Cambridge Healthtech Institute’s 3rd Annual

Lead Optimization for Drug Metabolism & Safety

Tools and Strategies for Predicting, Optimizing, and Improving PKPD and Safety

April 17, 2020



Lead compounds in drug discovery need to be optimized for both efficacy and safety. Unfortunately, some of the adverse events related to drug metabolism, clearance, and drug-drug interactions (DDI) do not surface until much later in drug development. This focused one-day symposium on Lead Optimization for Drug Metabolism & Safety will bring together experts from chemistry, ADME, DMPK, and pharmacology to talk about some of the factors that must be considered early in lead optimization for addressing safety concerns that may arise later. The symposium will cover some important concepts related to drug metabolism, biotransformation, drug transport, drug-drug interactions and more, using relevant case studies and recent research findings. It will offer an opportunity for attendees to network and share ideas with experts.

Final Agenda

Friday, April 17

7:30 am Registration Open and Morning Coffee

LEVERAGING IN SILICO TOOLS AND MACHINE LEARNING FOR ADME PREDICTIONS

7:55 Welcome and Opening Remarks

Tanuja Koppal, PhD, Senior Conference Director, Cambridge Healthtech Institute

Maria A. Miteva, PhD, Research Director, Molécules Thérapeutiques in silico (MTi), Inserm Institute

8:00 Integration of Structure-Based and Machine Learning Approaches to Predict Inhibition of Drug Metabolizing Enzymes

Maria MitevaMaria A. Miteva, PhD, Research Director, Molécules Thérapeutiques in silico (MTi), Inserm Institute

We will present an in silico approach to predict inhibition of drug metabolizing enzymes. We focus on Cytochrome P450 (CYP) responsible for the metabolism of 90% drugs and on sulfotransferase (SULT), a conjugate Phase II metabolizing enzyme. We have developed an original in silico approach for the prediction of CYP2C9 and SULT1A1 inhibition combining protein structure knowledge, dynamic behaviors in response to ligand binding and modern machine learning modeling.

8:30 Model Informed Human Pharmacokinetics and Dose Prediction: Beyond IVIVE of Compound Enzyme Stability

Yurong LaiYurong Lai, PhD, FAAPS, Senior Director, Drug Metabolism, Gilead Sciences

While in vitro in vivo extrapolation (IVIVE) approaches are suitable for drug candidates that are eliminated by metabolizing enzymes, more sophisticated prediction approaches (e.g. PBPK models) are required for drug candidates where transporter-mediated clearance mechanisms are involved. This talk will provide an update on tools for assessing in vitro transporter clearance parameters, PBPK model building, curve fitting and verification using preclinical PK data. The presentation will also provide case examples and highlight the gaps in human PK prediction.

9:00 Computational High-Throughput Lead Optimization Using New Free Energy Methods and Quantum Mechanical Force Fields

Darrin York, PhD, Henry Rutgers University Professor, Department of Chemistry and Chemical Biology, Director of the Laboratory for Biomolecular Simulation Research, Rutgers University

Computational lead refinement plays a vital role in drug discovery but has been hampered by the lack of affordable software with state-of-the-art methods that are highly efficient on commodity hardware. These issues have been overcome by the development and implementation of new GPU-accelerated alchemical free energy simulation methods with classical and quantum mechanical force fields in AMBER 2020. These high-throughput features enable new applications including drugs that target metal ion binding sites in metalloenzymes and covalent inhibitors.

9:30 Networking Coffee Break

10:00 Understanding the Applicability and Limitations of in silico and in vitro Safety Models towards the Design and Selection of the Safest Drug Candidates

Takafumi TakaiTakafumi Takai, PhD, Senior Scientist, Discovery Toxicology, Drug Safety Research & Evaluation, Takeda, Inc.

Many in silico and in vitro safety models are used during lead optimization to identify safety-related liabilities. A chemistry-based assessment of each model is essential to understand a model’s applicability and the relevance of any prediction from them. We will present several case studies of such analyses, including the in silico prediction of in vitro 3T3 phototoxicity and the use of in vitro cytotoxicity to predict in vivo toxicity findings.

10:30 FEATURED PRESENTATION: Artificial Intelligence and Small-Molecule Drug Metabolism

S. Joshua Swamidass, MD, PhD, Assistant Professor, Immunology and Pathology, Laboratory and Genomic Medicine; Faculty Lead, Translational Informatics, Institute for Informatics, Washington University

We have been building artificial intelligence models of metabolism and reactivity. Metabolism can both render toxic molecules safe and safe molecules toxic. The artificial intelligence models we use quantitatively summarize the knowledge from thousands of published studies. The hope is that we could more accurately model the properties of medicines, to determine whether metabolism renders drugs toxic or safe. This is just one of many places where artificial intelligence could give traction on the difficult questions facing the industry.

