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Correlation between the structure and skin permeability of compounds. Sci Rep 2021; 11:10076. [PMID: 33980965 PMCID: PMC8115152 DOI: 10.1038/s41598-021-89587-5] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2021] [Accepted: 04/28/2021] [Indexed: 12/02/2022] Open
Abstract
A three-descriptor quantitative structure–activity/toxicity relationship (QSAR/QSTR) model was developed for the skin permeability of a sufficiently large data set consisting of 274 compounds, by applying support vector machine (SVM) together with genetic algorithm. The optimal SVM model possesses the coefficient of determination R2 of 0.946 and root mean square (rms) error of 0.253 for the training set of 139 compounds; and a R2 of 0.872 and rms of 0.302 for the test set of 135 compounds. Compared with other models reported in the literature, our SVM model shows better statistical performance in a model that deals with more samples in the test set. Therefore, applying a SVM algorithm to develop a nonlinear QSAR model for skin permeability was achieved.
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Pecoraro B, Tutone M, Hoffman E, Hutter V, Almerico AM, Traynor M. Predicting Skin Permeability by Means of Computational Approaches: Reliability and Caveats in Pharmaceutical Studies. J Chem Inf Model 2019; 59:1759-1771. [PMID: 30658035 DOI: 10.1021/acs.jcim.8b00934] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/17/2023]
Abstract
The skin is the main barrier between the internal body environment and the external one. The characteristics of this barrier and its properties are able to modify and affect drug delivery and chemical toxicity parameters. Therefore, it is not surprising that permeability of many different compounds has been measured through several in vitro and in vivo techniques. Moreover, many different in silico approaches have been used to identify the correlation between the structure of the permeants and their permeability, to reproduce the skin behavior, and to predict the ability of specific chemicals to permeate this barrier. A significant number of issues, like interlaboratory variability, experimental conditions, data set building rationales, and skin site of origin and hydration, still prevent us from obtaining a definitive predictive skin permeability model. This review wants to show the main advances and the principal approaches in computational methods used to predict this property, to enlighten the main issues that have arisen, and to address the challenges to develop in future research.
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Affiliation(s)
- Beatrice Pecoraro
- Department of Clinical and Pharmaceutical Sciences , University of Hertfordshire , AL10 9AB Hatfield , United Kingdom
| | - Marco Tutone
- Department of Biological Chemical and Pharmaceutical Sciences and Technologies , University of Palermo , 90123 Palermo , Italy
| | - Ewelina Hoffman
- Department of Clinical and Pharmaceutical Sciences , University of Hertfordshire , AL10 9AB Hatfield , United Kingdom
| | - Victoria Hutter
- Department of Clinical and Pharmaceutical Sciences , University of Hertfordshire , AL10 9AB Hatfield , United Kingdom
| | - Anna Maria Almerico
- Department of Biological Chemical and Pharmaceutical Sciences and Technologies , University of Palermo , 90123 Palermo , Italy
| | - Matthew Traynor
- Department of Clinical and Pharmaceutical Sciences , University of Hertfordshire , AL10 9AB Hatfield , United Kingdom
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Ashrafi P, Sun Y, Davey N, Adams RG, Wilkinson SC, Moss GP. Model fitting for small skin permeability data sets: hyperparameter optimisation in Gaussian Process Regression. J Pharm Pharmacol 2018; 70:361-373. [PMID: 29341138 DOI: 10.1111/jphp.12863] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2017] [Accepted: 11/22/2017] [Indexed: 11/30/2022]
Abstract
OBJECTIVES The aim of this study was to investigate how to improve predictions from Gaussian Process models by optimising the model hyperparameters. METHODS Optimisation methods, including Grid Search, Conjugate Gradient, Random Search, Evolutionary Algorithm and Hyper-prior, were evaluated and applied to previously published data. Data sets were also altered in a structured manner to reduce their size, which retained the range, or 'chemical space' of the key descriptors to assess the effect of the data range on model quality. KEY FINDINGS The Hyper-prior Smoothbox kernel results in the best models for the majority of data sets, and they exhibited significantly better performance than benchmark quantitative structure-permeability relationship (QSPR) models. When the data sets were systematically reduced in size, the different optimisation methods generally retained their statistical quality, whereas benchmark QSPR models performed poorly. CONCLUSIONS The design of the data set, and possibly also the approach to validation of the model, is critical in the development of improved models. The size of the data set, if carefully controlled, was not generally a significant factor for these models and that models of excellent statistical quality could be produced from substantially smaller data sets.
