Core Project #1
Pharmacokinetic/Pharmacodynamic Modeling and Systems Analysis
David Z. D’Argenio, Ph.D.
The use of systems modeling and analysis is central to studying the absorption, distribution, metabolism and elimination of therapeutic agents, understanding how these compounds produce their effects, and for quantifying how genomic and other factors influence an individual’s therapeutic response to treatment. Over the past 40 years, systems modeling and simulation has evolved to become a critical component in efforts to understand and quantify the processes of uptake, disposition and action of therapeutic drugs. Methods for pharmacokinetic/ pharmacodynamic systems modeling and analysis impact all aspects of drug development including in vitro, animal and human testing, as well as drug therapy. Systems modeling methodologies developed for studying pharmacokinetic/pharmacodynamic processes confront many challenges related in part to the 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 complexity associated of the processes themselves. The overall goal of Core Project #1 is to develop, evaluate, apply and disseminate systems modeling and analysis methods that will improve the study of drug action in all phases of the drug development, leading to more effective treatments. This project focuses on the following problems:
- Although population PK/PD modeling is now used routinely in drug development, it is widely recognized that the existing methods used to identify and quantify the role of covariates in characterizing interindvidual variability are approximate and inefficient. In this aim, we will develop statistically robust and computationally practicable methods for Covariate Imbedding in Population PK/PD Modeling to allow the simultaneous modeling of covariates without the limitations of existing methods.
- System pharmacology models may serve to bridge both the discovery-development and the preclinical-clinical translational barriers in drug development. In this aim, we will construct a framework for developing Systems Pharmacology Pathway Models in Drug Discovery and Development that involves coupling existing systems biology network models to pharmacodynamic biomarker data obtained during preclinical drug development. We will develop this approach using examples involving acute myeloid leukemia and colorectal cancer, involving pathways for apoptosis, apoptosis evasion as well as the cell cycle progression.
- Assessing efficacy in clinical trials of therapies for treating chronic progressive diseases is challenging, in part because the response to extended treatment is often confounded by the disease progression. We will address this problem by developing and applying a framework for Physiologically-based Disease Progression Modeling. Our proposed approach uses systems physiology models of organ systems, which incorporate physiologically meaningful properties that characterize the disease process and thereby providing a rational basis for reflecting disease progression.
- 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 Modeling and Analysis, thereby enhancing the basic and clinical research efforts of other investigators – the raison d’être of a Research Resource. Toward this end, new capabilities and program enhancements will be added to ADAPT based on the work in Core Project #1.
Current Collaborative Research Projects provide applications for the methods and tools developed in Core Project #1, in such areas as metabolite diseases (Collaborative Project A4 – Dr. Pacini), oncology (Collaborative Project D1 – Dr. Gallo, Collaborative Project D2 – Drs. Beumer and Eiseman), and infectious diseases (Collaborative Project D3 – Dr. Kiser, Collaborative Project D4 – Dr. Acosta).
Song, G., Pacini, G., Ahrens, B. and D.Z. D’Argenio. Glucagon Increases Insulin Levels by Stimulating Insulin Secretion Without Effect on Insulin Clearance in Mice. Peptide (in press).
Dolton, M.J and D.Z D’Argenio. Population-based meta analysis of roxithromycin pharmacokinetics: Dosing implications of saturable absorption and protein binding. Journal of Antimicrobial Chemotherapy. (in press)
K.M. Gallegos, G.L. Drusano, D.Z. D’Argenio and A.N. Brown. Chikungunya Virus: In Vitro Response to Combination Therapy with Ribavirin and Interferon-α2a. Journal of Infectious Disease. 214:1192-1197, 2016. DOI 10.1093/infdis/jiw358.
Y. Zhang and D.Z. D’Argenio. Feedback Control Indirect Response Models. Journal of Pharmacokinetics and Pharmacodynamics. 43:343-358, 2016. DOI 10.1007/s10928-016-9479-8
L.S. Wu, L.C. Jimmerson, C.E. MacBrayne, J.J. Kiser and D.Z. D’Argenio. Modeling Ribavirin-induced Anemia in Chronic Hepatitis C Virus Patients. CPT Pharmacometrics Syst. Pharmacol. 5: 65–73, 2016. doi:10.1002/psp4.12058.
