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BBMD Used to Model Cancer Risk

“Science and Decisions” (the silver book) published by the National Academies of Sciences, ...

Citations for: Shao K, Shapiro AJ. (2018). A Web-Based System for Bayesian Benchmark Dose Estimation. Environ Health Perspect. 126(1):017002. http://dx.doi.org/10.1289/EHP1289.1-48.

Zhang, Y., Liu, Z., Wang, Z., Gao, H., Wang, Y., Cui, M., Peng, H., Xiao, Y., Jin, Y., Yu, D., et al. (2023). Health risk assessment of cadmium exposure by integration of an in silico physiologically based toxicokinetic model and in vitro tests. J Hazard Mater 443, 130191, 130191. http://dx.doi.org/10.1016/j.jhazmat.2022.130191.

Xiao, J., Fang, K., Zhang, S., Jiang, S., Liu, T., Lv, M., Liao, M., Cao, H., and Shi, Y. (2023). Inhalation bioaccessibility of inhaled triazole fungicides and health risk assessment during spraying. Pest Manag Sci 79, 1768-1776. http://dx.doi.org/10.1002/ps.7354.

Li, Y., Zhang, Z., Jiang, S., Xu, F., Tulum, L., Li, K., Liu, S., Li, S., Chang, L., Liddell, M., et al. (2023). Using transcriptomics, proteomics and phosphoproteomics as new approach methodology (NAM) to define biological responses for chemical safety assessment. Chemosphere 313, 137359, 137359. http://dx.doi.org/10.1016/j.chemosphere.2022.137359.

Al-Qaraleh, S.Y., Al-Zereini, W.A., and Oran, S.A. (2023). Phyto-Decoration of Selenium Nanoparticles Using Moringa peregrina (Forssk.) Fiori Aqueous Extract: Chemical Characterization and Bioactivity Evaluation. Biointerface Research in Applied Chemistry 13, 112. http://dx.doi.org/10.33263/briac132.112.

Zou, X., Wang, R., Yang, Z., Wang, Q., Fu, W., Huo, Z., Ge, F., Zhong, R., Jiang, Y., Li, J., et al. (2022). Family Socioeconomic Position and Lung Cancer Risk: A Meta-Analysis and a Mendelian Randomization Study. Front Public Health 10, 780538. http://dx.doi.org/10.3389/fpubh.2022.780538.

Yasuhiko, Y., Ishigami, M., Machino, S., Fujii, T., Aoki, M., Irie, F., Kanda, Y., and Yoshida, M. (2022). Comparison of the lower limit of benchmark dose confidence interval with no-observed-adverse-effect level by applying four different software for tumorigenicity testing of pesticides in Japan. Regul Toxicol Pharmacol 133, 105201. http://dx.doi.org/10.1016/j.yrtph.2022.105201.

Wheeler, M.W., Cortinas, J., Aerts, M., Gift, J.S., and Davis, J.A. (2022). Continuous Model Averaging for Benchmark Dose Analysis: Averaging Over Distributional Forms. Environmetrics 33, e2728. http://dx.doi.org/10.1002/env.2728.

Wang, S., Zhang, T., Liu, X., Yang, Z., Li, L., Shan, D., Gao, Y., Li, Y., Li, Y., Zhang, Y., and Wang, Q. (2022). Toxicity and toxicokinetics of the ethanol extract of Zuojin formula. BMC Complement Med Ther 22, 220. http://dx.doi.org/10.1186/s12906-022-03684-0.

Simeone, F.C., and Costa, A.L. (2022). Quantifying uncertainty in dose-response screenings of nanoparticles: a Bayesian data analysis. Nanotoxicology 16, 135-151. http://dx.doi.org/10.1080/17435390.2022.2038298.

Shao, K., and Shapiro, A.J. (2022). Erratum: "A Web-Based System for Bayesian Benchmark Dose Estimation". Environ Health Perspect 130, 39002. http://dx.doi.org/10.1289/EHP11205.

Shao, K., Ji, C., and Chiu, W.A. (2022). Using Prior Toxicological Data to Support Dose-Response Assessment horizontal line Identifying Plausible Prior Distributions for Dichotomous Dose-Response Models. Environ Sci Technol 56, 16506-16516. http://dx.doi.org/10.1021/acs.est.2c05872.