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Affiliation(s)
- Parivash Ashrafi
- School of Computer Science, University of Hertfordshire, Hatfield, UK
| | - Yi Sun
- School of Computer Science, University of Hertfordshire, Hatfield, UK
| | - Neil Davey
- School of Computer Science, University of Hertfordshire, Hatfield, UK
| | - Roderick G Adams
- School of Computer Science, University of Hertfordshire, Hatfield, UK
| | - Simon C Wilkinson
- Medical Toxicology Centre, Wolfson Unit, Medical School, University of Newcastle-upon-Tyne, Newcastle upon Tyne, UK
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Przybylak K, Madden J, Covey-Crump E, Gibson L, Barber C, Patel M, Cronin M. Characterisation of data resources for in silico modelling: benchmark datasets for ADME properties. Expert Opin Drug Metab Toxicol 2017; 14:169-181. [DOI: 10.1080/17425255.2017.1316449] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2022]
Affiliation(s)
- K.R. Przybylak
- School of Pharmacy and Chemistry, Liverpool John Moores University, Liverpool, UK
| | - J.C. Madden
- School of Pharmacy and Chemistry, Liverpool John Moores University, Liverpool, UK
| | | | - L. Gibson
- Lhasa Limited, Granary Wharf House, Leeds, UK
| | - C. Barber
- Lhasa Limited, Granary Wharf House, Leeds, UK
| | - M. Patel
- Lhasa Limited, Granary Wharf House, Leeds, UK
| | - M.T.D. Cronin
- School of Pharmacy and Chemistry, Liverpool John Moores University, Liverpool, UK
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Lindh M, Karlén A, Norinder U. Predicting the Rate of Skin Penetration Using an Aggregated Conformal Prediction Framework. Mol Pharm 2017; 14:1571-1576. [DOI: 10.1021/acs.molpharmaceut.7b00007] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Affiliation(s)
- Martin Lindh
- Department
of Medicinal Chemistry, Organic Pharmaceutical Chemistry, BMC, Uppsala University, Box 574, SE-751 23 Uppsala, Sweden
| | - Anders Karlén
- Department
of Medicinal Chemistry, Organic Pharmaceutical Chemistry, BMC, Uppsala University, Box 574, SE-751 23 Uppsala, Sweden
| | - Ulf Norinder
- Swetox, Karolinska Institutet, Unit of Toxicology
Sciences, Forskargatan
20, SE-151 36 Södertälje, Sweden
- Department
of Computer and Systems Sciences, Stockholm University, Forum 100, SE-164 40 Kista, Sweden
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Quantitative prediction of ionization effect on human skin permeability. Int J Pharm 2017; 522:222-233. [DOI: 10.1016/j.ijpharm.2017.03.009] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2017] [Revised: 03/05/2017] [Accepted: 03/06/2017] [Indexed: 12/12/2022]
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Steinmetz FP, Madden JC, Cronin MTD. Data Quality in the Human and Environmental Health Sciences: Using Statistical Confidence Scoring to Improve QSAR/QSPR Modeling. J Chem Inf Model 2015; 55:1739-46. [DOI: 10.1021/acs.jcim.5b00294] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Affiliation(s)
- Fabian P. Steinmetz
- School of Pharmacy and Chemistry, Liverpool John Moores University, Byrom Street, Liverpool, L3 3AF, England
| | - Judith C. Madden
- School of Pharmacy and Chemistry, Liverpool John Moores University, Byrom Street, Liverpool, L3 3AF, England
| | - Mark T. D. Cronin
- School of Pharmacy and Chemistry, Liverpool John Moores University, Byrom Street, Liverpool, L3 3AF, England
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Baba H, Takahara JI, Yamashita F, Hashida M. Modeling and Prediction of Solvent Effect on Human Skin Permeability using Support Vector Regression and Random Forest. Pharm Res 2015; 32:3604-17. [PMID: 26033768 DOI: 10.1007/s11095-015-1720-4] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2015] [Accepted: 05/19/2015] [Indexed: 10/23/2022]
Abstract
PURPOSE The solvent effect on skin permeability is important for assessing the effectiveness and toxicological risk of new dermatological formulations in pharmaceuticals and cosmetics development. The solvent effect occurs by diverse mechanisms, which could be elucidated by efficient and reliable prediction models. However, such prediction models have been hampered by the small variety of permeants and mixture components archived in databases and by low predictive performance. Here, we propose a solution to both problems. METHODS We first compiled a novel large database of 412 samples from 261 structurally diverse permeants and 31 solvents reported in the literature. The data were carefully screened to ensure their collection under consistent experimental conditions. To construct a high-performance predictive model, we then applied support vector regression (SVR) and random forest (RF) with greedy stepwise descriptor selection to our database. The models were internally and externally validated. RESULTS The SVR achieved higher performance statistics than RF. The (externally validated) determination coefficient, root mean square error, and mean absolute error of SVR were 0.899, 0.351, and 0.268, respectively. Moreover, because all descriptors are fully computational, our method can predict as-yet unsynthesized compounds. CONCLUSION Our high-performance prediction model offers an attractive alternative to permeability experiments for pharmaceutical and cosmetic candidate screening and optimizing skin-permeable topical formulations.
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Affiliation(s)
- Hiromi Baba
- Kyoto R&D Center, Maruho Co., Ltd., 93 Awata-cho, Chudoji, Shimogyo-ku, 600-8815, Kyoto, Japan. .,Department of Drug Delivery Research, Graduate School of Pharmaceutical Sciences, Kyoto University, 46-29, Yoshida-shimoadachicho, Sakyo-ku, Kyoto, 606-8501, Japan.
| | - Jun-ichi Takahara
- Kyoto R&D Center, Maruho Co., Ltd., 93 Awata-cho, Chudoji, Shimogyo-ku, 600-8815, Kyoto, Japan
| | - Fumiyoshi Yamashita
- Department of Drug Delivery Research, Graduate School of Pharmaceutical Sciences, Kyoto University, 46-29, Yoshida-shimoadachicho, Sakyo-ku, Kyoto, 606-8501, Japan
| | - Mitsuru Hashida
- Department of Drug Delivery Research, Graduate School of Pharmaceutical Sciences, Kyoto University, 46-29, Yoshida-shimoadachicho, Sakyo-ku, Kyoto, 606-8501, Japan.,Institute for Integrated Cell-Material Sciences, Kyoto University, 46-29, Yoshida-shimoadachicho, Sakyo-ku, Kyoto, 606-8501, Japan
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In Silico Predictions of Human Skin Permeability using Nonlinear Quantitative Structure–Property Relationship Models. Pharm Res 2015; 32:2360-71. [DOI: 10.1007/s11095-015-1629-y] [Citation(s) in RCA: 27] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2014] [Accepted: 01/13/2015] [Indexed: 10/24/2022]
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