Pharmacokinetic/Pharmacodynamic Systems Analysis
David Z. D'Argenio, Ph.D.
Project Leader
The use of mathematical modeling and associated computational methods is central to the study of the absorption, distribution and elimination (pharmacokinetics) of therapeutic drugs and to understanding how drugs produce their therapeutic and toxic effects (pharmacodynamics). From its inception, the field of pharmacokinetics and pharmacodynamics has incorporated methods of mathematical modeling, simulation and computation in an effort to understand and quantify the processes of uptake, disposition and action of therapeutic drugs. These methods for pharmacokinetic/pharmacodynamic systems analysis impact all aspects of drug development including in vitro, animal and human testing, as well as more routine drug therapy. Modeling methodologies developed for studying pharmacokinetic/ pharmacodynamic processes confront many challenges related, in part, to the severe restrictions on the number and type of measurements that are available from laboratory experiments and clinical trials, as well as the variability in the experiments and the uncertainty associated with the processes themselves. This Core Project focuses on three significant problems in pharmacokinetic/pharmacodynamic modeling and therapeutic drug development, and extends our past efforts to develop modeling methodologies tailored for use in the experimental study of sparse data systems.
The first specific aim involves the development of methods of pharmacokinetic/pharmacodynamic modeling for genetically polymorphic populations. Our goal is to investigate and develop robust methods for population PK/PD analysis that can identify subpopulations of patients with distinct pharmacokinetic and pharmacodynamic phenotypes. Subsequent determination of the genetic basis for identified PK/PD phenotypes will make it possible to select particular medications and dose regimens based on the genetic ability of individual patients to metabolize, eliminate, distribute and respond to specific drugs. We propose to investigate both parametric maximum likelihood and Bayesian methods for population analysis that incorporate a parameter mixture model. Our Collaborative Projects will provide numerous clinical applications for the methods developed, in such areas as pediatric antiviral therapy (Collaborative Project with Dr. John Rodman), anticancer drug development (Collaborative Project with Dr. Egorin), and pharmacogenetics of anticancer agents (Collaborative Project with Dr. Mark Ratain).
The second aim focuses on dose regimen design for molecularly targeted therapies. Our objective is to develop a general framework, together with computationally practical methods, for stochastic control of uncertain PK/PD systems in targeting molecular biomarkers. The methods and tools to be developed have application to clinical drug trials designed to identify biologically effective doses based on a drug’s molecular targets. In addressing this challenging problem, we will build upon our experience with stochastic control for pharmacokinetic end-points and with computational methods for Bayesian inference involving PK/PD models. The development of our methods will be guided by practical problems associated with antiretroviral drug therapy (Collaborative Project with Dr. Courtney Fletcher), pediatric anticancer drug development (Collaborative Project with Dr. John Rodman), and problems with bacterial resistance in antimicrobial drug therapy.
In the third specific aim we will develop approaches for physiological PK and molecular PD modeling in drug development. Our focus is on methods for physiologically-based pharmacokinetic modeling as well as mechanistically-based molecular modeling of drug action. The goal is to translate the knowledge gained in preclincal drug studies, to predict better both the pharmacokinetics and the time course of cellular drug action in humans. We propose to investigate physiological pharmacokinetic modeling approaches that formally incorporate available prior information in organ modeling, as well as tissue models based on transit time density functions. In addition we will explore models of cellular signaling pathways based on their response to drug action. Data from preclinical studies of anticancer drugs conducted in Collaborative Project (Dr. Egorin) and Collaborative Project (Dr. Ratain) will provide the opportunity to apply and evaluate the approaches developed.
Central to our overall research effort in pharmacokinetic/pharmacodynamic systems analysis are the collaborations referenced above, as well as our software development project ADAPT, together with the associated training Short Courses and dissemination Workshops. Taken together this ensemble of research, collaboration, software, training and dissemination activities are designed to advance biomedical research in therapeutic drug development through the development, implementation and evaluation of novel modeling methodologies and their distribution to the broader community of biomedical researchers.
