Prediction of the skin sensitising potential and potency of compounds via mechanism-based binary and ternary classification models

Toxicology in Vitro - Tập 59 - Trang 204-214 - 2019
Peiwen Di, Yongmin Yin1, Changsheng Jiang1, Yingchun Cai1, Weihua Li1, Yun Tang1, Guixia Liu1
1Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, 130 Meilong Road, Shanghai, 200237, China

Tài liệu tham khảo

Ankley, 2010, Adverse outcome pathways: a conceptual framework to support ecotoxicology research and risk assessment, Environ. Toxicol. Chem., 29, 730, 10.1002/etc.34 Annette, 2012, Non-animal test methods for predicting skin sensitization potentials, Arch. Toxicol., 86, 1273, 10.1007/s00204-012-0867-6 Aptula, 2005, Skin sensitization: reaction mechanistic applicability domains for structure-activity relationships, Chem. Res. Toxicol., 18, 1420, 10.1021/tx050075m Barratt, 1994, An expert system rulebase for identifying contact allergens, Toxicol. in Vitro, 8, 1053, 10.1016/0887-2333(94)90244-5 Basketter, 2007, The local lymph node assay: current position in the regulatory classification of skin sensitizing chemicals, Cutan. Ocul. Toxicol., 26, 293, 10.1080/15569520701556647 Benigni, 2008, Structure alerts for carcinogenicity, and the Salmonella assay system: a novel insight through the chemical relational databases technology, Mutat. Res., 659, 248, 10.1016/j.mrrev.2008.05.003 Berthold, 2007, KNIME: The Konstanz information miner, 319 Breiman, 2001, Random forests, Mach. Learn., 45, 5, 10.1023/A:1010933404324 Buehler, 1965, Delayed contact hypersensitivity in the Guinea pig, Arch. Dermatol., 91, 171, 10.1001/archderm.1965.01600080079017 Cao, 2012, Tree-based ensemble methods and their applications in analytical chemistry, Trends Anal. Chem., 40, 158, 10.1016/j.trac.2012.07.012 Chang, 2011, LIBSVM - a library for support vector machines, ACM. TIST, 2, 1, 10.1145/1961189.1961199 Clippinger, 2018, Pathway-based predictive approaches for non-animal assessment of acute inhalation toxicity, Toxicol. in Vitro, 52, 131, 10.1016/j.tiv.2018.06.009 Cortes, 1995, Support vector networks, Mach. Learn., 20, 273, 10.1007/BF00994018 Daniel, 2018, International regulatory requirements for skin sensitization testing, Regul. Toxicol. Pharmacol., 95, 52, 10.1016/j.yrtph.2018.03.003 Enoch, 2013, Predicting skin sensitization potency for Michael acceptors in the LLNA using quantum mechanics calculations, Chem. Res. Toxicol., 26, 767, 10.1021/tx4000655 Enoch, 2008, Identification of mechanisms of toxic action for skin sensitisation using a SMARTS pattern based approach, SAR QSAR Environ. Res., 19, 555, 10.1080/10629360802348985 Ezendam, 2016, State of the art in non-animal approaches for skin sensitization testing: from individual test methods towards testing strategies, Arch. Toxicol., 90, 2861, 10.1007/s00204-016-1842-4 Friedman, 2001, Greedy function approximation: a gradient boosting machine, Ann. Stat., 29, 1189, 10.1214/aos/1013203451 Goebel, 2012, Guiding principles for the implementation of non-animal safety assessment approaches for cosmetics: skin sensitisation, Regul. Toxicol. Pharmacol., 63, 40, 10.1016/j.yrtph.2012.02.007 Haykin, 1999 Kimber, 2001, Skin sensitization testing in potency and risk assessment, Toxicol. Sci., 59, 198, 10.1093/toxsci/59.2.198 Kleinstreuer, 2018, Non-animal methods to predict skin sensitization (II): an assessment of defined approaches, Crit. Rev. Toxicol., 48, 359, 