A data set of 500 chemicals evaluated for their ability to induce cleft palate in animal prenatal developmental studies was compiled from Toxicity Reference Database and the biomedical literature, which included 63 cleft palate active and 437 inactive chemicals. To characterize the potential molecular targets for chemical‐induced cleft palate, we mined the ToxCast high‐throughput screening database for patterns and linkages in bioactivity profiles and chemical structural descriptors. The following datasets can be obtained via the links and files in the Data section: Phase II ToxCast assay data results (Judson et al., 2010); The Gene Score data set derived from ToxCast; ToxRefDB version 1 (Knudsen et al., 2009; Martin, Judson, et al., 2009); The ToxPrint chemotypes (Yang et al., 2015). This dataset is associated with the following publication: Baker, N., N. Sipes, J. Franzosa, D. Belair, B. Abbott, R. Judson, and T. Knudsen. Characterizing cleft palate toxicants using ToxCast data, chemical structure, and the biomedical literature. Birth Defects Research. John Wiley & Sons, Inc., Hoboken, NJ, USA, 1-21, (2019).

Morphogenetic events are driven by cell-generated physical forces and complex cellular dynamics. To improve our capacity to predict developmental effects from cellular alterations, we built a multi-cellular agent-based model in CompuCell3D that recapitulates the cellular networks and collective cell behavior underlying growth and fusion of the mammalian secondary palate. The model incorporated multiple signaling pathways (TGF?, BMP, FGF, EGF, SHH) in a biological framework to recapitulate morphogenetic events from palatal outgrowth through midline fusion. It effectively simulated higher-level phenotypes (e.g., midline contact, medial edge seam (MES) breakdown, mesenchymal confluence, fusion defects) in response to genetic or environmental perturbations. Perturbation analysis of various control features revealed model functionality with respect to cell signaling systems and feedback loops for growth and fusion, diverse individual cell behaviors and collective cellular behavior leading to physical contact and midline fusion, and quantitative analysis of the TGF/EGF switch that controls MES breakdown – a key event in morphogenetic fusion. The virtual palate model was then executed with theoretical chemical perturbation scenarios to simulate switch behavior leading to a disruption of fusion following chronic (e.g., dioxin) and acute (e.g., retinoic acid, hydrocortisone) toxicant exposures. This computer model adds to similar systems models toward a ‘virtual embryo’ for simulation and quantitative prediction of adverse developmental outcomes following genetic perturbation and/or environmental. This dataset is associated with the following publication: Hutson, S., M. Leung, N. Baker, R. Spencer, and T. Knudsen. (CHEMICAL RESEARCH IN TOXICOLOGY) Computational Model of Secondary Palate Fusion and Disruption. CHEMICAL RESEARCH IN TOXICOLOGY. American Chemical Society, Washington, DC, USA, 30(4): 965-979, (2017).

Morphogenetic events are driven by cell-generated physical forces and complex cellular dynamics. To improve our capacity to predict developmental effects from cellular alterations, we built a multi-cellular agent-based model in CompuCell3D that recapitulates the cellular networks and collective cell behavior underlying growth and fusion of the mammalian secondary palate. The model incorporated multiple signaling pathways (TGF?, BMP, FGF, EGF, SHH) in a biological framework to recapitulate morphogenetic events from palatal outgrowth through midline fusion. It effectively simulated higher-level phenotypes (e.g., midline contact, medial edge seam (MES) breakdown, mesenchymal confluence, fusion defects) in response to genetic or environmental perturbations. Perturbation analysis of various control features revealed model functionality with respect to cell signaling systems and feedback loops for growth and fusion, diverse individual cell behaviors and collective cellular behavior leading to physical contact and midline fusion, and quantitative analysis of the TGF/EGF switch that controls MES breakdown – a key event in morphogenetic fusion. The virtual palate model was then executed with theoretical chemical perturbation scenarios to simulate switch behavior leading to a disruption of fusion following chronic (e.g., dioxin) and acute (e.g., retinoic acid, hydrocortisone) toxicant exposures. This computer model adds to similar systems models toward a ‘virtual embryo’ for simulation and quantitative prediction of adverse developmental outcomes following genetic perturbation and/or environmental. This dataset is associated with the following publication: Hutson, S., M. Leung, N. Baker, R. Spencer, and T. Knudsen. (CHEMICAL RESEARCH IN TOXICOLOGY) Computational Model of Secondary Palate Fusion and Disruption. CHEMICAL RESEARCH IN TOXICOLOGY. American Chemical Society, Washington, DC, USA, 30(4): 965-979, (2017).

