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Predictive Methods in Percutaneous Absorption pp 117–139Cite as

Squiggly Lines and Random Dots—You Can Fit Anything with a Nonlinear Model

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Abstract

This chapter describes the advances made in methodological aspects of modelling skin permeability by considering artificial intelligence, fuzzy logic and advanced Machine Learning methods and how they have been applied to skin absorption. It reviews the often-stunted contribution to the field of these methods and describes the advantages such methods have produced in recent years. In addition, the chapter also considers problems with these methods, particularly the lack of use in the skin permeability field due to the need for specific computational expertise that is not normally associated with the development of quantitative structure–permeability models, and discusses how such lack of widespread use can be tackled. In addition, as this chapter describes models that often fall into the “black box” category and do not therefore present a readily usable algorithm, an explicit output methods used to discern mechanistic aspects of the skin permeability process are discussed. These include feature selection methods, which have been applied to skin permeability and which have shown the non-independence and interrelationship of key descriptors on each other and that the need for an algorithm with discrete expressions may be of limited value.

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Notes

  1. 1.

    The Kubinyi function is a statistical parameter closely related to the Fisher ratio (F). Whereas the Fisher ratio (F) is often sensitive to changes in small d values, and poorly sensitive to changes in large d values, the Kubinyi function avoids these issues. In general, a larger Kubinyi values suggest a better linear equation. The Akaike’s information criterion is an indicator of the relative quality of a model for a particular set of data and thus is used in model selection.

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Author information

Authors and Affiliations

  1. The School of Pharmacy, Keele University, Keele, Staffordshire, UK

    Gary P. Moss

  2. College of Pharmacy, University of Georgia, Athens, Georgia, USA

    Darren R. Gullick

  3. Medical Toxicology Centre Institute of Cellular Medicine, Newcastle University, Newcastle upon Tyne, UK

    Simon C. Wilkinson

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  1. Gary P. Moss
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  2. Darren R. Gullick
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  3. Simon C. Wilkinson
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Correspondence to Gary P. Moss .

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Moss, G.P., Gullick, D.R., Wilkinson, S.C. (2015). Squiggly Lines and Random Dots—You Can Fit Anything with a Nonlinear Model. In: Predictive Methods in Percutaneous Absorption. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-47371-9_7

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