title: A framework for using structural, reactivity, metabolic and physicochemical similarity to evaluate the suitability of analogs for SAR-based toxicological assessments.
abstract: A systematic expert-driven process is presented for evaluating analogs for read across in SAR (structure activity relationship) toxicological assessments. The approach involves categorizing potential analogs based upon their degree of structural, reactivity, metabolic and physicochemical similarity to the chemical with missing toxicological data (target chemical). It extends beyond structural similarity, and includes differentiation based upon chemical reactivity and addresses the potential that an analog and target could show toxicologically significant metabolic convergence or divergence. In addition, it identifies differences in physicochemical properties, which could affect bioavailability and consequently biological responses observed in vitro or in vivo. The approach provides a stepwise decision tree for categorizing the suitability of analogs, which qualitatively characterizes the strength of the evidence supporting the hypothesis of similarity and level of uncertainty associated with their use for read across. The result is a comprehensive framework to apply chemical, biochemical and toxicological principles in a systematic manner to identify and evaluate factors that can introduce uncertainty into SAR assessments, while maximizing the appropriate use of all available data.
authors.affiliation: Central Product Safety Department, The Procter & Gamble Company, Miami Valley Innovation Center, 11810 E. Miami River Road, Cincinnati, OH 45040, USA. wu.s.3@pg.com
authors.forename: Shengde
authors.initials: S
authors.lastname: Wu
authors.forename: Karen
authors.initials: K
authors.lastname: Blackburn
authors.forename: Jack
authors.initials: J
authors.lastname: Amburgey
authors.forename: Joanna
authors.initials: J
authors.lastname: Jaworska
authors.forename: Thomas
authors.initials: T
authors.lastname: Federle
mesh_terms: D016208:Databases, Factual; D003663:Decision Trees; D005069:Evaluation Studies as Topic; D005103:Expert Systems; D008956:Models, Chemical; D015394:Molecular Structure; D021281:Quantitative Structure-Activity Relationship; D014116:Toxicology / Q000379:methods*