Objectives

· Demonstrate how PK/PD models that incorporate biomarkers and surrogate endpoints can be used to accelerate drug development

· Address novel PK/PD modeling approaches involving biomarkers and surrogate endpoints

· Suggest that PK/PD models can assist in evaluating the biological plausibility of biomarkers

· Provide a forum for discussion on application of PK/PD principles to biomarkers and surrogate endpoints

Agenda

Moderators: Carl C. Peck, M.D., Georgetown University Medical Center

Stephen C. Piscitelli, Pharm.D., National Institutes of Health Clinical Center

Introduction

Stephen C. Piscitelli, Pharm.D.

Case Studies

Modeling Hepatitis C Viral Dynamics and Interferon Treatment

Alan S. Perelson, Ph.D., Los Alamos National Laboratory

Pharmacokinetic/Pharmacodynamic Modeling and Simulation of Docetaxel Safety Endpoints

René Bruno, Ph.D., Rhône-Poulenc Rorer Rercherche-Development, France

Monoamine Oxidase Type B Case Study

Jerry M. Collins, Ph.D., Center for Drug Evaluation and Research, U.S. Food and Drug Administration

Pharmacokinetic/Pharmacodynamic Population Model Linking Cortisol Production With Fluticasone Concentration

Elena V. Mishina, Ph.D., Center for Drug Evaluation and Research, U.S. Food and Drug Administration

Panel Discussion With Audience Participation

Moderator: Carl C. Peck, M.D.

Panelists: Arthur J. Atkinson, Jr., M.D., National Institutes of Health Clinical Center

Lawrence J. Lesko, Ph.D., Center for Drug Evaluation and Research, U.S. Food and Drug Administration

Paul Rolan, M.D., University of Manchester and Medeval Co., United Kingdom

Merrill Egorin, M.D., University of Pittsburgh Cancer Institute

Stephen A. Williams, M.D., Ph.D., Pfizer, Inc.

Concluding Remarks and Formulation of Statement to Consensus Panel

ABSTRACTS

Accelerating Drug Development Using the U.S. Food and Drug Administration’s “Fast Track” and “Single Clinical Trial Effectiveness Standard” Procedures: Roles of Biomarker and Surrogate Endpoint Data That Are Analyzed Using Pharmacokinetic and Pharmacodynamic Methods

Carl C. Peck, M.D.

Enactment of the U.S. Food and Drug Administration (FDA) Modernization Act of 1997 (FDAMA) is the most profound regulatory development in three decades. In particular, the “fast track” provision and statutory recognition of the single clinical trial effectiveness standard provide powerful pathways for acceleration of drug development and regulatory approval of new drugs. The fast track provision of FDAMA legislatively confirms the Accelerated Approval Regulation within the Food and Drug Act and provides for FDA to facilitate development and expedite review of new drugs that address unmet medical needs. Approval of new fast track drugs may result from showing the efficacy of using a surrogate endpoint but is subject to postapproval validation of the surrogate endpoint or confirmation of clinical benefit. Linking (modeling) drug doses and concentrations (pharmacokinetics) with surrogate endpoint measurements (pharmacodyamics) can be used to strengthen the evidentiary basis supporting regulatory approval. The single clinical trial effectiveness standard described in FDAMA (and accompanying committee reports reflecting congressional intent) provides: “The Secretary [may] determine, based on relevant science” that substantial evidence of effectiveness may consist of “one adequate and well controlled clinical investigation and confirmatory evidence (obtained prior to or after such investigation),” where “confirmatory evidence” comprises “scientifically sound data from any investigation in the NDA [New Drug Application] that provides substantiation as to the safety and effectiveness of the new drug.” Furthermore, “confirmatory evidence” may “consist of earlier clinical trials, pharmacokinetic data, or other appropriate scientific studies.” Additional regulatory guidance concerning circumstances in which a single effectiveness trial, including a pharmacokinetic, pharmacodynamic, or clinical endpoint trial, may be sufficient for regulatory approval can be found in a recently published FDA guidance on evidence of effectiveness (FDA Guidance for Industry 1998). Although these two provisions of FDAMA codify regulatory practices already utilized by FDA in a limited fashion through recent regulations or discretionary practices, their statutory status provides a powerful incentive for expanded applications. For those instances in which pharmacokinetic and pharmacodynamic analyses of biomarker or surrogate endpoint data are applicable, advanced pharmacometric methods already exist (e.g., clinical pharmacology and population pharmacokinetic techniques, modeling and simulations of clinical trials, and pharmacogenetic techniques). Nevertheless, widespread application of these provisions will be possible only after several barriers are overcome. These barriers include (1) broad understanding and acceptance of the sometimes complex quantitative concepts and methods utilizing kinetic techniques, (2) limited numbers of scientists who are trained to use such methods, and (3) a paucity of validated surrogate endpoints and routine methods for advancing biomarkers to surrogate endpoint status.

