Cambridge Healthtech Institute’s 2nd Annual

Optimizing Drug Metabolism & Pharmacokinetics

Innovative Tools and Strategies for Lead Optimization and Early Dosing

June 18-19, 2019

Lead compounds in drug discovery need to be optimized for both efficacy and safety. Unfortunately, some of the adverse events related to drug metabolism, transport, drug-drug interactions and drug clearance do not surface until much later in development. Improvements in cell-based assays, new predictive models for in vitro and in silico testing, and the emphasis on fail fast, fail early has driven the need for more efficient and effective PKPD and ADME testing. Cambridge Healthtech Institute’s conference on Optimizing Drug Metabolism & Pharmacokinetics will bring together experts from discovery chemistry, ADME, DMPK, and PKPD groups to talk about some of the drug metabolism issues that are important, from lead optimization to early dosing in humans. The talks and discussions will cover what’s new and relevant in ADME and PKPD assessments using relevant case studies, research findings, and highlighting use of innovative assays and technologies.

Final Agenda

Tuesday, June 18

7:00 am Registration Open and Morning Coffee


8:00 Chairperson’s Remarks

Li Di, PhD, Research Fellow, Pharmacokinetics, Dynamics and Metabolism, Pfizer, Inc.

8:10 Structural Attributes Influencing Unbound Tissue Distribution

Di_LiLi Di, PhD, Research Fellow, Pharmacokinetics, Dynamics and Metabolism, Pfizer, Inc.

Asymmetric tissue distribution can lead to higher or lower tissue free drug concentration than free plasma concentration (Kpuu). Understanding of the structural attributes influencing tissue Kpuu can provide design principles for medicinal chemists to enhance or decrease tissue exposure. In this presentation, physiochemical properties that affect tissue Kpuu will be discussed for five tissues (muscle, liver, brain, heart and adipose) with 50 structurally diverse compounds.

8:40 CASE STUDY: Intracellular Pharmacokinetics (ICPK) — An Emerging Area in Drug Discovery and Development

Reichel_AndreasAndreas Reichel, PhD, Head of Research Pharmacokinetics, Bayer AG

In the talk I will discuss mechanisms that control intracellular drug concentrations, experimental approaches to estimate unbound intracellular concentrations and their applicability to advance our understanding of intracellular drug-target interactions. Using case studies, I would illustrate how the field of ICPK can offer novel opportunities that can be used to address key questions in drug discovery, thereby enhancing our understanding of the intracellular fate of drugs and the impact on drug efficacy and safety.

9:10 Intestinal Excretion is Becoming an Important Attribute for Drug Discovery

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

Metabolic stability screening has resulted in new drugs or drug candidates that are metabolically stable and are subject to direct excretion for elimination. While urinary and biliary excretion is common, intestinal excretion has not been recognized as a common drug elimination pathway. This presentation will discuss intestinal excretion with examples in relation to metabolism, re-absorption, and transporter properties in drug discovery.

9:40 Grand Opening Coffee Break in the Exhibit Hall with Poster Viewing


10:25 CASE STUDY: Application of Mechanistic PKPD Models in Preclinical to Clinical Translation of Biotherapeutics

Alison Betts, PhD, Associate Research Fellow, Translational Modeling & Simulation, Biomedicine Design, Pfizer Worldwide R&D

Translational PK/PD modeling is the integration of in silico, in vitro and in vivo preclinical data with mechanism-based models to predict effects of drugs across different biological systems. Successful implementation of translational PK/PD in drug discovery can have substantial impact on efficiency and quality of decision making. Three case studies will be presented, outlining impact on design/selection of lead compounds and prediction of clinical efficacious doses and regimens.

10:55 CASE STUDY: Tapping Modelling and Simulation to Select and Optimize a Lead Biologic Candidate

Renu Singh Dhanikula, PhD, Senior Research Investigator, Metabolism and Pharmacokinetics, Bristol-Myers Squibb

The presentation will demonstrate how we can employ modelling and simulation as a strategy early in discovery and optimization of biologics to select a lead candidate and potentially reduce potential toxicity risks associated with the target.

11:25 Sponsored Presentation (Opportunity Available)

11:55 Transition to Lunch

12:00 pm Luncheon Presentation (Sponsorship Opportunity Available) or Enjoy Lunch on Your Own

12:30 Session Break


1:05 Chairperson’s Remarks

Jayaprakasam Bolleddula, PhD, Director, DMPK and Clinical Pharmacology, Agios Pharmaceuticals

1:10 Molecular Modeling and Data Mining in Biotransformation for Metabolism-Directed Lead Optimization

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

Two distinct modeling approaches, structure-based modeling and data mining will be discussed for their application in drug metabolism and biotransformation. Structure-based modeling with crystal structures of drug metabolizing enzymes was applied for metabolism-derived drug design to solve metabolism-related problems such as regioselectivity, stereoselectivity, metabolic switch and mechanism-based inactivation. Pattern recognition-type data mining was used to facilitate mass spectrometry-based metabolite identification.

1:40 Navigating Transporter Sciences in Pharmacokinetics Characterization Using Extended Clearance Classification System (ECCS)

Varma_ManthenaManthena Varma, PhD, Associate Research Fellow, PDM, Medicine Design, Worldwide R&D, Pfizer, Inc.

Extended clearance classification system (ECCS) is a framework based on simple drug properties (i.e., ionization permeability and MW) developed to predict predominant clearance mechanism. I will discuss the paradigm of applying ECCS framework in mapping the role of clinically relevant drug transporters in early discovery and development, thereby implementing the right strategy to allow reliable optimization of drug exposure and predict/evaluate the pharmacokinetic changes due to DDIs or genetic polymorphisms.