Sample, B.E., Johnson, M.S., Hull, R.N., Kapustka, L., Landis, W.G., Murphy, C.A., Sorensen, M., Mann, G., Gust, K.A., Mayfield, D.B., et al. (2022). Key challenges and developments in wildlife ecological risk assessment: Problem formulation. Integr Environ Assess Manag. http://dx.doi.org/10.1002/ieam.4710.

Maurer, L.L., Alexander, M.S., Bachman, A.N., Grimm, F.A., Lewis, R.J., North, C.M., Wojcik, N.C., and Goyak, K.O. (2022). An interdisciplinary framework for derivation of occupational exposure limits. Front Public Health 10, 1038305, 1038305. http://dx.doi.org/10.3389/fpubh.2022.1038305.

Li, X., Ni, M., Xiong, W., Tian, L., Yang, Z., Zhang, L., and Chen, J. (2022). Transcriptomics analysis and benchmark concentration estimating-based in vitro test with IOSE80 cells to unveil the mode of action for female reproductive toxicity of bisphenol A at human-relevant levels. Ecotoxicol Environ Saf 237, 113523. http://dx.doi.org/10.1016/j.ecoenv.2022.113523.

Li, X., He, X., Le, Y., Guo, X., Bryant, M.S., Atrakchi, A.H., McGovern, T.J., Davis-Bruno, K.L., Keire, D.A., Heflich, R.H., and Mei, N. (2022). Genotoxicity evaluation of nitrosamine impurities using human TK6 cells transduced with cytochrome P450s. Arch Toxicol 96, 3077-3089. http://dx.doi.org/10.1007/s00204-022-03347-6.

Ji, C., Weissmann, A., and Shao, K. (2022). A computational system for Bayesian benchmark dose estimation of genomic data in BBMD. Environ Int 161, 107135. http://dx.doi.org/10.1016/j.envint.2022.107135.

Green, J.W., Foudoulakis, M., Fredricks, T., Bean, T., Maul, J., Plautz, S., Valverde, P., Schapaugh, A., Sopko, X., and Gao, Z. (2022). Statistical analysis of avian reproduction studies. Environmental Sciences Europe 34, 31. http://dx.doi.org/10.1186/s12302-022-00603-5.

Chen, Q., Chou, W.C., and Lin, Z. (2022). Integration of Toxicogenomics and Physiologically Based Pharmacokinetic Modeling in Human Health Risk Assessment of Perfluorooctane Sulfonate. Environ Sci Technol 56, 3623-3633. http://dx.doi.org/10.1021/acs.est.1c06479.

Yao, M., Zeng, Q., Luo, P., Sun, B., Liang, B., Wei, S., Xu, Y., Wang, Q., Liu, Q., and Zhang, A. (2021). Assessing the risk of coal-burning arsenic-induced liver damage: a population-based study on hair arsenic and cumulative arsenic. Environ Sci Pollut Res Int 28, 50489-50499. http://dx.doi.org/10.1007/s11356-021-14273-y.

Spînu, N. (2021). Modelling of quantitative Adverse Outcome Pathways (Liverpool John Moores University (United Kingdom)). http://dx.doi.org/https://researchonline.ljmu.ac.uk/id/eprint/15012/.

Shi, P., Yan, H., Fan, X., and Xi, S. (2021). A benchmark dose analysis for urinary cadmium and type 2 diabetes mellitus. Environ Pollut 273, 116519. http://dx.doi.org/10.1016/j.envpol.2021.116519.

Shao, K., Zhou, Z., Xun, P., and Cohen, S.M. (2021). Bayesian benchmark dose analysis for inorganic arsenic in drinking water associated with bladder and lung cancer using epidemiological data. Toxicology 455, 152752. http://dx.doi.org/10.1016/j.tox.2021.152752.

Mikkonen, A.T., Martin, J., Dourson, M.L., Hinwood, A., and Johnson, M.S. (2021). Suggestions for Improving the Characterization of Risk from Exposures to Per and Polyfluorinated Alkyl Substances. Environ Toxicol Chem 40, 871-886. http://dx.doi.org/10.1002/etc.4931.

Lent, E.M., Sussan, T.E., Leach, G.J., and Johnson, M.S. (2021). Using Evidence Integration Techniques in the Development of Health-Based Occupational Exposure Levels. Int J Toxicol 40, 178-195. http://dx.doi.org/10.1177/1091581820970494.