Key References

FDA Guidance for Industry. “Providing Clinical Evidence of Effectiveness for Human Drugs and Biological Products”. May 1998. FDA Web page: www.fda.gov

FDA Modernization Act of 1997. FDA Web page: www.fda.gov

Modeling Hepatitis C Viral Dynamics and Interferon Treatment

Alan S. Perelson, Ph.D.

To better understand the dynamics of the hepatitis C virus (HCV) and the antiviral effect of interferon alpha‑2b (IFN‑alpha), the viral decline in 23 patients during therapy was analyzed with a mathematical model. The analysis suggests that the major initial effect of IFN‑alpha is to block the production or release of virions from infected cells in a dose‑dependent manner. The analysis also allowed the estimation of the virion clearance rate from serum and the half‑life of infected cells. Fast‑infected cell death rates, deduced over the first 14 days of treatment, were predictive of the virus being undetectable by polymerase chain reaction after 3 months of therapy.

Key References

Neumann AU, Lam NP, Dahari H, Gretch DR, Wiley TE, Layden TJ, Perelson AS. Hepatitis C viral dynamics in vivo and the antiviral efficacy of interferon‑alpha therapy. Science 1998;282:103‑107.

Pharmacokinetic/Pharmacodynamic Modeling and Simulation of

Docetaxel Safety Endpoints

René Bruno, Ph.D., and C. Veyrat‑Follet, Ph.D.

Population pharmacokinetic/pharmacodynamic (PK/PD) was prospectively integrated into the clinical development of docetaxel to assess the PK profile in a large population of patients and investigate systemic exposure as a prognostic factor for clinical outcome. PK was studied at first course in 24 phase II studies of docetaxel monotherapy in various tumor types (640 patients) using 4 randomized, sparse‑sampling schedules (full‑screen approach). Three covariates were found to be important for docetaxel CL (NONMEM analysis), including liver function: 27‑percent reduction of CL in patients with concomitant elevations of transaminases (T) > 1.5 x ULN and alkaline phosphatase (AP) > 2.5 x ULN. In addition, CL was an independent predictor of the odds of grade 4 and febrile neutropenia (logistic regression). A safety analysis conducted in a larger population confirmed that these patients had a very poor safety profile compared with others. The safety profile of other patients was similar to that typically observed for this class of drugs. The identification of this subpopulation of patients at risk was fully accounted for to address safety concerns raised during the review of the dossier. The impact of these findings will be explored using the simulation of clinical trial approach.

Key References

Bruno R, Hille D, Riva A, Vivier N, ten Bokkel Huinnink WW, van Oosterom AT, Kaye SB, Verweij J, Fossella FV, Valero V, Rigas JR, Seidman AD, Chevalier B, Fumoleau P, Burris HA, Ravdin PM, Sheiner LB. Population pharmacokinetics/pharmacodynamics of docetaxel in phase II studies in patients with cancer. J Clin Oncol 1998;16:187‑196.

Bruno R, Vivier N, Vergniol JC, De Phillips SL, Montay G, Sheiner LB. A population pharmacokinetic model for docetaxel (Taxotere): Model building and validation. J Pharmacokinet Biopharm 1996;24:153‑172.

