Research in Focus: Pharmacokinetics, Pharmacodynamics and Systems Pharmacology

ISSUE SEVEN: JANUARY 2016

RESEARCH IN

FOCUS Pharmacokinetics, Pharmacodynamics and Systems Pharmacology Harvey Wong, BSc(Pharm), PhD

INTRODUCTION

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RUG DISCOVERY AND DEVELOPMENT HAS BEEN PLAGUED BY CLINICAL ATTRITION RATES EXCEEDING 90%, WHERE THE PRIMARY CAUSE WAS IDENTIFIED BY KOLA AND LANDIS (NATURE REVIEWS DRUG DISCOVERY 3, 711-716, 2004) AS LACK OF EFFICACY AND UNEXPECTED SAFETY FINDINGS. The authors highlighted that while poor pharmacokinetics was identified as a main cause for drug attrition in the early 1990s, this was no longer a leading cause for attrition 10 years later. Advances in preclinical in vitro and in vivo methodologies for predicting human pharmacokinetics served to reduce the drug failure rate due to poor pharmacokinetics such that by 2000, lack of efficacy became the main cause of drug attrition. In an updated look at causes for drug failures in Phase II (between 2008-2010; Arrowsmith, Nature Reviews Drug Discovery 10, 328-329, 2011), and Phase III and drug submission (between 2007-2010; Arrowsmith Nature Reviews Drug Discovery 10, 87, 2011), lack of efficacy remained the primary cause of attrition contributing to 51% and 66% of drug failures, respectively.

Mathematical modeling techniques such as pharmacokinetic/pharmacodynamics (PK/PD) modeling, physiologically based pharmacokinetic (PBPK) modeling, and systems pharmacology modeling have recently come to the forefront as means to integrate the vast amount of preclinical pharmacokinetic and pharmacodynamic data that is generated in the drug discovery setting. PK/PD and systems pharmacology analyses involves the use of mathematical functions to quantitatively inter-relate drug behavior and its’ effects on the biological system of interest. These types of analyses are particularly helpful in understanding exposure-response relationships where there are apparent disconnects in drug concentration and effect related to temporal delays in drug action. In addition, mechanism-based PK/PD and systems pharmacology models allow the quantitative assessment of known species differences in factors such as pharmacokinetics or in vivo drug potency, and enable a more robust characterization of concentration-pharmacodynamic/effect relationships from frequently variable in vivo preclinical efficacy data. More recently, there has been an increase in reports describing the utility of mathematical modeling to help interpret and translate the results of preclinical studies to human. Finally, the establishment of PK/PD or systems pharmacology models enables integration of all available data, and allows for leveraging of these data for prospective simulations.

Just as an early assessment of preclinical pharmacokinetics helped to reduce drug failures due to poor human pharmacokinetics in the 1990s, a better understanding of drug effect using preclinical pharmacokinetic/pharmacodynamic or systems pharmacology analyses offers an important tool to potentially reduce drug attrition due to a lack of efficacy by offering a means to translate preclinical efficacy data to humans. Finally, PBPK modeling serves as a similar tool to enable translation of preclinical pharmacokinetic data to humans and can be used in combination with PK/PD and systems pharmacology models.

Having recently arrived at UBC, my aim is to establish my laboratory to further work on the use of mathematical modeling techniques as preclinical translation tool in drug discovery and development and as a tool to probe biological pathways.

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SELECTED PROJECTS SYSTEMS PHARMACOLOGY APPROACHES IN UNDERSTANDING DRUG RESISTANCE

