Bridging Bench and Bedside with Quantitative Model-Based Translational Pharmacology

Bridging Bench and Bedside with Quantitative Model-Based Translational Pharmacology

Tuesday, April 24, 2012

The New York Academy of Sciences

Species differences in pharmacological response exist and are well known. Yet it remains common practice to select and advance compounds to clinical trials based upon gross pharmacological responses observed in animals. Not surprisingly, safety and efficacy findings that fail to translate from preclinical models account for most of the unacceptably high rate of attrition currently experienced by the pharmaceutical industry. This challenge has largely served as the impetus for emerging translational research efforts. Pharmacokinetic-pharmacodynamic (PK-PD) modeling and systems pharmacology are essential tools in the translational research toolkit for the systematic, quantitative integration of diverse preclinical information for the sake of rational drug design, candidate selection and development. In this symposium, experts from both areas of practice will review guiding principles, general considerations and specific applications in quantitative translational research.

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Presented by

  • American Chemical Society, New York Section

Agenda

* Presentation times are subject to change.


Tuesday April 24, 2012

8:30 AM

Registration and Continental Breakfast

9:00 AM

Introduction
Tristan Maurer, PharmD, PhD, Pfizer

9:10 AM

Probing Drug Exposure-Response Relationships in Inflammatory Bone Loss with Translational Pharmacodynamic Systems Modeling
Donald E. Mager, PharmD, PhD, State University of New York at Buffalo

9:55 AM

Integrating Drug Discovery and Experimental Chemotherapy Using Tumor-based Pharmacokinetic/Pharmacodynamic Investigations
James M. Gallo, PhD, Mount Sinai School of Medicine

10:40 AM

Coffee Break

11:15 AM

Successful Application of PK-PD Modeling to Translate Preclinical Biomarker Response to Clinical Proof of Mechanism (POM) Signal: Case Study with a Kappa Opioid Receptor (KOR) Antagonist
Cheng Chang, PhD, Pfizer

12:00 PM

From Target Selection to the Minimum Acceptable Biological Effect Level for Human Study: Use of Mechanism-Based PK/PD Modeling to Design Safe and Efficacious Biologics
Ramprasad Ramakrishna, PhD, Novartis Institutes for BioMedical Research

12:45 PM

Lunch Break

1:30 PM

Translational Pharmacokinetics and Pharmacodynamics of an FcRn-Variant Anti-CD4 Monoclonal Antibody from Preclinical Model to Phase I Study
Eric Stefanich, PhD, Genentech, Inc.

2:15 PM

Model-Based Discovery and Development of Novel Therapies for Type-2 Diabetes Mellitus
Tristan S. Maurer, PharmD, PD, Pfizer

3:00 PM

Coffee break

3:30 PM

Practical Theoretic Guidance for the Design of Tumor-Targeting Agents
Dane Wittrup, PhD, MIT

4:15 PM

Optimization of MM-111 and Lapatinib Dosing Regimens using Mathematical Modeling and Quantitative Biology
Matthew Onsum, PhD, Merrimack Pharmaceuticals

5:00 PM

Networking reception

6:00 PM

Close

Speakers

Organizers

Mercedes Beyna, MS

Pfizer

Mercedes Beyna is currently a research scientist at Pfizer, where she is using molecular, cellular, genetic, and imaging approaches in the quest to understand the biology underlying autism spectrum disorders. Captivated by neuroscience, she has worked in the field for over 10 years, in both academic and industrial laboratory settings. Mercedes attended Binghamton University, earning her undergraduate degree in Biology, and subsequently received her Master's Degree in Biology from New York University. As an active member of the Biochemical Pharmacology Discussion Group, she enjoys developing interesting and educational symposia.

