Visual evoked potential data from rats exposed to toluene Electroretinogram data from rats exposed to toluene Counts of rod and m-cone photoreceptor cells in retinas of rats exposed to toluene. This dataset is associated with the following publication: Boyes , W., M. Bercegeay, L. Degn , T. Beasley , P. Evansky , J.C. Mwanza, A. Geller , C. Pinckney, M.T. Nork, and P.J. Bushnell. Toluene Inhalation Exposure for 13 Weeks Causes Persistent Changes in Electroretinograms of Long-Evans Rats. NEUROTOXICOLOGY. Elsevier B.V., Amsterdam, NETHERLANDS, 53: 257-270, (2016).

Visual evoked potential data from rats exposed to toluene Electroretinogram data from rats exposed to toluene Counts of rod and m-cone photoreceptor cells in retinas of rats exposed to toluene. This dataset is associated with the following publication: Boyes , W., M. Bercegeay, L. Degn , T. Beasley , P. Evansky , J.C. Mwanza, A. Geller , C. Pinckney, M.T. Nork, and P.J. Bushnell. Toluene Inhalation Exposure for 13 Weeks Causes Persistent Changes in Electroretinograms of Long-Evans Rats. NEUROTOXICOLOGY. Elsevier B.V., Amsterdam, NETHERLANDS, 53: 257-270, (2016).

In an effort to address a major challenge in chemical safety assessment, alternative approaches for characterizing systemic effect levels, a predictive model was developed. Systemic effect levels were curated from ToxRefDB, HESS-DB and COSMOS-DB from numerous study types totaling 4382 in vivo studies for 1201 chemicals. Observed systemic effects in mammalian models are a complex function of chemical dynamics, kinetics, and inter- and intra-individual variability. In order to address the complexity problem, systemic effect levels were modeled at the study-level by leveraging study covariates (e.g., study type, strain, administration route) in addition to multiple descriptor sets, including chemical (ToxPrint, PaDEL, and Physchem), biological (ToxCast), and kinetic descriptors. Using Random Forest modeling with cross-validation and external validation procedures, study-level covariates alone accounted for approximately 20% of the variance reducing the root mean squared error (RMSE) from 0.96 log10 mg/kg/day to 0.85 log10 mg/kg/day, providing a baseline performance metric (lower expectation of model performance). A consensus model developed using a combination of study-level covariates, chemical, biological, and kinetic descriptors explained a total of 38% of the variance with an RMSE of 0.76 log10 mg/kg/day. A benchmark model (upper expectation of model performance) was also developed with an RMSE of 0.5 log10 mg/kg/day by incorporating study-level covariates and the mean effect level per chemical. To achieve a representative chemical-level prediction, the minimum study-level predicted and observed effect level per chemical were compared reducing the RMSE from 1.1 to 0.8 log10 mg/kg/day. Although biological descriptors did not improve model performance, the final model was enriched for biological descriptors that indicated xenobiotic metabolism gene expression, oxidative stress, and cytotoxicity, demonstrating the importance of accounting for kinetics and non-specific bioactivity in predicting systemic effect levels. Herein, we have generated an externally predictive model of systemic effect levels for use as a safety assessment tool and have generated forward predictions for thousands of chemicals. This dataset is associated with the following publication: Truong, L., G. Ouedraogo, L. Pham, J. Clouzeau, S. Loisel-Joubert, D. Blanchet, H. Noçairi, W. Setzer, R. Judson, C. Grulke, K. Mansouri, and M. Martin. (Archives of Toxicology) Predicting In Vivo Effect Levels for Repeat Dose Systemic Toxicity using Chemical, Biological, Kinetic and Study Covariates. Archives of Toxicology. Springer, New York, NY, USA, 92(2): 587-600, (2018).

In an effort to address a major challenge in chemical safety assessment, alternative approaches for characterizing systemic effect levels, a predictive model was developed. Systemic effect levels were curated from ToxRefDB, HESS-DB and COSMOS-DB from numerous study types totaling 4382 in vivo studies for 1201 chemicals. Observed systemic effects in mammalian models are a complex function of chemical dynamics, kinetics, and inter- and intra-individual variability. In order to address the complexity problem, systemic effect levels were modeled at the study-level by leveraging study covariates (e.g., study type, strain, administration route) in addition to multiple descriptor sets, including chemical (ToxPrint, PaDEL, and Physchem), biological (ToxCast), and kinetic descriptors. Using Random Forest modeling with cross-validation and external validation procedures, study-level covariates alone accounted for approximately 20% of the variance reducing the root mean squared error (RMSE) from 0.96 log10 mg/kg/day to 0.85 log10 mg/kg/day, providing a baseline performance metric (lower expectation of model performance). A consensus model developed using a combination of study-level covariates, chemical, biological, and kinetic descriptors explained a total of 38% of the variance with an RMSE of 0.76 log10 mg/kg/day. A benchmark model (upper expectation of model performance) was also developed with an RMSE of 0.5 log10 mg/kg/day by incorporating study-level covariates and the mean effect level per chemical. To achieve a representative chemical-level prediction, the minimum study-level predicted and observed effect level per chemical were compared reducing the RMSE from 1.1 to 0.8 log10 mg/kg/day. Although biological descriptors did not improve model performance, the final model was enriched for biological descriptors that indicated xenobiotic metabolism gene expression, oxidative stress, and cytotoxicity, demonstrating the importance of accounting for kinetics and non-specific bioactivity in predicting systemic effect levels. Herein, we have generated an externally predictive model of systemic effect levels for use as a safety assessment tool and have generated forward predictions for thousands of chemicals. This dataset is associated with the following publication: Truong, L., G. Ouedraogo, L. Pham, J. Clouzeau, S. Loisel-Joubert, D. Blanchet, H. Noçairi, W. Setzer, R. Judson, C. Grulke, K. Mansouri, and M. Martin. (Archives of Toxicology) Predicting In Vivo Effect Levels for Repeat Dose Systemic Toxicity using Chemical, Biological, Kinetic and Study Covariates. Archives of Toxicology. Springer, New York, NY, USA, 92(2): 587-600, (2018).

