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A Model for Predicting the Interindividual Variability of Drug-Drug Interactions
M Tod1,2,3, L Bourguignon4,5,6, N Bleyzac7,8
1Pharmacie, Groupement Hospitalier Nord, Hospices Civils de Lyon, Lyon, France. michel.tod@univ-lyon1.fr.
The AAPS Journal
|December 8, 2016
Summary
This study introduces a new model to predict the variability of drug-drug interaction AUC ratios. The model accurately forecasts the standard deviation of the AUC ratio, aiding in anticipating interaction consequences.
Area of Science:
- Pharmacokinetics
- Drug Metabolism
- Pharmacology
Background:
- Drug-drug interactions (DDIs) are common and impact drug efficacy and safety.
- Cytochrome P450 enzymes mediate many DDIs, leading to altered drug exposure.
- Predicting the variability of interaction outcomes is crucial for clinical risk assessment.
Purpose of the Study:
- To develop and validate a predictive model for the interindividual variability of the AUC ratio (Rauc) in DDIs.
- To quantify the standard deviation of the natural logarithm of the AUC ratio (sd(Ln(Rauc))).
- To assess the model's performance using literature data.
Main Methods:
- A model was derived linking sd(Ln(Rauc)) to substrate metabolism and interactor potency.
- Bayesian hierarchical modeling was employed to estimate model parameters using 56 literature studies.
- Model performance was evaluated using prediction error (PE) metrics.
Main Results:
- The developed model accurately predicted sd(Ln(Rauc)), with a median PE of 0.998.
- Prediction error was within the acceptable range (0.5-2) in 52 out of 56 evaluated studies.
- The model identified factors influencing Rauc variability, showing minimal sd(Ln(Rauc)) for Rauc=1 and maximal values for strong interactions.
Conclusions:
- The proposed approach provides a reliable method for predicting DDI variability.
- This tool can enhance the anticipation of clinical consequences arising from DDIs.
- Understanding DDI variability is essential for personalized medicine and drug development.
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