Cheng Chang, PhD

Pfizer

Dr. Cheng Chang is a Principle Scientist at the Department of Pharmacokinetics, Dynamics and Metabolism, Pfizer, Inc. He received a B.S. in biochemistry and molecular biology from the University of Science and Technology of China and a Ph.D. from the Ohio State University in the areas of biophysics and computer-aided drug design. Since 2006, he has been working at Pfizer, enabling advancement of discovery programs in neuroscience by establishing in vitro-to-in vivo, preclinical-to-clinical pharmacology translation using quantitative modeling and simulations. He is experienced in quantifying exposure response relationships, designing preclinical experiments, and projecting clinical efficacious doses. He has authored over 20 peer-reviewed research papers, review articles and book chapters.

Anis Khan, PhD

Merck

Dr. Anis Khan is an Associate Director in the Modeling & Simulation department in Early Stage Development at Merck & Co., Inc. He received his PhD in Pharmaceutical Sciences at the University of Southern California and subsequently did a Postdoctoral Fellowship at Palo Alto Medical Foundation and School of Medicine at Stanford University in the area of infectious diseases. Since 2007, he has been involved in various aspects of quantitative drug development including translation, dose optimization and risk/benefit analysis for decision making at Merck. He has contributed to twenty-eight peer-reviewed research articles, reviews and one book chapter.

Tristan Maurer, PharmD, PhD

Pfizer

Dr. Maurer is a Senior Director of Systems Modeling and Simulation in the Department of Pharmacokinetics, Dynamics and Metabolism at Pfizer Inc. He received a Pharm.D. at the University of Georgia in 1993 and a Ph.D. from The University at Buffalo, State University of New York in 1999. During his 13 years with Pfizer, he has been involved in the development and application of quantitative model-based approaches to predicting pharmacokinetics and pharmacodynamics in support of the discovery of numerous clinical candidates across Pfizer’s cardiovascular, metabolic and neurological disease areas. Dr. Maurer also serves on a multidisciplinary group of senior Pfizer scientists which set standards by which the quality of clinical candidates are assessed for the sake of risk mitigation and organizational learning. He has contributed to 40 peer-reviewed journal articles covering the development and application of models to predicting brain penetration, drug-drug interactions, pharmacokinetics and pharmacology. Currently, he leads a diverse group of systems modeling and simulation experts which employ fit-for purpose approaches to support target and modality evaluation, chemical optimization and early clinical development across the disease areas of neuroscience, cardiovascular and metabolism.

Jennifer Henry, PhD

The New York Academy of Sciences

Speakers

Cheng Chang, PhD

Pfizer

James M. Gallo, PhD

Mount Sinai School of Medicine

James M. Gallo is a Professor in the Department of Pharmacology and Systems Therapeutics at Mount Sinai School of Medicine in NYC. He directs a lab on brain tumor chemotherapy that has projects on drug discovery, drug transporters and drug resistance. A common theme of his research is to examine drug disposition and dynamics in brain tumors that is gradually being integrated with "omics" and systems biology approaches. It is hoped that these efforts will lead to new therapeutic strategies, and in so doing illustrate the importance of quantitative pharmacology. He is a member of a number of scientific organizations, a reviewer for numerous journals, and a past Member of the NIH Clinical Oncology and Developmental Therapeutic study sections. He has over 120 peer-reviewed journal articles. His research is funded by NIH and the National Brain Tumor Society.

Donald E. Mager, PharmD, PhD

State University of New York at Buffalo

Dr. Mager is an Associate Professor of Pharmaceutical Sciences at the University at Buffalo, State University of New York (UB). He received the University at Buffalo Young Investigator Award in 2006 and the New Investigator Award in Pharmacokinetics, Pharmacodynamics, and Drug Metabolism from AAPS in 2007. Dr. Mager served as a Visiting Professor at the University Paris Descartes in January from 2007 – 2012. He currently serves on the Clinical Pharmacology Advisory Committee to the FDA and the Editorial Advisory Boards of Biopharmaceutics and Drug Disposition and Journal of Pharmacokinetics and Pharmacodynamics. He was also elected as a Fellow to the American College of Clinical Pharmacology, as Chair of the Clinical Pharmacology and Translational Research section of AAPS, and as a member of the Board of Directors to the American Society of Pharmacometrics. His research focuses on identifying molecular and physiological factors that control the pharmacological properties of various drugs including immunomodulatory and anti-cancer drugs. He has contributed to over 69 peer-reviewed publications.

