Drug-drug interaction predictions with PBPK models and optimal multiresponse sampling time designs: application to midazolam and a phase I compound. Part 2: clinical trial results.Report as inadecuate




Drug-drug interaction predictions with PBPK models and optimal multiresponse sampling time designs: application to midazolam and a phase I compound. Part 2: clinical trial results. - Download this document for free, or read online. Document in PDF available to download.

* Corresponding author 1 IRIS - Institut de Recherches Internationales Servier 2 Technologie Servier 3 CAPR - Centre for Applied Pharmacokinetic Research 4 School of Pharmacy and Pharmaceutical Sciences 5 Modèles et méthodes de l-évaluation thérapeutique des maladies chroniques

Abstract : PURPOSE: To compare results of population PK analyses obtained with a full empirical design FD and an optimal sparse design MD in a Drug-Drug Interaction DDI study aiming to evaluate the potential CYP3A4 inhibitory effect of a drug in development, SX, on a reference substrate, midazolam MDZ. Secondary aim was to evaluate the interaction of SX on MDZ in the in vivo study. Methods To compare designs, real data were analysed by population PK modelling technique using either FD or MD with NONMEM FOCEI for SX and with NONMEM FOCEI and MONOLIX SAEM for MDZ. When applicable a Wald test was performed to compare model parameter estimates, such as apparent clearance CL-F, across designs. To conclude on the potential interaction of SX on MDZ PK, a Student paired test was applied to compare the individual PK parameters i.e. logAUC and logCmax obtained either by a non-compartmental approach NCA using FD or from empirical Bayes estimates EBE obtained after fitting the model separately on each treatment group using either FD or MD. RESULTS: For SX, whatever the design, CL-F was well estimated and no statistical differences were found between CL-F estimated values obtained with FD CL-F = 8.2 l-h and MD CL-F = 8.2 l-h. For MDZ, only MONOLIX was able to estimate CL-F and to provide its standard error of estimation with MD. With MONOLIX, whatever the design and the administration setting, MDZ CL-F was well estimated and there were no statistical differences between CL-F estimated values obtained with FD 72 l-h and 40 l-h for MDZ alone and for MDZ with SX, respectively and MD 77 l-h and 45 l-h for MDZ alone and for MDZ with SX, respectively. Whatever the approach, NCA or population PK modelling, and for the latter approach, whatever the design, MD or FD, comparison tests showed that there was a statistical difference P < 0.0001 between individual MDZ logAUC obtained after MDZ administration alone and co-administered with SX. Regarding Cmax, there was a statistical difference P < 0.05 between individual MDZ logCmax obtained under the 2 administration settings in all cases, except with the sparse design with MONOLIX. However, the effect on Cmax was small. Finally, SX was shown to be a moderate CYP3A4 inhibitor, which at therapeutic doses increased MDZ exposure by a factor of 2 in average and almost did not affect the Cmax. CONCLUSION: The optimal sparse design enabled the estimation of CL-F of a CYP3A4 substrate and inhibitor when co-administered together and to show the interaction leading to the same conclusion as the full empirical design.





Author: Marylore Chenel - François Bouzom - Fanny Cazade - Kayode Ogungbenro - Leon Aarons - France Mentré -

Source: https://hal.archives-ouvertes.fr/



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