Decision Context Approach Results Impacts Read-across is a popular data gap filling technique within category and analogue approaches for regulatory purposes. Acceptance of read-across remains a challenge with several efforts underway for identifying and addressing uncertainties. To date, these approaches have been qualitative in nature. Here, an algorithmic approach to facilitate read-across using ToxCast in vitro bioactivity data in conjunction with chemical descriptor information to predict in vivo outcomes in guideline (and guideline-like) testing studies from ToxRefDB is demonstrated. The read-across prediction for a given chemical is based on the similarity weighted endpoint outcomes of its nearest neighbors, calculated using in vitro bioactivity and chemical structure descriptors, called Generalized Read-across (GenRA). GenRA is a first step in systemizing read-across by providing performance metrics and enabling the scientific confidence of a prediction to be objectively assessed. References Disruptive Innovation in Chemical Evaluation Generalized Read-across (GenRA) prediction using chemical and biological information Imran Shah,1 Jie Liu,1,2,3 Richard S. Judson,1 Russell S. Thomas,1 Grace Patlewicz1* 1NCCT/ORD/US EPA, 2ORISE, 3UALR epa.gov/research CSS BoSC Meeting 2016 Presenting author: orcid.org/0000-0003-3863-9689| Grace Patlewicz l email: patlewicz.grace@epa.gov l I. Data 1,778 Chemicals 3,239 Structure descriptors (chm) 820 Bioactivity assays (bio) ToxCast 574 Apical outcomes (tox) ToxRefDB II. Define Local neighborhoods Use K-means analysis to group chemicals by similarity Use cluster stability analysis ~ 100 local neighborhoods III. GenRA Use GenRA to predict apical outcomes in local neighbor hoods Evaluate impact descriptors (chm, bio, bc) on prediction Quantify uncertainty Read-across acceptance is based on a subjective expert judgement assessment No objective means of assessing performance of a read-across Critical need for an objective measure of uncertainty in read-across prediction Data – Representing chemicals 1,778 Chemicals from ToxCast Phases I, II, III Each chemical represented by binary chemical (chm), bioactivity (bio) and toxicity (tox) vectors: 3,239 binary chemical structure descriptors (RDKit): Morgan fingerprints, Molecular Access System (MACCS) keys, and topological torsion fingerprints. Presence of structural feature denoted as “1” 820 binary bioactivity descriptors from ToxCast: If chemical is active in an assay then “1” else “0” 574 apical outcomes from ToxRefDB aggregated at the level of guideline study and target organ. If there is a significant effect at target site “1” else “0” Data –in vivo toxicity information from ToxRefDB Local neighborhoods (Clusters) α ij k j β j α ij k j α β, i s Σ x s Σ = y ghbours nearestnei = k Jaccard similarity: Impacts GenRA – performance in each cluster Use GenRA to predict the similarity weighted toxicity scores for each Toxicity type (𝛽𝛽) Descriptor ={chm,bio,bc} (α ) No. of nearest neighbours (𝑘𝑘) Similarity score threshold ( 𝑠𝑠𝑖𝑖𝑖𝑖 𝛼𝛼) Calculate performance by comparing predicted 𝑦𝑦𝑡𝑡𝑡𝑡𝑡𝑡and true 𝑥𝑥𝑡𝑡𝑡𝑡𝑡𝑡for all chemicals using area under ROC curve (AUC) Analyzing local neighborhood of a target chemical Comparing the read-across performance between different toxicity effects and their chemical representations Assessing the impact of the number of neighbors and the similarity threshold on the read-across prediction Bioactivity descriptors were often found to be more predictive of in vivo toxicity outcomes than chemical descriptors. Predictions were still context dependent on the endpoint and the chemical neighborhood under study. GenRA provides a first step in a tiered approach of investigating the utility of bioactivity data in concert with chemical structural information to make read-across predictions, and for systematically evaluating the uncertainty in those predictions. Next steps in progress: • Use of other chemical descriptor sets that encode