Tristan Maurer, PharmD, PhD

Pfizer

Matthew Onsum, PhD

Merrimack Pharmaceuticals

Dr. Onsum is an Associate Director of Translational Research at Merrimack Pharmaceuticals. He received his B.S., M.S., and Ph.D. degrees in Mechanical Engineering from the University of California, Berkeley. His doctoral work, under the supervision of Adam Arkin and Kameshwar Poolla, used both computational and wet biology to study how immune cells track and capture invading microbes. Additionally, he was a member of the Alliance for Cellular Signaling where he developed mathematical models of GPCR mediated calcium signaling and model validation software. He spent two years at AstraZeneca R&D Boston, where he used model simulations to help identify new drug targets. He is currently at Merrimack Pharmaceuticals where he is leading the translational research program for MM-111, a bi-specific antibody against ErbB3 that uses an ErbB2 targeting arm to enhance avidity and inhibitor potency.

Ramprasad Ramakrishna, PhD

Novartis Institutes for BioMedical Research

Ramprasad ‘Prasad’ Ramakrishna has a B.Tech in Chemical Engineering from the Indian Institute of Technology, Madras and a Ph.D in Chemical Engineering from Purdue University. His research and scientific career has focused on mathematical modeling in biomedical and drug discovery areas including bacterial physiology, computational chemistry, pharmacometrics and disease models. Prasad is in the modeling group at Novartis Pharmaceuticals.

Eric Stefanich, PhD

Genentech, Inc.

Eric Stefanich is a Scientist in the Department of Pharmacokinetic and Pharmacodynamic (PK/PD) Sciences at Genentech Inc., a member of the Roche Group. Eric has 22 years of pharmaceutical/biotechnology industry experience and has worked at Genentech for over 17 of those years. Currently Eric supports advancement of large molecule therapeutics for autoimmune/inflammatory diseases and tumor immunology from Research through Phase I. His expertise is in pharmacology, including support of clinical candidate selection for novel therapeutics, projecting human PK and target efficacious dose, and selecting first in human dose and dose regimen for clinical studies. Eric has been involved in numerous regulatory filings with the FDA and ex-US health authorities. He has also been an author on 22 published manuscripts as well as an inventor on 3 patents related to his work at Genentech.

Dane Wittrup, PhD

MIT

Abstracts

Successful Application of PK-PD Modeling to Translate Preclinical Biomarker Response to Clinical Proof of Mechanism (POM) Signal: Case Study with a Kappa Opioid Receptor (KOR) Antagonist
Cheng Chang, PhD, Pfizer

PK-PD modeling greatly enables quantitative implementation of the “learn and confirm” paradigm across different stages of drug discovery and development. This work describes successful prospective application of this concept in the discovery and early development of a novel κ-opioid receptor (KOR) antagonist, PF-04455242, where PK-PD understanding from preclinical biomarker responses enabled successful prediction of clinical response in a Proof of Mechanism (POM) study. Preclinical data obtained in rats included time-course measures of the KOR antagonist (PF-04455242), a KOR agonist (spiradoline) and a KOR-mediated biomarker response (prolactin secretion) in plasma. Clinical data included time-course measures of PF-04455242 and prolactin in 24 healthy volunteers following a spiradoline challenge and single oral doses of PF-04455242 (18 mg and 30 mg). In both species, PF-04455242 successfully reversed spiradoline-induced prolactin response. A competitive antagonism model was developed and implemented within NONMEM to describe the effect of PF-04455242 on spiradoline-induced prolactin elevation in rats and humans. The PK-PD model-based estimate of Ki for PF-04455242 in rats was 414 ng/mL. Accounting for species differences in unbound fraction, in vitro Ki and brain penetration provided a predicted human Ki of 44.4 ng/mL. This prediction was in good agreement with that estimated via application of the proposed PK-PD model to the clinical data (i.e. 39.2 ng/mL). These results illustrate the utility of the proposed PK-PD model in supporting the quantitative translation of preclinical studies into an accurate clinical expectation.

