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

Intestinal absorption in human is
routinely predicted in drug discovery
using in vitro assays such as permeability in the
Madin-Darby canine kidney cell line. In silico 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 in vitro permeability, are barriers
to widespread adoption. Because of the categorical nature of the in vitro permeability assay, intrinsic assay variability,
and the challenges often encountered when translating in vitro data to an in vivo drug property, validation based
solely on in vitro data might not be a good characterization
of the usefulness of the in silico tool. In this
work, we analyze the performance of three different in silico models in predicting the in vitro and in
vivo 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 in silico models can be successfully used
to complement the discovery toolbox for characterizing in
vivo intestinal permeability, defined using fraction of dose
absorbed in human (Fa) and human jejunal permeability (Peff). While the in vitro permeability
models outperformed the in silico approach at predicting
each of the in vivo end points explored, the gap
in predictivity between the in vitro and the in vivo data was generally comparable to the gap between in silico and in vitro data. The in vitro and in silico 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 in silico models for predicting
permeability as well as from a careful analysis of the in
silico to in vivo disconnects.