antiretroviral drugs and cardiovascular diseases including hypertension and cancer. Professor C Dandara is a current HUGO Executive Board Member. He is a Fellow of the Academy of Science of South Africa (ASSAf Fellow), a Fellow of the African Academy of Science (AAS-Fellow) and was part of the inaugural World Academy of Sciences (TWAS) Young Affiliate/Alumni (TYAN) Executive Committee (2016-2021). He has a track record of successful supervision of postgraduate students and has >160 publications in international peer-reviewed journals.

  • Challenges in Predicting PK for Transporter Substrates
    Contains 3 Component(s)

    This webinar is intended to provide education and updates of this evolving area to scientists and graduate students.

    This webinar is organized by ISSX M&S and transporter FG jointly. It will cover from in vitro to the human PK prediction for transporter substrates with focus on the current challenge and possible solutions related to:

    1) Critical In vitro methods /parameters needed for a reliable PBPK model predictions, data quality, gaps and how to overcome the limitations of in vitro system; 

    2) Key model structure and population parameters (including special populations) for transporter substrates - gaps in addressing multiple transporter involvement and interplay with enzymes; 

    3) Status of model validation for index substrates, applications in regulatory interaction and filing -challenges and future direction.

    The webinar will include sufficient time for Q&A with invited field expert as panelists.

    Xiaomin Liang

    Xiaomin Liang, Ph.D., is a Senior Scientist II in the Department of Drug Metabolism at Gilead Sciences Inc. She obtained her B.S. degree in molecular toxicology from the University of California, Berkeley in 2011, and her Ph.D. degree in pharmaceutical sciences with a focus on membrane transporters, transporter-mediated drug disposition, and pharmacokinetics (PK) from the University of California, San Francisco in 2016. Following a brief fellowship at the Office of Clinical Pharmacology at the FDA, she joined Gilead in 2017. Her primary research interests are in the applications of transporter biology, in vitro methodologies, and mechanism-based PK/PBPK modeling to understand the ADME of compounds and translate preclinical data to predict human PK.

    Ying-Hong Wang

    Dr. Ying-Hong Wang is a senior reviewer in the Division of Pharmacometrics, Office of Clinical Pharmacology, Office of Translational Sciences (OTS)/CDER. Prior to joining the FDA, she worked for 14 years at Merck, where she was responsible for preclinical and clinical development of numerous compounds in multiple therapeutic areas. She chaired the Quantitative Prediction of Drug Interaction Working Group and developed the internal guidelines for DDI prediction. Her modeling work on predicting and characterizing enzyme and transporter-mediated DDI and hepatic impairment contributed to the NDA approvals of grazoprevir/elbasvir and letermovir. Dr. Wang earned her Ph.D. from the Oregon Health Science University and completed her Clinical Pharmacology Fellowship training at the Indiana University-Purdue University Indianapolis. Her current research focuses on applications of PBPK modeling in CYP induction, transporter-mediated DDI, and hepatic impairment.

    Kunal Taskar

    Kunal Taskar, Ph.D., is currently working as Director, Global Head of PBPK Modelling at GSK. Kunal completed his doctorate more than a decade ago and postdoctoral research at Texas Tech University Health Sciences department, USA, in Quentin Smith’s lab with research focused on Neuropharmacokinetics and role of transporters in drug delivery across the blood-brain barrier (BBB).

    Kunal’s experience and research focus include: PBPK modeling of small/large molecules and new modalities for PK and dose predictions, drug-drug interaction predictions and mechanistic understanding of the clinically occurring drug–drug interactions; and application of PBPK modeling in special populations including pediatrics, pregnancy, and organ impairment. His expertise also includes transporter mediated drug delivery and intracellular drug concentrations, especially the role of uptake and efflux transporters in drug pharmacokinetics-pharmacodynamics in disease and toxicology; novel transporters and role in drug disposition and use of endogenous probes and modeling for drug mediated transporter modulations.

