General Framework to Quantitatively Predict Pharmacokinetic Induction Drug-Drug Interactions Using In Vitro Data.
Sandra Grañana-Castillo1, Angharad Williams1, Thao Pham1
1Pharmacology and Therapeutics, University of Liverpool, Liverpool, UK.
Clinical Pharmacokinetics
|March 29, 2023
Summary
A new algorithm predicts drug-drug interaction (DDI) magnitude using in vitro data, aiding early drug development. This tool correctly classifies 70.5% of interactions, improving safety for patients on multiple medications.
Area of Science:
- Pharmacology and Drug Metabolism
- Computational Chemistry and Cheminformatics
Background:
- Metabolic inducers in polypharmacy pose risks due to unstudied drug-drug interactions (DDIs).
- Clinical trials for DDIs are limited, leaving most interactions unexplored.
- Predicting DDI magnitude is crucial for patient safety and drug development.
Purpose of the Study:
- To develop and validate an algorithm for predicting the magnitude of induction DDIs.
- To integrate in vitro data related to drug-metabolizing enzymes for DDI prediction.
- To provide a rapid screening tool for potential DDIs in early drug development.
Main Methods:
- Developed an algorithm integrating in vitro parameters like fraction unbound and enzyme induction potential.
- Generated an in vitro metabolic metric (IVMM) combining substrate metabolism fraction and enzyme activity fold increase.
- Correlated predicted area under the curve ratio (AUCratio) with clinical AUCratio for 319 DDIs.
Main Results:
- The algorithm identified IVMM and fraction unbound in plasma as significant predictors.
- The developed algorithm correctly classified 70.5% of DDIs into categories: no, mild, moderate, and strong induction.
- DDIs were considered well-classified if predictions matched observations or were within a 1.5-fold ratio.
Conclusions:
- A novel algorithm effectively predicts the magnitude of induction drug-drug interactions using in vitro data.
- This tool offers a rapid and advantageous screening method for identifying potential DDIs.
- The findings support the use of in vitro data for early-stage DDI assessment in drug development.
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