Core Project #1
Pharmacokinetic/Pharmacodynamic
Systems Analysis
David Z. D’Argenio, Ph.D.
Project
Leader
The use of mathematical modeling is central to the study of the absorption, distribution and elimination of therapeutic drugs (pharmacokinetics) and to understanding how drugs produce their effects (pharmacodynamics). From its inception the field of pharmacokinetics and pharmacodynamics has incorporated methods of mathematical modeling, simulation and computation in an effort to better 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 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. The overall goal of Core Project #1 is to develop, evaluate and apply modeling methods that will improve the study of drug action in all phases of the drug development process. In this proposal we will focus on the following specific problems:
• The first aim is directed to learning how genetic factors influence both drug kinetics and dynamics (pharmacogenetics). The specific aim is to develop, evaluate and apply robust methods for Bayesian population PK/PD analysis with parameter mixture models that can identify subpopulations of patients with distinct pharmacokinetic and pharmacodynamic phenotypes and classify study subjects into the identified subpopulations. Subsequent determination of the genetic basis for identified PK/PD phenotypes may 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.
• The second specific aim focuses on developing modeling methods for population PK/PD analysis with covariates (e.g., demographic, physiological, disease status, cellular biomarker, genetic) to better quantify intersubject differences in drug action. In this aim we propose to investigate methods for combined parameter/covariate population analysis using mixture models in which covariates are also treated as random, and to develop ML, MAP and Bayesian solutions. Our proposed approach simultaneously incorporates all measured covariates into the population analysis and does not require an explicit second stage covariate model, thus avoiding the some of the problems associated with commonly used step-wise schemes for covariate modeling.
• The third aim is to develop a feedback autoregulartory framework for PD modeling. We propose to investigate a class of simple autoregulatory feedback control models and combine them with the class of indirect response models, thus developing a PD modeling approach that allows the drug’s effects to be quantified independently of the action of any endogenous autoregulatory mechanisms. Our proposed approach may result in a more accurate estimation of in vivo drug potency when the drug target is itself subject to endogenous regulation.
• An overarching goal of Core Project #1 is to provide advanced modeling and analysis methods to the broader biomedical research community through the ADAPT software for PK/PD systems analysis, thereby enhancing the basic and clinical research efforts of other investigators - the raison d’être of a Research Resource. Toward this end, several new capabilities and program enhancements will be added to ADAPT including: a population analysis program with MAP estimation for Normal parameter models; ML and MAP population analysis with finite mixture models currently under development; combined parameter/covariate population modeling; interoperability with existing cell-signaling pathway model repositories.
Our
Collaborative Projects will provide numerous basic and clinical
applications for the methods and tools developed in Core Project #1, in
such areas as pediatric anticancer therapy (Collaborative Project #1 -
Dr. Relling), anticancer drug development (Collaborative Project #2 -
Dr. Egorin), antiretroviral therapy (Collaborative Project #3 - Dr.
Fletcher) and organ transplant (Collaborative Project #4 - Dr.
Burkhart).
Selected Publications
Wang, J., Weiss, M. and D.Z. D'Argenio. A note on population analysis of dissolution-absorption models using the inverse gaussian function. Jounral of Clinical Pharmacology 48:719-725, 2008. [PDF – 357 KB]
Jacob, E., Scorsone, K., Blaney, S.M., D’Argenio, D.Z., and S.L. Berg. Synergy of karenitecin and mafosfamide in pediatric leukemia, medulloblastoma, and neuroblastoma cell lines. Pediatric Blood & Cancer 50:757-760, 2008. [PDF – 80 KB]
Beumer, J.H., J.L. Eiseman, R.A. Parise, J.A. Florian, E. Joseph, D.Z. D’Argenio, R.S. Parker, B. Kay, J.M. Covey and M. J. Egorin. Plasma pharmacokinetics and oral bioavailability of 3,4,5,6-tetrahydrouridine (THU), a cytidine deaminase inhibitor, in mice. Cancer Chemotherapy and Pharmacology 62:457-464, 2008. [PDF – 401 KB]
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]
Horton, T.M., Gannavarapu, A., Blaney, S.M., D’Argenio, D.Z., Plon, S.E. and S.L. Berg. Bortezomib interactions with chemotherapy agents in acute leukemia in vitro.Cancer Chemotherapy and Pharmacology 58(1):13-23, 2006. [PDF – 382 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.
