The application of Gaussian processes in the predictions of permeability across mammalian and polydimethylsiloxane membranes

Yi Sun, Rod Adams, Neil Davey, Gary P. Moss, Maria Prapopopolou, Marc B. Brown, Gary P. Martin, Simon C. Wilkinson

Abstract


The problem of predicting the rate of percutaneous absorption of a drug is an important issue, particular with the increasing use of the skin as a means of moderating and controlling drug delivery. One key feature of this problem domain is that human skin permeability to penetrants (often characterised by Kp, the permeability coefficient) has been shown to be inherently non-linear when mathematically related to the key physicochemical parameters of penetrants. The aims of the current study were to apply and validate Gaussian process regression methods to datasets for membranes other than human skin, and to explore how the nature of the dataset may influence its analysis. Permeability data for absorption across rodent and pig skin, and polydimethylsiloxane Silastic® membranes was collected from the literature. Two QSPR methods were applied to compare to the Gaussian process models. The results demonstrated that Gaussian process models with different covariance functions outperform the QSPR model for human, pig and rodent datasets, but in general are not good for Silastic® membranes. These results suggest that the physicochemical parameters employed in this study might not be appropriate for developing models that represent this membrane. In addition, the results show the size of the datasets, in both absolute and comparative senses, appears to influence model quality.


Full Text: PDF DOI: 10.5430/air.v1n2p86

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Artificial Intelligence Research

ISSN 1927-6974 (Print)   ISSN 1927-6982 (Online)

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