This repository contains code, input and output files associated with the GenRA acute toxicity case study that was published by Helman et al (2019) in Computational Toxicology. This dataset is associated with the following publication: Helman, G., I. Shah, and G. Patlewicz. Transitioning the Generalised Read-Across approach (GenRA) to quantitative predictions: A case study using acute oral toxicity data. Computational Toxicology. Elsevier B.V., Amsterdam, NETHERLANDS, 12(November 2019): 100097, (2019).

This repository contains code, input and output files associated with the GenRA acute toxicity case study that was published by Helman et al (2019) in Computational Toxicology. This dataset is associated with the following publication: Helman, G., I. Shah, and G. Patlewicz. Transitioning the Generalised Read-Across approach (GenRA) to quantitative predictions: A case study using acute oral toxicity data. Computational Toxicology. Elsevier B.V., Amsterdam, NETHERLANDS, 12(November 2019): 100097, (2019).

These new analysis builds on the baseline GenRA approach and presents a proof of concept of how other contexts of similarity namely physchem can be implemented into a search strategy for identification of analogues and how this impacts performance of read-across. Chemicals Involved: Same ToxRef dataset as used in the original GenRA manuscript. This dataset is associated with the following publication: Helman, G., I. Shah, and G. Patlewicz. Extending the Generalised Read-Across approach (GenRA): A systematic analysis of the impact of physicochemical property information on read-across performance. Computational Toxicology. Elsevier B.V., Amsterdam, NETHERLANDS, 8: 34-50, (2018).

Read-across is a popular data gap filling technique within category and analogue approaches for regulatory purposes. Acceptance of read-across remains an ongoing challenge with several efforts underway for identifying and addressing uncertainties. Here we demonstrate an algorithmic, automated approach to evaluate the utility of using in vitro bioactivity data (“bioactivity descriptors”, from EPA’s ToxCast program) in conjunction with chemical descriptor information to derive local validity domains (specific sets of nearest neighbors) to facilitate read-across for a number of in vivo repeated dose toxicity study types. Over 3400 different chemical structure descriptors were generated for a set of 976 chemicals and supplemented with the outcomes from 821 in vitro assays. The read-across prediction for a given chemical was based on the similarity weighted endpoint outcomes of its nearest neighbors. The approach enabled a performance baseline for read-across predictions of specific study outcomes to be established. Bioactivity descriptors were often found to be more predictive of in vivo toxicity outcomes than chemical descriptors or a combination of both. This generic read across (GenRA) is intended to form a first step in systemizing read-across prediction and serves as a useful tool as part of a screening level hazard assessment for new untested chemicals. This dataset is associated with the following publication: Shah , I., J. Liu , R.S. Judson , R.S. Thomas , and G. Patlewicz. (Reg. Tox. Pharm.) Systematically evaluating read-across prediction and performance using a local validity approach characterized by chemical structure and bioactivity information. REGULATORY TOXICOLOGY AND PHARMACOLOGY. Elsevier Science Ltd, New York, NY, USA, 79: 12-24, (2016).

Read-across is a popular data gap filling technique within category and analogue approaches for regulatory purposes. Acceptance of read-across remains an ongoing challenge with several efforts underway for identifying and addressing uncertainties. Here we demonstrate an algorithmic, automated approach to evaluate the utility of using in vitro bioactivity data (“bioactivity descriptors”, from EPA’s ToxCast program) in conjunction with chemical descriptor information to derive local validity domains (specific sets of nearest neighbors) to facilitate read-across for a number of in vivo repeated dose toxicity study types. Over 3400 different chemical structure descriptors were generated for a set of 976 chemicals and supplemented with the outcomes from 821 in vitro assays. The read-across prediction for a given chemical was based on the similarity weighted endpoint outcomes of its nearest neighbors. The approach enabled a performance baseline for read-across predictions of specific study outcomes to be established. Bioactivity descriptors were often found to be more predictive of in vivo toxicity outcomes than chemical descriptors or a combination of both. This generic read across (GenRA) is intended to form a first step in systemizing read-across prediction and serves as a useful tool as part of a screening level hazard assessment for new untested chemicals. This dataset is associated with the following publication: Shah , I., J. Liu , R.S. Judson , R.S. Thomas , and G. Patlewicz. (Reg. Tox. Pharm.) Systematically evaluating read-across prediction and performance using a local validity approach characterized by chemical structure and bioactivity information. REGULATORY TOXICOLOGY AND PHARMACOLOGY. Elsevier Science Ltd, New York, NY, USA, 79: 12-24, (2016).

Here are all of the data files used for this manuscript. Please note that this is all published data. Imran Shah 1.1060+ Chemicals and Chemical controls 2. Chemical descriptors (chm): 2048 Morgan (mrgn) 2048 Topological Torsion (tptr) 729 ToxPrints (toxp) 3. Transcriptomic descriptors(bio): 95 Gene (ge) 189 Assay (asy) 4. 922 Toxicity outcomes(tox) 5. 86 Predefined Chemical Clusters

The dataset contains the outputs for the analogue searches conducted for the chemical of interest, p,p'-DDD. This dataset is associated with the following publication: Lizarraga, L., J. Dean, J. Kaiser, S. Wesselkamper, J. Lambert, and J. Zhao. A Case Study on the Application of An Expert-driven Read-Across Approach in Support of Quantitative Risk Assessment of p,p’-Dichlorodiphenyldichloroethane. REGULATORY TOXICOLOGY AND PHARMACOLOGY. Elsevier Science Ltd, New York, NY, USA, 103: 301-313, (2019).