Enhancing drug-drug Interaction Prediction by Integrating Physiologically-Based Pharmacokinetic Model with Fraction Metabolized by CYP3A4.
Expert Opin Drug Metab Toxicol
; 19(10): 721-731, 2023.
Article
em En
| MEDLINE
| ID: mdl-37746740
ABSTRACT
BACKGROUND:
Enhancing the precision of drug-drug interaction (DDI) prediction is essential for improving drug safety and efficacy. The aim is to identify the most effective fraction metabolized by CY3A4 (fm) for improving DDI prediction using physiologically based pharmacokinetic (PBPK) models. RESEARCH DESIGN ANDMETHODS:
The fm values were determined for 33 approved drugs using a human liver microsome for in vitro measurements and the ADMET Predictor software for in silico predictions. Subsequently, these fm values were integrated into PBPK models using the GastroPlus platform. The PBPK models, combined with a ketoconazole model, were utilized to predict AUCR (AUCcombo with ketoconazole/AUCdosing alone), and the accuracy of these predictions was evaluated by comparison with observed AUCR.RESULTS:
The integration of in vitro fm method demonstrates superior performance compared to the in silico fm method and fm of 100% method. Under the Guest-limits criteria, the integration of in vitro fm achieves an accuracy of 76%, while the in silico fm and fm of 100% methods achieve accuracies of 67% and 58%, respectively.CONCLUSIONS:
Our study highlights the importance of in vitro fm data to improve the accuracy of predicting DDIs and demonstrates the promising potential of in silico fm in predicting DDIs.Palavras-chave
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Citocromo P-450 CYP3A
/
Cetoconazol
Tipo de estudo:
Prognostic_studies
/
Risk_factors_studies
Limite:
Humans
Idioma:
En
Ano de publicação:
2023
Tipo de documento:
Article