Show simple item record

dc.contributor.authorAshrafi, Parivash
dc.contributor.authorSun, Yi
dc.contributor.authorDavey, Neil
dc.contributor.authorWilkinson, Simon Charles
dc.contributor.authorMoss, Gary
dc.date.accessioned2020-01-10T01:08:26Z
dc.date.available2020-01-10T01:08:26Z
dc.date.issued2019-11-14
dc.identifier.citationAshrafi , P , Sun , Y , Davey , N , Wilkinson , S C & Moss , G 2019 , ' The influence of diffusion cell type and experimental temperature on machine learning models of skin permeability ' , Journal of Pharmacy and Pharmacology , pp. 1-12 . https://doi.org/10.1111/jphp.13203
dc.identifier.issn0022-3573
dc.identifier.otherPURE: 18537455
dc.identifier.otherPURE UUID: b2f0676e-21ad-448f-84eb-6b8f3f3b52ee
dc.identifier.otherScopus: 85075203036
dc.identifier.urihttp://hdl.handle.net/2299/22055
dc.description© 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.
dc.description.abstractObjectives The 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. Methods Data 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 findings Using 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. Conclusions This 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.en
dc.format.extent12
dc.language.isoeng
dc.relation.ispartofJournal of Pharmacy and Pharmacology
dc.rightsEmbargoed
dc.subjectFranz diffusion cells
dc.subjectdataset design
dc.subjectflow-through diffusion cells
dc.subjectmachine learning
dc.subjectpercutaneous absorption
dc.subjectPharmacology
dc.subjectPharmaceutical Science
dc.titleThe influence of diffusion cell type and experimental temperature on machine learning models of skin permeabilityen
dc.contributor.institutionSchool of Engineering and Computer Science
dc.contributor.institutionCentre for Computer Science and Informatics Research
dc.contributor.institutionDepartment of Computer Science
dc.contributor.institutionSchool of Computer Science
dc.description.statusPeer reviewed
dc.date.embargoedUntil2020-10-26
dc.identifier.urlhttp://www.scopus.com/inward/record.url?scp=85075203036&partnerID=8YFLogxK
dc.relation.schoolSchool of Engineering and Computer Science
dc.relation.schoolSchool of Computer Science
dc.description.versiontypeFinal Accepted Version
dcterms.dateAccepted2019-11-14
rioxxterms.versionAM
rioxxterms.versionofrecordhttps://doi.org/10.1111/jphp.13203
rioxxterms.licenseref.uriOther
rioxxterms.licenseref.startdate2020-10-26
rioxxterms.typeJournal Article/Review
herts.preservation.rarelyaccessedtrue
herts.date.embargo2020-10-26
herts.rights.accesstypeEmbargoed


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record