Role of physicochemical properties in the estimation of skin permeability: In vitro data assessment by Partial Least-squares regression

SAR and QSAR in Environmental Research

Volumn 21, Issue 5-6, 2010, Pages 481-494

  Chauhan, P ^a   Shakya, M ^a  

^a MAULANA AZAD NATIONAL INSTITUTE OF TECHNOLOGY   (India)

Author keywords

In vitro dataset; Molecular descriptors; Physicochemical parameters; Quantitative structure permeability relationships; Skin penetration

Indexed keywords

LEAST SQUARES APPROXIMATIONS; REGRESSION ANALYSIS; STATISTICAL TESTS;

DESCRIPTORS; IN VITRO DATASET; IN-VITRO; MOLECULAR DESCRIPTORS; PARTIAL LEAST SQUARE REGRESSION; PHYSICO - CHEMICAL PARAMETERS; QUANTITATIVE STRUCTURE-PERMEABILITY RELATIONSHIP; QUANTITATIVE STRUCTURES; SKIN PENETRATION; SKIN PERMEABILITY;

GENETIC ALGORITHMS;

ANIMALIA;

EID: 77956405653     PISSN: 1062936X     EISSN: 1029046X     Source Type: Journal    
DOI: 10.1080/1062936X.2010.501819     Document Type: Article

References (32)