Lent, E.M., Leach, G., and Johnson, M.S. (2021). Development of health-based environmental screening levels for insensitive munitions constituents. Human and Ecological Risk Assessment: An International Journal 27, 1543-1567. http://dx.doi.org/10.1080/10807039.2020.1859352.

Korchevskiy, A. (2021). Using benchmark dose modeling for the quantitative risk assessment: Carbon nanotubes, asbestos, glyphosate. J Appl Toxicol 41, 148-160. http://dx.doi.org/10.1002/jat.4063.

Jo, S., Park, B., Chung, Y., Kim, J., Lee, E., Lee, J., and Choi, T. (2021). Bayesian semiparametric mixed effects models for meta-analysis of the literature data : An application to cadmium toxicity studies. Stat Med 40, 3762-3778. http://dx.doi.org/10.1002/sim.8996.

Hsieh, N.H., Chen, Z., Rusyn, I., and Chiu, W.A. (2021). Risk Characterization and Probabilistic Concentration-Response Modeling of Complex Environmental Mixtures Using New Approach Methodologies (NAMs) Data from Organotypic in Vitro Human Stem Cell Assays. Environ Health Perspect 129, 17004. http://dx.doi.org/10.1289/EHP7600.

Edler, L. (2021). Benchmark Dose Approach in Regulatory Toxicology. In Regulatory Toxicology, F.X. Reichl, and M. Schwenk, eds. (Springer, Cham), pp. 339-374. http://dx.doi.org/10.1007/978-3-030-57499-4_93.

Chiu, W.A., and Paoli, G.M. (2021). Recent Advances in Probabilistic Dose-Response Assessment to Inform Risk-Based Decision Making. Risk Anal 41, 596-609. http://dx.doi.org/10.1111/risa.13595.

Chang, Y., Rager, J.E., and Tilton, S.C. (2021). Linking coregulated gene modules with polycyclic aromatic hydrocarbon-related cancer risk in the 3D human bronchial epithelium. Chem Res Toxicol 34, 1445-1455. http://dx.doi.org/10.1021/acs.chemrestox.0c00333.

Alamri, F.S., Boone, E.L., and Edwards, D.J. (2021). A Bayesian Monotonic Non-parametric Dose-Response Model. Human and Ecological Risk Assessment: An International Journal 27, 2104-2123. http://dx.doi.org/10.1080/10807039.2021.1956298.

Wheeler, M.W., Blessinger, T., Shao, K., Allen, B.C., Olszyk, L., Davis, J.A., and Gift, J.S. (2020). Quantitative Risk Assessment: Developing a Bayesian Approach to Dichotomous Dose-Response Uncertainty. Risk Anal 40, 1706-1722. http://dx.doi.org/10.1111/risa.13537.

Wang, T., Tu, Y., Zhang, G., Gong, S., Wang, K., Zhang, Y., Meng, Y., Wang, T., Li, A., Christiani, D.C., et al. (2020). Development of a benchmark dose for lead-exposure based on its induction of micronuclei, telomere length changes and hematological toxicity. Environ Int 145, 106129. http://dx.doi.org/10.1016/j.envint.2020.106129.

Li, X., He, X., Chen, S., Guo, X., Bryant, M.S., Guo, L., Manjanatha, M.G., Zhou, T., Witt, K.L., and Mei, N. (2020). Evaluation of pyrrolizidine alkaloid-induced genotoxicity using metabolically competent TK6 cell lines. Food Chem Toxicol 145, 111662. http://dx.doi.org/10.1016/j.fct.2020.111662.

Jensen, S.M., Kluxen, F.M., Streibig, J.C., Cedergreen, N., and Ritz, C. (2020). bmd: an R package for benchmark dose estimation. PeerJ 8, e10557. http://dx.doi.org/10.7717/peerj.10557.

Hatherell, S., Baltazar, M.T., Reynolds, J., Carmichael, P.L., Dent, M., Li, H., Ryder, S., White, A., Walker, P., and Middleton, A.M. (2020). Identifying and Characterizing Stress Pathways of Concern for Consumer Safety in Next-Generation Risk Assessment. Toxicol Sci 176, 11-33. http://dx.doi.org/10.1093/toxsci/kfaa054.