2:10 Sponsored Presentation (Opportunity Available)

2:25 Refreshment Break in the Exhibit Hall with Poster Viewing

2:30-2:45 Speed Networking: Young Professionals

3:10 Modelling Inactivation Parameters From Time-Dependent Inhibition Improves Clinical DDI Prediction Compared to Standard Practice

Jaydeep Yadav, PhD, Scientist, Pharmacokinetics and Drug Metabolism, Amgen, Inc.

Time-dependent inactivation (TDI) of CYPs is a leading cause of clinical drug-drug interactions (DDIs). We compared the current practice for TDI analysis (replot method) with a modelling approach incorporating multiple binding kinetics, quasi-irreversible inactivation, sequential metabolism, inhibitor depletion, and membrane partitioning (numerical method). Inactivation parameters (KI and kinact) from both methods were used to predict 77 clinically observed DDIs. The numerical method outperformed the standard replot approach 81% of the time.

3:40 Emerging Role of Endogenous Biomarkers in Prediction of Pharmacokinetics Based Drug-Drug Interactions

Bolleddula_JayaprakasamJayaprakasam Bolleddula, PhD, Director, DMPK and Clinical Pharmacology, Agios Pharmaceuticals

DDIs can lead to fatal adverse events or lack of therapeutic benefit and are a major cause of drug withdrawal. Although in vitro evaluation and model-based simulations are used to predict a DDI, oftentimes clinical studies with probe substrates or inhibitors are required for quantitative determination. Recently, there has been an increased interest in utilizing endogenous markers to evaluate preliminary DDI potential of an investigational compound. A summary of the utility of endogenous biomarkers in DDI prediction will be presented.

4:10 Transition to Keynote


5:20 Taste of New England Welcome Reception in the Exhibit Hall with Poster Viewing

5:25 Meet the Plenary Keynotes

6:25 Find your Table, Meet your Moderator

6:30 Breakout Discussion Groups 

7:30 Close of Day

Wednesday, June 19

7:00 am Registration Open and Morning Coffee


8:00 Chairperson’s Remarks

Wilson Shou, PhD, Senior Principal Scientist, Discovery Chemistry Platforms, Bristol-Myers Squibb Co.

8:05 High-Throughput and Ultra High-Throughput Mass Spectrometry for HT-ADME Support

Wilson Shou, PhD, Senior Principal Scientist, Discovery Chemistry Platforms, Bristol-Myers Squibb Co.

LC-MS readout for HT-ADME assays typically has slow cycle times (minutes/sample) that are not amenable to high throughput screening efforts. Here we investigated several novel approaches that directly introduce the contents of sample wells into the mass spectrometer for high-throughput and ultra-high-throughput analysis, and detailed results from various HT-ADME applications will be presented.

8:35 Quantitative Analysis of Major Biotransformation on Antibody-Drug Conjugates

Cong Wei, PhD, Group Leader, Drug Metabolism and Pharmacokinetics, Biogen

Antibody-drug conjugate (ADC) is a complex structure that combines an antibody (~150 kDa) with a small molecule drug (often a cytotoxin) through a chemical linker. Due to the large size of ADC, it is challenging to study biotransformations on the intact ADC. A strategy and workflow will be presented to identify and quantify the major biotransformation of the ADC, which is conjugated via hinge-cysteines to an auristatin payload with a maleimide-containing linker.

9:05 High-Content Metabolic Stability in Drug Discovery

Maria Fitzgerald, Scientific Director, Early ADME, DMPK, Sanofi US

Understanding the structure-metabolism relationship early in drug discovery can enable a more efficient and effective compound design optimization. Traditional metabolic stability, metabolite identification and phenotyping experiments can be labor intensive when performed separately. Using a combination of automation, high resolution mass spectrometry and advanced software, we have developed an assay to assess metabolic stability, molecular soft spots, and CYP phenotyping combined into a single experiment.

9:35 Coffee Break in the Exhibit Hall with Poster Viewing

10:05 Poster Winner Announced

10:20 In silico ADME in Drug Discovery

Xu Xin, PhD, Director, Pharmacokinetics, National Center for Advancing Translational Sciences (NCATS), National Institutes of Health

The rapid advancement in automation has enabled the generation of compounds with diverse chemical structures and key in vitro ADME properties very quickly. The database generated with these in vitro ADME properties lays the foundation for in silico ADME modeling. The importance of quality of ADME database, in silico modeling efforts at NCATS and potential application of in silico ADME models to accelerate drug discovery research will be discussed in this presentation.

10:50 KEYNOTE PRESENTATION: A Case Study in Machine Learning — Integrating Metabolism, Toxicity, and Real-World Evidence

Swamidass_JoshuaS. Joshua Swamidass, MD, PhD, Associate Professor, Department of Immunology and Pathology, Division of Laboratory and Genomic Medicine; Faculty Lead, Translational Informatics, Institute for Informatics, 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. Deep learning can also be used to understand drug toxicity as it arises in clinical data and why some patients are affected.

11:50 Enjoy Lunch on Your Own

12:30 Transition to Plenary


2:20 Booth Crawl and Dessert Break in the Exhibit Hall with Poster Viewing

2:25 Meet the Plenary Keynotes

3:05 Close of Conference

Stay on to attend Wednesday, June 19 - Thursday, June 20

Predicting Drug Toxicity

Recommended Short Course

SC2: Optimizing Drug Metabolism, Drug Clearance and Drug-Drug Interactions