bmdrc: Python package for quantifying phenotypes from chemical exposures with benchmark dose modeling
Though chemical exposures are known to potentially have negative impacts on health, including contributing to chronic diseases such as cancer, the quantitative contribution of risk is not fully understood for every chemical. A commonly used approach to quantify levels of risk is to measure the propo...
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| Veröffentlicht in: | PLoS computational biology Jg. 21; H. 7; S. e1013337 |
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28.07.2025
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| Abstract | Though chemical exposures are known to potentially have negative impacts on health, including contributing to chronic diseases such as cancer, the quantitative contribution of risk is not fully understood for every chemical. A commonly used approach to quantify levels of risk is to measure the proportion of organisms (such as a total number of zebrafish on a plate or mice in a cage) with abnormal behavioral responses or morphology at increasing concentrations of chemical exposure. A particular challenge with processing the proportional data from these assays is the appropriate estimation of chemical concentration levels that result in malformations or acute toxicity, as these values typically vary between experimental measurements. The recommended approach by the Environmental Protection Agency (EPA) is to fit benchmark dose curves with specific filters and model fitting steps, which are crucial to properly processing the proportional data. Several tools exist for the fitting of benchmark dose response curves, but none are standalone Python libraries built to process both morphological and behavioral data as proportions with all the EPA recommended filters, filter parameters, models, and model parameters. Thus, here we present the benchmark dose response curve ( bmdrc ) Python library, which was built to closely follow these EPA guidelines with helpful visualizations of filters and fitted model curves, and reports for reproducibility purposes. bmdrc is open-source and has demonstrated utility as a support package to an existing web portal for information on chemicals ( https://srp.pnnl.gov ). Our package will support any toxicology analysis where the response is a proportional value at increasing levels of a concentration of a chemical or chemical mixture. |
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| AbstractList | Though chemical exposures are known to potentially have negative impacts on health, including contributing to chronic diseases such as cancer, the quantitative contribution of risk is not fully understood for every chemical. A commonly used approach to quantify levels of risk is to measure the proportion of organisms (such as a total number of zebrafish on a plate or mice in a cage) with abnormal behavioral responses or morphology at increasing concentrations of chemical exposure. A particular challenge with processing the proportional data from these assays is the appropriate estimation of chemical concentration levels that result in malformations or acute toxicity, as these values typically vary between experimental measurements. The recommended approach by the Environmental Protection Agency (EPA) is to fit benchmark dose curves with specific filters and model fitting steps, which are crucial to properly processing the proportional data. Several tools exist for the fitting of benchmark dose response curves, but none are standalone Python libraries built to process both morphological and behavioral data as proportions with all the EPA recommended filters, filter parameters, models, and model parameters. Thus, here we present the benchmark dose response curve (bmdrc) Python library, which was built to closely follow these EPA guidelines with helpful visualizations of filters and fitted model curves, and reports for reproducibility purposes. bmdrc is open-source and has demonstrated utility as a support package to an existing web portal for information on chemicals (https://srp.pnnl.gov). Our package will support any toxicology analysis where the response is a proportional value at increasing levels of a concentration of a chemical or chemical mixture. Though chemical exposures are known to potentially have negative impacts on health, including contributing to chronic diseases such as cancer, the quantitative contribution of risk is not fully understood for every chemical. A commonly used approach to quantify levels of risk is to measure the proportion of organisms (such as a total number of zebrafish on a plate or mice in a cage) with abnormal behavioral responses or morphology at increasing concentrations of chemical exposure. A particular challenge with processing the proportional data from these assays is the appropriate estimation of chemical concentration levels that result in malformations or acute toxicity, as these values typically vary between experimental measurements. The recommended approach by the Environmental