Predicting Passive Permeability of Drug-like Molecules from Chemical Structure: Where Are We?

Intestinal absorption in human is routinely predicted in drug discovery using <i>in vitro</i> assays such as permeability in the Madin-Darby canine kidney cell line. <i>In silico</i> models trained on these data are used in drug discovery efforts to prioritize novel chemical targets for synthesis; however, their proprietary nature and the limited validation available, which is usually restricted to predicting <i>in vitro</i> permeability, are barriers to widespread adoption. Because of the categorical nature of the <i>in vitro</i> permeability assay, intrinsic assay variability, and the challenges often encountered when translating <i>in vitro</i> data to an <i>in vivo</i> drug property, validation based solely on <i>in vitro</i> data might not be a good characterization of the usefulness of the <i>in silico</i> tool. In this work, we analyze the performance of three different <i>in silico</i> models in predicting the <i>in vitro</i> and <i>in vivo</i> permeability of 300 marketed drugs and 86 discovery compounds. The models differ in their approach (mechanistic vs quantitative structure–activity relationship) and the degree of complexity; one of them is a linear equation based on seven simple physicochemical descriptors and is presented for the first time in this work. Results show that <i>in silico</i> models can be successfully used to complement the discovery toolbox for characterizing <i>in vivo</i> intestinal permeability, defined using fraction of dose absorbed in human (Fa) and human jejunal permeability (<i>P</i>_eff). While the <i>in vitro</i> permeability models outperformed the <i>in silico</i> approach at predicting each of the <i>in vivo</i> end points explored, the gap in predictivity between the <i>in vitro</i> and the <i>in vivo</i> data was generally comparable to the gap between <i>in silico</i> and <i>in vitro</i> data. The <i>in vitro</i> and <i>in silico</i> approaches shared many of the same outliers, which can often be explained by the route of drug absorption (paracellular vs transcellular, active vs passive). Data suggest that the discovery process can greatly benefit from an early adoption of <i>in silico</i> models for predicting permeability as well as from a careful analysis of the <i>in silico</i> to <i>in vivo</i> disconnects