BACKGROUND AND OBJECTIVES: An approach was recently proposed for quantitative predictions of cytochrome P450 (CYP) 3A4-mediated drug-drug interactions. This approach relies solely on in vivo data. It is based on two characteristic parameters: the contribution ratio (CR; i.e. the fraction of victim drug clearance due to metabolism by a specific CYP) and the inhibition ratio (IR) of the inhibitor. Knowledge of these parameters allows forecasting of the ratio between the area under the plasma concentration-time curve (AUC) of the victim drug when the inhibitor is co-administered and the AUC of the victim drug administered alone. The goals of our study were to extend this method to CYP2D6-mediated interactions, to validate it, and to forecast the magnitude of a large number of interactions that have not been studied so far. METHODS: A three-step approach was pursued. First, initial estimates of CRs and IRs were obtained by several methods, using data from the literature. Second, an external validation of these initial estimates was carried out, by comparing the predicted AUC ratios with the observed values. Third, refined estimates of CRs and IRs were obtained by orthogonal regression in a Bayesian framework. RESULTS: Thirty-nine AUC ratios were available for external validation. The mean prediction error of the ratios was 0.31, while the mean prediction absolute error was 1.14. Seventy AUC ratios were available for the global analysis. Final estimates of CRs and IRs were obtained for 39 substrates and 11 inhibitors, respectively. The mean prediction error of the AUC ratios was 0.04, while the mean prediction absolute error was 0.51. CONCLUSIONS: Predictive distributions for 615 possible interactions were obtained, giving detailed information on some drugs or inhibitors that have been poorly studied so far, such as metoclopramide, bupropion and terbinafine.
BACKGROUND AND OBJECTIVES: An approach was recently proposed for quantitative predictions of cytochrome P450 (CYP) 3A4-mediated drug-drug interactions. This approach relies solely on in vivo data. It is based on two characteristic parameters: the contribution ratio (CR; i.e. the fraction of victim drug clearance due to metabolism by a specific CYP) and the inhibition ratio (IR) of the inhibitor. Knowledge of these parameters allows forecasting of the ratio between the area under the plasma concentration-time curve (AUC) of the victim drug when the inhibitor is co-administered and the AUC of the victim drug administered alone. The goals of our study were to extend this method to CYP2D6-mediated interactions, to validate it, and to forecast the magnitude of a large number of interactions that have not been studied so far. METHODS: A three-step approach was pursued. First, initial estimates of CRs and IRs were obtained by several methods, using data from the literature. Second, an external validation of these initial estimates was carried out, by comparing the predicted AUC ratios with the observed values. Third, refined estimates of CRs and IRs were obtained by orthogonal regression in a Bayesian framework. RESULTS: Thirty-nine AUC ratios were available for external validation. The mean prediction error of the ratios was 0.31, while the mean prediction absolute error was 1.14. Seventy AUC ratios were available for the global analysis. Final estimates of CRs and IRs were obtained for 39 substrates and 11 inhibitors, respectively. The mean prediction error of the AUC ratios was 0.04, while the mean prediction absolute error was 0.51. CONCLUSIONS: Predictive distributions for 615 possible interactions were obtained, giving detailed information on some drugs or inhibitors that have been poorly studied so far, such as metoclopramide, bupropion and terbinafine.
Authors: Thomas Giessmann; Christiane Modess; Ute Hecker; Michael Zschiesche; Peter Dazert; Christiane Kunert-Keil; Rolf Warzok; Georg Engel; Werner Weitschies; Ingolf Cascorbi; Heyo K Kroemer; Werner Siegmund Journal: Clin Pharmacol Ther Date: 2004-03 Impact factor: 6.875
Authors: John-Michael Sauer; Amanda J Long; Barbara Ring; Jennifer S Gillespie; Nathan P Sanburn; Karl A DeSante; David Petullo; Mark R VandenBranden; Charles B Jensen; Steven A Wrighton; Brian P Smith; Holly A Read; Jennifer W Witcher Journal: J Pharmacol Exp Ther Date: 2003-11-10 Impact factor: 4.030