Predicting Oral Absorption for Compounds Outside the Rule of Five Property Space

The estimation of the extent of absorption of drug candidates intended for oral drug delivery is an important selection criteria in drug discovery. The use of cell-based transwell assays examining flux across cell-monolayers (e.g., Caco-2 or MDCK cells) usually provide satisfactory predictions of the extent of absorption in vivo. These predictions often fall short of expection for molecules outside the traditional low molecular weight property space. In this manuscript the transwell permeability assay was modified to circumvent potential issues that can be encountered when evaluating the aforementioned drug molecules. Particularly, the addition of albumin in the acceptor compartment to reduce potential binding to cells and the acceptor compartment, improved the predictive power of the assay. Cellular binding and lysosomal trapping effects are significantly reduced for larger molecules, particularly lipophilic bases under these more physiological conditions, resulting in higher recovery values and a better prediction power. The data indicate that lysosomal trapping does not impact the rate of absorption of lipophilic bases in general but is rather an exception. Finally, compounds believed to permeate by passive mechanisms were used in a calibration curve for the effective prediction of the fraction absorbed of molecules of interest in current medicinal chemistry efforts. (C) 2021 American Pharmacists Association (R). Published by Elsevier Inc. All rights reserved.

Automated summary

Estimating the extent of absorption of drug candidates is crucial in drug discovery, and cell-based transwell assays are commonly used to predict in vivo absorption. By modifying the transwell permeability assay with the addition of albumin to reduce binding issues, the predictive power for larger molecules, especially lipophilic bases, was improved. Under more physiological conditions, cellular binding and lysosomal trapping effects were significantly reduced, leading to higher recovery values and better prediction power.