# Improving prediction of drug interactions mediated by time-dependent inhibitors

> **NIH NIH R01** · TEMPLE UNIV OF THE COMMONWEALTH · 2020 · $396,250

## Abstract

Project Summary
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.

## Key facts

- **NIH application ID:** 10154855
- **Project number:** 2R01GM114369-05
- **Recipient organization:** TEMPLE UNIV OF THE COMMONWEALTH
- **Principal Investigator:** Kenneth Ray Korzekwa
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $396,250
- **Award type:** 2
- **Project period:** 2016-01-01 → 2024-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10154855

## Citation

> US National Institutes of Health, RePORTER application 10154855, Improving prediction of drug interactions mediated by time-dependent inhibitors (2R01GM114369-05). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10154855. Licensed CC0.

---

*[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*