Computational prediction of oral drug absorption based on absorption rate constants in humans

Models for predicting oral drug absorption kinetics were developed by correlating absorption rate constants in humans (K-a) with computational molecular descriptors. The K-a values of a set of 22 passively absorbed drugs were derived from human plasma time-concentration profiles using a deconvolution approach. The K-a values correlated well with experimental values of fraction of dose absorbed in humans (FA), better than the values of human jejunal permeability (P-eff) which have previously been used to assess the in vivo absorption kinetics of drugs. The relationships between the K-a values of the 22 structurally diverse drugs and computational molecular descriptors were established with PLS analysis. The analysis showed that the most important parameters describing log K-a were polar surface area (PSA), number of hydrogen bond donors (HBD), and log D at a physiologically relevant pH. Combining log D at pH 6.0 with PSA or HBD resulted in models with Q(2) and R-2 values ranging from 0.74 to 0.76. An external data set of 169 compounds demonstrated that the models were able to predict K-a values that correlated well with experimental FA values. Thus, it was shown that, using a combination of only two computational molecular descriptors, it is possible to predict with good accuracy the K-a value for a new drug candidate.