Pharmaceutical research · 2011
In vitro and in silico strategies to identify OATP1B1 inhibitors and predict clinical drug-drug interactions.
СтатьяПоддержка исследования: вне правительства СШАНазвание и аннотация: Перевод в очереди
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Karlgren Maria, Ahlin Gustav, Bergström Christel A S, Svensson Richard, Palm Johan, Artursson Per
Аннотация
PURPOSE: To establish in vitro and in silico models that predict clinical drug-drug interactions (DDIs) with the OATP1B1 (SLCO1B1) transporter.
METHODS: The inhibitory effect of 146 drugs and drug-like compounds on OATP1B1-mediated transport was studied in HEK293 cells. A computational model was developed to predict OATP1B1 inhibition. Concentration-dependent effects were investigated for six compounds; clinical DDIs were predicted by calculating change in exposure (i.e. R-values) in eight different ways.
RESULTS: Sixty-five compounds were identified as OATP1B1 inhibitors at 20 μM. The computational model predicted the test set with 80% accuracy for inhibitors and 91% for non-inhibitors. In vitro-in vivo comparisons underscored the importance of using drugs with known clinical effects as references. Thus, reference drugs, cyclosporin A, gemfibrozil, and fenofibrate, provided an inhibition interval to which three antiviral drugs, atazanavir, lopinavir, and amprenavir, could be compared and their clinical DDIs with OATP1B1 classified.
CONCLUSIONS: Twenty-two new OATP1B1 inhibitors were identified, a predictive OATP1B1 inhibition in silico model was developed, and successful predictions of clinical DDIs were obtained with OATP1B1.
METHODS: The inhibitory effect of 146 drugs and drug-like compounds on OATP1B1-mediated transport was studied in HEK293 cells. A computational model was developed to predict OATP1B1 inhibition. Concentration-dependent effects were investigated for six compounds; clinical DDIs were predicted by calculating change in exposure (i.e. R-values) in eight different ways.
RESULTS: Sixty-five compounds were identified as OATP1B1 inhibitors at 20 μM. The computational model predicted the test set with 80% accuracy for inhibitors and 91% for non-inhibitors. In vitro-in vivo comparisons underscored the importance of using drugs with known clinical effects as references. Thus, reference drugs, cyclosporin A, gemfibrozil, and fenofibrate, provided an inhibition interval to which three antiviral drugs, atazanavir, lopinavir, and amprenavir, could be compared and their clinical DDIs with OATP1B1 classified.
CONCLUSIONS: Twenty-two new OATP1B1 inhibitors were identified, a predictive OATP1B1 inhibition in silico model was developed, and successful predictions of clinical DDIs were obtained with OATP1B1.