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

Abstract

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 (P). 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.

Carvacrol: An In Silico Approach of a Candidate Drug on HER2, PI3Kα, mTOR, hER‐α, PR, and EGFR Receptors in the Breast Cancer

“…In addition, carvacrol exhibited a great %HIA, which would suggest that the molecule could be absorbed throughout the intestinal segments upon oral administration. This latter statement has been also confirmed by using Caco-2 cell monolayers or MDCK cells as predicting model, in which both models are recommended as a simplified in vitro model of intestinal absorption in drug development [ 38 , 39 ]. In this case, carvacrol displayed recommended values ranges for an ideal drug of 3712 and 2042 nm/s, respectively.…”

Divergent effects of strontium and calcium‐sensing receptor positive allosteric modulators (calcimimetics) on human osteoclast activity

“…Intriguingly, cinacalcet, but not AC‐265347 or BTU‐compound 13, pharmacochaperoned the endoplasmic reticulum‐retained CaS receptor‐G670E mutant to the cell surface. Although we cannot rule out that these compounds differentially access intracellular compartments, they are all predicted to be cell permeable (pkCSM predictor; Broccatelli et al, ; Pires et al, ), Collectively, these data indicate that the different calcimimetics preferentially stabilize different conformational states of the CaS receptor and thus engender biased modulation and/or exhibit location bias (Irannejad et al, ) through access to different receptor pools.…”

“…This suggests that cinacalcet can access and stabilize a conformation of the CaS receptor‐G670E variant that is trafficked to the cell surface, but AC‐265347 and BTU‐compound 13 cannot (Figure ). All three compounds are lipophilic and are predicted to have high cell permeability (Supporting Information Table S2; Broccatelli et al, ; Pires et al, ), suggesting that the effects could be due to distinct receptor conformations stabilized by the calcimimetics. However, we cannot rule out that divergent active transport or permeability of the drugs leads to differential access to the intracellular CaS receptor‐G670E variant.…”

chemalot and chemalot_knime: Command line programs as workflow tools for drug discovery

“…The command line programs and KNIME framework are used at Genentech to create, validate and apply QSPR models for properties important to lead optimization such as metabolic stability [42], permeability [43] and solubility. Here, we present an example KNIME workflow to show how command line programs are used to construct a QSPR model to predict solubility (Fig.…”