10.1080/10408444.2018.1429386 Li, 2007, A new descriptor selection scheme for SVM in unbalanced class problem: a case study using skin sensitisation dataset, SAR QSAR Environ. Res., 18, 423, 10.1080/10629360701428474 Magnusson, 1969, The identification of contact allergens by animal assay. The Guinea pig maximization test, J. Invest. Dermatol., 52, 268, 10.1038/jid.1969.42 Mehling, 2012, Non-animal test methods for predicting skin sensitization potentials, Arch. Toxicol., 86, 1273, 10.1007/s00204-012-0867-6 OECD, 1992 OECD, 2010 OECD, 2013, Guidance Document on Developing and Assessing Adverse Outcome Pathways OECD, 2016, Guidance document on the reporting of defined approaches to be used within integrated approaches to testing and assessment OECD, 2016, Guidance Document on the Reporting of Defined Approaches and Individual Information Sources to Be Used within Integrated Approaches to Testing and Assessment (IATA) for Skin Sensitization Patlewicz, 2014, Towards AOP application - implementation of an integrated approach to testing and assessment (IATA) into a pipeline tool for skin sensitization, Regul. Toxicol. Pharmacol., 69, 529, 10.1016/j.yrtph.2014.06.001 Peiser, 2012, Allergic contact dermatitis: epidemiology, molecular mechanisms, in vitro methods and regulatory aspects, Cell. Mol. Life Sci., 69, 763, 10.1007/s00018-011-0846-8 Quinlan, 1993 Reisinger, 2015, Systematic evaluation of non-animal test methods for skin sensitisation safety assessment, Toxicol. in Vitro, 29, 259, 10.1016/j.tiv.2014.10.018 Specht, 1990, Probabilistic neural networks, Neural Netw., 3, 109, 10.1016/0893-6080(90)90049-Q Strickland, 2016, Integrated decision strategies for skin sensitization hazard, J. Appl. Toxicol., 36, 1150, 10.1002/jat.3281 Teubner, 2013, Computer models versus reality: how well do in silico models currently predict the sensitization potential of a substance, Regul. Toxicol. Pharmacol., 67, 468, 10.1016/j.yrtph.2013.09.007 Tollefsen, 2014, Applying adverse outcome pathways (AOPs) to support integrated approaches to testing and assessment (IATA), Regul. Toxicol. Pharmacol., 70, 629, 10.1016/j.yrtph.2014.09.009 Türkşen, 2011, A review of developments in fuzzy system models: fuzzy rule bases to fuzzy functions, Sci. Iran., 18, 522, 10.1016/j.scient.2011.04.001 Urbisch, 2016, Peptide reactivity associated with skin sensitization: the QSAR toolbox and TIMES compared to the DPRA, Toxicol. in Vitro, 34, 194, 10.1016/j.tiv.2016.04.005 van der Veen, 2014, Evaluating the performance of integrated approaches for hazard identification of skin sensitizing chemicals, Regul. Toxicol. Pharmacol., 69, 371, 10.1016/j.yrtph.2014.04.018 Watson, 2008, Naïve Bayes classification using 2D pharmacophore feature triplet vectors, J. Chem. Inf. Model., 48, 166, 10.1021/ci7003253 Yang, 2018, admetSAR 2.0: web-service for prediction and optimization of chemical ADMET properties, Bioinformatics, 1 Yang, 2018, In silico prediction of chemical toxicity for drug design using machine learning methods and structural alerts, Front. Chem., 6, 1 Yap, 2011, PaDEL-descriptor: an open source software to calculate molecular descriptors and fingerprints, J. Comput. Chem., 32, 1466, 10.1002/jcc.21707 Zhang, 2006, A novel automated lazy learning QSAR (ALL-QSAR) approach: method development, applications, and virtual screening of chemical databases using validated ALL-QSAR models, J. Chem. Inf. Model., 46, 1984, 10.1021/ci060132x