Development of a Gaussian Process – Feature Selection Model to Characterise (poly)dimethylsiloxane (Silastic®) Membrane Permeation

© 2020 Royal Pharmaceutical Society, Journal of Pharmacy and Pharmacology. This is an open access article under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits use, distribution and reproduction in any medium, provided the original work is properly cited.Objectives The current study aims to determine the effect of physicochemical descriptor selection on models of polydimethylsiloxane permeation. Methods 2,942 descriptors were calculated for a dataset of 77 chemicals. Data was processed to remove redundancy, single values, imbalanced and highly correlated data, yielding 1,363 relevant descriptors. For four independent test sets feature selection methods were applied and modelled via a variety of Machine Learning methods. Key findings Two sets of molecular descriptors which can provide improved predictions, compared to existing models, have been identified. Best permeation predictions were found with Gaussian Process methods. The molecular descriptors describe lipophilicity, partial charge and hydrogen bonding as key determinants of PDMS permeation. Conclusions This study highlights important considerations in the development of relevant models and in the construction and use of the datasets used in such studies, particularly that highly correlated descriptors should be removed from datasets. Predictive models are improved by the methodology adopted in this study, notably the systematic evaluation of descriptors, rather than simply using any and all available descriptors, often based empirically on in vitro experiments. Such findings also have clear relevance to a number of other fieldsPeer reviewe

Partitioning of Nonsteroidal Antiinflammatory Drugs in Lipid Membranes: A Molecular Dynamics Simulation Study

Using the potential of mean constrained force method, molecular dynamics simulations with atomistic details were performed to examine the partitioning and nature of interactions of two nonsteroidal antiinflammatory drugs, namely aspirin and ibuprofen, in bilayers of dipalmitoylphosphatidylcholine. Two charge states (neutral and anionic) of the drugs were simulated to understand the effect of protonation or pH on drug partitioning. Both drugs, irrespective of their charge state, were found to have high partition coefficients in the lipid bilayer from water. However, the values and trends of the free energy change and the location of the minima in the bilayer are seen to be influenced by the drug structure and charge state. In the context of the transport of the drugs through the bilayer, the charged forms were found to permeate fully hydrated in contrast to the neutral forms that permeate unhydrated

Penetration of metals through the skin barrier

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