Development of a Gaussian Process – feature selection model to characterise (poly)dimethylsiloxane (Silastic®) membrane permeation

The current study aims to determine the effect of physicochemical descriptor selection on models of polydimethylsiloxane permeation.

[1]  Neil Davey,et al.  The application of feature selection to the development of Gaussian process models for percutaneous absorption , 2010, The Journal of pharmacy and pharmacology.

[2]  Michael H. Abraham.  Scales of Solute Hydrogen-Bonding: Their Construction and Application to Physicochemical and Biochemical Processes , 2010 .

[3]  Cardona Alzate,et al.  Predicción y selección de variables con bosques aleatorios en presencia de variables correlacionadas , 2020 .

[4]  Michael S. Roberts,et al.  Epidermal permeability: Penetrant structure relationships. 2. The effect of h-bonding groups in penetrants on their diffusion through the stratum corneum , 1996 .

[5]  W D Plummer,et al.  Power and sample size calculations for studies involving linear regression. , 1998, Controlled clinical trials.

[6]  Brian W. Barry,et al.  On the non-Gaussian distribution of human skin permeabilities , 1992 .

[7]  J W Green,et al.  Rapid integrity assessment of rat and human epidermal membranes for in vitro dermal regulatory testing: correlation of electrical resistance with tritiated water permeability. , 2002, Toxicology in vitro : an international journal published in association with BIBRA.

[8]  G Lallement,et al.  In vitro percutaneous penetration of organophosphorus compounds using full-thickness and split-thickness pig and human skin. , 2007, Toxicology in vitro : an international journal published in association with BIBRA.

[9]  Michael H. Abraham,et al.  Hydrogen bonding part 46: a review of the correlation and prediction of transport properties by an lfer method: physicochemical properties, brain penetration and skin permeability , 1999 .

[10]  Jonathan Hadgraft,et al.  Simultaneous monitoring of drug and solvent diffusion across a model membrane using ATR-FTIR spectroscopy. , 2009, European journal of pharmaceutical sciences : official journal of the European Federation for Pharmaceutical Sciences.

[11]  Neil Davey,et al.  The influence of diffusion cell type and experimental temperature on machine learning models of skin permeability , 2019, The Journal of pharmacy and pharmacology.

[12]  Gary P Moss,et al.  Formulation and characterization of a captopril ethyl ester drug-in-adhesive-type patch for percutaneous absorption , 2010, Drug development and industrial pharmacy.

[13]  Yakov Frum,et al.  Evidence that drug flux across synthetic membranes is described by normally distributed permeability coefficients. , 2007, European journal of pharmaceutics and biopharmaceutics : official journal of Arbeitsgemeinschaft fur Pharmazeutische Verfahrenstechnik e.V.

[14]  George Lee,et al.  Evaluating feature selection strategies for high dimensional, small sample size datasets , 2011, 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[15]  Neil Davey,et al.  Model fitting for small skin permeability data sets: hyperparameter optimisation in Gaussian Process Regression , 2018, The Journal of pharmacy and pharmacology.

[16]  M T D Cronin,et al.  Quantitative structure-permeability relationships (QSPRs) for percutaneous absorption. , 2002, Toxicology in vitro : an international journal published in association with BIBRA.

[17]  Russell O. Potts,et al.  A Predictive Algorithm for Skin Permeability: The Effects of Molecular Size and Hydrogen Bond Activity , 1995, Pharmaceutical Research.

[18]  V. Meidan,et al.  Assessment of drug permeability distributions in two different model skins. , 2005, International journal of pharmaceutics.

[19]  G P Moss,et al.  An Investigation of the Mechanism of Flux Across Polydimethylsiloxane Membranes by use of Quantitative Structure‐Permeability Relationships , 1998, The Journal of pharmacy and pharmacology.

[20]  Russell O. Potts,et al.  Predicting Skin Permeability , 1992, Pharmaceutical Research.

[21]  A. D. Woolfson.  Development and characterisation of a moisture-activated bioadhesive drug delivery system for percutaneous local anaesthesia , 1998 .

[22]  Pedro Larrañaga,et al.  Bioinformatics Advance Access published August 24, 2007 A review of feature selection techniques in bioinformatics , 2022 .

[23]  Michael Wolzt,et al.  In vitro vs. in vivo tape stripping: validation of the porcine ear model and penetration assessment of novel sucrose stearate emulsions. , 2012, European journal of pharmaceutics and biopharmaceutics : official journal of Arbeitsgemeinschaft fur Pharmazeutische Verfahrenstechnik e.V.

[24]  F. Agakov,et al.  Application of high-dimensional feature selection: evaluation for genomic prediction in man , 2015, Scientific Reports.

[25]  Michael S. Roberts,et al.  Epidermal permeability penetrant structure relationships .1. An analysis of methods of predicting penetration of monofunctional solutes from aqueous solutions , 1995 .

[26]  Michael H. Abraham,et al.  Hydrogen bonding: XVII. The characterisation of 24 gas-liquid chromatographic stationary phases studied by Poole and co-workers. including molten salts, and evaluation of solute-stationary phase interactions , 1991 .

[27]  Neil Davey,et al.  The application of stochastic machine learning methods in the prediction of skin penetration , 2011, Appl. Soft Comput..

[28]  Neil Davey,et al.  The application of Gaussian processes in the prediction of percutaneous absorption , 2009 .

[29]  LarrañagaPedro,et al.  A review of feature selection techniques in bioinformatics , 2007 .

[30]  W. Loh,et al.  REGRESSION TREES WITH UNBIASED VARIABLE SELECTION AND INTERACTION DETECTION , 2002 .

[31]  J. Bouwstra,et al.  Lipid organization in human and porcine stratum corneum differs widely, while lipid mixtures with porcine ceramides model human stratum corneum lipid organization very closely. , 2008, Biochimica et biophysica acta.

