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

Authors
Xu-G; Hughes-Oliver-JM; Brooks-JD; Yeatts-JL; Baynes-RE
Source
SAR QSAR Environ Res 2013 Feb; 24(2):135-156
NIOSHTIC No.
20042645
Abstract
Quantitative structure-activity relationship (QSAR) models are being used increasingly in skin permeation studies. The main idea of QSAR modelling is to quantify the relationship between biological activities and chemical properties, and thus to predict the activity of chemical solutes. As a key step, the selection of a representative and structurally diverse training set is critical to the prediction power of a QSAR model. Early QSAR models selected training sets in a subjective way and solutes in the training set were relatively homogenous. More recently, statistical methods such as D-optimal design or space-filling design have been applied but such methods are not always ideal. This paper describes a comprehensive procedure to select training sets from a large candidate set of 4534 solutes. A newly proposed 'Baynes' rule', which is a modification of Lipinski's 'rule of five', was used to screen out solutes that were not qualified for the study. U-optimality was used as the selection criterion. A principal component analysis showed that the selected training set was representative of the chemical space. Gas chromatograph amenability was verified. A model built using the training set was shown to have greater predictive power than a model built using a previous dataset [1].
Keywords
Quantitative-analysis; Chemical-analysis; Analytical-models; Models; Skin; Skin-absorption; Skin-exposure; Chemical-structure; Structural-analysis; Biological-effects; Chemical-properties; Chemical-reactions; Training; Solvents; Gas-chromatography; Pharmacodynamics; Chemical-kinetics; Analytical-processes; Analytical-chemistry; Molecular-structure; Metalworking-fluids; Biocides; Author Keywords: QSAR model; training set selection; Baynes' Rule; U-optimal design; principal component analysis (PCA); applicability domain (AD)
Contact
R.E.Baynes, Center for Chemical Toxicology Research and Pharmacokinetics, North Carolina State University, College of Veterinary Medicine, Raleigh, NC 27606, USA
CODEN
SQERED
Publication Date
20130201
Document Type
Journal Article
Email Address
rebaynes@ncsu.edu
Funding Amount
875919
Funding Type
Grant
Fiscal Year
2013
NTIS Accession No.
NTIS Price
Identifying No.
Grant-Number-R01-OH-003669; B20130612
Issue of Publication
2
ISSN
1062-936X
Priority Area
Disease and Injury: Allergic and Irritant Dermatitis
Source Name
SAR and QSAR in Environmental Research
State
NC; VA
Performing Organization
North Carolina State University, Raleigh, North Carolina
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