Transitioning the generalised read-across approach (GenRA) to quantitative predictions: A case study using acute oral toxicity data

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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).

Quantitative Prediction of Repeat Dose Toxicity Values using GenRA

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Per Imran Shah, this was the Data used in and published as supplemental material for this manuscript. Table S1. Aggregated point of departure (POD) data obtained from ToxRefDB v2.0. Table S2. Chemical structure descriptor data from DSSTox. Table S3. Chemical cluster membership. Table S5. GenRA optimal predictions for each endpoint category and cluster.

Quantitative Prediction of Repeat Dose Toxicity Values using GenRA

공공데이터포털

Per Imran Shah, this was the Data used in and published as supplemental material for this manuscript. Table S1. Aggregated point of departure (POD) data obtained from ToxRefDB v2.0. Table S2. Chemical structure descriptor data from DSSTox. Table S3. Chemical cluster membership. Table S5. GenRA optimal predictions for each endpoint category and cluster.

Generalised Read-Across Prediction using genra-py

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Read-across (RAX) is a widely used data gap filling approach and the authors have developed a data-driven tool, called GenRA, to support expert-driven RAX. This work describes a stand-alone Python 3 package, called genra-py, which enables end-users to conduct hazard identification and point of departure (POD) estimation using GenRA. This dataset is associated with the following publication: Shah, I., T. Tate, and G. Patlewicz. Generalised Read-Across Prediction using genra-py. BIOINFORMATICS. Oxford University Press, Cary, NC, USA, 37(19): 3380-3381, (2021).

Repeat-dose toxicity prediction with Generalized Read-Across (GenRA) using targeted transcriptomic data

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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

Repeat-dose toxicity prediction with Generalized Read-Across (GenRA) using targeted transcriptomic data

공공데이터포털

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

Generalised Read-Across (GenRA) refinements

공공데이터포털

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).

Generalised Read-Across (GenRA) refinements

공공데이터포털

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).

Analogue search results for p,p'-DDD

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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).

Analogue search results for p,p'-DDD

공공데이터포털

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).