Cambridge Healthtech Institute 第2回

Lead Optimization for Drug Metabolism & Safety
( 薬物代謝と安全性の改善に向けたリード化合物の最適化 )




化合物の構造が薬様特性に及ぼす影響を理解している化学者が増えれば、医薬品開発に向け化合物を最適化する作業を加速させることができます。創薬段階のリード化合物は、有効性と安全性の両方を最適化する必要がありますが、薬物代謝、クリアランス、薬物間相互作用 (DDI) に関連する不都合な事象は、医薬品開発プロジェクトのかなり後の段階まで表面化しないことがあります。薬物代謝と安全性の改善に向けたリード化合物の最適化をテーマにしたこのシンポジウムでは、化学、吸収・分布・代謝・排泄 (ADME) 、薬物代謝と薬物動態 (DMPK) 、薬理学などの分野の研究者が一堂に会し、リード化合物最適化の早期段階で、とりわけ安全面の問題に対応するため考慮すべき要因について議論します。またこのシンポジウムでは、ケーススタディや最新の研究成果を援用しながら、薬物代謝、生体内変化、薬物輸送、DDIなどに関連する重要な概念を明らかにするセッションも予定されています。

Final Agenda

Friday, April 12

7:30 am Registration Open and Morning Coffee


7:55 Welcome and Opening Remarks

Tanuja Koppal, PhD, Conference Director

Ganesh Rajaraman, PhD, MBA, Associate Director, DMPK, Celgene Corporation

8:00 ADME Strategies in Beyond the Rule of Five Space

Ganesh Rajaraman, PhD, MBA, Associate Director, DMPK, Celgene Corporation

As drug discovery is increasingly pushing new frontiers in deep hydrophobic targets, protein-protein interactions, protein degraders with PROTACS, etc., it requires compounds ‘beyond the rule of five’ (bRO5; Lipinski’s rule). This poses major challenges with respect to permeability and oral bioavailability. Current in vitro tools are of limited value in predicting in vivo results, making it challenging to come up with a rational SAR strategy to improve on properties. The talk aims at exploring current challenges and attempts at possible solutions.

8:30 A Chemical Toxicologist’s Perspective on the Validation and Application of Cutting-Edge in vitro Toxicity Assays for Lead Optimization

Tomoya Yukawa, PhD, Associate Scientific Fellow, Discovery Toxicology, Drug Safety Research & Evaluation, Takeda Pharmaceutical Company

There is a strong focus on the development of new in vitro assays that are predictive of adverse events linked to drug attrition. To leverage these assays for lead optimization, local validation analyses based on target class, mode-of-action and chemotype-similarity are essential to ensure applicability and utility. We present several case studies of validation/application of such assays including a 3D-liver microtissue model, a proximal tubule cell model and a hematopoietic stem cell derived myeloid model.

9:00 Networking Coffee Break


9:30 Biotransformation of Antibody Drug Conjugates (ADCs) - Pathways and Enzymes

Donglu Zhang, PhD, Principal Scientist, Department of Drug Metabolism and Pharmacokinetics, Genentech, Inc.

Biotransformation of an ADC involves both hydrolysis of the protein portion and metabolism of payloads in addition to linker metabolism. Examples will be given to demonstrate biotransformation of commonly used peptide and disulfide linkers in which both cleavage and immolation are important. Further biotransformation of payloads could be important as DNA alkylation of DNA alkylators should be considered as a disposition pathway.

10:00 Modeling and Simulation to Study the Impact of Transporters on Drug Disposition and to Improve in vitro to in vivo Extrapolation (IVIVE)

Priyanka Kulkarni, PhD, Scientist, Pharmacokinetics and Drug Metabolism, Amgen, Inc.

IVIVE of transporter substrates is an industry-wide challenge due to multiple complicating factors. Modeling and simulation tools were used to address such experimentally challenging systems. Compartmental and semi-physiological models were used to assess the impact of uptake transporters on drug distribution and to determine system-independent “true” inhibition parameters of efflux transporters, respectively. Together, these results demonstrate the use of modeling and simulation techniques to improve IVIVE of transporter substrates and inhibitors.

10:30 Success and Challenges in Predicting Transporter Mediated Drug Disposition and Clearance from in vitro to in vivo Extrapolation

Na Li, PhD, Senior Scientist, Pharmacokinetics and Drug Metabolism, Amgen, Inc.

11:00 Sponsored Presentation (Opportunity Available)

11:15 Luncheon Presentation (Sponsorship Opportunity Available) or Enjoy Lunch on Your Own

12:00 pm Session Break


1:00 Chairperson’s Remarks

Kari Morrissey, PhD, Scientist, Clinical Pharmacology, Genentech, Inc.

1:05 Understanding Transporter-Mediated DDIs – Regulatory DDI Guidance and Industry Case Studies

Michelle Liao, PhD, Associate Director, Clinical Pharmacology and DMPK, Clovis Oncology

Transporter-mediated clinically relevant drug-drug interactions (DDIs) are widely recognized. Drug regulatory agencies worldwide have issued guidance regarding transporter DDI in (1) evaluation of important drug transporters during preclinical drug development, (2) design of clinical DDI studies, and (3) drug labeling. This presentation will compare this DDI guidance and illustrate these concepts with case studies.

1:35 Determining the Clinical Relevance of DDI Predictions

Kari Morrissey, PhD, Scientist, Clinical Pharmacology, Genentech, Inc.

Interactions between drugs can have serious implications; therefore, it is important to understand the potential for and clinical relevance of DDIs early in drug development. This presentation will provide practical considerations and strategies on (1) incorporating nonclinical DDI predictions into clinical development plans, (2) timing, design and conduct of dedicated DDI studies, (3) interpretation of clinical data to determine the clinical relevance of a DDI and (4) implications of clinically relevant DDIs on product labeling.

2:05 Sponsored Presentation (Opportunity Available)

2:35 Networking Refreshment Break


3:05 FEATURED PRESENTATION: A Case Study in Machine Learning: Integrating Metabolism, Toxicity, and Real-World Evidence

S. Joshua Swamidass, MD, PhD, Assistant Professor, Department of Immunology and Pathology, Washington University

Many medicines become toxic only after bioactivation by metabolizing enzymes, sometimes into chemically reactive species. Idiosyncratic reactions are the most difficult to predict, and often depend on bioactivation. Recent advances in deep learning can model bioactivation pathways with increasing accuracy, and these approaches are giving us deeper understanding of why some drugs become toxic and others do not. At the same time, deep learning can be used to understand drug toxicity as it arises in clinical data and why some patients are affected, but not others.

3:35 Modeling in Drug Metabolism for Drug Design and Development

Hao Sun, PhD, Principal Pharmacokineticist, DMPK, Seattle Genetics

4:05 Quantitative Prediction of Complex Drug-Drug Interactions Involving CYP3A and P-glycoprotein: A Case Study of Anticancer Drug Bosutinib

Shinji Yamazaki, PhD, Department of Pharmacokinetics, Dynamics and Metabolism, La Jolla Laboratories, Pfizer Worldwide Research and Development

4:35 Close of Conference

* 不測の事態により、事前の予告なしにプログラムが変更される場合があります。