Computational Prediction of Oral Drug Absorption Based on Absorption Rate Constants in Humans

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Abstract
Models for predicting oral drug absorption kinetics were developed by correlating absorption rate constants in humans (Ka) with computational molecular descriptors. The Ka values of a set of 22 passively absorbed drugs were derived from human plasma time−concentration profiles using a deconvolution approach. The Ka values correlated well with experimental values of fraction of dose absorbed in humans (FA), better than the values of human jejunal permeability (Peff) which have previously been used to assess the in vivo absorption kinetics of drugs. The relationships between the Ka 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 Ka 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 Q2 and R2 values ranging from 0.74 to 0.76. An external data set of 169 compounds demonstrated that the models were able to predict Ka 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 Ka value for a new drug candidate.