Protection Agency (EPA) is to fit benchmark dose curves with specific filters and model fitting steps, which are crucial to properly processing the proportional data. Several tools exist for the fitting of benchmark dose response curves, but none are standalone Python libraries built to process both morphological and behavioral with all the EPA recommended filters, filter parameters, models, and model parameters. Thus, here we present the benchmark dose response curve (bmdrc) Python library, which was built to closely follow these EPA guidelines with helpful visualizations of filters and fitted model curves, and reports for reproducibility purposes. bmdrc is open-source and has demonstrated utility as a support package to an existing web portal for information on chemicals (https://srp.pnnl.gov). Our package will support any toxicology analysis where the response is a proportional value at increasing levels of a concentration of a chemical or chemical mixture.Though chemical exposures are known to potentially have negative impacts on health, including contributing to chronic diseases such as cancer, the quantitative contribution of risk is not fully understood for every chemical. A commonly used approach to quantify levels of risk is to measure the proportion of organisms (such as a total number of zebrafish on a plate or mice in a cage) with abnormal behavioral responses or morphology at increasing concentrations of chemical exposure. A particular challenge with processing the proportional data from these assays is the appropriate estimation of chemical concentration levels that result in malformations or acute toxicity, as these values typically vary between experimental measurements. The recommended approach by the Environmental Protection Agency (EPA) is to fit benchmark dose curves with specific filters and model fitting steps, which are crucial to properly processing the proportional data. Several tools exist for the fitting of benchmark dose response curves, but none are standalone Python libraries built to process both morphological and behavioral with all the EPA recommended filters, filter parameters, models, and model parameters. Thus, here we present the benchmark dose response curve (bmdrc) Python library, which was built to closely follow these EPA guidelines with helpful visualizations of filters and fitted model curves, and reports for reproducibility purposes. bmdrc is open-source and has demonstrated utility as a support package to an existing web portal for information on chemicals (https://srp.pnnl.gov). Our package will support any toxicology analysis where the response is a proportional value at increasing levels of a concentration of a chemical or chemical mixture. |
| Author | Waters, Katrina M. Degnan, David J. Gosline, Sara M. Bramer, Lisa M. Tanguay, Robyn L. Truong, Lisa |
| AuthorAffiliation | 1 Earth and Biological Sciences Directorate, Pacific Northwest National Laboratory, Richland, Washington, United States of America 2 Sinnhuber Aquatic Research Laboratory, Department of Environmental and Molecular Toxicology, Oregon State University, Corvallis, Oregon, United States of America The University of Melbourne Faculty of Science, AUSTRALIA |
| AuthorAffiliation_xml | – name: 2 Sinnhuber Aquatic Research Laboratory, Department of Environmental and Molecular Toxicology, Oregon State University, Corvallis, Oregon, United States of America – name: The University of Melbourne Faculty of Science, AUSTRALIA – name: 1 Earth and Biological Sciences Directorate, Pacific Northwest National Laboratory, Richland, Washington, United States of America |
| Author_xml | – sequence: 1 givenname: David J. orcidid: 0000-0001-5737-7173 surname: Degnan fullname: Degnan, David J. – sequence: 2 givenname: Lisa M. surname: Bramer fullname: Bramer, Lisa M. – sequence: 3 givenname: Lisa surname: Truong fullname: Truong, Lisa – sequence: 4 givenname: Robyn L. surname: Tanguay fullname: Tanguay, Robyn L. – sequence: 5 givenname: Sara M. surname: Gosline fullname: Gosline, Sara M. – sequence: 6 givenname: Katrina M. orcidid: 0000-0003-4696-5396 surname: Waters fullname: Waters, Katrina M. |
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| Cites_doi | 10.1016/j.atmosenv.2007.12.010 10.1093/bioinformatics/bty878 10.1093/bioinformatics/btw680 10.1371/journal.pone.0146021 10.1111/risa.13537 10.1016/j.reprotox.2019.07.013 10.18637/jss.v026.i05 10.1021/es800511x 10.1016/j.comtox.2018.11.001 10.1038/s41597-023-02021-5 10.1021/acs.est.8b04752 10.7717/peerj.10557 10.1016/j.yrtph.2022.105261 10.1016/j.scitotenv.2019.133971 10.1289/EHP1289 10.1289/ehp.1307539 10.1073/pnas.1618475114 10.1021/es800453n 10.1021/acs.est.5b00800 10.1111/risa.17451 10.1016/j.taap.2010.10.016 10.1038/s41467-021-22249-2 10.22427/NTP-DATA-002-00062-0001-0000-1 10.1093/bioinformatics/btaa700 10.1080/10473289.1991.10466911 |
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| Title | bmdrc: Python package for quantifying phenotypes from chemical exposures with benchmark dose modeling |
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