Development and Evaluation of Two-Parameter Linear Free Energy Models for the Prediction of Human Skin Permeability Coefficient of Neutral Organic Chemicals

Development and Evaluation of Two-Parameter Linear Free Energy Models for the Prediction of Human Skin Permeability Coefficient of Neutral Organic Chemicals | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research article Development and Evaluation of Two-Parameter Linear Free Energy Models for the Prediction of Human Skin Permeability Coefficient of Neutral Organic Chemicals Sana Naseem, Yasuyuki Zushi, Deedar Nabi This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-60132/v3 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 19 Mar, 2021 Read the published version in Journal of Cheminformatics → Version 3 posted 9 You are reading this latest preprint version Show more versions Abstract The experimental values of skin permeability coefficients, required for dermal exposure assessment, are not readily available for many chemicals. The existing estimation approaches are either less accurate or require many parameters that are not readily available. Furthermore, current estimation methods are not easy to apply to complex environmental mixtures. We present two models to estimate the skin permeability coefficients of neutral organic chemicals. The first model, referred to here as the 2-parameter partitioning model (PPM), exploits a linear free energy relationship (LFER) of skin permeability coefficient with a linear combination of partition coefficients for octanol-water and air-water systems. The second model is based on the retention time information of nonpolar analytes on comprehensive two-dimensional gas chromatography (GC×GC). The PPM successfully explained variability in the skin permeability data ( n = 175) with R 2 = 0.82 and root mean square error ( RMSE ) = 0.47 log unit. In comparison, the US-EPA’s model DERMWIN exhibited an RMSE of 0.78 log unit. The Zhang model a 5-parameter LFER equation based on experimental Abraham solute descriptors (ASDs) performed slightly better with an RMSE value of 0.44 log unit. However, the Zhang model is limited by the scarcity of experimental ASDs. The GC×GC model successfully explained the variance in skin permeability data of nonpolar chemicals ( n = 79) with R 2 = 0.90 and RMSE = 0.23 log unit. The PPM can easily be implemented in US-EPA’s Estimation Program Interface Suite (EPI Suite™). The GC×GC model can be applied to the complex mixtures of nonpolar chemicals. Environmental Engineering Bioinformatics Skin Permeability Linear Free Energy Relationship (LFER) Modeling Abraham Solvation Model GC×GC model Complex Mixtures Dermal Permeability Coefficient Program (DERMWIN™) Figures Figure 1 Figure 2 Full Text Supplementary Files GraphicalAbstract.jpg TableS1.csv TableS3.csv TableS11.csv TableS4.csv SI091920.pdf TableS12.csv TableS14.csv TableS15.csv Cite Share Download PDF Status: Published Journal Publication published 19 Mar, 2021 Read the published version in Journal of Cheminformatics → Version 3 posted Review # 2 received at journal 24 Jan, 2021 Editorial decision: Major revision 24 Jan, 2021 Reviewer # 2 agreed at journal 03 Jan, 2021 Review # 1 received at journal 29 Dec, 2020 Reviewer # 1 agreed at journal 22 Dec, 2020 Editor assigned by journal 21 Sep, 2020 Reviewers invited by journal 21 Sep, 2020 Submission checks completed at journal 20 Sep, 2020 Editor invited by journal 20 Sep, 2020 You are reading this latest preprint version Show more versions Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-60132","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research article","associatedPublications":[],"authors":[{"id":2566286,"identity":"655ba54e-065e-44f6-9644-f7f852b80bfe","order_by":0,"name":"Sana Naseem","email":"","orcid":"https://orcid.org/0000-0003-1918-3516","institution":"National University of Sciences and Tecnology","correspondingAuthor":false,"prefix":"","firstName":"Sana","middleName":"","lastName":"Naseem","suffix":""},{"id":2566287,"identity":"71ab5075-e856-45d4-b59e-c1e5549f69f8","order_by":1,"name":"Yasuyuki Zushi","email":"","orcid":"https://orcid.org/0000-0001-8062-1592","institution":"National Institute of Advanced