Chou, W.C., and Lin, Z. (2020). Probabilistic human health risk assessment of perfluorooctane sulfonate (PFOS) by integrating in vitro, in vivo toxicity, and human epidemiological studies using a Bayesian-based dose-response assessment coupled with physiologically based pharmacokinetic (PBPK) modeling approach. Environ Int 137, 105581. http://dx.doi.org/10.1016/j.envint.2020.105581.

Chang, Y., Huynh, C.T.T., Bastin, K.M., Rivera, B.N., Siddens, L.K., and Tilton, S.C. (2020). Classifying polycyclic aromatic hydrocarbons by carcinogenic potency using in vitro biosignatures. Toxicol In Vitro 69, 104991. http://dx.doi.org/10.1016/j.tiv.2020.104991.

Wheeler, M.W., Piegorsch, W.W., and Bailer, A.J. (2019). Quantal Risk Assessment Database: A Database for Exploring Patterns in Quantal Dose-Response Data in Risk Assessment and its Application to Develop Priors for Bayesian Dose-Response Analysis. Risk Anal 39, 616-629. http://dx.doi.org/10.1111/risa.13218.

Shao, K., Chen, Q., and Wang, Z. (2019). Quantifying association between liver tumor incidence and early-stage liver weight increase - An NTP data analysis. Toxicol Rep 6, 674-682. http://dx.doi.org/10.1016/j.toxrep.2019.07.001.

Pham, L.L., Watford, S., Friedman, K.P., Wignall, J., and Shapiro, A.J. (2019). Python BMDS: A Python interface library and web application for the canonical EPA dose-response modeling software. Reprod Toxicol 90, 102-108. http://dx.doi.org/10.1016/j.reprotox.2019.07.013.

Kvasnicka, J., Stylianou, K.S., Nguyen, V.K., Huang, L., Chiu, W.A., Burton, G.A., Jr., Semrau, J., and Jolliet, O. (2019). Human Health Benefits from Fish Consumption vs. Risks from Inhalation Exposures Associated with Contaminated Sediment Remediation: Dredging of the Hudson River. Environ Health Perspect 127, 127004. http://dx.doi.org/10.1289/EHP5034.

Kullar, S.S., Shao, K., Surette, C., Foucher, D., Mergler, D., Cormier, P., Bellinger, D.C., Barbeau, B., Sauve, S., and Bouchard, M.F. (2019). A benchmark concentration analysis for manganese in drinking water and IQ deficits in children. Environ Int 130, 104889. http://dx.doi.org/10.1016/j.envint.2019.05.083.

De Pretis, F., and Osimani, B. (2019). New Insights in Computational Methods for Pharmacovigilance: E-Synthesis, a Bayesian Framework for Causal Assessment. Int J Environ Res Public Health 16. http://dx.doi.org/10.3390/ijerph16122221.

De Pretis, F., Landes, J., and Osimani, B. (2019). E-Synthesis: A Bayesian Framework for Causal Assessment in Pharmacosurveillance. Front Pharmacol 10, 1317. http://dx.doi.org/10.3389/fphar.2019.01317.

Yang, G., Li, J., Wang, Y., Chen, C., Zhao, H., and Shao, K. (2018). Quantitative ecotoxicity analysis for pesticide mixtures using benchmark dose methodology. Ecotoxicol Environ Saf 159, 94-101. http://dx.doi.org/10.1016/j.ecoenv.2018.04.055.

Chiu, W.A., Axelrad, D.A., Dalaijamts, C., Dockins, C., Shao, K., Shapiro, A.J., and Paoli, G. (2018). Beyond the RfD: Broad Application of a Probabilistic Approach to Improve Chemical Dose-Response Assessments for Noncancer Effects. Environ Health Perspect 126, 067009. http://dx.doi.org/10.1289/EHP3368

Ji, C., Weissmann, A., and Shao, K. (2022). A computational system for Bayesian benchmark dose estimation of genomic data in BBMD. Environ. Int. 161, 12, 107135. http://dx.doi.org/10.1016/j.envint.2022.107135.

Shao, K., Ji, C., and Chiu, W.A. (2022). Using Prior Toxicological Data to Support Dose-Response Assessment horizontal line Identifying Plausible Prior Distributions for Dichotomous Dose-Response Models. Environ Sci Technol 56, 16506-16516. http://dx.doi.org/10.1021/acs.est.2c05872.