Integrating Drug Discovery and Experimental Chemotherapy Using Tumor-based Pharmacokinetic / Pharmacodynamic Investigations
James M. Gallo, PhD, Mount Sinai School of Medicine

A series of preclinical pharmacological investigations will be presented that span both drug discovery and experimental chemotherapy related to brain tumors. Each vignette will highlight the importance of examining drug disposition and dynamics in tumors and will cover topics such as drug interactions and drug resistance. In one hypothetical example the integration of systems biology and pharmacodynamics is cast as a dynamic model as a novel and precise means to understand drug activity based on different genomic alterations. Although drug discovery and development of anticancer drugs and experimental chemotherapy may have distinct goals there are areas of overlap that can generate a sound quantitative foundation that can enable a more seamless transition of pharmacological data and strategies to the clinic. Advantages of tumor-based PK/PD may be realized in a variety of areas but ultimately lead to devising more rational and effective drug treatment regimens based on an understanding of the determinants of drug activity.

Probing Drug Exposure-Response Relationships in Inflammatory Bone Loss with Translational Pharmacodynamic Systems Modeling
Donald E. Mager, PharmD, PhD, University at Buffalo, State University of New York, Department of Pharmaceutical Sciences

Bone loss associated with inflammatory diseases results from a metabolic imbalance in regulatory mechanisms controlling cellular bone turnover. Assessing exposure-response relationships for drugs that alter bone homeostasis is complicated by an array of complex molecular and cellular signaling pathways. Although mathematical models of signal transduction in bone are available, their translational nature may be limited without considering temporal patterns of drug exposure and mechanistic biomarkers. The goal of this paperis to highlight examples of linking cellular systems models with the pharmacokinetics, (patho-)physiological processes, and mechanisms of drug action to understand and predict the time-course of pharmacological effects. Such pharmacodynamic systems models show a clear advantage of assessing the role of physiologically important factors on drug response and thus provide new testable hypotheses for designing effective therapeutic strategies for osteolytic diseases.

Model-Based Discovery and Development of Novel Therapies for Type-2 Diabetes Mellitus
Tristan S. Maurer, PharmD, PhD, Pfizer Inc.

One of the most serious threats to sustained pharmaceutical innovation is the rapidly rising cost of research and development experienced over the past several decades. This exponential rise in cost has been attributed to a variety of factors including increasing technology investments, rising regulatory and commercial hurdles, and late-stage attrition. Among these factors, attrition due to insufficient efficacy and / or safety stands out as a major contributor. Historically high rates of efficacy and safety related attrition suggest that that there is often insufficient knowledge upon which critical decisions in drug discovery are made, namely: which biochemical targets to pursue and which chemical matter to bring forward into the clinic. Preclinical modeling and simulation (e.g. systems biology, PK/PD) is a useful approach to integrating biological information toward the selection of the most promising targets and the design of the most promising chemical matter. This presentation will provide the rationale for quantitative modeling and simulation, guiding principles for successful implementation of fit for purpose models and examples illustrative of value added in the discovery of novel therapies for type 2 diabetes mellitus (GPR119 agonists, SGLT2 inhibitors and Glucokinase activators).

Optimization of MM-111 and Lapatinib Dosing Regimens Using Mathematical Modeling and Quantitative Biology
Matthew Onsum, PhD, Merrimack Pharmaceuticals

Designing optimal dosing regimens for targeted biologic therapies is an open problem in translational oncology. There are at least two reasons for this: First, biologics are generally well tolerated so it is not guaranteed that the maximum tolerated dose (MTD) will be observed in a Phase 1 dose escalation; and second, since targeted therapies work in only a subset of patients, there will be limited efficacy data available to determine a Phase 2 dose. This problem is further compounded by the use of combination drug treatments.