1. T. Degim, Understanding skin penetration: Computer aided modeling and data interpretation, Curr. Comput. Aided Drug Des. 1 (2005), pp. 11-19.
2. T. Degim, New tools and approaches for predicting skin permeability, Drug Discov. Today 11 (2006), pp. 517-523.
3. J. Kielhorn, S.M. Kollmuß, and I. Mangelsdorf, The Dermal Absorption, Fraunhofer Institute Toxicology and Experimental Medicine, Hanover, 2005, pp. 90-91.
4. B.E. Vecchia and A.L. Bunge, in Transdermal drug delivery, in Skin Absorption Database and Predictive Equations, R.H. Guy and J. Hadgraft, eds., Marcel Dekker, New York, 2003, pp. 57-142.
5. M.S. Roberts, W.J. Pugh, J. Hadgraft, and A.C. Watkinson, Epidermal permeability-penetrant structure relationships: 1. An analysis of methods of predicting penetration of monofunctional solutes from aqueous solutions, Int. J. Pharm. 126 (1995), pp. 219-233.
6. W.J. Pugh, I.T. Degim, and J. Hadgraft, Epidermal permeability-penetrant structure relationships: 4, QSAR of permeant diffusion across human stratum corneum in terms of molecular weight, H-bonding and electronic charge, Int. J. Pharm. 197 (2000), pp. 203-211.
7. L.J. Chen, G.P. Lian, and L.J. Han, Prediction of human skin permeability using artificial neural network (ANN) Modeling, Acta Pharmacol. Sin. 28 (2007), pp. 591-600.
8. S. Mitragotri, Modeling skin permeability to hydrophilic and hydrophobic solutes based on four permeation pathways, J. Control. Rel. 86 (2003), pp. 69-92.
9. G. Lian, L. Chen, and L. Han, An evaluation of mathematical models for predicting skin permeability, J. Pharm. Sci. 97 (2008), pp. 584-598.
10. L. Chen, G. Lian, and L. Han, Modeling transdermal permeation. Part I. Predicting skin permeability of both hydrophobic and hydrophilic solutes, AIChE J. 56 (2010), pp. 1136-1146.
11. G.L. Flynn, Physicochemical determinants of skin absorption, in Principles of Route-to-Route Extrapolation for Risk Assessment, T.R. Gerrity and C.J. Henry, eds., Elsevier, New York, 1990, pp. 93-127.
12. R.O. Potts and R.H. Guy, Predicting skin permeability, Pharm. Res. 9 (1992), pp. 663-669.
13. E.J. Lien and H. Gao, QSAR analysis of skin permeability of various drugs in man as compared to in vivo and in vitro studies in rodents, Pharm. Res. 12 (1995), pp. 583-587.
14. M.D. Barratt, Quantitative structure-activity relationships for skin permeability, Toxicol. In Vitro 9 (1995), pp. 27-37.
15. R.O. Potts and R.H. Guy, A predictive algorithm for skin permeability: The effects of molecular size and hydrogen bond activity, Pharm. Res. 12 (1995), pp. 1628-1633.
16. M.H. Abraham, H.S. Chadha, and R.C. Mitchell, The factors that influence skin penetration of solutes, J. Pharm. Pharmacol. 47 (1995), pp. 8-16.
17. L.A. Kirchner, R.P. Moody, E. Doyle, R. Bose, J. Jeffrey, and I. Chu, The prediction of skin permeability by using physicochemical data, Altern. Lab Anim. 25 (1997), pp. 359-370.
18. J.J. Hostynek and P.S. Magee, Modelling in vivo human skin absorption, Quant. Struct. Act. Relat. 16 (1997), pp. 473-479.
19. M.H. Abraham, H.S. Chadha, F. Martins, R.C. Mitchell, M.W. Bradbury, and J.A. Gratton, 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, Pestic. Sci. 55 (1999), pp. 78-88.
20. B.D. Gute, G.D. Grunwald, and S.C. Basak, Prediction of the dermal penetration of polycyclic aromatic hydrocarbons (PAHs): A hierarchical QSAR approach, SAR QSAR Environ. Res. 10 (1999), pp. 1-15.
21. M.T.D. Cronin, J.C. Dearden, G.P. Moss, and G. Murray-Dickson, Investigation of the mechanism of flux across human skin in vitro by quantitative structure-permeability relationships, Eur. J. Pharm. Sci. 7 (1999), pp. 325-330.
22. J.C. Dearden, M.T.D. Cronin, H. Patel, and O.A. Raevsky, QSAR prediction of human skin permeability coefficients, J. Pharm. Pharmacol. 52 (2000), p. 221.
23. H. Patel, W. Berge, and M.T.D. Cronin, Quantitative structure-activity relationships (QSARs) for the prediction of skin permeation of exogenous chemicals, Chemosphere 48 (2002), pp. 603-613.
24. T.A. Roy, A.J. Krueger, C.R. Mackerer, W. Neil, A.M. Arroyo, and J.J. Yang, SAR models for estimating the percutaneous absorption of polynuclear aromatic hydrocarbons, SAR QSAR Environ. Res. 9 (1998), pp. 171-185.
25. A. Wilschut, W.F. Ten-Berge, P.J. Robinson, and T.E. McKone, Estimating skin permeation. The validation of five mathematical skin permeation models, Chemosphere 30 (1995), pp. 1275-1296.
26. J.C. Dearden, M.T.D. Cronin, and K.L.E. Kaiser, How not to develop a quantitative structureactivity or structure-property relationship (QSAR/QSPR), SAR QSAR Environ. Res. 20 (2009), pp. 241-266.
27. S. Geinoz, R.H. Guy, B. Testa, and P.A. Carrupt, Quantitative structure-permeation relationships (QSPeRs) to predict skin permeation: A critical evaluation, Pharm. Res. 21 (2004), pp. 83-92.
28. EDETOX database. Available at http://edetox.ncl.ac.uk/. Accessed 24 December 2009.
29. A. Tropsha, P. Gramatica, and V.K. Gombar, The importance of being earnest: Validation is the absolute essential for successful application and interpretation of QSAPR models, QSAR Comb. Sci. 22 (2003), pp. 69-77.
30. B. Lei, J. Li, H. Liu, and X. Yao, Accurate prediction of aquatic toxicity of aromatic compounds based on genetic algorithm and least square support vector machines, QSAR Comb. Sci. 27 (2008), pp. 850-865.
31. R.W. Kennard and L. Stone, Computer aided design of experiments, Technometrics 11 (1969), pp. 137-148.
32. M. Daszykowski, S. Serneels, K. Kaczmarek, P. Van Espen, C. Croux, and B. Walczak, TOMCAT: A MATLAB toolbox for multivariate calibration techniques, Chemom. Intell. Lab. Syst. 85 (2007), pp. 269-277.