Laurence Gabriel1, Michel Tod2,3, Sylvain Goutelle4,5,6. 1. Service Pharmaceutique, Hôpital Gériatrique Pierre Garraud, Hospices Civils de Lyon, 136 rue du Commandant Charcot, 69322, Lyon Cedex 05, France. 2. ISPB-Faculté de Pharmacie de Lyon, Université de Lyon, Université Lyon 1, Lyon, France. 3. Service Pharmaceutique, Hôpital de la Croix-Rousse, Hospices Civils de Lyon, Lyon, France. 4. Service Pharmaceutique, Hôpital Gériatrique Pierre Garraud, Hospices Civils de Lyon, 136 rue du Commandant Charcot, 69322, Lyon Cedex 05, France. sylvain.goutelle@chu-lyon.fr. 5. ISPB-Faculté de Pharmacie de Lyon, Université de Lyon, Université Lyon 1, Lyon, France. sylvain.goutelle@chu-lyon.fr. 6. UMR CNRS 5558, Laboratoire de Biométrie et Biologie Evolutive, Université Lyon 1, Villeurbanne, France. sylvain.goutelle@chu-lyon.fr.
Abstract
BACKGROUND: A simple method to predict drug-drug interactions mediated by cytochrome P450 enzymes (CYPs) on the basis of in vivo data has been previously applied for several CYP isoforms but not for CYP1A2. The objective of this study was to extend this method to drug interactions caused by CYP1A2 inhibitors and inducers. METHODS: First, initial estimates of the model parameters were obtained using data from the literature. Then, an external validation of these initial estimates was performed by comparing model-based predicted area under the concentration-time curve (AUC) ratios with observations not used in the initial estimation. Third, refined estimates of the model parameters were obtained by Bayesian orthogonal regression using Winbugs software, and predicted AUC ratios were compared with all available observations. Finally, predicted AUC ratios for all possible substrates-inhibitors and substrates-inducers were computed. RESULTS: A total of 100 AUC ratios were retrieved from the literature. Model parameters were estimated for 19 CYP1A2 substrate drugs, 26 inhibitors and seven inducers, including tobacco smoking. In the external validation, the mean prediction error of the AUC ratios was -0.22, while the mean absolute error was 0.97 (37 %). After the Bayesian estimation step, the mean prediction error was 0.11, while the mean absolute error was 0.43 (22 %). The AUC ratios for 625 possible interactions were computed. CONCLUSION: This analysis provides insights into the interaction profiles of drugs poorly studied so far and can help to identify and manage significant interactions in clinical practice. Those results are now available to the community via a web tool ( http://www.ddi-predictor.org ).
BACKGROUND: A simple method to predict drug-drug interactions mediated by cytochrome P450 enzymes (CYPs) on the basis of in vivo data has been previously applied for several CYP isoforms but not for CYP1A2. The objective of this study was to extend this method to drug interactions caused by CYP1A2 inhibitors and inducers. METHODS: First, initial estimates of the model parameters were obtained using data from the literature. Then, an external validation of these initial estimates was performed by comparing model-based predicted area under the concentration-time curve (AUC) ratios with observations not used in the initial estimation. Third, refined estimates of the model parameters were obtained by Bayesian orthogonal regression using Winbugs software, and predicted AUC ratios were compared with all available observations. Finally, predicted AUC ratios for all possible substrates-inhibitors and substrates-inducers were computed. RESULTS: A total of 100 AUC ratios were retrieved from the literature. Model parameters were estimated for 19 CYP1A2 substrate drugs, 26 inhibitors and seven inducers, including tobacco smoking. In the external validation, the mean prediction error of the AUC ratios was -0.22, while the mean absolute error was 0.97 (37 %). After the Bayesian estimation step, the mean prediction error was 0.11, while the mean absolute error was 0.43 (22 %). The AUC ratios for 625 possible interactions were computed. CONCLUSION: This analysis provides insights into the interaction profiles of drugs poorly studied so far and can help to identify and manage significant interactions in clinical practice. Those results are now available to the community via a web tool ( http://www.ddi-predictor.org ).
Authors: Kenneth Bachmann; Donald White; Luis Jauregui; Jules I Schwartz; Nancy G B Agrawal; Ralph Mazenko; Patrick J Larson; Arturo G Porras Journal: J Clin Pharmacol Date: 2003-10 Impact factor: 3.126
Authors: Angela van der Plas; Sandrine Pouly; Nicolas Blanc; Christelle Haziza; Guillaume de La Bourdonnaye; Bjorn Titz; Julia Hoeng; Nikolai V Ivanov; Brindusa Taranu; Annie Heremans Journal: Toxicol Rep Date: 2020-10-29