The overarching goal of this work is to improve predictions of drug-drug interactions (DDI) due to time dependent inactivation (TDI) of cytochrome P450 (CYP) enzymes. The current funding period has resulted in new understandings on mechanisms of metabolite intermediate complex (MIC) formation, and novel models for complex enzyme kinetics based on numerical approaches. DDI predictions in the presence of MIC formation, partial inactivation, and non- Michaelis-Menten multiple binding, are now possible with our new methods. These new results have led us to new questions and hypotheses for improving DDI predictions for TDIs due to sequential metabolism, and TDIs that are also activators. Activators require models that include victim-perpetrator-enzyme complexes. Additionally, it has become clear that sequential metabolism involves diffusion of formed metabolites out of hepatocytes. Therefore, we are developing novel membrane permeability-limited dynamic models for improved predictions of victim PK.
Three specific aims are proposed.
Under Aim 1, in vitro TDI assays and ADME data will be collected. Our published numerical methods will be used for data analysis and TDI modeling. Kinetics of sequential metabolism and metabolite diffusion out of the cell will be evaluated with novel confocal microscopy experiments. Data will be modeled with partial differential equations to characterize analyte levels over time and distance across the cell. In situ sequential metabolism and spatial distribution in rat liver will be quantified in rat liver slices with MALDI-FTMS.
In Aim 2, human as well as rat fully permeability- or perfusion-limited PBPK models will be developed, with novel incorporation of fenestrated vs. non-fenestrated vasculature, explicit membranes, and metabolism and active transport in/out of major organs. The models will be validated with clinical C-t profiles of 19 compounds (mix of acids, bases, and neutrals), and rat single IV dosing data from 10 compounds.
In aim 3, in vitro data obtained from Aim 1 will be incorporated into the new PBPK model framework from Aim 2. Clinical and rat DDI will be predicted, and goodness of prediction will be compared to current standard prediction methods. The proposed studies will uncover mechanisms and kinetics of TDI due to sequential metabolism, activation, and as yet unknown processes. The larger significance of this work lies in marked improvement in the prediction of human drug disposition (absorption, distribution, and elimination) for drug discovery and development.
The goal of this work is to improve predictions of drug-drug interactions (DDI) due to time dependent inactivation (TDI) of cytochrome P450 (CYP) enzymes. Data will be generated in cells and in rodent models, and human published DDI data will be collected from the literature. New mathematical models will be built to better predict drug interaction liability in the clinic.
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