Image: Martin Dee

Despite recent breakthroughs in cancer treatment with the development of molecularly targeted anti-cancer agents, the eventual emergence of drug resistance remains a problem. The causes of drug resistance in tumor cells include a diversity of mechanisms such as increases in drug efflux due to drug transporters, alterations in the expression or mutations of the drug target, and changes in cancer signaling pathways, among others. The various mechanisms, their interaction with one another, and their overall contribution to drug resistance are poorly understood. For example, the overexpression of MDR1, a drug transporter, has been associated with cancer treatment failures. However, results from clinical trials of specific MDR1 inhibitors such as zosuquidar and tariquidar have been disappointing highlighting an incomplete understanding of the contribution of drug transporters to drug resistance. The focus of my research program is to better understand the emergence of drug resistance related to alterations in cancer signaling pathways using a systems pharmacology approach. In particular, we wish to map quantitative relationships between components of signaling pathways that are altered with cancer, and characterize how they change and compensate with molecularly targeted therapy and subsequent emergence of drug resistance. We have experience quantitating relationships between drug concentration, target modulation and biological effect for kinase pathways whose activation are associated with certain types of cancer (Figure 1). The new work on drug resistance builds upon our understanding of these relationships. Our research will be based on quantitative experimental results generated from in vitro studies using tumor cells as well as in vivo studies in preclinical tumor models (i.e. xenografts, allografts, etc).

The proposed research utilizes my laboratory’s expertise in building complex mathematical models as well as our experience in the oncology field. Since systems pharmacology models naturally evolve as more information becomes available, our work can serve as a platform for an evolving understanding of cancer drug resistance mechanisms. Ultimately, systems pharmacology models arising from our proposed research will provide a means to better understand how anti-cancer drugs can be effectively administered to reduce the emergence of resistance and cancer therapy failures.

Figure 1. (A) Pharmacokinetic/Pharmacodynamic/Efficacy (PK/PD/Efficacy) model used to characterize relationships between drug concentration, target modulation and anti-tumor effect. (B) Relationship between target modulation (%pERK Decrease) and anti-tumor efficacy (K) for a MEK inhibitor derived from PK/PD/Efficacy model. Reference: Wong et al., Clin Cancer Res, 18: 3090-3099, 2012.

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OTHER PROJECTS My laboratory is particularly interested in the development of new methods of data analysis for toxicology studies. In vivo toxicology as a discipline has been rather empirical in nature relying on simple classical pharmacokinetic parameters (Cmax , tmax , and AUC) to assess the rate and extent of drug exposure, and to assess the preclinical therapeutic index for new drug candidates. There is very limited quantitative information comparing the responsiveness in preclinical species and humans to the modulation of biological pathways that result in toxicological consequences. The application of PK/PD principles to perform a toxicokinetic/toxicodynamic (TK/TD) analysis will provide a better means to quantitate species similarities and differences in toxicological response and sensitivity. These work allow for better translation of preclinical toxicology data to humans. We have performed such work on the consequences of inhibitor apoptosis protein (IAP) inhibition in rats, dogs and humans (Figure 2) and demonstrated that the dog is uniquely sensitive to IAP inhibition compared to the rat and human. TK/ TD analysis is an emerging area, and my laboratory is interested in continued research in this area.

Figure 2. (A) Toxicokinetic/Toxicodynamic model used to characterize inflammatory response (MCP-1 increase) in rat and dog. Predicted MCP-1 change in humans following a range of doses of an IAP inhibitor assuming responsiveness to IAP inhibition is similar to the rat (B) or the dog (C).

Reference: Wong et al., Tox Sci. 130: 205-213, 2012.

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RELEVANT PUBLICATIONS Schuck E, Bohnert T, Chakravarty A, Damian-Iordache V, Gibson C, Hsu C-P, Heimbach T, Krishnatry AS, Liederer BM, Lin J, Maurer T, Mettetal JT, Mudra DR, Nijsen MJMA, Raybon J, Schroeder P, Schuck V, Suryawanshi S, Su Y, Trapa P, Tsai A, Vakilynejad M, Wang S, and Wong H. (2015) Preclinical Pharmacokinetic/Pharmacodynamic Modeling and Simulation in Pharmaceutical Industry: An IQ Consortium Survey Examining the Current Landscape. AAPS J, 17:462-473. DOI: 10.1208/ s12248-014-9716-2.