This dataset consists of data from in vitro developmental neurotoxicity new approach methodologies (DNT-NAMs). Assays include evaluations of proliferation, apoptosis, viability, neurite outgrowth, synaptogenesis and network formation. A variety of cell models are utilized depending on the assay, and each assay may have multiple endpoints. Data have been collected and evaluated using the ToxCast PipeLine (tcpl). This dataset is associated with the following publication: Carstens, K., A. Carpenter, M. Martin, J. Harrill, T. Shafer, and K. Friedman. Integrating Data From In Vitro New Approach Methodologies for Developmental Neurotoxicity. TOXICOLOGICAL SCIENCES. Society of Toxicology, RESTON, VA, 187(1): 62-79, (2022).

This dataset consists of data from in vitro developmental neurotoxicity new approach methodologies (DNT-NAMs). Assays include evaluations of proliferation, apoptosis, viability, neurite outgrowth, synaptogenesis and network formation. A variety of cell models are utilized depending on the assay, and each assay may have multiple endpoints. Data have been collected and evaluated using the ToxCast PipeLine (tcpl). This dataset is associated with the following publication: Carstens, K., A. Carpenter, M. Martin, J. Harrill, T. Shafer, and K. Friedman. Integrating Data From In Vitro New Approach Methodologies for Developmental Neurotoxicity. TOXICOLOGICAL SCIENCES. Society of Toxicology, RESTON, VA, 187(1): 62-79, (2022).

A database of embryo-fetal developmental toxicity (EFDT) studies of 379 pharmaceutical compounds in rat and rabbit. This dataset is not publicly accessible because: this paper uses EPA public data to build new datasets and analysis by non-EPA authors. It can be accessed through the following means: EPA data is publicly accessible. Format: N/A. This dataset is associated with the following publication: Theunissen, P., S. Beken, B. Beyer, W. Breslin, G.D. 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 , and M. Martin. (Crit. Rev. Tox.) Comparing rat and rabbit embryo-fetal developmental toxicity studies for 379 pharmaceuticals: On systemic dose and developmental effects. CRITICAL REVIEWS IN TOXICOLOGY. CRC Press LLC, Boca Raton, FL, USA, 1-13, (2016).

We use a supervised machine learning strategy to systematically investigate the relative importance of study type, machine learning algorithm, and type of descriptor on predicting in vivo repeat-dose toxicity at the organ-level. A total of 985 compounds were represented using chemical structural descriptors, ToxPrint chemotype descriptors, and bioactivity descriptors from ToxCast in vitro high-throughput screening assays. Using ToxRefDB, a total of 35 target organ outcomes were identified that contained at least 100 chemicals (50 positive and 50 negative). Supervised machine learning was performed using Naïve Bayes, k-nearest neighbor, random forest, classification and regression trees, and support vector classification approaches. Model performnce was assessed based on F1 scores using five-fold cross-validation with balanced bootstrap replicates. Fixed effects modeling showed the variance in F1 scores was explained mostly by target organ outcome, followed by descriptor type, machine learning algorithm, and interactions between these three factors. A combination of bioactivity and chemical structure or chemotype descriptors were the most predictive. Model performance improved with more chemicals (up to a maximum of 24%) and these gains were correlated (ρ= 0.92) with the number of chemicals. This dataset is associated with the following publication: Liu, J., G. Patlewicz, A. Williams, R. Thomas, and I. Shah. (Chemical Research in Toxicology) Predicting organ toxicity using in vitro bioactivity data and chemical structure. CHEMICAL RESEARCH IN TOXICOLOGY. American Chemical Society, Washington, DC, USA, 30: 2046−2059, (2017).

We use a supervised machine learning strategy to systematically investigate the relative importance of study type, machine learning algorithm, and type of descriptor on predicting in vivo repeat-dose toxicity at the organ-level. A total of 985 compounds were represented using chemical structural descriptors, ToxPrint chemotype descriptors, and bioactivity descriptors from ToxCast in vitro high-throughput screening assays. Using ToxRefDB, a total of 35 target organ outcomes were identified that contained at least 100 chemicals (50 positive and 50 negative). Supervised machine learning was performed using Naïve Bayes, k-nearest neighbor, random forest, classification and regression trees, and support vector classification approaches. Model performnce was assessed based on F1 scores using five-fold cross-validation with balanced bootstrap replicates. Fixed effects modeling showed the variance in F1 scores was explained mostly by target organ outcome, followed by descriptor type, machine learning algorithm, and interactions between these three factors. A combination of bioactivity and chemical structure or chemotype descriptors were the most predictive. Model performance improved with more chemicals (up to a maximum of 24%) and these gains were correlated (ρ= 0.92) with the number of chemicals. This dataset is associated with the following publication: Liu, J., G. Patlewicz, A. Williams, R. Thomas, and I. Shah. (Chemical Research in Toxicology) Predicting organ toxicity using in vitro bioactivity data and chemical structure. CHEMICAL RESEARCH IN TOXICOLOGY. American Chemical Society, Washington, DC, USA, 30: 2046−2059, (2017).

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).