Selection of appropriate training and validation set chemicals for modelling dermal permeability by U-optimal design

References

• Vijay , V , White , EM , Kaminski , M.D. Jr , Riviere , JE and Baynes , RE . 2009 . Dermal permeation of biocides and aromatic chemicals in three generic formulations of metalworking fluids . J. Toxic. Environ. Health, Part A: Current Issues , 72 : 832 – 841 .
   PubMed Web of Science ®Google Scholar
• Barratt , MD . 1995 . Quantitative structure-activity relationships for skin permeability . Toxicol. Vitro , 9 : 27 – 37 .
   PubMed Web of Science ®Google Scholar
• Potts , RO and Guy , RH . 1992 . Predicting skin permeability . Pharm. Res. , 9 : 663 – 669 .
   PubMed Web of Science ®Google Scholar
• Abraham , MH . 1993 . Scales of solute hydrogen-bonding: Their construction and application to physicochemical and biochemical processes . Chem. Soc. Rev. , 22 : 73 – 83 .
   Web of Science ®Google Scholar
• ADME BOXES 4.95, Pharma Algorithms, Toronto, Canada, 2009, software available at http://pharma-algorithms.com/adme_boxes.htm
   Google Scholar
• Wold , S and Dunn , WJ . 1983 . Multivariate quantitative structure-activity relationships (QSAR): Conditions for their applicability . J. Chem. Inf. Comput. Sci. , 23 : 6 – 13 .
   Google Scholar
• Eriksson , L and Johansson , E . 1996 . Multivariate design and modeling in QSAR . Chemom. Intell. Lab. Syst. , 34 : 1 – 19 .
   Web of Science ®Google Scholar
• Flynn , GL . 1990 . “ Physicochemical determinants of skin absorption ” . In Principles of Route-to-Route Extrapolation for Risk Assessment , Edited by: Gerrity , T.R. and Henry , C.J. 93 – 127 . Amsterdam , , Amsterdam : Elsevier .
   Google Scholar
• Furusjö , E , Svenson , A , Rahmberg , M and Andersson , M . 2006 . The importance of outlier detection and training set selection for reliable environmental QSAR predictions . Chemosphere , 63 : 99 – 108 .
   PubMed Web of Science ®Google Scholar
• Vijay , V , Baynes , RE , Young , SS and Riviere , JE . 2009 . Selection of appropriate training set of chemicals for modeling dermal permeability using uniform coverage design . QSAR Comb. Sci. , 28 : 1478 – 1486 .
   Google Scholar
• Lipinski , CA , Lombardo , F , Dominy , BW and Feeney , PJ . 1997 . Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings . Adv. Drug Delivery Rev. , 23 : 3 – 25 .
   Web of Science ®Google Scholar
• SAS Institute, SAS/QC software: Usage and reference, Version 6, Institute SAS, Cary, NC, 1995
   Google Scholar
• Abraham , MH and Martins , F . 2004 . Human skin permeation and partition: General linear free-energy relationship analyses . J. Pharm. Sci. , 93 : 1508 – 1523 .
   PubMed Web of Science ®Google Scholar
• Gramatica , P . 2007 . Principles of QSAR models validation: Internal and external . QSAR Comb. Sci. , 26 : 694 – 701 .
   Google Scholar
• Weininger , D . 1988 . Smiles, a chemical language and information-system. 1. Introduction to methodology and encoding rules . J. Chem. Inf. Comput. Sci. , 28 : 31 – 36 .
   Google Scholar
• ChemDraw Ultra 6.0, CambridgeSoft Corporation, Cambridge, MA, 2001, software available at http://products.camsoft.com
   Google Scholar
• JChem for Excel 5.4.1.157, ChemAxon, Budapest, Hungary, 2001, software available at http://www.chemaxon.com
   Google Scholar
• Magnusson , BM , Pugh , WJ and Roberts , MS . 2004 . Simple rules defining the potential of compounds for transdermal delivery or toxicity . Pharm. Res. , 21 : 1047 – 1054 .
   PubMed Web of Science ®Google Scholar
• Lam , RLH , Welch , WJ and Young , SS . 2002 . Uniform coverage designs for molecule selection . Technometrics , 44 : 99 – 109 .
   Web of Science ®Google Scholar
• Higgs , RE , Bemis , KG , Watson , IA and Wikel , JH . 1997 . Experimental designs for selecting molecules from large chemical databases . J. Chem. Inf. Comput. Sci. , 37 : 861 – 870 .
   Google Scholar
• Reynolds , CH , Tropsha , A , Pfahler , LB , Druker , R , Chakravorty , S , Ethiraj , G and Zheng , W . 2001 . Diversity and coverage of structural sublibraries selected using the SAGE and SCA algorithms . J. Chem. Inf. Comput. Sci. , 41 : 1470 – 1477 .
   PubMedGoogle Scholar
• SAS 9.2, SAS Institute Inc., Cary, NC, 2008, software available at http://www.sas.com
   Google Scholar
• Johnson , RA and Wichern , DW . 2006 . Applied Multivariate Statistical Analysis , 6th ed. , 430 – 436 . Upper Saddle River , NJ : Pearson Prentice Hall .
   Google Scholar
• Riviere , JE and Brooks , JD . 2005 . Predicting skin permeability from complex chemical mixtures . Toxicol. Appl. Pharmacol. , 208 : 99 – 110 .
   PubMed Web of Science ®Google Scholar
• Baynes , RE , Brooks , JD , Barlow , BM and Riviere , JE . 2002 . Physiochemical determinants of linear alkylbenzene sulfonate (LAS) disposition in skin exposed to aqueous cutting fluid mixtures . Toxicol. Indust. Health , 18 : 237 – 248 .
   PubMed Web of Science ®Google Scholar
• Gramatica , P . 2004 . Evaluation of Different Statistical Approaches for the Validation of Quantitative Structure-Activity Relationships , Ispra : European Centre for the Validation of Alternative Methods .
   Google Scholar
• Netzeva , TI , Saliner , G and Worth , AP . 2005 . Comparison of the applicability domain of a QSAR for estrogenicity with a large chemical inventory . Environ. Toxicol. Chem. , 25 : 1223 – 1230 .
   Web of Science ®Google Scholar
• Pavan , M , Ntezeva , TI and Worth , AP . 2006 . Validation of a QSAR model for acute toxicity . SAR QSAR Environ. Res. , 17 : 147 – 171 .
   PubMed Web of Science ®Google Scholar
• Breiman , L . 1998 . Arcing classifiers (with discussion and a rejoinder by the author) . Ann. Stat. , 26 : 801 – 849 .
   Web of Science ®Google Scholar
• Liaw , A and Wiener , M . 2002 . Classification and Regression by randomForest . R News , 2 ( 3 ) : 18 – 22 .
   Google Scholar
• Hawkins , DM , Kraker , JJ , Basak , SC and Mills , D . 2008 . QSPR checking and validation: A case study with hydroxy radical reaction rate constant . SAR QSAR Environ. Res. , 19 : 525 – 539 .
   PubMed Web of Science ®Google Scholar
• OECD, Guidance Document on the Validation of (Quantitative) Structure–Activity Relationships [(Q)SAR] Models, ENV/JM/MONO(2007)2, OECD Environmental Health and Safety Publications Series on Testing and Assessment No. 69, Organisation for Economic Co-operation and Development, Paris, 2007
   Google Scholar