Veyrat‑Follet C, Bruno R, Montay G, Rhodes GR. Application of clinical trial simulation in exploring the safety profile of docetaxel (Taxotere) in cancer patients. Clin Pharmacol Ther 1999;65:198 (abstract).

Monoamine Oxidase Type B Case Study

Jerry M. Collins, Ph.D.

The pharmacokinetic/pharmacodynamic arena poses a series of questions about new drugs in development. These same questions also apply to the customized management of individual patients: Does the drug work at its target? What is dose‑response at the target? What is the duration of drug action? In most cases, obtaining answers to these questions is slow, resource intensive, and generally problematic. Biomarkers (e.g., external imaging of radiolabeled exogenous compounds) offer an alternative approach to rapid answers. Fowler and colleagues at the Brookhaven National Laboratory have elegantly demonstrated the use of positron emission tomography in this context. They applied 11C‑labeled deprenyl as a phenotypic probe for monoamine oxidase, type B (MAO‑B). This probe is covalently bound to the enzyme. After a tracer dose is given, the patterns of radiodistribution that are imaged externally correspond to variations in enzymatic activity. For lazabemide, an investigational agent for inhibiting MAO‑B, the initial human studies answered all three questions by demonstrating that (1) the enzyme was inhibited in situ, (2) there was a clear dose‑response curve for inhibition, and (3) the frequency of dosing to maintain inhibition would be either once or twice a day. This type of study is far more common in neuropharmacology than other therapeutic areas. The development of suitable probes for use as exogenous biomarkers in other disease types could stimulate many more relevant PK/PD studies, with consequent benefits in drug development and individualized patient treatment.

Key References

Fowler JS, Volkow ND, Logan J, Schyler DJ, MacGregor RR, Wang GJ, Wolf AP, Pappas N, Alexoff D, Shea C, et al. Monoamine oxidase B (MAO B) inhibitor therapy in Parkinson's disease: The degree and reversibility of human brain MAO B inhibition by Ro 19 6327. Neurology 1993;43:1984‑1992.

Fowler JS, Volkow ND, Logan J, Wang GJ, MacGregor RR, Schyler D, Wolf AP, Pappas N, Alexoff D, Shea C, et al. Slow recovery of human brain MAO B after L‑deprenyl (Selegeline) withdrawal. Synapse 1994;18:86‑93.

Pharmacokinetic/Pharmacodynamic Population Model Linking Cortisol Production With Fluticasone Concentration

Elena V. Mishina, Ph.D.

Fluticasone propionate (FP) is a novel corticosteroid with potent anti‑inflammatory activity (receptor affinity is 18 times higher than for dexamethasone) and low systemic bioavailability. It has shown a significant therapeutic efficacy in the treatment of asthma. The main adverse effect—suppression of adrenal function—has been assessed by the measurement of plasma cortisol level. For the prediction of pharmacodynamic effect based on the dose of drug, modeling is applied. Modeling of cortisol (CT) suppression has additional complexities due to nonstationary secretion of cortisol (circadian rhythm) and downregulation effect. Previous models describe cortisol secretion as a cosine function or as sum of linear functions. We applied a superposition of two Bateman functions to characterize the pulsatile circadian rhythm of cortisol in plasma. CT and FP levels were measured over 24 hours in 12 healthy subjects after inhalation of 0.5, 1, and 2 mg of FP and placebo. NONMEM was used to model pharmacokinesis/

pharmacodynamics. PK of FP was best characterized by a two‑compartment model with first‑order absorption (CL/F 158.8 L/hr, V/F 2800 L, Ka 7.1 hr‑1). CT plasma levels were described by two pulsatile Bateman functions over 24 hours. The effect of FP was modeled using the Hill equation with inhibition of cortisol secretion. Cortisol peaks were estimated to occur in pulses at 3:30 a.m. (large sharp) and 1:30 p.m. (small shallow) with an amplitude ratio of 7:1. The IC50 of FP was 350 pcg/mL, which is consistent with predicted values for receptor affinity. Cortisol suppression (adverse effect) will have negligible values at the therapeutic doses (50 mg).