Cambridge Healthtech Institute 제2회

Lead Optimization for Drug Metabolism & Safety
(약물 대사와 안전성 개선을 위한 리드 화합물의 최적화)

안전성의 예측과 평가를 시행하여 약제 설계에 포함하기 위한 툴과 전략

2019년 4월 12일

 

화합물의 구조가 약물 유사 특성에 미치는 영향을 이해하고 있는 화학자가 증가하면 의약품 개발을 위해 화합물을 최적화하는 작업을 가속시킬 수 있습니다. Drug Discovery 단계의 리드 화합물은 유효성과 안전성 양측을 최적화할 필요가 있으나, 약물 대사, 클리어런스, 약물간 상호작용(DDI)에 관련된 이상반응(adverse events)은 의약품 개발 프로젝트 이후 단계까지 표면화하지 않는 것이 있습니다. 약물 대사와 안전성 개선을 위한 리드 화합물의 최적화를 테마로 한 이 심포지엄에서는 화학, 흡수·분포·대사·배설(ADME), 약물 대사와 약물 동태(DMPK), 약리학 등 분야의 연구자가 한자리에 모여 리드 화합물 최적화의 조기 단계에서 특히 안전면 문제에 대응하기 위해 고려해야 할 요인에 대해 논의합니다. 또한 이 심포지엄에서는 사례 연구 및 최신 연구 성과를 이용하면서 약물 대사, 생체내 변화, 약물 운송, DDI 등에 관련된 중요한 개념을 다루는 세션도 예정되어 있습니다.


Final Agenda

Friday, April 12

7:30 am Registration Open and Morning Coffee

새로운 화학적 환경과 의약품 모달리티의 최적화

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

약물 운송과 클리어런스에 관한 연구 성과

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

약물간 상호작용 평가

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

ADME/DMPK 예측을 위한 AI

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

* 주최측 사정에 따라 사전 예고없이 프로그램이 변경될 수 있습니다.