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A Genetic Algorithm-Based Approach for Quantitative Prediction of Drug-Drug Interactions Caused by Cytochrome P450 3A
Veronica Di Paolo1, Francesco Maria Ferrari2, Italo Poggesi3
1Department of Comparative Biomedicine and Food Science, University of Padua, Viale dell'Università 16, 35020 Legnaro, Italy.
Pharmaceuticals (Basel, Switzerland)
|June 26, 2026
Summary
A genetic algorithm predicts equine drug-drug interactions (DDIs) involving cytochrome P450 3A (CYP3A). This computational tool accurately estimates potential DDIs when horses receive CYP3A substrates with interacting drugs.
Area of Science:
- Pharmacology
- Computational Biology
- Veterinary Medicine
Background:
- Drug-drug interactions (DDIs) involving cytochrome P450 3A (CYP3A) are a significant concern in equine medicine.
- Predicting these interactions is crucial for ensuring horse safety and optimizing drug therapy.
- Existing methods for predicting equine CYP3A-mediated DDIs are limited.
Purpose of the Study:
- To develop and validate a genetic algorithm (GA)-based computational approach for predicting DDIs in horses.
- To specifically address DDIs mediated by CYP3A inhibition or induction.
- To provide a tool for estimating the clinical relevance of potential DDIs.
Main Methods:
- Utilized area under the concentration-time curve ratios (AUCRs) from in vivo equine DDI studies.
- Computed the contribution ratio (CR) of CYP3A metabolism for substrates.
- Calculated inhibitory (IR) or inducing (IC) potency/efficacy of interacting drugs.
Main Results:
- Successfully predicted AUCRs for 9 CYP3A substrates, 12 inhibitors, and 1 inducer in horses.
- Achieved high prediction accuracy, with over 96% of predictions within the 50-200% range of observed values.
- Validated the GA-based method against existing in vivo DDI data.
Conclusions:
- The proposed GA-based method is a valuable tool for predicting equine DDIs.
- This approach can estimate the clinical significance of DDIs when CYP3A substrates and interacting drugs are co-administered.
- The method aids in anticipating and managing potential adverse drug events in horses.
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