The major factors determining drug responses are the input and disposition rates controlling pharmacokinetics, drug distribution to the site of action (biophase), the mechanism of drug action in altering mediator or receptor level, and transduction processes. May improvements in quantitating pharmacologic responses came from our recognition that diverse pharmacodynamic effects can be characterized using a family of four basic (and extended) indirect response models. These (and most) models require analysis using differential equations which usually cannot be fully integrated. This project seeks to characterize and quantify the properties of drugs acting by diverse mechanisms.
Specific aims i nclude development of a compendium with comparison of the array of relevant mechanism-based PK/PD models, further development of indirect response models with a precursor compartment including the occurrence of feedback alterations; evolution of extended indirect lifespan models for application to natural cell responses including effects such as bone marrow stimulation and/or cytotoxicity; and the detailed characterization of non-linear and time- dependent transduction models which may be applied to numerous membrane receptor (e.g., G-protein) mediated responses. Advanced methods of calculus and stimulations will be employed to seek exact or approximate solutions or behaviors for these models to identify how the onset, extent, return, duration, AUC, and steady-stage of responses are controlled, to recovery parameters more easily from experimental data, and to discriminate among diverse models available to describe typical data, and to determine how to optimize drug dosing regimens. These efforts will yield insights, methods, and resources for understanding and quantitating the time course of drug responses as related to major mechanisms of action.
|Nguyen, Ly M; Singh, Aman P; Wiczling, Pawel et al. (2018) Dynamics of Erythropoietic Biomarkers in Response to Treatment With Erythropoietin in Belgrade Rats. Front Pharmacol 9:316|
|Ramakrishnan, Vidya; Mager, Donald E (2018) Network-Based Analysis of Bortezomib Pharmacodynamic Heterogeneity in Multiple Myeloma Cells. J Pharmacol Exp Ther 365:734-751|
|Nanavati, Charvi; Mager, Donald E (2017) Sequential Exposure of Bortezomib and Vorinostat is Synergistic in Multiple Myeloma Cells. Pharm Res 34:668-679|
|Miao, Xin; Koch, Gilbert; Straubinger, Robert M et al. (2016) Pharmacodynamic modeling of combined chemotherapeutic effects predicts synergistic activity of gemcitabine and trabectedin in pancreatic cancer cells. Cancer Chemother Pharmacol 77:181-93|
|Nanavati, Charvi; Mager, Donald E (2016) Calculated Log D Is Inversely Correlated With Select Camptothecin Clearance and Efficacy in Colon Cancer Xenografts. J Pharm Sci 105:1561-6|
|Chudasama, Vaishali L; Ovacik, Meric A; Abernethy, Darrell R et al. (2015) Logic-Based and Cellular Pharmacodynamic Modeling of Bortezomib Responses in U266 Human Myeloma Cells. J Pharmacol Exp Ther 354:448-58|
|Singh, Aman P; Krzyzanski, Wojciech; Martin, Steven W et al. (2015) Quantitative prediction of human pharmacokinetics for mAbs exhibiting target-mediated disposition. AAPS J 17:389-99|
|Zhu, Xu; Straubinger, Robert M; Jusko, William J (2015) Mechanism-based mathematical modeling of combined gemcitabine and birinapant in pancreatic cancer cells. J Pharmacokinet Pharmacodyn 42:477-96|
|Zhao, Jie; Cao, Yanguang; Jusko, William J (2015) Across-Species Scaling of Monoclonal Antibody Pharmacokinetics Using a Minimal PBPK Model. Pharm Res 32:3269-81|
|McCune, Jeannine S; Vicini, Paolo; Salinger, David H et al. (2015) Population pharmacokinetic/dynamic model of lymphosuppression after fludarabine administration. Cancer Chemother Pharmacol 75:67-75|
Showing the most recent 10 out of 111 publications