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Development of a Gaussian Process – feature selection model to characterise (poly)dimethylsiloxane (Silastic®) membrane permeation

Lookup NU author(s): Dr Simon Wilkinson

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This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).


Abstract

© 2020 Royal Pharmaceutical Society, Journal of Pharmacy and PharmacologyObjectives: The current study aims to determine the effect of physicochemical descriptor selection on models of polydimethylsiloxane permeation. Methods: A total of 2942 descriptors were calculated for a data set of 77 chemicals. Data were processed to remove redundancy, single values, imbalanced and highly correlated data, yielding 1363 relevant descriptors. For four independent test sets, feature selection methods were applied and modelled via a variety of Machine Learning methods. Key findings: Two sets of molecular descriptors which can provide improved predictions, compared to existing models, have been identified. Best permeation predictions were found with Gaussian Process methods. The molecular descriptors describe lipophilicity, partial charge and hydrogen bonding as key determinants of PDMS permeation. Conclusions: This study highlights important considerations in the development of relevant models and in the construction and use of the data sets used in such studies, particularly that highly correlated descriptors should be removed from data sets. Predictive models are improved by the methodology adopted in this study, notably the systematic evaluation of descriptors, rather than simply using any and all available descriptors, often based empirically on in vitro experiments. Such findings also have clear relevance to a number of other fields.


Publication metadata

Author(s): Sun Y, Hewitt M, Wilkinson SC, Davey N, Adams RG, Gullick DR, Moss GP

Publication type: Article

Publication status: Published

Journal: Journal of Pharmacy and Pharmacology

Year: 2020

Pages: epub ahead of print

Online publication date: 04/04/2020

Acceptance date: 08/03/2009

Date deposited: 20/04/2020

ISSN (print): 0022-3573

ISSN (electronic): 2042-7158

Publisher: Blackwell Publishing Ltd

URL: https://doi.org/10.1111/jphp.13263

DOI: 10.1111/jphp.13263


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