Industrial Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Yasuyuki","middleName":"","lastName":"Zushi","suffix":""},{"id":2566288,"identity":"f152e05c-6f3d-49c5-887a-6dfc64965501","order_by":2,"name":"Deedar Nabi","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABK0lEQVRIie2PsUrEQBCG5wikirl2wt3pK0QWAuLhs2QJpAoiCJJCYiQQm9NeBH2FiK3FhoVLkweIeOBdYyUYEURB1PW0SLHBw0pkP2Zh2ZmP+RdAofiLMACcX7CzP3UbjU4MoP2kJLbbeF1EAR1hEcUskrx6uYTN7kmShtMwWobeiDzWMBxkbInbEsUqx97a6Ba2cZKnlVtyAv3SQQY+yZjpuRLFrgIHDQY0rmgqitEYA0cE4zRjBmEy5eb+yXoVypmY36Jv0Z5QSM3gvV2pDL33uSUTitiluYCBLYKxuSILZpU+We8zpOeTPEF3zFdT9HewtD1yzE1P9n2z4LOrOzakp9cHs4fn3Wili95FHYYbg6PikKNE+abR0r8Ci6MZ7UIbv1AUCoXiP/IBGUNuf437FM0AAAAASUVORK5CYII=","orcid":"https://orcid.org/0000-0002-0188-0404","institution":"National University of Sciences and Technology","correspondingAuthor":true,"prefix":"","firstName":"Deedar","middleName":"","lastName":"Nabi","suffix":""}],"badges":[],"createdAt":"2020-08-15 10:52:51","currentVersionCode":3,"declarations":"","doi":"10.21203/rs.3.rs-60132/v3","doiUrl":"https://doi.org/10.21203/rs.3.rs-60132/v3","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s13321-021-00503-5","type":"published","date":"2021-03-19T15:00:34+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":2536075,"identity":"71a9530f-17d8-443f-beb9-3e5c447b9032","added_by":"auto","created_at":"2020-09-22 14:53:16","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":309437,"visible":true,"origin":"","legend":"Dimensionality analysis for the PPM training set. Top panels show the results obtained by the Principal Component Analysis (PCA) ran on 175 × 5 matrix, [E S A B V], of Abraham solute descriptors for the training set of the Zhang Model in the form of (a) Scree Plot of eigenvalues (i.e., the amount of variation retained by each principal component), and (b) the correlation circle showing the relationship and quality of representation, square cosine (cos2), of variables in first two dimensions. Lower panels show (d) the distribution of quality of representation, Cos2, into the first five dimensions obtained by the PCA, and (e) the correlogram of the correlation matrix obtained respectively by the PCA and Pearson correlation analysis of 175 × 8 matrix, [E S A B V log K_(o-w) log K_(a-w) log⁡〖K_p 〗 ]. In Panel (b), the length of arrowed line from the origin shows the quality of representation of variable. Angles between the arrowed lines show the degree of correlations: Descriptor A is almost orthogonal to E, S, B and V descriptors, which are mutually positively correlated. In Panel (c), color intensity and size of the circle are proportional to the quality of presentation of a variable. In Panel (d), Blue and Red color respectively show positive and negative correlations between the pair. The value of correlation coefficient for each pair of variables is shown in each square. All correlations, shown here, were statistically significant (p \u003c 0.05). In Panel c and d, Dim. stands for the dimension. ","description":"","filename":"fig1.png","url":"https://assets-eu.researchsquare.com/files/rs-60132/v3/fig1.png"},{"id":2536076,"identity":"5ff92755-237c-4835-b63b-ca37f3e7bb39","added_by":"auto","created_at":"2020-09-22 14:53:16","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":365569,"visible":true,"origin":"","legend":"Linear regression plot for (a) Two - Parameter Partitioning Model (PPM), and (b) GC×GC Model. Upper and lower green lines bound 95% confidence interval around the regression line (dotted black line in the middle). Lower panels show (c) scatterplot obtained by comparing the prediction of log⁡〖K_p 〗 from three models, Zhang model (green triangles), DERMWIN (red square) and PPM (purple crosses), with the experimental values. Panel (d) shows the result of independent validation of the GC×GC Model obtained by comparing the predictions (green circles) for 52 nonpolar chemicals - which were analyzed on the GC×GC - with the predictions of the Zhang model. Predictions of DERMWIN (red squares) also shown for comparative purpose. In the lower panels, the dotted line in the middle shows 1:1 agreement, and upper and lower dotted lines indicate 1:2 agreement between the reference and predicted values. 