[32]  Michael H. Abraham,et al.  The Factors that Influence Skin Penetration of Solutes * , 1995 .

[33]  Sam Efromovich.  Adaptive estimation of error density in nonparametric regression with small sample size , 2007 .

[34]  J Hadgraft,et al.  Thermodynamic considerations of solvent/enhancer uptake into a model membrane. , 2010, International journal of pharmaceutics.

[35]  F Paquet,et al.  INCORPORATION AND DISTRIBUTION OF URANIUM IN RATS AFTER A CONTAMINATION ON INTACT OR WOUNDED SKIN , 2007, Health physics.

[36]  Ulisses Braga-Neto,et al.  Bolstered error estimation , 2004, Pattern Recognit..

[37]  Geoffrey E. Hinton,et al.  Visualizing Data using t-SNE , 2008 .

[38]  Neil Davey,et al.  The application and limitations of mathematical modelling in the prediction of permeability across mammalian skin and polydimethylsiloxane membranes , 2011, The Journal of pharmacy and pharmacology.

[39]  M. Abraham,et al.  Algorithms for Skin Permeability Using Hydrogen Bond Descriptors: the Problem of Steroids * , 1997, The Journal of pharmacy and pharmacology.

[40]  R. Tibshirani.  Regression Shrinkage and Selection via the Lasso , 1996 .

[41]  Edward R. Dougherty,et al.  What should be expected from feature selection in small-sample settings , 2006, Bioinform..

[42]  Neil Davey,et al.  The application of Gaussian processes in the prediction of percutaneous absorption , 2009, ESANN.

[43]  Brian W. Barry,et al.  Effects of penetration enhancer treatment on the statistical distribution of human skin permeabilities , 1995 .

[44]  Gary P Moss,et al.  Design, synthesis and characterization of captopril prodrugs for enhanced percutaneous absorption , 2006, The Journal of pharmacy and pharmacology.

[45]  R. F. Fedors,et al.  A method for estimating both the solubility parameters and molar volumes of liquids , 1974 .

[46]  Leo Breiman,et al.  Random Forests , 2001, Machine Learning.

[47]  S. Agatonovic-Kustrin,et al.  ANN modeling of the penetration across a polydimethylsiloxane membrane from theoretically derived molecular descriptors. , 2001, Journal of pharmaceutical and biomedical analysis.

[48]  Bernard Testa,et al.  Quantitative Structure–Permeation Relationships for Solute Transport Across Silicone Membranes , 2002, Pharmaceutical Research.

[49]  M D Barratt.  Quantitative structure-activity relationships for skin permeability. , 1995, Toxicology in vitro : an international journal published in association with BIBRA.

[50]  Chen Yisheng,et al.  Prediction of flux through polydimethylsiloxane membranes using atomic charge calculations , 1993 .

[51]  Geoffrey E. Hinton,et al.  Bayesian Learning for Neural Networks , 1995 .

[52]  Scott C Wasdo,et al.  A correlation of flux through a silicone membrane with flux through hairless mouse skin and human skin in vitro. , 2009, International journal of pharmaceutics.

[53]  Neil Davey,et al.  The Application of Gaussian Processes in the Predictions of Permeability across Mammalian Membranes , 2012, ICANN.

[54]  Michael S. Roberts,et al.  Epidermal permeability — Penetrant structure relationships: 3. The effect of hydrogen bonding interactions and molecular size on diffusion across the stratum corneum , 1996 .

[55]  Michael H. Abraham,et al.  Scales of solute hydrogen-bonding: their construction and application to physicochemical and biochemical processes , 2010 .

[56]  Yisheng Chen,et al.  Prediction of flux through polydimethylsiloxane membranes using atomic charge calculations: Application to an extended data set , 1996 .

[57]  Weixin Yao,et al.  Fully Bayesian logistic regression with hyper-LASSO priors for high-dimensional feature selection , 2014, Journal of Statistical Computation and Simulation.

[58]  Bernhard P. Wenkers,et al.  THE STATISTICAL DISTRIBUTION OF IN-VIVO SKIN PERMEABILITY , 1999 .

[59]  Annette L. Bunge,et al.  Chemical transport in silicone rubber membranes from pure powders and saturated aqueous solutions , 2007 .

[60]  Carl E. Rasmussen,et al.  Gaussian processes for machine learning , 2005, Adaptive computation and machine learning.

[61]  Jonathan Hadgraft,et al.  P147 diffusion of deuterated ethanol in synthetic membranes using ATR-FTIR spectroscopy , 1994 .

[62]  Mark T D Cronin,et al.  Quantitative structure-permeability relationships for percutaneous absorption: re-analysis of steroid data. , 2002, International journal of pharmaceutics.

[63]  Neil Davey,et al.  The application of Gaussian processes in the predictions of permeability across mammalian and polydimethylsiloxane membranes , 2012, Artif. Intell. Res..

[64]  Gary P. Moss,et al.  Predictive Methods in Percutaneous Absorption , 2015, Springer Berlin Heidelberg.

[65]  Jan Komorowski,et al.  Selection of Significant Features Using Monte Carlo Feature Selection , 2016, Challenges in Computational Statistics and Data Mining.

[66]  Jonathan Hadgraft,et al.  Influence of membrane-solvent-solute interactions on solute permeation in model membranes. , 2007, International journal of pharmaceutics.

[67]  Gavin Brown,et al.  Conditional Likelihood Maximisation: A Unifying Framework for Information Theoretic Feature Selection , 2012, J. Mach. Learn. Res..

[68]  R. Langer,et al.  Permeation of steroids through human skin. , 1995, Journal of pharmaceutical sciences.