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Chi2019 - In Silico Prediction of PAMPA Effective Permeability Using a Two-QSAR Approach

ABSTRACT: The authors provide a dataset of 200 small molecules and their experimentally measured permeability in a PAMPA assay. Using this data, we have trained a model that predicts the logarithm of the effective permeability coefficient. Model Type: Predictive machine learning model. Model Relevance: Predicts pampa-permeability. Model Encoded by: Miquel Duran Frigola (Ersilia) Metadata Submitted in BioModels by: Zainab Ashimiyu-Abdusalam Implementation of this model code by Ersilia is available here: https://github.com/ersilia-os/eos97yu

SUBMITTER: Zainab Ashimiyu-Abdusalam

PROVIDER: MODEL2406030003 | BioModels | 2024-06-03

REPOSITORIES: BioModels

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

Chi Cheng-Ting CT Lee Ming-Han MH Weng Ching-Feng CF Leong Max K MK

International journal of molecular sciences 20190628 13

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 str ...[more]

PMID: 31261723

Similar Datasets

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

Project description: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.

| S-EPMC6651837 | biostudies-literature

Sun2017 - predictive and interpretable models for PAMPA permeability 7

Project description:Parallel Artificial Membrane Permeability is an in vitro surrogate to determine the permeability of drugs across cellular membranes. PAMPA at pH 7.4 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: The model predicts a chemical compound as highly or low/moderate permeable. Model Encoded by: Pauline (Ersilia) Metadata Submitted in BioModels by: Zainab Ashimiyu-Abdusalam Implementation of this model code by Ersilia is available here: https://github.com/ersilia-os/eos9tyg

2024-04-23 | MODEL2404220001 | BioModels

Jiménez-Luna2021 - Coloring molecules for Caco-2 cell permeability

Project description:By combining a Message-Passing Graph Neural Network (MPGNN) and a Forward fully connected Neural Network (FNN) with an integrated gradients explainable artificial intelligence (XAI) method, the authors developed MolGrad and tested it on a number of ADME predictive tasks such as pCaco-2 cell permeability as the case for this model. MolGrad incorporates explainable features to facilitate interpretation of the predictions. This model has been trained using experimental data on the permeability of molecules across Caco2 cell membranes (Papp, cm s-1). Model Type: Predictive machine learning model. Model Relevance: Predicts Log10 of the Passive permeability in cm s-1. Model Encoded by: Miquel Duran-Frigola (Ersilia) Metadata Submitted in BioModels by: Zainab Ashimiyu-Abdusalam Implementation of this model code by Ersilia is available here: https://github.com/ersilia-os/eos1af5

2024-05-21 | MODEL2405210006 | BioModels

Merget2013 - Mycobacterium tuberculosis permeability prediction tool

Project description:MycPermCheck predicts potential to permeate the Mycobacterium tuberculosis cell membrane based on physicochemical properties. Due to the lack of reliable experimental datapoints, the authors defined the training set using molecules that are active against M.tb. Model Type: Predictive machine learning model. Model Relevance: Probability of permeability across the M.tb cell wall. Model Encoded by: Miquel Duran-Frigola (Ersilia) Metadata Submitted in BioModels by: Zainab Ashimiyu-Abdusalam Implementation of this model code by Ersilia is available here: https://github.com/ersilia-os/eos8d8a

2024-05-21 | MODEL2405210004 | BioModels

Vascular Permeability and Apoptosis are Separable Processes in Retinal Ischemia-Reperfusion Injury

Project description:Effects of Ischemic Preconditioning, Bevacizumab and Etanercept Ischemia and reperfusion injury provides an acute model of ischemic retinopathy that includes neurodegeneration and VEGF-dependent vascular permeability and is amenable to rapid drug testing. The distinct effects of ischemic preconditioning and bevacizumab demonstrate that the apoptotic and vascular responses to ischemia may be separated and that VEGF expression is not neuroprotective following ischemic-reperfusion. Using transient ischemia followed by reperfusion (IR) to model ischemic retinal disease, this study compares the effects of ischemic preconditioning (IPC) and therapies targeting vascular endothelial growth factor (VEGF) and tumor necrosis factor α (TNFα) on retinal apoptosis, vascular permeability and mRNA biomarker expression. Only the Ischemic Preconditioning (not Bevacizumab and Etanercept treated samples) were hybridized to arrays. Study contains 6 replicates of control and 6 IP treated retinal samples.