Weiss, M., Pacini, G., Tura, A., Kautzky Willer, A. and D. Z. D’Argenio. Human insulin dynamics in women: a physiologically based model. Am. J. Physiol. Regul. Integr. Comp. Physiol. 310:R268-R274, 2016. DOI: 10.1152/ajpregu.00113.2015.
Wu, L.S., J.E. Rower, J.R. Burton, Jr., P.L. Anderson, K.P. Hammond, F. Baouchi-Mokrane, G.T. Everson, T.J. Urban, D.Z. D’Argenio and J.J. Kiser. Population pharmacokinetic modeling of plasma and intracellular ribavirin concentrations in patients with chronic hepatitis C virus infection. Antimicrobial Agents and Chemotherapy 59(4):2179-2188, 2015. [PMC4356818]
Y. Zhang, C-P. Hsu, J-F. Lu, Y-N. Sun and D.Z. D’Argenio. FLT3 and CDK4/6 inhibitors: signaling mechanisms and tumor burden in subcutaneous and orthotopic mouse models of acute myeloid leukemia. Journal of Pharmacokinetics and Pharmacodynamics 41:675-692, 2014. [PMC4226810]
Wang, K., D.Z. D’Argenio, E.P. Acosta, A.N. Sheth, C. Delille, J.L. Lennox, and I. Ofotokun. Integrated population pharmacokinetic/viral dynamic modeling of lopinavir/ritonavir in HIV-1 treatment naïve patients. Clinical Pharmacokinetics 53:361-371, 2014. [PMC3962720]
Zhou A., G. Pacini, B. Ahrén, and D.Z. D’Argenio. Glucagon clearance is regulated by nutritional state: Evidence from experimental studies in mice. Diabetologia 57(4):801-808, 2014. [PMC3947415]
Zhu, R., Y. Zheng, W.S. Putnam, J. Visich, M.D. Eisner, J.G. Matthews, K.E. Rosen and D.Z. D’Argenio. Population-based efficacy modeling of omalizumab in patients with severe allergic asthma inadequately controlled with standard therapy. The AAPS Journal 53(4):361-371, 2014. [PMC3675747]
Marmarelis, V.Z., D.C. Shin, Y. Zhang, A. Kautzky-Willer, G. Pacini and D.Z. D’Argenio. Analysis of intravenous glucose tolerance test data using parametric and nonparametric modeling: application to a population at risk for diabetes. Journal of Diabetes Science & Technology 7(4):952-962, 2013. [PMC3879759]
Zhu, R., J.J. Kiser, H.I. Seifart, C.J. Werely, C.D. Mitchell, D.Z. D’argenio and C.V. Fletcher. The Pharmacogenetics of NAT2 enzyme maturation in perinatally HIV exposed infants receiving isoniazid. The Journal of Clinical Pharmacology 52(4):511-519, 2012. [PMC3182303]
Kay, B.P., C-P. Hsu, J-F. Lu, Y-N. Sun, S. Bai, Y. Xin, and D.Z. D’Argenio. Intracellular-signaling tumor-regression modeling of the pro-apoptotic receptor agonists dulanermin and conatumumab. Journal of Pharmacokinetics and Pharmacodynamics 39: 577-590, 2012. [PMC3487388]
Wu, C.C., D.Z., D’Argenio, S. Asgharzadeh, and T.J. Triche. TARGETgene: A tool for identification of potential therapeutic targets in cancer. PLoS One 7:e43305, 2012. [PMC3432038]
Kiser, J., R. Zhu, D.Z. D’Argenio, M. Cotton, R. Bobat, G.D. McSherry, S.A. Madhi, V.J. Carey, H.I. Seifart, C.J. Werely, and C.V. Fletcher. Isoniazid pharmacokinetics, pharmacodynamics and dosing in South African infants. Therapeutic Drug Monitoring 34:446-451, 2012. [PMC3397663]
Beumer, J.H., R.A. Parise, B. Kanterewicz, M. Petkovich, D.Z. D’Argenio, and P.A. Hershberger. A local effect of CYP24 inhibition on lung tumor xenograft exposure to 1,25-dihydroxyvitamin D3 is revealed using a novel LC-MS/MS assay. Steroids 77:477-483, 2012. [PMC3303948]
Beringer, P.M., H. Owens, A. Nguyen, D. Benitez, A. Rao, and D.Z. D’Argenio. Pharmacokinetics of doxycycline in adults with cystic fibrosis. Antimicrobial Agents and Chemotherapy 56:70-74, 2012. [PMC3256044]
Chan, H.M, R. Jian, B. Ahrens, G. Pacini, and D.Z. D’Argenio. Effects of increasing doses of glucagon-like peptide-1 on insulin-releasing phases during intravenous glucose administration in mice.