    Kunal is a member of American Society of Clinical Pharmacology and Therapeutics (ASCPT), International Society for the Study of Xenobiotics (ISSX) and the International Brain Barriers Society. Kunal is a member of several IQ consortiums including MIDD Pilot Program WG, Transporters, Induction PBPK and Pediatric PBPK. He is the founder member and Chair of the ASCPT QP PBPK Community. He received the 2014 AAPS Pharmaceutical Research Meritorious Manuscript Award for a manuscript that was published in the same journal in 2012. He has actively published and given invited talks and conducted workshops at international conferences.

    Dr. Christine Bowman

    Christine Bowman is a Principal Scientist in the Drug Metabolism and Pharmacokinetics Department at Genentech, Inc.  She is a DMPK project lead for discovery and development projects and her research interests include improving in vitro to in vivo extrapolation with new in vitro methods and PBPK modeling, with a specific focus on transporters.  Prior to joining Genentech, Christine received her PhD from the University of California, San Francisco under Dr. Leslie Benet.

    Xiaoyan Chu

    PhD

    Dr. Xiaoyan Chu is a Senior Director in the Department of Pharmacokinetics, Dynamics, Metabolism, and Bioanalytics at Merck & Co., Inc. in West Point, PA, USA. She obtained her PhD from the Department of Molecular Pharmacokinetics at the Graduate School of Pharmaceutical Sciences, University of Tokyo, Japan. After completing her post-doctoral research at the Department of Pharmaceutical Sciences, College of Pharmacy, University of Michigan., she joined the Department of Pharmacokinetics, Pharmacodynamics and Drug Metabolism at Merck & Co., Inc.   As the leader for the Transporter Science Group, her main responsibilities are to develop transporter related research and operational strategies to support Merck’s discovery and development portfolio, and to evaluate and establish new technologies and approaches to study the role of transporters in pharmacokinetics, efficacy, and toxicity of drugs. She has authored over 80 peer-reviewed research papers, book chapters, and has been invited to speak at over 40 national/international scientific conferences.  She is the member of the International Transport Consortium (ITC) and serves as the Chair of the ISSX Transporter Focus Group from 2019 to 2022. She is also a member of the Editorial Board of Drug Metabolism & Disposition (DMD) and serves on the Industry Advisory Board at the College of Pharmacy & Pharmaceutical Sciences, Washington State University.

    Bridget Morse

    PhD

    Dr. Bridget Morse is currently a Senior Director in PK/PD and scientific lead of Quantitative Clinical Pharmacology at Lilly Research Laboratories. She received her Pharm.D. from Butler University and her Ph.D. in Pharmaceutical Sciences from the University at Buffalo. Bridget has served in the pharmaceutical industry for 10 years as a subject matter expert in pharmacokinetics, transporters, clinical pharmacology and drug-drug interactions, particularly in the use of modeling for substrates of hepatic transporters. She chaired the IQ OATP1B Biomarkers Working Group from 2020-2023, is current Chair of the AAPS Drug Transporter Community and sits on the Editorial Advisory Board for the AAPS Journal. She has over 30 published journal articles and co-authors a recurring book chapter on drug transporters in Foye’s Principles of Medicinal Chemistry.

    Manthena Varma

    PhD

    Dr. Manthena Varma, PhD is Research Fellow, at Pfizer Inc, Groton, CT. Dr. Varma received his B. Pharm. degree from the Kakatiya University, India, and an M.S. degree and PhD in Pharmaceutics, from the National Institute of Pharmaceutical Education and research (NIPER), Punjab, India.  Later, Dr. Varma worked as a Post Doctoral Fellow at the Department of Pharmaceutics, University of Minnesota (Minneapolis). Dr. Varma holds an Adjunct faculty position in the Department of Pharmacy at the University of Rhode Island. His research focus include ADME/PK technologies and strategies in drug discovery and development, role of drug transporters and transporter-enzyme interplay (extended clearance) in ADME/PK, clinical pharmacokinetics and DDI predictions via mechanistic (PBPK) modeling. Varma supported preclinical and clinical development of several Pfizer compounds in the oncology, diabetes, and in NAFLD/obesity areas. He published about 150 original articles/reviews on a variety of ADME/PK topics.