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The influence of diffusion cell type and experimental temperature on machine learning models of skin permeability. / Ashrafi, Parivash; Sun, Yi; Davey, Neil; Wilkinson, Simon Charles; Moss, Gary.

In: Journal of Pharmacy and Pharmacology, 14.11.2019, p. 1-12.

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@article{b2f0676e21ad448f84eb6b8f3f3b52ee,
title = "The influence of diffusion cell type and experimental temperature on machine learning models of skin permeability",
abstract = "ObjectivesThe aim of this study was to use Gaussian process regression (GPR) methods to quantify the effect of experimental temperature (Texp) and choice of diffusion cell on model quality and performance.MethodsData were collated from the literature. Static and flow‐through diffusion cell data were separated, and a series of GPR experiments was conducted. The effect of Texp was assessed by comparing a range of datasets where Texp either remained constant or was varied from 22 to 45 °C.Key findingsUsing data from flow‐through diffusion cells results in poor model performance. Data from static diffusion cells resulted in significantly greater performance. Inclusion of data from flow‐through cell experiments reduces overall model quality. Consideration of Texp improves model quality when the dataset used exhibits a wide range of experimental temperatures.ConclusionsThis study highlights the problem of collating literature data into datasets from which models are constructed without consideration of the nature of those data. In order to optimise model quality data from only static, Franz‐type, experiments should be used to construct the model and Texp should either be incorporated as a descriptor in the model if data are collated from a range of studies conducted at different temperatures.",
keywords = "Franz diffusion cells, dataset design, flow-through diffusion cells, machine learning, percutaneous absorption",
author = "Parivash Ashrafi and Yi Sun and Neil Davey and Wilkinson, {Simon Charles} and Gary Moss",
note = "{\textcopyright} 2019 Royal Pharmaceutical Society. This is the peer reviewed version of the following article: The influence of diffusion cell type and experimental temperature on machine learning models of skin permeability, which has been published in final form at https://doi.org/10.1111/jphp.13203 . This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Use of Self-Archived Versions. ",
year = "2019",
month = nov,
day = "14",
doi = "10.1111/jphp.13203",
language = "English",
pages = "1--12",
journal = "Journal of Pharmacy and Pharmacology",
issn = "0022-3573",
publisher = "Pharmaceutical Press",

}

RIS

TY - JOUR

T1 - The influence of diffusion cell type and experimental temperature on machine learning models of skin permeability

AU - Ashrafi, Parivash

AU - Sun, Yi

AU - Davey, Neil

AU - Wilkinson, Simon Charles

AU - Moss, Gary

N1 - © 2019 Royal Pharmaceutical Society. This is the peer reviewed version of the following article: The influence of diffusion cell type and experimental temperature on machine learning models of skin permeability, which has been published in final form at https://doi.org/10.1111/jphp.13203 . This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Use of Self-Archived Versions.

PY - 2019/11/14

Y1 - 2019/11/14

N2 - ObjectivesThe aim of this study was to use Gaussian process regression (GPR) methods to quantify the effect of experimental temperature (Texp) and choice of diffusion cell on model quality and performance.MethodsData were collated from the literature. Static and flow‐through diffusion cell data were separated, and a series of GPR experiments was conducted. The effect of Texp was assessed by comparing a range of datasets where Texp either remained constant or was varied from 22 to 45 °C.Key findingsUsing data from flow‐through diffusion cells results in poor model performance. Data from static diffusion cells resulted in significantly greater performance. Inclusion of data from flow‐through cell experiments reduces overall model quality. Consideration of Texp improves model quality when the dataset used exhibits a wide range of experimental temperatures.ConclusionsThis study highlights the problem of collating literature data into datasets from which models are constructed without consideration of the nature of those data. In order to optimise model quality data from only static, Franz‐type, experiments should be used to construct the model and Texp should either be incorporated as a descriptor in the model if data are collated from a range of studies conducted at different temperatures.

AB - ObjectivesThe aim of this study was to use Gaussian process regression (GPR) methods to quantify the effect of experimental temperature (Texp) and choice of diffusion cell on model quality and performance.MethodsData were collated from the literature. Static and flow‐through diffusion cell data were separated, and a series of GPR experiments was conducted. The effect of Texp was assessed by comparing a range of datasets where Texp either remained constant or was varied from 22 to 45 °C.Key findingsUsing data from flow‐through diffusion cells results in poor model performance. Data from static diffusion cells resulted in significantly greater performance. Inclusion of data from flow‐through cell experiments reduces overall model quality. Consideration of Texp improves model quality when the dataset used exhibits a wide range of experimental temperatures.ConclusionsThis study highlights the problem of collating literature data into datasets from which models are constructed without consideration of the nature of those data. In order to optimise model quality data from only static, Franz‐type, experiments should be used to construct the model and Texp should either be incorporated as a descriptor in the model if data are collated from a range of studies conducted at different temperatures.

KW - Franz diffusion cells

KW - dataset design

KW - flow-through diffusion cells

KW - machine learning

KW - percutaneous absorption

UR - http://www.scopus.com/inward/record.url?scp=85075203036&partnerID=8YFLogxK

U2 - 10.1111/jphp.13203

DO - 10.1111/jphp.13203

M3 - Article

SP - 1

EP - 12

JO - Journal of Pharmacy and Pharmacology

JF - Journal of Pharmacy and Pharmacology

SN - 0022-3573

ER -