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14:53:18","extension":"csv","order_by":8,"title":"","display":"","copyAsset":false,"role":"supplement","size":2099,"visible":true,"origin":"","legend":"","description":"","filename":"TableS14.csv","url":"https://assets-eu.researchsquare.com/files/rs-60132/v3/TableS14.csv"},{"id":2536086,"identity":"7687a00f-6221-4e34-b0dd-f0da6654881a","added_by":"auto","created_at":"2020-09-22 14:53:18","extension":"csv","order_by":9,"title":"","display":"","copyAsset":false,"role":"supplement","size":544,"visible":true,"origin":"","legend":"","description":"","filename":"TableS15.csv","url":"https://assets-eu.researchsquare.com/files/rs-60132/v3/TableS15.csv"}],"financialInterests":"","formattedTitle":"Development and Evaluation of Two-Parameter Linear Free Energy Models for the Prediction of Human Skin Permeability Coefficient of Neutral Organic Chemicals","fulltext":[{"header":"Full Text","content":"\u003cp\u003eThis preprint is available for \u003ca href='/article/rs-60132/latest.pdf' target='_blank'\u003edownload as a PDF\u003c/a\u003e.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"journal-of-cheminformatics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"chin","sideBox":"Learn more about [Journal of Cheminformatics](https://jcheminf.biomedcentral.com/)","snPcode":"13321","submissionUrl":"https://submission.nature.com/new-submission/13321/3","title":"Journal of Cheminformatics","twitterHandle":"@jcheminf","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Skin Permeability, Linear Free Energy Relationship (LFER) Modeling, Abraham Solvation Model, GC×GC model, Complex Mixtures, Dermal Permeability Coefficient Program (DERMWIN™)","lastPublishedDoi":"10.21203/rs.3.rs-60132/v3","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-60132/v3","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe experimental values of skin permeability coefficients, required for dermal exposure assessment, are not readily available for many chemicals. The existing estimation approaches are either less accurate or require many parameters that are not readily available. Furthermore, current estimation methods are not easy to apply to complex environmental mixtures. We present two models to estimate the skin permeability coefficients of neutral organic chemicals. The first model, referred to here as the 2-parameter partitioning model (PPM), exploits a linear free energy relationship (LFER) of skin permeability coefficient with a linear combination of partition coefficients for octanol-water and air-water systems. The second model is based on the retention time information of nonpolar analytes on comprehensive two-dimensional gas chromatography (GC×GC). The PPM successfully explained variability in the skin permeability data (\u003cem\u003en\u003c/em\u003e = 175) with \u003cem\u003eR\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e = 0.82 and root mean square error (\u003cem\u003eRMSE\u003c/em\u003e) = 0.47 \u003cem\u003elog\u003c/em\u003e unit. In comparison, the US-EPA’s model DERMWIN exhibited an \u003cem\u003eRMSE\u003c/em\u003e of 0.78 \u003cem\u003elog\u003c/em\u003e unit. The Zhang model \u003c/p\u003e\u003cp\u003e\u0026nbsp;a 5-parameter LFER equation based on experimental Abraham solute descriptors (ASDs) performed slightly better with an \u003cem\u003eRMSE\u003c/em\u003e value of 0.44 \u003cem\u003elog\u003c/em\u003e unit. However, the Zhang model is limited by the scarcity of experimental ASDs. The GC×GC model successfully explained the variance in skin permeability data of nonpolar chemicals (\u003cem\u003en\u003c/em\u003e = 79) with \u003cem\u003eR\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e = 0.90 and \u003cem\u003eRMSE\u003c/em\u003e = 0.23 \u003cem\u003elog\u003c/em\u003e unit.\u0026nbsp;The PPM can easily be implemented in US-EPA’s Estimation Program Interface Suite (EPI Suite™). \u0026nbsp;The GC×GC model can be applied to the complex mixtures of nonpolar chemicals.\u003c/p\u003e","manuscriptTitle":"Development and Evaluation of Two-Parameter Linear Free Energy Models for the Prediction of Human Skin Permeability Coefficient of Neutral Organic Chemicals","msid":"","msnumber":"","nonDraftVersions":[{"code":"","date":"2021-03-03 00:00:00","doi":"","editorialEvents":[{"type":"editorInvitedReview","content":"","date":"2021-03-03T00:00:00+00:00","index":1,"fulltext":"Recommendation: Reviewer's comments unavailable due to the journal's 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