The Permeability of an Artificial Membrane for Wide Range of pH in Human Gastrointestinal Tract: Experimental Measurements and Quantitative StructureActivity Relationship

Oja, Mare; Maran, Uko

doi:10.1002/minf.201400147

Cited by 23 publications

(25 citation statements)

References 50 publications

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“…However, PAMPA permeability is sensitive to the assay conditions (vide supra). To warrant data consistency and to minimize the variations in assay conditions among different data sources [34,38,39,40,41,42,51,55,75,77,78,79,80], only those molecules assayed by Oja and Maran [39,41,42] were selected in this study since they generated the largest quantity of data. If there were two or more available efflux ratio data for a given compound and in close range, the average values were then taken in order to warrant better consistency.…”

Section: Methodsmentioning

confidence: 99%

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

Chi

Lee

Weng

et al. 2019

IJMS

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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.

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Section: Methodsmentioning

confidence: 99%

“…As such, in silico approaches play an increasing role in ADME/Tox profiling [27]. In fact, numerous qualitative structure–activity relationship (QSAR) models have been developed to predict PAMPA permeability [7,23,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47].…”

Section: Introductionmentioning

confidence: 99%

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

Chi

Lee

Weng

et al. 2019

IJMS

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“…13–15 A number of physical models have been proposed to predict passive membrane permeation, in an attempt to simulate the underlying physical permeation process. 16 The majority of the physical models exhibited poor predictability based on the small datasets, partly due to complexity of the permeation process and lack of reliable parameters to formulate the equations associated with the process.…”

Section: Introductionmentioning

confidence: 99%

Highly predictive and interpretable models for PAMPA permeability

Sun

Nguyen

Kerns

et al. 2017

Bioorganic & Medicinal Chemistry

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Cell membrane permeability is an important determinant for oral absorption and bioavailability of a drug molecule. An in-silico model predicting drug permeability is described, which is built based on a large permeability dataset of 7488 compound entries or 5,435 structurally unique molecules measured by the same lab using parallel artificial membrane permeability assay (PAMPA). On the basis of customized molecular descriptors, the support vector regression (SVR) model trained with 4,071 compounds with quantitative data is able to predict the remaining 1,364 compounds with the qualitative data with an area under the curve of receiver operating characteristic (AUC-ROC) of 0.90. The support vector classification (SVC) model trained with half of the whole dataset comprised of both the quantitative and the qualitative data produced accurate predictions to the remaining data with the AUC-ROC of 0.88. The results suggest that the developed SVR model is highly predictive and provides medicinal chemists a useful in-silico tool to facilitate design and synthesis of novel compounds with optimal drug-like properties, and thus accelerate the lead optimization in drug discovery.

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“…Only a limited number of studies reported models for PAMPA permeability in literature. 31–35 Most studies either use a different kind of assay to measure PAMPA permeability or use small datasets comprising a few hundred compounds. Searching by keyword “PAMPA” resulted in a total of 935 assay records and 1015 measurements on ChEMBL.…”

Section: Resultsmentioning

confidence: 99%