Michel Tod1,2,3, S Goutelle4,5, N Bleyzac6, L Bourguignon4,5. 1. Pharmacie, Groupement Hospitalier Nord, Hospices Civils de Lyon, Lyon, France. michel.tod@univ-lyon1.fr. 2. EMR 3738, Faculté de Médecine Lyon-Sud, Université Lyon 1, Chemin du Grand Revoyet, BP 12, 69921, Oullins, Lyon, France. michel.tod@univ-lyon1.fr. 3. Faculté de Pharmacie, Université Lyon 1, Lyon, France. michel.tod@univ-lyon1.fr. 4. Pharmacie, Groupement Hospitalier Nord, Hospices Civils de Lyon, Lyon, France. 5. Faculté de Pharmacie, Université Lyon 1, Lyon, France. 6. EMR 3738, Faculté de Médecine Lyon-Sud, Université Lyon 1, Chemin du Grand Revoyet, BP 12, 69921, Oullins, Lyon, France.
Abstract
BACKGROUND AND OBJECTIVE: The In Vivo Mechanistic Static Model (IMSM) is a powerful method used to predict the magnitude of drug-drug interactions (DDIs) mediated by cytochromes. The objective of this study was to extend the IMSM paradigm to DDIs mediated by efflux transporters and cytochromes. METHODS: First, a generic model for this kind of interaction was devised. A flexible approach was then developed to estimate the characteristic parameters [the contribution ratios (CRs) and inhibition or induction potencies (IXs)] from clinical data by non-linear regression. Next, this approach was applied to the DDIs mediated by P-glycoprotein (P-gp) and cytochrome P450 (CYP) 3A4/3A5 in a large set of victim drugs and interactors. Lastly, the model and associated parameters were used to identify the DDIs most at risk of overexposure. RESULTS: A total of 25 substrates and 26 interactors (three inducers, 23 inhibitors) could be considered in the regression analysis. The number of observations [area under the plasma concentration-time curve ratios or renal clearance ratios (Robs)] was 138. Fifty CRs and 57 IXs were estimated. The proportions of predictions within 0.67- to 1.5-fold Robs and within 0.5- to 2-fold Robs were 79% and 93% for the internal validation and 76% and 88% for the external validation, respectively. The median fold error was 0.98 (the ideal value is 1) and the interquartile range of the fold error was 0.36. The relative standard error of parameter estimates was a maximum of 15%. CONCLUSIONS: The IMSM approach was successfully extended to DDIs mediated by P-gp and CYP3A4/3A5. The method revealed good predictive performances by internal and external validation.
BACKGROUND AND OBJECTIVE: The In Vivo Mechanistic Static Model (IMSM) is a powerful method used to predict the magnitude of drug-drug interactions (DDIs) mediated by cytochromes. The objective of this study was to extend the IMSM paradigm to DDIs mediated by efflux transporters and cytochromes. METHODS: First, a generic model for this kind of interaction was devised. A flexible approach was then developed to estimate the characteristic parameters [the contribution ratios (CRs) and inhibition or induction potencies (IXs)] from clinical data by non-linear regression. Next, this approach was applied to the DDIs mediated by P-glycoprotein (P-gp) and cytochrome P450 (CYP) 3A4/3A5 in a large set of victim drugs and interactors. Lastly, the model and associated parameters were used to identify the DDIs most at risk of overexposure. RESULTS: A total of 25 substrates and 26 interactors (three inducers, 23 inhibitors) could be considered in the regression analysis. The number of observations [area under the plasma concentration-time curve ratios or renal clearance ratios (Robs)] was 138. Fifty CRs and 57 IXs were estimated. The proportions of predictions within 0.67- to 1.5-fold Robs and within 0.5- to 2-fold Robs were 79% and 93% for the internal validation and 76% and 88% for the external validation, respectively. The median fold error was 0.98 (the ideal value is 1) and the interquartile range of the fold error was 0.36. The relative standard error of parameter estimates was a maximum of 15%. CONCLUSIONS: The IMSM approach was successfully extended to DDIs mediated by P-gp and CYP3A4/3A5. The method revealed good predictive performances by internal and external validation.
Authors: Manthena V S Varma; Jian Lin; Yi-An Bi; Charles J Rotter; Odette A Fahmi; Justine L Lam; Ayman F El-Kattan; Theunis C Goosen; Yurong Lai Journal: Drug Metab Dispos Date: 2013-02-07 Impact factor: 3.922