Selected
Publications
Wang, X., A. Schumitzky and D.Z. D’Argenio. Nonlinear random effects mixture models: Maximum likelihood estimation via the EM algorithm. Computational Statistics and Data Analysis 51:6614-6623, 2007. [PDF – 635 KB]
Beumer, J.H., E. Joseph, M.H. Egorin, R.S. Parker, D.Z. D’Argenio, J.M. Covey and J.L. Eiseman. A mass balance and disposition study of the DNA-methyltransferase inhibitor zebularine (NSC 309132) and three of its metabolites in mice.Clinical Cancer Research 12(19):5826-5833, 2006. [PDF – 324 KB]
Zhou, Z., J.H. Rodman, P.M. Flynn, B.L. Robbins, C.K. Wilcox and D.Z. D’Argenio. Model for intracellular Lamivudine metabolism in peripheral blood mononuclear cells ex vivo and in human immunodeficiency virus type 1-infected adolescents. Antimicrobial Agents and Chemotherapy 50(8):2686-2694, 2006. [PDF – 327 KB]
Holleran J.L., Parise, R.A., Joseph, E., Eiseman, J.L., Covey, J.M., Glaze, E., Lyubimov, A.V., D'Argenio, D.Z. and M.J. Egorin. Plasma Pharmacokinetics, Oral Bioavailability, and Interspecies Scaling of the DNA Methyltransferase Inhibitor, Zebularine. Clinical Cancer Research 11(10):3862-3868, 2005. [PDF - 263 KB]
D'Argenio, D.Z. (ed.). Advanced Methods of Pharmacokinetic and Pharmacodynamic Systems Analysis Volume 3, Kluwer Academic Publishers, Boston, 2004.
Xu, L., J.L. Eiseman, M.J. Egorin, and D.Z. D'Argenio. Physiologically-Based Pharmacokinetic and Molecular Pharmacodynamics of 17-(allyalamino)-17-demethoxygeldanamycin and its Active Metabolite in Tumor-Bearing Mice. Journal of Pharmacokinetics and Pharmacodynamics 30:185-219, 2003. [PDF - 437 KB]
Bading, J.R., P.B. Yoo, J.D. Fissekis, M.M. Alauddin, D.Z. D'Argenio, D.Z. and P.S. Conti. Kinetic Modeling of 5-Fluorouracil Anabolism in Colorectal Adenocarcinoma: A Positron Emission Tomography Study in Rats. Cancer Research 63:3667-3674, 2003. [PDF - 137 KB]
Zamboni, W.C., D.Z. D'Argenio, C.F. Stewart, T. MacVittie, B.J. Delauter, A.M. Farese, D.M. Potter, N.M. Kubat, D. Tubergen & M.J. Egorin. Pharmacodynamic model of topotecan-induced time course of neutropenia. Clinical Cancer Research, 7:2301-2308, 2001. [PDF - 92.6 KB]
Drusano, G.L., D.Z. D'Argenio, S.L. Preston, C. Barone, W. Symonds, S. LaFon, M. Rogers, W. Prince, A. Bye and J.A. Bilello. Use of Drug Effect Interaction Modeling with Monte Carlo Simulation to Examine the Impact of Dosing Interval on the Projected Antiviral Activity of the Combination of Abacavir and Amprenavir. Antimicrobial Agents in Chemotherapy, 44:1655-1659, 2000.
Snyder, S., D.Z. D'Argenio, O. Weislow, J.A. Bilello, and G.L. Drusano. The Triple Combination of Indinavir-Zidovudine-Lamivudine is Highly Synergistic. Antimicrobial Agents in Chemotherapy, 44:1051-1058, 2000.
Drusano, G.L., D.Z. D'Argenio and W. Symonds, W. Symonds, P. A. Bilello, J. McDowell, B. Sadler, A. Bye, and J. A. Bilello. Nucleoside analog 1592489 and human immunodeficiency virus protease inhibitor 141W94 are synergistic in vitro. Antimicrobial Agents and Chemotherapy, 42:2153-2159, 1998.
