Evidence map›Paper›PMID 33392947›Full record

ArticleJournal of computer-aided molecular design2021

Predicting PAMPA permeability using the 3D-RISM-KH theory: are we there yet?

Dipankar Roy, Devjyoti Dutta, David S Wishart, Andriy Kovalenko

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Article in Journal of computer-aided molecular design, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
  2. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Dipankar RoyDepartment of Mechanical Engineering, University of Alberta, 10-203 Donadeo Innovation Centre for Engineering, 9211-116 Street NW, Edmonton, AB, T6G 1H9, Canada.ORCID 0000-0002-4703-0130
Devjyoti DuttaDepartments of Biological Sciences and Computing Science, University of Alberta, Edmonton, AB, T6G 2E8, Canada.ORCID 0000-0002-2516-5624
David S WishartDepartments of Biological Sciences and Computing Science, University of Alberta, Edmonton, AB, T6G 2E8, Canada.ORCID 0000-0002-3207-2434
Andriy KovalenkoDepartment of Mechanical Engineering, University of Alberta, 10-203 Donadeo Innovation Centre for Engineering, 9211-116 Street NW, Edmonton, AB, T6G 1H9, Canada. andriy.kovalenko@ualberta.ca.ORCID 0000-0001-5033-4314

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The parallel artificial membrane permeability assay (PAMPA), a non-cellular lab-based assay, is extensively used to measure the permeability of pharmaceutical compounds. PAMPA experiments provide a working mimic of a molecule passing through cells and PAMPA values are widely used to estimate drug absorption parameters. There is an increased interest in developing computational methods to predict PAMPA permeability values. We developed an in silico model to predict the permeability of compounds based on the PAMPA assay. We used the three-dimensional reference interaction site model (3D-RISM) theory with the Kovalenko-Hirata (KH) closure to calculate the excess chemical potentials of a large set of compounds and predicted their apparent permeability with good accuracy (mean absolute error or MAE = 0.69 units) when compared to a published experimental data set. Furthermore, our in silico PAMPA protocol performed very well in the binary prediction of 288 compounds as being permeable or impermeable (precision = 94%, accuracy = 93%). This suggests that our in silico protocol can mimic the PAMPA assay and could aid in the rapid discovery or screening of potentially therapeutic drug leads that can be delivered to a desired tissue.

Indexed as

Computer SimulationDatabases, ChemicalMachine LearningModels, MolecularOrganic ChemicalsPermeabilityQuantitative Structure-Activity RelationshipSolventsThermodynamicsOrganic ChemicalsSolvents3D-RISM-KHClassificationMachine learningMolecular solvation theoryPAMPA

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.