American Journal of Physiology Regulatory, Integrative and Comparative Physiology 300:R1126-R1133, 2011. [PMC3293513]
Beumer, J.H., J.L. Eisenman, J.A. Gilbert, J.L. Holleran, A.E. Yellow-Duke, D.M. Clausen, D.Z. D’Argenio, M.M. Ames, P.A. Hershberger, R.A. Parise, L. Bai, J.M. Covey, and M.J. Egorin. Plasma Pharmacokinetics and oral bioavailability of the 3,4,5,6-tetrahydrouridine(THU) prodrug, triacetyl-THU (taTHU), in mice. Cancer Chemotherapy and Pharmacology 67(2):421-430, 2011. [PMC2954253]
Lee, S.S., C. Ghosn, Z. Yu, L. Zacharias, H. Kao, C. Lanni, N. Abdelfattah, B. Kuppermann, K.G. Csaky, D.Z. D’Argenio, J.A. Burke, P. Hughes, and M.R. Robinson. Vitreous VEGF clearance is increased following vitrectomy. Ophthalmol Vis Sci. 51:2135-2138, 2010.
Wu, C.C., S. Asgharzadeh, T.J. Triche and D.Z. D’Argenio. Prediction of human functional genetic networks from heterogeneous data using RVM-based ensemble learning. Bioinformatics 26(6):807-813, 2010. [PDF] — [PMC2832827]
Wang, X., A. Schumitzky and D.Z. D’Argenio. Population pharmacokinetic/pharmacodyanamic mixture models via maximum a posteriori estimation. Computational Statistics & Data Analysis 53(12):3907-3915, 2009. [PDF]— [PMC2743512]
Wang, J., M. Weiss and D.Z. D’Argenio. A note on population analysis of dissolution-absorption models using the inverse gaussian function. Jounral of Clinical Pharmacology 48(6):719-725, 2008. [PDF] — [PMC2648518]
Jacob, E., K. Scorsone, S.M. Blaney, D.Z. D’Argenio and S.L. Berg. Synergy of karenitecin and mafosfamide in pediatric leukemia, medulloblastoma, and neuroblastoma cell lines. Pediatric Blood & Cancer 50(4):757-760, 2008. [PDF] — [PMC2975705]
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(3):457-464, 2008. [PDF] — [PMC2677692]
D’Argenio, D.Z., X. Wang and Z. Zhou. Application of bayesian methods for laboratory to clinical translation and for identifying hidden subpopulations. Chinical Journal of Clinical Pharmacology Therapy 12:1114-1121, 2007.
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(12):6614-6623, 2007. [PDF]— [PMC2743159]
Horton, T.M., A. Gannavarapu, S.M. Blaney, D.Z. D’Argenio, S.E. Plon and S.L. Berg. Bortezomib interactions with chemotherapy agents in acute leukemia in vitro.Cancer Chemotherapy and Pharmacology 58(1):13-23, 2006. [PDF]
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]
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] — [PMC1538647]
Holleran, J.L., R.A. Parise, E. Joseph, J.L. Eiseman, J.M. Covey, E. Glaze, A.V. Lyubimov, D.Z. D’Argenio 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]
D’Argenio, D.Z. (ed.). Advanced Methods of Pharmacokinetic and Pharmacodynamic Systems Analysis Volume 3, Kluwer Academic Publishers, Boston, 2004.
D’Argenio, D.Z. and A. Schumitzky. ADAPT II User’s Guide: Pharmacokinetic/Pharmacodynamic Systems Analysis Software. Biomedical Simulations Resource, Los Angeles, 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. (ed.) Advanced Methods of Pharmacokinetic and Pharmacodynamic Systems Analysis, Vol. I. Plenum Press, New York, 1991.