D'Argenio, D.Z. & A. Schumitzky. ADAPT II User's Guide: Pharmacokinetic/Pharmacodynamic Systems Analysis Software. Biomedical Simulations Resource, Los Angeles, 1997.
D'Argenio, D.Z. and K-S. Park. Uncertain Pharmacokinetic/Pharmacodynamic Systems: Design, Estimation and Control. Control Eng. Practice, 5:1707-1716, 1997.
D'Argenio, D.Z. (ed.). Advanced Methods of Pharmacokinetic and Pharmacodynamic Systems Analysis Vol II, Plenum Press, New York, 1995.
D'Argenio, D.Z. and J.H. Rodman. Targeting the Systemic Exposure of Teniposide in the Population and the Individual Using a Stochastic Therapeutic Objective. Journal of Pharmacokinetics and Biopharmaceutics, 21: 223-251, 1993.
Kayne, L.H., D.Z. D'Argenio, J.H. Meyer, S.H. Ming, N. Jamgotchian and D.B.N. Lee. Analysis of Segmental Phosphate Absorption in Intact Rats: A Compartmental Analysis Approach. Journal of Clinical Investigation, 91:915-922, 1993.
D'Argenio, D.Z. (ed.) Advanced Methods of Pharmacokinetic and Pharmacodynamic Systems Analysis, Vol. I. Plenum Press, New York, 1991.
D'Argenio, D.Z. Incorporating prior parameter uncertainty in the design of sampling schedules for pharmacokinetic parameter estimation experiments. Math. Biosc., 99:105-118, 1990.
Shadmehr, R. and D.Z. D'Argenio. A Neural Network for Nonlinear Bayesian Estimation in Drug Therapy. Neural Computation, 2:218-227, 1990.
Maneval, D., D.Z. D'Argenio and W. Wolf. A Kinetic Model of Tc-99m DMSA in the Rat. European Journal of Nuclear Medicine, 16:29-34, 1990.
D'Argenio, D.Z. and D. Katz. Application of Stochastic Control Methods to the Problem of Individualizing Intravenous Theophylline Therapy. Biomedical Measurement Informatics and Control, 2:115-122, 1988.
D'Argenio, D.Z., A. Schumitzky, and W. Wolf. Simulation of Linear Compartment Models with Application to Nuclear Medicine Kinetic Modeling. Computer Methods and Programs in Biomedicine, 27:47-54, 1988.
Brechner, R.R., D.Z. D'Argenio, R. Dehalan, and W. Wolf. Noninvasive Estimation of Bound and Mobile Platinum Compounds in the Kidney using a Radiopharmacokinetic Model. Journal of Pharmacological Sciences, 75:873-877, 1986.
Katz, D. and D.Z. D'Argenio. Implementation and Evaluation of Control Strategies for Individualizing Dosage Regimens, with Application to the Aminoglycoside Antibiotics. Journal of Pharmacokinetics and Biopharmaceutics, 14:523-537, 1986.
Katz, D. and D.Z. D'Argenio. Discrete Approximation of Multivariate Densities with Application to Bayesian Estimation. Computational Statistics & Data Analysis, 2:27-36, 1984.
D'Argenio, D.Z. and K. Khakmahd. Adaptive Control of Theophylline Therapy: Importance of Blood Sampling Times. Journal of Pharmacokinetics and Biopharmaceutics, 11:547-559, 1983.
D'Argenio, D.Z. and D. Katz, D. Sampling Strategies for Noncompartmental Estimation of Mean Residence Time. Journal of Pharmacokinetics and Biopharmaceutics, 11:435-446, 1983.
Katz, D. and D.Z. D'Argenio. Experimental Design for Estimating Integrals by Numerical Quadrature with Applications to Pharmacokinetic Systems. Biometrics, 39:621-628, 1983.
D'Argenio, D.Z. Optimal Sampling Times for Pharmacokinetic Experiments. Journal of Pharmacokinetics and Biopharmaceutics, 9:739-756, 1981.
D'Argenio, D.Z. and A. Schumitzky. A Program Package for Simulation and Parameter Estimation in Pharmacokinetics. Computer Programs in Biomedicine, 9:115-134, 1979.
