Dataset Information

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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Publications

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]

PMID: 35030421

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Project description:Chronic injury in kidney transplants remains a major cause of graft loss. The aim of this study was to identify a predictive gene set capable of classifying renal grafts at risk for progressive injury due to fibrosis.The Genomics of Chronic Allograft Rejection (GoCAR) study is a prospective, multicenter study. Biopsies obtained prospectively 3 months after transplantation from renal allograft recipients (n=159) with stable renal function were analyzed for gene expression by microarray. Genes were sought which correlated with subsequent 12-month Chronic Allograft Damage Index (CADI) but neither CADI in the 3 month biopsy nor other histological or clinical parameters. We identified a set of 13 genes that was independently predictive for the development of fi brosis at 1 year (ie, CADI-12 >=2). The gene set had high predictive capacity (area under the curve [AUC] 0·967), which was superior to that of baseline clinical variables (AUC 0·706) and clinical and pathological variables (AUC 0·806). Furthermore routine pathological variables were unable to identify which histologically normal allografts would progress to fi brosis (AUC 0·754), whereas the predictive gene set accurately discriminated between transplants at high and low risk of progression (AUC 0·916). The 13 genes also accurately predicted early allograft loss (AUC 0·842 at 2 years and 0·844 at 3 years). We validated the predictive value of this gene set in an independent cohort from the GoCAR study (n=45, AUC 0·866) and two independent, publically available expression datasets (n=282, AUC 0·831 and n=24, AUC 0·972). Biopsies obtained prospectively 3 months after transplantation from renal allograft recipients (n=159) with stable renal function were analyzed for gene expression by Affymetrix exon arrays. Genes that were specifically correlated with 12-month Chronic Allograft Damage Index (CADI) but neither CADI in the 3 month biopsy nor other histological or clinical parameters were identified by correlation analysis. The minimal geneset was further identified to predict the progressors/non progressors and validated by qPCR in an independent cohort and two public datasets. This dataset is part of the TransQST collection.

Dataset Information

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

Publications

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

Similar Datasets

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