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        The application of Gaussian processes in the prediction of percutaneous absorption

        Author
        Moss, Gary P.
        Sun, Yi
        Prapopoulou, Maria
        Davey, N.
        Adams, Roderick
        Pugh, W. John
        Brown, Marc
        Attention
        2299/4843
        Abstract
        Objectives The aim was to assess mathematically the nature of a skin permeability dataset and to determine the utility of Gaussian processes in developing a predictive model for skin permeability, comparing it with existing methods for deriving predictive models.Methods Principal component analysis was carried out in order to determine the nature of the dataset. MatLab software was used to assess the performance of Gaussian process, single linear networks (SLN) and quantitative structure-permeability relationships (QSPRs) using a range of statistical measures.Key findings Principal component analysis showed that the dataset is inherently non-linear. The Gaussian process model yielded a predictive model that provides a significantly more accurate estimate of skin absorption than previous models, particularly QSPRs (which were consistently worse than Gaussian process or SLN models), and does so across a wider range of molecular properties. Gaussian process models appear particularly capable of providing excellent predictions where previous studies have shown QSPRs to fail, such as where penetrants have high log P and high molecular weight.Conclusions A non-linear approach was more appropriate than QSPRs or SLNs for the analysis of the dataset employed herein, as the prediction and confidence values in the prediction given by the Gaussian process are better than with other methods examined. Gaussian process provides a novel way of analysing skin absorption data that is substantially more accurate, statistically robust and reflective of our empirical understanding of skin absorption than the QSPR methods so far applied to skin absorption.
        Publication date
        2009-09
        Published in
        Journal of Pharmacy and Pharmacology
        Published version
        https://doi.org/10.1211/jpp/61.09.0003
        Other links
        http://hdl.handle.net/2299/4843
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