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In Silico Approach

  • Hiromi BabaEmail author
  • Jun-ichi Takahara
  • Fumiyoshi Yamashita
Chapter

Abstract

Quantitative structure–property relationship (QSPR) modeling is generally used to develop in silico prediction models of skin permeability based on the compound molecular structures. Due to the improved quality and size of permeability datasets and the use of promising machine learning techniques, appropriate molecular descriptors, and model validation, current QSPR models provide a highly reliable and efficient option for assessing skin permeability of numerous candidate compounds for use in dermatological medicines and cosmetics. While computational models can predict skin permeability of even yet-to-be synthesized or virtually generated compounds, the traditional prediction models have been limited to only evaluating the skin permeability of permeants in aqueous solutions. Such models cannot evaluate the effects of solvents on skin permeability that constitute a crucial problem for the optimization of topical formulations’ compositions. This has motivated the recent development of models that can predict even the complicated solvent effects on skin permeability with quantitative accuracy. Here, we introduce the general procedures of QSPR modeling for predicting skin permeability and review the existing models including the newest models that can predict solvent effects on skin permeability.

Keywords

In silico prediction Quantitative structure–property relationship Skin permeability Machine learning Solvent effect Vehicle effect 

References

  1. 1.
    Franz TJ (1975) Percutaneous absorption on the relevance of in vitro data. J Invest Dermatol 64(3):190–195CrossRefPubMedGoogle Scholar
  2. 2.
    Bartek MJ et al (1972) Skin permeability in vivo: comparison in rat, rabbit, pig and man. J Invest Dermatol 58(3):114–123CrossRefPubMedGoogle Scholar
  3. 3.
    Potts RO, Guy RH (1992) Predicting skin permeability. Pharm Res 9(5):663–669CrossRefPubMedGoogle Scholar
  4. 4.
    Cronin MT et al (1999) Investigation of the mechanism of flux across human skin in vitro by quantitative structure-permeability relationships. Eur J Pharm Sci 7(4):325–330CrossRefPubMedGoogle Scholar
  5. 5.
    Moss GP, Cronin MT (2002) Quantitative structure-permeability relationships for percutaneous absorption: re-analysis of steroid data. Int J Pharm 238(1–2):105–109CrossRefPubMedGoogle Scholar
  6. 6.
    Patel H et al (2002) Quantitative structure-activity relationships (QSARs) for the prediction of skin permeation of exogenous chemicals. Chemosphere 48(6):603–613CrossRefPubMedGoogle Scholar
  7. 7.
    Lim CW et al (2002) Prediction of human skin permeability using a combination of molecular orbital calculations and artificial neural network. Biol Pharm Bull 25(3):361–366CrossRefPubMedGoogle Scholar
  8. 8.
    Abraham MH, Martins F (2004) Human skin permeation and partition: general linear free-energy relationship analyses. J Pharm Sci 93(6):1508–1523CrossRefPubMedGoogle Scholar
  9. 9.
    Katritzky AR et al (2006) Skin permeation rate as a function of chemical structure. J Med Chem 49(11):3305–3314CrossRefPubMedGoogle Scholar
  10. 10.
    Basak SC et al (2007) A quantitative structure-activity relationship (QSAR) study of dermal absorption using theoretical molecular descriptors. SAR QSAR Environ Res 18(1–2):45–55CrossRefPubMedGoogle Scholar
  11. 11.
    Chen LJ et al (2007) Prediction of human skin permeability using artificial neural network (ANN) modeling. Acta Pharmacol Sin 28(4):591–600CrossRefPubMedGoogle Scholar
  12. 12.
    Neely BJ et al (2009) Nonlinear quantitative structure-property relationship modeling of skin permeation coefficient. J Pharm Sci 98(11):4069–4084CrossRefPubMedPubMedCentralGoogle Scholar
  13. 13.
    Chauhan P, Shakya M (2010) Role of physicochemical properties in the estimation of skin permeability: in vitro data assessment by Partial Least-Squares Regression. SAR QSAR Environ Res 21(5–6):481–494CrossRefPubMedGoogle Scholar
  14. 14.
    Khajeh A, Modarress H (2014) Linear and nonlinear quantitative structure-property relationship modelling of skin permeability. SAR QSAR Environ Res 25(1):35–50CrossRefPubMedGoogle Scholar
  15. 15.
    Patel J (2013) Science of the science, drug discovery and artificial neural networks. Curr Drug Discov Technol 10(1):2–7PubMedGoogle Scholar
  16. 16.
    Breiman L (2001) Random forests. Mach Learn 45(1):5–32CrossRefGoogle Scholar
  17. 17.
    Smola AJ, Schölkopf B (2004) A tutorial on support vector regression. Stat Comput 14(3):199–222CrossRefGoogle Scholar
  18. 18.
    Atobe T et al (2015) Artificial neural network analysis for predicting human percutaneous absorption taking account of vehicle properties. J Toxicol Sci 40(2):277–294CrossRefPubMedGoogle Scholar
  19. 19.
    Baba H et al (2015) Modeling and prediction of solvent effect on human skin permeability using support vector regression and random forest. Pharm Res 32(11):3604–3617CrossRefPubMedGoogle Scholar
  20. 20.
    Ghafourian T et al (2010) Validated models for predicting skin penetration from different vehicles. Eur J Pharm Sci 41(5):612–616CrossRefPubMedGoogle Scholar
  21. 21.
