In Silico Prediction of PAMPA Effective Permeability Using a Two-QSAR Approach

Abstract

Oral administration is the preferred and predominant route of choice for medication. As such, drug absorption is one of critical drug metabolism and pharmacokinetics (DM/PK) parameters that should be taken into consideration in the process of drug discovery and development. The cell-free in vitro parallel artificial membrane permeability assay (PAMPA) has been adopted as the primary screening to assess the passive diffusion of compounds in the practical applications. A classical quantitative structure–activity relationship (QSAR) model and a machine learning (ML)-based QSAR model were derived using the partial least square (PLS) scheme and hierarchical support vector regression (HSVR) scheme to elucidate the underlying passive diffusion mechanism and to predict the PAMPA effective permeability, respectively, in this study. It was observed that HSVR executed better than PLS as manifested by the predictions of the samples in the training set, test set, and outlier set as well as various statistical assessments. When applied to the mock test, which was designated to mimic real challenges, HSVR also showed better predictive performance. PLS, conversely, cannot cover some mechanistically interpretable relationships between descriptors and permeability. Accordingly, the synergy of predictive HSVR and interpretable PLS models can be greatly useful in facilitating drug discovery and development by predicting passive diffusion.

Partial Least Square Model (PLS) as a Tool to Predict the Diffusion of Steroids Across Artificial Membranes

“…Apart from prednisolone 21-sodium succinate (PSA = 141 Å), which has been classified as an outlier, PSA values for all steroids evaluated in the present study were below the cutoff value (PSA < 110 Å) suggested for the identification of poorly permeable compounds. Even though it's been recognized that lower PSA contributes to higher permeability [41], that trend was not confirmed here, mainly due to the absence of extreme variations in the PSA values and considering the relatively narrow range of PSA for the tested steroids.…”

“…The utility of in silico models in predicting drug permeability across biological membranes has been recognized as a time-and cost-efficient tool to facilitate drug discovery and development. The PLS model has been previously employed to identify the most critical molecular parameters affecting the permeability and retention of 17b-carboxamide steroids across an artificial membrane (parallel artificial membrane permeability assay (PAMPA)), as a means to predict their permeability across human skin [41]. In another study, Zhang et al, (2015) confirmed the good predictability of the PLS model, highlighting its potential utility as a high-throughput screening tool of placental drug permeability [18].…”

“…It defines the polar part of a molecule and correlates with passive molecular transport through membranes. It has been previously observed that compounds with PSA < 60 Å are highly permeable, in contrast to those with PSA > 120 Å that are poorly permeable [41]. In that context, optimal permeability has been recognized when PSA is below 120 Å.…”

Alternatives to Biological Skin in Permeation Studies: Current Trends and Possibilities

“…The PAMPA was initially designed to estimate passive gastrointestinal absorption and blood-brain barrier permeability. A skin-PAMPA model was developed by adding synthetic certramides, cholesterol, and stearic acid [88]. In comparison to the diffusion cells, the PAMPA is preferred owing to its short time period required for the high-throughput screening of compounds for obtaining a permeation profile.…”

Artificial Intelligence in Drug Discovery: A Comprehensive Review of Data-driven and Machine Learning Approaches

“…One is the human colon carcinoma (Caco-2) cell line permeability assay, and the other is the parallel artificial membrane permeability assay (PAMPA). In recent years, there have been numerous efforts to predict the absorption potential of compounds by using machine learning and molecular descriptors [119][120][121][122][123][124]. Fredlund et al [119] designed an in vitro assay to measure intrinsic permeability in Caco-2 cells and built prediction models with the data measured by a designed experiment.…”