This dataset includes cardiovascular and pulmonary endpoints from rats exposed to either filtered air, isoprene-smog or toluene-smog. This dataset is associated with the following publication: Snow , S., J. Krug, J. Turlington, J. Richards, M. Schladweiler, A. Ledbetter, Q. Krantz, C. King, M. Gilmour, S. Gavett, U. Kodavanti, A. Farraj, and M. Hazari. Differential Cardiopulmonary Effects of Isoprene- versus Toluene-generated Photochemically-Aged Smog in Rats. ATMOSPHERIC ENVIRONMENT. Elsevier B.V., Amsterdam, NETHERLANDS, 295: 119525, (2023).

This paper uses EPA public data to build new datasets and analysis by non-EPA authors. This dataset is not publicly accessible because: Data was not collected in EPA labs or paid for by EPA. It can be accessed through the following means: This paper uses EPA public data to build new datasets and analysis by non-EPA authors. Format: N/A. This dataset is associated with the following publication: Theunissen, P., S. Beken, B. Beyer, W. Breslin, G. Cappon, C. Chen, G. Chmielewski, L. De Schaepdrijver, B. Enright, J. Foreman, W. Harrouk, K. Hew, A. Hoberman, J. Hui, T. Knudsen , S. Laffan, S. Makris , M. Martin , M. McNerney, C. Siezen, D. Stanislaus, J. Stewart, K. Thompson, B. Tornesi, G. Weinbauer, S. Wood, J. Van der Laan, and A. Piersma. (Crit. Rev. Tox.) Comparison of rat and rabbit embryo-fetal developmental toxicity data for 379 pharmaceuticals: on the nature and severity of developmental effects. CRITICAL REVIEWS IN TOXICOLOGY. CRC Press LLC, Boca Raton, FL, USA, 1-11, (2016).

This paper uses EPA public data to build new datasets and analysis by non-EPA authors. This dataset is not publicly accessible because: Data was not collected in EPA labs or paid for by EPA. It can be accessed through the following means: This paper uses EPA public data to build new datasets and analysis by non-EPA authors. Format: N/A. This dataset is associated with the following publication: Theunissen, P., S. Beken, B. Beyer, W. Breslin, G. Cappon, C. Chen, G. Chmielewski, L. De Schaepdrijver, B. Enright, J. Foreman, W. Harrouk, K. Hew, A. Hoberman, J. Hui, T. Knudsen , S. Laffan, S. Makris , M. Martin , M. McNerney, C. Siezen, D. Stanislaus, J. Stewart, K. Thompson, B. Tornesi, G. Weinbauer, S. Wood, J. Van der Laan, and A. Piersma. (Crit. Rev. Tox.) Comparison of rat and rabbit embryo-fetal developmental toxicity data for 379 pharmaceuticals: on the nature and severity of developmental effects. CRITICAL REVIEWS IN TOXICOLOGY. CRC Press LLC, Boca Raton, FL, USA, 1-11, (2016).

ToxRefDB comprises information from over fifty years of in vivo toxicity data. The database includes information for over 1000 chemicals, and is being used as a primary source of data for evaluating efforts of the ToxCast program [4,5], as well as for numerous predictive and retrospective analyses. This dataset is associated with the following publication: Watford, S., L. Pham, J. Wignall, R. Shin, M.T. Martin, and K. Friedman. ToxRefDB version 2.0: Improved utility for predictive and retrospective toxicology analyses. REPRODUCTIVE TOXICOLOGY. Elsevier Science Ltd, New York, NY, USA, 89: 145-158, (2019).

individual values for liver detoxification for each human sample and for each chemical. This dataset is associated with the following publication: Moser, G., and S. Padilla. Esterase detoxification of acetylcholinesterase inhibitors using human liver samples in vitro. TOXICOLOGY. Elsevier Science Ltd, New York, NY, USA, 11-20, (2016).

individual values for liver detoxification for each human sample and for each chemical. This dataset is associated with the following publication: Moser, G., and S. Padilla. Esterase detoxification of acetylcholinesterase inhibitors using human liver samples in vitro. TOXICOLOGY. Elsevier Science Ltd, New York, NY, USA, 11-20, (2016).