    Ghafourian T et al (2010) Modelling the effect of mixture components on permeation through skin. Int J Pharm 398(1–2):28–32CrossRefPubMedGoogle Scholar
  22. 22.
    Riviere JE, Brooks JD (2007) Prediction of dermal absorption from complex chemical mixtures: incorporation of vehicle effects and interactions into a QSPR framework. SAR QSAR Environ Res 18(1–2):31–44CrossRefPubMedGoogle Scholar
  23. 23.
    Riviere JE, Brooks JD (2011) Predicting skin permeability from complex chemical mixtures: dependency of quantitative structure permeation relationships on biology of skin model used. Toxicol Sci 119(1):224–232CrossRefPubMedGoogle Scholar
  24. 24.
    van Ravenzwaay B, Leibold E (2004) A comparison between in vitro rat and human and in vivo rat skin absorption studies. Hum Exp Toxicol 23(9):421–430CrossRefPubMedGoogle Scholar
  25. 25.
    Moss GP et al (2011) The application and limitations of mathematical modelling in the prediction of permeability across mammalian skin and polydimethylsiloxane membranes. J Pharm Pharmacol 63(11):1411–1427CrossRefPubMedGoogle Scholar
  26. 26.
    Baba H et al (2015) In silico predictions of human skin permeability using nonlinear quantitative structure-property relationship models. Pharm Res 32(7):2360–2371CrossRefPubMedGoogle Scholar
  27. 27.
    Vecchia BE, Bunge AL (2002) Skin absorption databases and predictive equations. In: Guy R, Hadgraft J (eds) Transdermal drug delivery, 2nd edn. Marcel Dekker, New York, pp 57–141Google Scholar
  28. 28.
    Netzeva TI et al (2005) Current status of methods for defining the applicability domain of (quantitative) structure-activity relationships. The report and recommendations of ECVAM Workshop 52. Altern Lab Anim 33(2):155–173PubMedGoogle Scholar
  29. 29.
    Magnusson BM et al (2004) Molecular size as the main determinant of solute maximum flux across the skin. J Invest Dermatol 122(4):993–999CrossRefPubMedGoogle Scholar
  30. 30.
    Flynn GL (1990) Physicochemical determinants of skin absorption. In: Gerrity TR, Henry CJ (eds) Principles of route-to-route extrapolation for risk assessment, 1st edn. Elsevier, New York, pp 93–127Google Scholar
  31. 31.
    Kirchner LA et al (1997) The prediction of skin permeability by using physicochemical data. Altern Lab Anim 25:359–370Google Scholar
  32. 32.
    Neumann D et al (2006) A fully computational model for predicting percutaneous drug absorption. J Chem Inf Model 46(1):424–429CrossRefPubMedGoogle Scholar
  33. 33.
    Buchwald P, Bodor N (2001) A simple, predictive, structure-based skin permeability model. J Pharm Pharmacol 53(8):1087–1098CrossRefPubMedGoogle Scholar
  34. 34.
    Lien EJ, Gao H (1995) QSAR analysis of skin permeability of various drugs in man as compared to in vivo and in vitro studies in rodents. Pharm Res 12(4):583–587CrossRefPubMedGoogle Scholar
  35. 35.
    Tropsha A (2010) QSAR in drug discovery. In: Merz KM et al (eds) Drug design structure- and ligand-based approaches. Cambridge University Press, Cambridge, pp 151–164CrossRefGoogle Scholar
  36. 36.
    Shahlaei M (2013) Descriptor selection methods in quantitative structure-activity relationship studies: a review study. Chem Rev 113(10):8093–8103CrossRefPubMedGoogle Scholar
  37. 37.
    Moss GP et al (2009) The application of Gaussian processes in the prediction of percutaneous absorption. J Pharm Pharmacol 61(9):1147–1153CrossRefPubMedGoogle Scholar
  38. 38.
    Marsland S (2014) Machine learning: an algorithmic perspective, 2nd edn. CRC Press, New YorkGoogle Scholar
  39. 39.
    Golbraikh A, Tropsha A (2002) Beware of q2! J Mol Graph Model 20(4):269–276CrossRefPubMedGoogle Scholar
  40. 40.
    Chirico N, Gramatica P (2011) Real external predictivity of QSAR models: how to evaluate it? Comparison of different validation criteria and proposal of using the concordance correlation coefficient. J Chem Inf Model 51(9):2320–2335CrossRefPubMedGoogle Scholar
  41. 41.
    Chirico N, Gramatica P (2012) Real external predictivity of QSAR models. Part 2. New intercomparable thresholds for different validation criteria and the need for scatter plot inspection. J Chem Inf Model 52(8):2044–2058CrossRefPubMedGoogle Scholar
  42. 42.
    Consonni V et al (2009) Comments on the definition of the Q2 parameter for QSAR validation. J Chem Inf Model 49(7):1669–1678CrossRefPubMedGoogle Scholar
  43. 43.
    Ojha PK et al (2011) Further exploring rm2 metrics for validation of QSPR models. Chemom Intell Lab Syst 107(1):194–205CrossRefGoogle Scholar
  44. 44.
    Roy K et al (2012) Comparative studies on some metrics for external validation of QSPR models. J Chem Inf Model 52(2):396–408CrossRefPubMedGoogle Scholar
  45. 45.
    Roy PP, Roy K (2008) On some aspects of variable selection for partial least squares regression models. QSAR Comb Sci 27(3):302–313CrossRefGoogle Scholar
  46. 46.
    Barry BW (2004) Breaching the skin’s barrier to drugs. Nat Biotechnol 22(2):165–167CrossRefPubMedGoogle Scholar

Copyright information

© Springer Japan KK 2017

Authors and Affiliations

  • Hiromi Baba
    • 1
    • 2
    Email author
  • Jun-ichi Takahara
    • 1
  • Fumiyoshi Yamashita
    • 2
  1. 1.Kyoto R&D Center, Maruho Co., Ltd.KyotoJapan
  2. 2.Graduate School of Pharmaceutical SciencesKyoto UniversityKyotoJapan

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