title: Novel Uses of In Vitro Data to Develop Quantitative Biological Activity Relationship Models for in Vivo Carcinogenicity Prediction.
abstract: The availability of large in vitro datasets enables better insight into the mode of action of chemicals and better identification of potential mechanism(s) of toxicity. Several studies have shown that not all in vitro assays can contribute as equal predictors of in vivo carcinogenicity for development of hybrid Quantitative Structure Activity Relationship (QSAR) models. We propose two novel approaches for the use of mechanistically relevant in vitro assay data in the identification of relevant biological descriptors and development of Quantitative Biological Activity Relationship (QBAR) models for carcinogenicity prediction. We demonstrate that in vitro assay data can be used to develop QBAR models for in vivo carcinogenicity prediction via two case studies corroborated with firm scientific rationale. The case studies demonstrate the similarities between QBAR and QSAR modeling in: (i) the selection of relevant descriptors to be used in the machine learning algorithm, and (ii) the development of a computational model that maps chemical or biological descriptors to a toxic endpoint. The results of both the case studies show: (i) improved accuracy and sensitivity which is especially desirable under regulatory requirements, and (ii) overall adherence with the OECD/REACH guidelines. Such mechanism based models can be used along with QSAR models for prediction of mechanistically complex toxic endpoints.
authors.affiliation: Department of Mathematics, Computer Science and Statistics, Marquette University, 1313 W. Wisconsin Avenue, Milwaukee, WI 53233, USA fax: (414) 288-5472. prachi.pradeep@marquette.edu.
authors.forename: Prachi
authors.initials: P
authors.lastname: Pradeep
authors.affiliation: Department of Electrical and Computer Engineering, Marquette University, 1515 W. Wisconsin Avenue, Milwaukee, WI 53233, USA.
authors.forename: Richard J
authors.initials: RJ
authors.lastname: Povinelli
authors.affiliation: Department of Mathematics, Computer Science and Statistics, Marquette University, 1313 W. Wisconsin Avenue, Milwaukee, WI 53233, USA fax: (414) 288-5472.
authors.forename: Stephen J
authors.initials: SJ
authors.lastname: Merrill
authors.affiliation: Department of Mathematics, Computer Science and Statistics, Marquette University, 1313 W. Wisconsin Avenue, Milwaukee, WI 53233, USA fax: (414) 288-5472.
authors.forename: Serdar
authors.initials: S
authors.lastname: Bozdag
authors.affiliation: School of Pharmacy, Concordia University Wisconsin, 12800 N. Lake Shore Drive, Mequon, WI 53097, USA.
authors.forename: Daniel S
authors.initials: DS
authors.lastname: Sem
mesh_terms: D002273:Carcinogens / Q000737:chemistry / Q000633:toxicity*; D016208:Databases, Factual; D000069550:Machine Learning; D008954:Models, Biological
chemical_list: D002273:Carcinogens
keywords: Biological descriptors; Carcinogenicity; Computational toxicology; In vitro data; OECD; Quantitative biological activity relationship (QBAR); REACH