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 the study of the absorption, distribution and elimination of therapeutic drugs, understanding how drugs produce their effects and quantifying how genomic and other factors influence an individual’s therapeutic response to treatment. Over the past 30 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 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 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 mode effective treatments. In this proposal we will focus on the following problems:
- While 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 statistically flawed 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 continuous and categorical covariates without the limitations of existing methods.
- 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.
- 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.
- 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 A1 – 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. Gumbo).
W. Chen, D.Z. D’Argenio, A. Sipos, K-J Kim, and E.D. Crandall. Biokinetic Modeling of Polystyrene Nanoparticle Interactions with Primary Rat Lung Alveolar Epithelial Cell Monolayers: Uptake from Apical Fluid, Intracellular Processing and Egress from Cells. American Journal of Physiology-Regulatory, Integrative and Comparative Physiology. 330:R36-R43, 2021. doi: 10.1152/ajpregu.00184.2020.
A G. Drusano, S. Kim, M. Almoslem, S. Schmidt, D.Z. D’Argenio, J. Myrick, B. Duncanson, J. Nole, D. Brown, C. Peloquin, M. Neely, W. Yamada, and A. Louie. The Funnel: A screening technique for identifying optimal two-drug combination chemotherapy regimens. Antimicrobial Agents and Chemotherapy. 65(2):e02172-20, 2021. doi: 10.1128/AAC.02172-20.
W. Chen, B. Boras, T. Sung, W. Hu, M.E Spilker, and D.Z. D’Argenio. Predicting chemotherapy-induced neutropenia and granulocyte-colony stimulating factor response using model-based in vitro to clinical translation. The AAPS Journal. 22:143, 2020. doi: 10.1208/s12248-020-00529-x.
S. Jaisue, C. Pongsakul, D.Z. D’Argenio and P. Sermsappasuk. Population pharmacokinetic modeling of vancomycin in Thai patients with heterogenous and unstable renal function. Therapeutic Drug Monitoring. 42:856-865, 2020. doi: 10.1097/FTD.0000000000000801.
C. Wang, C. Winstein, D.Z. D’Argenio and N. Schweighofer. The efficiency, efficacy, and retention of motor training in chronic stroke neurorehabilitation and neural repair. Neurorehabilitation & Neural Repair. 34:881-890, 2020. doi: 10.1177/1545968320948609.
S. Hu and D.Z. D’Argenio. Predicting monoclonal antibody pharmacokinetics following subcutaneous administration via whole-body physiologically-based modeling. Journal of Pharmacokinetics and Pharmacodynamics. 47:385-409, 2020. doi:10.1007/s10928-020-09691-3.
W. Chen, B. Boras, T. Sung, Y. Yu, J. Zheng, D. Wang, W. Hu, M.E. Spilker, D.Z. D’Argenio. A Physiological Model of Granulopoiesis to Predict Clinical Drug Induced Neutropenia from in vitro Bone Marrow Studies. Journal of Pharmacokinetics and Pharmacodynamics. 27:163-182, 2020. doi: 10.1007/s10928-020-09680-6.
D.Z. D’Argenio and K-S Bae Analytical solution of linear multi-compartment models with non-zero initial condition and its implementation. Translational and Clinical Pharmacology. 27:43-51, 2019. doi: 10.12793/tcp.2019.27.2.43.
Park, A.J., Wang, J., Jayne, J., Fukushima, L., Rao, A.P., D’Argenio, D.Z, Beringer, P.M. Pharmacokinetics of tedizolid in plasma and sputum of adults with cystic fibrosis. Antimicrobial Agents and Chemotherapy. 62, e00550-18, 2018. doi: 10.1128/AAC.00550-18.
T.J. Bensman, J. Wang, J. Jayne, L. Fukushima, A.P. Rao, D.Z. D’Argenio, P.M. Beringer. Pharmacokinetic-Pharmacodynamic Target Attainment Analyses to Determine Optimal Dosing of Ceftazidime-Avibactam for the Treatment of Acute Pulmonary Exacerbations in Cystic Fibrosis. Antimicrobial Agents and Chemotherapy. 61, e00988-17, 2017. doi: 10.1128/AAC.00988-17
Y. Zhang, Hu, K, Beumer, J, Bakkenist, C.J. and D.Z. D’Argenio. RAD-ADAPT: Software for modelling clonogenic assay data in radiation biology. DNA Repair. 52:24-39, 2017. PubMed: 28254357 doi: 10.1016/j.dnarep.2017.02.004
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. 72:1129-1136, 2017. doi:10.1093/jac/dkw553.