title: A three-tier QSAR modeling strategy for estimating eye irritation potential of diverse chemicals in rabbit for regulatory purposes.
abstract: Experimental determination of the eye irritation potential (EIP) of chemicals is not only tedious, time and resource intensive, it involves cruelty to test animals. In this study, we have established a three-tier QSAR modeling strategy for estimating the EIP of chemicals for the use of pharmaceutical industry and regulatory agencies. Accordingly, a qualitative (binary classification: irritating, non-irritating), semi-quantitative (four-category classification), and quantitative (regression) QSAR models employing the SDT, DTF, and DTB methods were developed for predicting the EIP of chemicals in accordance with the OECD guidelines. Structural features of chemicals responsible for eye irritation were extracted and used in QSAR analysis. The external predictive power of the developed QSAR models were evaluated through the internal and external validation procedures recommended in QSAR literature. In test data, the two and four category classification QSAR models (DTF, DTB) rendered accuracy of >93%, while the regression QSAR models (DTF, DTB) yielded correlation (R(2)) of >0.92 between the measured and predicted EIPs. Values of various statistical validation coefficients derived for the test data were above their respective threshold limits (except rm(2) in DTF), thus put a high confidence in this analysis. The applicability domain of the constructed QSAR models were defined using the descriptors range and leverage approaches. The QSAR models in this study performed better than any of the previous studies. The results suggest that the developed QSAR models can reliably predict the EIP of diverse chemicals and can be useful tools for screening of candidate molecules in the drug development process.
authors.affiliation: ETRC, Gomtinagar, Lucknow 226010, India.
authors.forename: Nikita
authors.initials: N
authors.lastname: Basant
authors.affiliation: Environmental Chemistry Division, CSIR-Indian Institute of Toxicology Research, Post Box 80, Mahatma Gandhi Marg, Lucknow 226001, India.
authors.forename: Shikha
authors.initials: S
authors.lastname: Gupta
authors.affiliation: Environmental Chemistry Division, CSIR-Indian Institute of Toxicology Research, Post Box 80, Mahatma Gandhi Marg, Lucknow 226001, India. Electronic address: kpsingh_52@yahoo.com.
authors.forename: Kunwar P
authors.initials: KP
authors.lastname: Singh
mesh_terms: D000826:Animal Testing Alternatives / Q000379:methods*; D000818:Animals; D057225:Data Mining; D016208:Databases, Factual; D005128:Eye Diseases / Q000139:chemically induced*; D007509:Irritants / Q000737:chemistry / Q000145:classification / Q000633:toxicity*; D008958:Models, Molecular; D015233:Models, Statistical; D015394:Molecular Structure; D021281:Quantitative Structure-Activity Relationship; D011817:Rabbits; D012044:Regression Analysis; D018570:Risk Assessment; D018675:Toxicity Tests / Q000379:methods*
chemical_list: D007509:Irritants
keywords: Eye irritation potential; Qualitative and semi-quantitative QSAR; Regression QSAR; Structural diversity; Structural features; Three-tier strategy