Structure-Based Prediction of the Nonspecific Binding of Drugs to Hepatic Microsomes

For the accurate prediction of in vivo hepatic clearance or drug-drug interaction potential through in vitro microsomal metabolic data, it is essential to evaluate the fraction unbound in hepatic microsomal incubation media. Here, a structure-based in silico predictive model of the nonspecific binding (fu(mic), fraction unbound in hepatic microsomes) for 86 drugs was successfully developed based on seven selected molecular descriptors. The R-2 of the predicted and observed log((1-fu(mic))/fu(mic)) for the training set (n=64) and test set (n=22) were 0.82 and 0.85, respectively. The average fold error (AFE, calculated by fu(mic) rather than log((1-fu(mic))/fu(mic))) of the in silico model was 1.33 (n=86). The predictive capability of fu(mic) for neutral drugs compared well to that for basic compounds (R-2=0.82, AFE=1.18 and fold error values were all below 2, except for felodipine and progesterone) in our model. This model appears to perform better for neutral compounds when compared to models previously published in the literature. Therefore, this in silico model may be used as an additional tool to estimate fu(mic) and for predicting in vivo hepatic clearance and inhibition potential from in vitro hepatic microsomal studies.