2010-05-31 | E-GEOD-20521 | biostudies-arrayexpress

Menichetti2019 - Drug−Membrane Permeability across Chemical Space

Project description:Using Coarse Grained (CG) models, where several atoms are aggregated into a single bead, the authors obtain a set of 500,000 compounds with their simulated permeability across a single-component DOPC lipid bilayer. With this approach, the authors are able to cover a large and representative portion of the chemical space. We have used the data generated in this publication to train a simple regression model to predict compound permeability. Model Type: Predictive machine learning model. Model Relevance: Predicition of Passive permeability based on simulations Model Encoded by: Miquel Duran-Frigola (Ersilia) Metadata Submitted in BioModels by: Zainab Ashimiyu-Abdusalam Implementation of this model code by Ersilia is available here: https://github.com/ersilia-os/eos2hbd

2024-08-07 | MODEL2408070001 | BioModels

Physiological Vascular Permeability Requires Induction of Endothelial NR4A1 by Progesterone Receptor [ChIP-Seq]

Project description:Vascular permeability is frequently associated with inflammation and it is triggered by chemokines and by a cohort of secreted permeability factors, such as VEGF. In contrast, here we showed that the physiological vascular permeability that precedes implantation is directly controlled by progesterone receptor (PR) and it is independent of VEGF. Both global and endothelial-specific deletion of PR block physiological vascular permeability in the uterus while misexpression of PR in the endothelium of other organs results in ectopic vascular leakage. Integration of genome-wide transcriptional profile of endothelium and ChIP-sequencing revealed that PR induces a NR4A1 (Nur77/TR3) specific transcriptional program that broadly regulates vascular permeability in response to progesterone. This program triggers concurrent suppression of several junctional proteins and leads to an effective, timely and venule-specific regulation of vascular barrier function. Silencing NR4A1 blocks PR-mediated permeability responses indicating a direct link between PR and NR4A1. These results reveal a previously unknown function for progesterone receptor on endothelial cell biology with consequences to physiological vascular permeability and implications to the clinical use of progestins and anti-progestins on blood vessel integrity. Examination of PR binding sites in HUVEC cells using ChIP-seq (non-infected-negative control, PR infected followed by ligand treatment-PR+P or vehicle PR)

2014-01-01 | E-GEOD-43786 | biostudies-arrayexpress

Physiological Vascular Permeability Requires Induction of Endothelial NR4A1 by Progesterone Receptor [RNA-Seq]

Project description:Vascular permeability is frequently associated with inflammation and it is triggered by chemokines and by a cohort of secreted permeability factors, such as VEGF. In contrast, here we showed that the physiological vascular permeability that precedes implantation is directly controlled by progesterone receptor (PR) and it is independent of VEGF. Both global and endothelial-specific deletion of PR block physiological vascular permeability in the uterus while misexpression of PR in the endothelium of other organs results in ectopic vascular leakage. Integration of genome-wide transcriptional profile of endothelium and ChIP-sequencing revealed that PR induces a NR4A1 (Nur77/TR3) specific transcriptional program that broadly regulates vascular permeability in response to progesterone. This program triggers concurrent suppression of several junctional proteins and leads to an effective, timely and venule-specific regulation of vascular barrier function. Silencing NR4A1 blocks PR-mediated permeability responses indicating a direct link between PR and NR4A1. These results reveal a previously unknown function for progesterone receptor on endothelial cell biology with consequences to physiological vascular permeability and implications to the clinical use of progestins and anti-progestins on blood vessel integrity. Examination of PR target genes in human umbilical vein endothelial cells (HUVECs) using RNA-seq (PR infected only -PR only and PR infected followed by ligand treatment-PR+P)

2014-01-01 | E-GEOD-43788 | biostudies-arrayexpress

Physiological Vascular Permeability Requires Induction of Endothelial NR4A1 by Progesterone Receptor [RNA-Seq]

2014-01-01 | GSE43788 | GEO

Physiological Vascular Permeability Requires Induction of Endothelial NR4A1 by Progesterone Receptor [ChIP-Seq]

2014-01-01 | GSE43786 | GEO