Substantivity of sunscreens. In vitro evaluation of the transdermal permeation characteristics of some benzophenone derivatives.

The in vitro permeation through excised hairless mouse skin of a series of 4-O-(N, N-dimethylaminoalkyl)-benzophenones, non-quaternarized and quaternarized, and of two commercial benzophenone sunscreens, taken as reference compounds, was investigated. The aim of the study was to verify the skin penetration of the highly skin-substantive quaternary ammonium derivatives, in comparison with their parent, non-quaternarized compounds. While the quaternary derivatives proved unable to permeate the skin during the period of observation (45 h), their parent amine hydrochlorides and the reference sunscreens (2-hydroxy-4-methoxy-benzophenone-5-sulphonic acid and 2,2′-dihydroxy-4,4′-dimethoxy-benzophenone 5,5′-sodium disulphonate), showed appreciable transdermal fluxes. These data indicate that the presence of a quaternary ammonium group in a molecule, besides inducing a high affinity for cutaneous keratin, may result in hindered or reduced transdermal (and possibly systemic) absorption. Both features may contribute in improving the safety of a cosmetic sunscreen

The application of machine learning to the modelling of percutaneous absorption: An overview and guide

Machine learning (ML) methods have been applied to the analysis of a range of biological systems. This paper reviews the application of these methods to the problem domain of skin permeability and addresses critically some of the key issues. Specifically, ML methods offer great potential in both predictive ability and their ability to provide mechanistic insight to, in this case, the phenomena of skin permeation. However, they are beset by perceptions of a lack of transparency and, often, once a ML or related method has been published there is little impetus from other researchers to adopt such methods. This is usually due to the lack of transparency in some methods and the lack of availability of specific coding for running advanced ML methods. This paper reviews critically the application of ML methods to percutaneous absorption and addresses the key issue of transparency by describing in detail – and providing the detailed coding for – the process of running a ML method (in this case, a Gaussian process regression method). Although this method is applied here to the field of percutaneous absorption, it may be applied more broadly to any biological system.Peer reviewe