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Williams2022 - Permeable compound selection using in vitro ADME data (PAMPA pH 5).

ABSTRACT: Parallel Artificial Membrane Permeability is an in vitro surrogate to determine the permeability of drugs across cellular membranes. PAMPA at pH 5 was experimentally determined in a dataset of 5,473 unique compounds by the NIH-NCATS. 50% of the dataset was used to train a classifier (SVM) to predict the permeability of new compounds, and validated on the remaining 50% of the data, rendering an AUC = 0.88. The Peff was converted to logarithmic, log Peff value lower than 2.0 were considered to have low to moderate permeability, and those with a value higher than 2.5 were considered as high-permeability compounds. Model Type: Predictive machine learning model. Model Relevance: Predicting compound permeability across cellular membranes. Model Encoded by: Pauline Banye (Ersilia) Metadata Submitted in BioModels by: Zainab Ashimiyu-Abdusalam Implementation of this model code by Ersilia is available here: https://github.com/ersilia-os/eos81ew

SUBMITTER: Zainab Ashimiyu-Abdusalam  

PROVIDER: MODEL2404220004 | BioModels | 2024-04-22

REPOSITORIES: BioModels

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Using in vitro ADME data for lead compound selection: An emphasis on PAMPA pH 5 permeability and oral bioavailability.

Williams Jordan J   Siramshetty Vishal V   Nguyễn Ðắc-Trung ÐT   Padilha Elias Carvalho EC   Kabir Md M   Yu Kyeong-Ri KR   Wang Amy Q AQ   Zhao Tongan T   Itkin Misha M   Shinn Paul P   Mathé Ewy A EA   Xu Xin X   Shah Pranav P  

Bioorganic & medicinal chemistry 20220105

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 Scien  ...[more]

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