Wong H, Gould SE, La H, Budha N, Darbonne WC, Kadel EE III, Halladay JS, Alicke B, Erickson R, Flygare JA, Portera C, Mamounas M, Hop CECA and Fairbrother WJ. (2013) Learning and Confirming with Preclinical Studies: Modeling and Simulation in the Discovery of GDC-0917, an IAP Antagonist. Drug Metab and Disp, 41:2104-2113. DOI: 10.1124/dmd.113.053926.

Wong H, Budha N, West K, Blackwood B, Ware JA, Yu R, Darbonne WC, Gould SE, Steigerwalt R, Erickson R, Hop CECA, LoRusso P, Eckhardt SG, Wagner A, Chan IT, Mamounas M, Flygare J and Fairbrother WJ. (2012) Dogs are More Sensitive to Antagonists of Inhibitor of Apoptosis (IAP) Proteins than Rats and Humans: A Translational Toxicokinetic/Toxicodynamic Analysis. Tox Sci, 130: 205-213. DOI: 10.1093/toxsci/kfs235.

Wong H, Choo EF, Alicke B, Ding X, La H, McNamara E, Theil FP, Tibbitts J, Friedman LS, Hop CECA, and Gould SE. (2012) Anti-tumor activity of targeted and cytotoxic agents in murine subcutaneous tumor models correlates with clinical response.

Clin Cancer Res, 18: 3846-3855. DOI: 10.1158/1078-0432.CCR-12-0738.

Wong H, Vernillet L, Peterson A, Ware JA, Lee L, Martini J-F, Yu P, Li C, Del Rosario G, Choo EF, Hoeflich KP, Shi Y, Aftab BT, Aoyama R, Lam ST, Belvin M, and Prescott J. (2012) Bridging the Gap between Preclinical and Clinical Studies Using PK-PD Modeling: An Analysis of GDC-0973, a MEK Inhibitor. Clin Cancer Res, 18: 3090-3099. DOI: 10.1158/1078-0432.CCR-120445.

Wong H, Alicke B, West K, Pacheco P, La H, Januario T, Yauch RL, de Sauvage FJ and Gould SE. (2011) Pharmacokinetic/ Pharmacodynamic Analysis of Vismodegib in Preclinical Models of Mutational and Ligand-Dependent Hedgehog Pathway Activation. Clin Cancer Res, 17: 4682-4692. DOI: 10.1158/1078-0432.CCR-11-0975.

Liu L, Di Paolo J, Barbosa J, Rong H, Reif K and Wong H. (2011) Anti-Arthritis Effect of a Novel Bruton’s Tyrosine Kinase Inhibitor in Rat Collagen-Induced Arthritis and Mechanism-Based Pharmacokinetic/Pharmacodynamic Modeling: Relationships between Inhibition of BTK Phosphorylation and Efficacy. J Pharmacol and Exp Ther, 338: 154-163. DOI: 10.1124/jpet.111.181545.

Salphati L, Wong H, Belvin M, Edgar KA, Prior WW, Sampath D and Wallin JJ. (2010) Pharmacokinetic-Pharmacodynamic Modeling of Tumor Growth Inhibition and Biomarker Modulation by the Novel PI3K Inhibitor 2-(1H-Indazol-4-yl)-6-(4-methanesulfonyl-piperazin-1-ylmethyl)-4-morpholin-4-yl-thieno[3,2-d]pyrimidine (GDC-0941). Drug Metab and Disp, 38: 1436-1442. DOI: 10.1124/dmd.110.032912.

Wong H, Belvin M, Herter S, Hoeflich KP, Murray LJ, Wong L and Choo EF. (2009) Pharmacodynamics of 2-{4-[(1E)-1-(Hydroxyimino)2,3-dihydro-1H-inden-5-yl]-3-(pyridine-4-yl)-1H-pyrazol-1-yl}ethan-1-ol (GDC-0879), a Potent and Selective B-Raf Kinase Inhibitor: Relationships between Systemic Concentrations, pMEK1 Inhibition, and Efficacy. J Pharmacol Exp Ther, 329: 360-367. DOI: 10.1124/jpet.108.148189.

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