11:00 Sponsored Presentation (Opportunity Available)

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

12:00 pm Session Break

ADME STRATEGIES FOR NEW DRUG MODALITIES

1:00 Chairperson’s Remarks

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

1:05 Local Metabolism Leads to Better Understanding of Tissue Drug Concentration for New Modalities

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

For small molecule drugs, liver is the major organ for drug clearance. Liver in vitro systems can be used to predict in vivo PK. Plasma drug concentration is a good surrogate for tissue concentrations. For new modalities especially drug conjugates, there is a universal lysosomal degradation of proteins for clearance and generation of active drugs. The efficacy and toxicity is supported by the drug in tissue that is released locally in a right form at a proper concentration range from a conjugate. This talk discusses the importance of tissue metabolism.

1:35 In vitro ADME Challenges in Assessing New Modalities

Zhengyin_YanZhengyin Yan, PhD, Principal Scientist, Department of Drug Metabolism and Pharmacokinetics, Genentech Inc.

New modalities such as covalent modulators, macrolides, macrocyclic peptides and antibody-drug conjugates have distinct physicochemical properties, and they pose a variety of unique challenges in assessing various ADME properties including metabolic stability, plasma protein binding and CYP inhibition. As a result, inaccurate in vitro ADME data can be reported unnoticeably. This presentation will present some case studies as well as alternative strategies on how to overcome those technical challenges and safeguard data quality.

2:05 Sponsored Presentation (Opportunity Available)

2:35 Networking Refreshment Break

REACTIVE METABOLITES, DRUG TRANSPORTERS AND DRUG CLEARANCE

3:00 Chairperson’s Remarks

Mark Grillo, PhD, Staff Scientist, Drug Metabolism & Pharmacokinetics, MyoKardia, Inc.

3:05 Reactive Drug Metabolite Assessment in Drug Discovery and Development

Mark GrilloMark Grillo, PhD, Staff Scientist, Drug Metabolism & Pharmacokinetics, MyoKardia, Inc.

Chemically-reactive drug metabolites, formed by enzyme-mediated metabolic activation usually in the liver, are perceived as an unwanted feature of drug candidates. These reactive metabolites may covalently bind to protein nucleophiles in vivo leading to subsequent immune-based idiosyncratic toxicities such as hepatotoxicity. The goal is to eliminate or minimize metabolic activation liabilities of drug candidates leading to the increased probability of safer drugs being developed. This talk will review up-to-date risk assessment of reactive drug metabolites.

3:35 Successful Prediction of Hepatic Clearance and Recent Improvements to IVIVE

Jasleen SodhiJasleen Sodhi, PhD Candidate, Laboratory of Dr. Leslie Benet, University of California San Francisco

Accurate prediction of human pharmacokinetic properties is critically important to progress compounds with favorable PK properties. Of particular importance is the prediction of hepatic clearance, which largely determines drug exposure and contributes to projections of dose, drug half-life and bioavailability. This lecture will cover common in vitro techniques used to predict hepatic clearance of new chemical entities, as well as recent advancements and current challenges in IVIVE.

4:05 In vitro and in silico Tools to Facilitate the Assessment of Transporter-Mediated Drug Interactions

Cen Guo, PhD, Manager, Clinical Pharmacology, Pfizer

Drug-drug interactions (DDIs) mediated by hepatic transporters can have important implications in drug efficacy and safety. However, the accuracy and efficiency for the assessment of transporter mediated DDIs need improvement. This presentation will review some new tools to improve the DDI assessment, including cellular models combined with in silico tools to aid data interpretation, in vitro assays to characterize transporter probe cocktail and endogenous biomarkers which can be used for in vivo DDI assessment.

4:35 ADME Characterization and Challenges on New Biotherapeutic Modalities

Cong Wei, PhD, Senior Scientist and Group Leader, Drug Metabolism and Pharmacokinetics, Biogen Inc.

Over the last decade, an increasing number of new therapeutic modalities such as antibody-drug conjugate, monoclonal antibody, fusion protein, siRNA and oligonucleotide are entering clinical trials for the treatment of various diseases. Different from small molecules, ADME characterization on those new biotherapeutic modalities are focused on bioanalytical and biotransformation studies, and challenges are remaining. This talk will summarize and include some case studies on in vivo bioanalysis and biotransformation of the biotherapeutic molecules.

5:05 Close of Symposium

* The program is subject to change without notice, due to unforeseen reason.

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Update History
2020/02/10
Agenda,Sponsor updated
2019/12/25
Agenda,Sponsor updated
2019/11/25
Sponsor updated
2019/10/31
Agenda,Sponsor updated