Abstract
The problem of predicting the rate of percutaneous absorption of a drug is an important issue with the increasing use of the skin as a means of moderating and controlling drug delivery. The aim of the current study was to explore whether including another species skin data in a training set can improve predictions of the human skin permeability coefficient. Permeability data for absorption across rodent skin was collected from the literature. The Gaussian process model was applied to the data, and this was compared to two QSPR methods. The results demonstrate that data from non-human skin can provide useful information in the prediction of the permeability of human skin.
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Sun, Y., Brown, M.B., Prapopoulou, M., Adams, R., Davey, N., Moss, G.P. (2012). The Application of Gaussian Processes in the Predictions of Permeability across Mammalian Membranes. In: Villa, A.E.P., Duch, W., Érdi, P., Masulli, F., Palm, G. (eds) Artificial Neural Networks and Machine Learning – ICANN 2012. ICANN 2012. Lecture Notes in Computer Science, vol 7553. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33266-1_63
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DOI: https://doi.org/10.1007/978-3-642-33266-1_63
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