MM-111 is a novel bispecific antibody that inhibits ErbB3 (HER3), using an ErbB2 (HER2) targeting arm to enhance avidity and inhibitor potency. Pre-clinical data shows that combining MM-111 with the ErbB2 inhibitor lapatinib (Tykerb®) results in synergistic growth inhibition of HER2hi tumors.

We developed a mathematical model of MM-111 and lapatinib’s mechanism of action, incorporating ErbB-family signal transduction, transcriptional regulation, and proliferation, with pre-clinical PK models of the two drugs. Using this model, we designed optimal dosing schedules which were validated in vivo, and will be used to inform future clinical studies. By integrating PK/PD and mechanistic biochemical models, we were able to rationally design combination dosing strategies to maximize therapeutic synergy.

From Target Selection to the Minimum Acceptable Biological Effect Level for Human Study: Use of Mechanism-based PK/PD Modeling to Design Safe and Efficacious Biologics
Ramprasad Ramakrishna, PhD, Novartis Institutes for BioMedical Research

In this presentation, two applications of mechanism-based modeling are presented with their utility from candidate selection to first-in-human dosageselection. The first example is for a monoclonal antibody against acytomegalovirus glycoprotein complex, which involves an antibody binding model and a viral load model. The model was used as part of a feasibility analysis prior to antibody generation, setting the specifications for the affinity needed to achieve a desired level of clinical efficacy. The second example is a pharmacokinetic-pharmacodynamic model based on a single dose pharmacology study in cynomolgus monkey using data onpharmacokinetics, receptor occupancy and the dynamics of target cell depletion and recovery. The model was used to estimate the MABEL, here defined as theminimumacceptable biological effect level against which adose is selected fora first-in-human study. From these applications, we demonstrate thatmechanism-based PK/PD binding models are usefulfor predicting human response to biologics compounds. Especially, such models have the ability to integrate preclinical and clinical, in vitro and in vivoinformation and facilitate rational decision making during various stages of drug discovery and translational research.

Translational Pharmacokinetics and Pharmacodynamics of an FcRn-Variant Anti-CD4 Monoclonal Antibody from Preclinical Model to Phase I Study
Eric Stefanich, PhD, Genentech, Inc.

MTRX1011A is a humanized anti-CD4 antibody with an amino acid substitution (N434H) to improve its binding to the neonatal Fc receptor (FcRn). Pharmacokinetic and pharmacodynamic (PK/PD) data in baboons suggest that the increased binding to FcRn reduces the nonspecific elimination rate (Kel) of MTRX1011A by ~50% but does not affect its PK–PD relationship. The human PK/PD data of MTRX1011A from a phase I study in patients with rheumatoid arthritis (RA) were compared with those previously reported for TRX1, its predecessor antibody, using population PK–PD modeling. The results suggest a comparable PK–PD relationship but no significant difference between the Kel values of the two antibodies. However, the results may have been confounded by the differences in the clinical populations in which the two antibodies were studied and the presence of preexisting immunoglobulin M (IgM) antibodies in the RA sera that recognize N434H in MTRX1011A. This study highlights the challenges in translating from animal studies to human application the effects of FcRn-directed mutations on the PK of monoclonal antibodies.

Practical Theoretic Guidance for the Design of Tumor-Targeting Agents
Dane Wittrup, PhD, MIT

Theoretical analyses of targeting agent pharmacokinetics provides specific guidance with respect to desirable design objectives such as agent size, affinity, and target antigen. These analyses suggest that IgG-sized macromolecular constructs exhibit the most favorable balance between systemic clearance and vascular extravasation, resulting in maximal tumor uptake. Quantitative predictions of the effects of dose and binding affinity on tumor uptake and penetration are also provided. The single bolus dose required for saturation of xenografted tumors in mice can be predicted from knowledge of antigen expression level and metabolic half-life. The role of high binding affinity in tumor uptake can be summarized as: essential for small peptides, less important for antibodies, and negligible for nanoparticles.

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