Bridging Bench and Bedside with Quantitative Model-Based Translational Pharmacology

Agenda

* Presentation times are subject to change.

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 K_i for PF-04455242 in rats was 414 ng/mL. Accounting for species differences in unbound fraction, in vitro K_i and brain penetration provided a predicted human K_i 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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