Using in vitro ADME data for lead compound selection: An emphasis on PAMPA pH 5 permeability and oral bioavailability. (15th February 2022)

• Record Type: Journal Article
• Authors: Williams, Jordan; Siramshetty, Vishal; Nguyễn, Ðắc-Trung; Padilha, Elias Carvalho; Kabir, Md.; Yu, Kyeong-Ri; Wang, Amy Q.; Zhao, Tongan; Itkin, Misha; Shinn, Paul; Mathé, Ewy A.; Xu, Xin; Shah, Pranav
• Is Part Of: Bioorganic & medicinal chemistry. Volume 56(2022)
• Publication Date: 2022-02-15
• Subjects: Quantitative structure activity relationship -- PAMPA -- ADME -- Oral bioavailability -- Machine learning -- In silico models; 5-CV 5-fold cross validation -- ACN Acetonitrile -- ADME Absorption, Distribution, Metabolism, Excretion -- AI Artificial Intelligence -- ANN Artificial Neural Network -- AUC Area Under the Curve -- BACC Balanced Accuracy -- DMSO Dimethyl sulfoxide -- DNN Deep Neural Network -- GBM Generalized Boosted Models -- GCNN Graph Convolutional Neural Network -- GI Gastrointestinal -- IV Intravenous -- MDCK Madin-Darby Canine Kidney cells -- ML Machine Learning -- MSR Minimum Significant Ratio -- MW Molecular Weight -- NCATS National Center for Advancing Translational Sciences -- PAMPA Parallel Artificial Membrane Permeability Assay -- PEOE Partial Equalization of Orbital Electronegativities -- PK Pharmacokinetic -- PLS Partial Least Squares -- PO Per Os (orally administered) -- QSAR Quantitative Structure Activity Relationship -- RF Random Forest -- ROC Receiver Operating Characteristic -- S.D. Standard Deviation -- TPSA Total Polar Surface Area -- UPLC/MS Ultra-high Performance Liquid Chromatography Mass Spectrometry -- UV Ultraviolet -- XGBoost eXtreme Gradient Boosting; Bioorganic chemistry -- Periodicals; Pharmaceutical chemistry -- Periodicals; Biochemistry -- Periodicals; Chemistry, Clinical -- Periodicals; Chemistry, Organic -- Periodicals; Chimie bio-organique -- Périodiques; Chimie pharmaceutique -- Périodiques; 615.19
• DOI: 10.1016/j.bmc.2021.116588
• Languages: English
• ISSNs: 0968-0896

Abstract

Graphical abstract: Abstract: Membrane permeability plays an important role in oral drug absorption. Caco-2 and Madin-Darby Canine Kidney (MDCK) cell culture systems have been widely used for assessing intestinal permeability. Since most drugs are absorbed passively, Parallel Artificial Membrane Permeability Assay (PAMPA) has gained popularity as a low-cost and high-throughput method in early drug discovery when compared to high-cost, labor intensive cell-based assays. At the National Center for Advancing Translational Sciences (NCATS), PAMPA pH 5 is employed as one of the Tier I absorption, distribution, metabolism, and elimination (ADME) assays. In this study, we have developed a quantitative structure activity relationship (QSAR) model using our ∼6500 compound PAMPA pH 5 permeability dataset. Along with ensemble decision tree-based methods such as Random Forest and eXtreme Gradient Boosting, we employed deep neural network and a graph convolutional neural network to model PAMPA pH 5 permeability. The classification models trained on a balanced training set provided accuracies ranging from 71% to 78% on the external set. Of the four classifiers, the graph convolutional neural network that directly operates on molecular graphs offered the best classification performance. Additionally, an ∼85% correlation was obtained between PAMPA pH 5 permeability and in vivo oral bioavailability in mice and rats. These results suggest that data from this assay (experimental or predicted) can be used to rank-order compounds for preclinical in vivo testing with a high degree of confidence, reducing cost and attrition as well as accelerating the drug discovery process. Additionally, experimental data for 486 compounds (PubChem AID: 1645871) and the best models have been made publicly available (https://opendata.ncats.nih.gov/adme/ ).