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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.

De Pretis, F., Zhou, Y., Xun, P., & Shao, K. (2023). Benchmark dose modeling for epidemiological dose–response assessment using prospective cohort studies. Risk Analysis. https://doi.org/10.1111/risa.14196

Dolan, D. G., Bercu, J. P., Graham, J. C., & Barle, E. L. (2023). Occupational Toxicology in the Pharmaceutical Industry. In Patty’s Toxicology (pp. 1–39). https://doi.org/10.1002/0471125474.tox112.pub3

Gomez-Villalba, L. S., Salcines, C., & Fort, R. (2023). Application of Inorganic Nanomaterials in Cultural Heritage Conservation, Risk of Toxicity, and Preventive Measures. Nanomaterials, 13(9), 1454. https://doi.org/10.3390/nano13091454

Jang, S., Shao, K., & Chiu, W. A. (2023). Beyond the cancer slope factor: Broad application of Bayesian and probabilistic approaches for cancer dose-response assessment. Environment International, 175, 107959. https://doi.org/10.1016/j.envint.2023.107959

Ji, C., & Shao, K. (2023). The Effect of Historical Data-Based Informative Prior on Benchmark Dose Estimation of Toxicogenomics. Chemical Research in Toxicology, 36(8), 1345–1354. https://doi.org/10.1021/acs.chemrestox.3c00088

Korchevskiy, A. A., & Wylie, A. G. (2023). Toxicological and epidemiological approaches to carcinogenic potency modeling for mixed mineral fiber exposure: the case of fibrous balangeroite and chrysotile. Inhalation Toxicology, 35(7-8), 185–200. https://doi.org/10.1080/08958378.2023.2213720

Li, Y., Li, X., Cournoyer, P., Choudhuri, S., Guo, L., & Chen, S. (2023). Induction of apoptosis by cannabidiol and its main metabolites in human Leydig cells. Archives of Toxicology, 1–15. https://doi.org/10.1007/s00204-023-03609-x

Liu, X., Zhang, L., Liu, J., Zaya, G., Wang, Y., Xiang, Q., Li, J., & Wu, Y. (2023). 6:2 Chlorinated Polyfluoroalkyl Ether Sulfonates Exert Stronger Thyroid Homeostasis Disruptive Effects in Newborns than Perfluorooctanesulfonate: Evidence Based on Bayesian Benchmark Dose Values from a Population Study. Environmental Science & Technology, 57(31), 11489–11498. https://doi.org/10.1021/acs.est.3c03952

Long, Q., Zhang, Z., Li, Y., Zhong, Y., Liu, H., Chang, L., Ying, Y., Zuo, T., Wang, Y., & Xu, P. (2023). Phosphoproteome reveals long-term potentiation deficit following treatment of ultra-low dose soman exposure in mice. Journal of Hazardous Materials, 459, 132211. https://doi.org/10.1016/j.jhazmat.2023.132211

Luo, Y.-S. (2023). Bayesian-Based Probabilistic Risk Assessment of Fipronil in Food: A Case Study in Taiwan. Toxics, 11(8), 677. https://doi.org/10.3390/toxics11080677

Noruzi, M., Rezvanfar, M. A., & Daghighi, S. M. (2023). Benchmark dose. In Reference Module in Biomedical Sciences. https://doi.org/10.1016/b978-0-12-824315-2.00786-7

Rattner, B. A., Bean, T. G., Beasley, V. R., Berny, P., Eisenreich, K. M., Elliott, J. E., Eng, M. L., Fuchsman, P. C., King, M. D., Mateo, R., Meyer, C. B., O’Brien, J. M., & Salice, C. J. (2023). Wildlife ecological risk assessment in the 21st century: Promising technologies to assess toxicological effects. Integrated Environmental Assessment and Management. https://doi.org/10.1002/ieam.4806

Wheeler, M. W. (2023). An investigation of non-informative priors for Bayesian dose-response modeling. Regulatory Toxicology and Pharmacology, 141, 105389. https://doi.org/10.1016/j.yrtph.2023.105389

Xiao, J., Fang, K., Zhang, S., Jiang, S., Liu, T., Lv, M., Liao, M., Cao, H., & Shi, Y. (2023). Inhalation bioaccessibility of inhaled triazole fungicides and health risk assessment during spraying. Pest Management Science, 79(5), 1768–1776. https://doi.org/10.1002/ps.7354

Yahyaeian, A. E., Shahidi, M., Mousavi, T., & Daniali, M. (2023). Uncertainty factors. In Reference Module in Biomedical Sciences. https://doi.org/10.1016/b978-0-12-824315-2.00401-2

Yan, Z. F., Gu, Z. G., Fan, Y. H., Li, X. L., Niu, Z. M., Duan, X. R., Mallah, A. M., Zhang, Q., Yang, Y. L., Yao, W., & Wang, W. (2023). Benchmark Dose Assessment for Coke Oven Emissions-Induced Mitochondrial DNA Copy Number Damage Effects*. Biomedical and Environmental Sciences, 36(6), 490–500. https://doi.org/10.3967/bes2023.060

Yao, M., Zeng, Q., Luo, P., Yang, G., Li, J., Sun, B., Liang, B., & Zhang, A. (2023). Assessing the health risks of coal-burning arsenic-induced skin damage: A 22-year follow-up study in Guizhou, China. Science of the Total Environment, 905, 167236. https://doi.org/10.1016/j.scitotenv.2023.167236

Bioinformatics, I. I. f. B., & statistical. (2022). EFSA Platform for Bayesian Benchmark Dose Analysis. EFSA Supporting Publications, 19(12). https://doi.org/10.2903/sp.efsa.2022.en-7740

Chen, Q., Chou, W.-C., & Lin, Z. (2022). Integration of Toxicogenomics and Physiologically Based Pharmacokinetic Modeling in Human Health Risk Assessment of Perfluorooctane Sulfonate. Environmental Science & Technology, 56(6), 3623–3633. https://doi.org/10.1021/acs.est.1c06479

Green, J. W., Foudoulakis, M., Fredricks, T., Bean, T., Maul, J., Plautz, S., Valverde, P., Schapaugh, A., Sopko, X., & Gao, Z. (2022). Statistical analysis of avian reproduction studies. Environmental Sciences Europe, 34(1), 31. https://doi.org/10.1186/s12302-022-00603-5

Ji, C., Weissmann, A., & Shao, K. (2022). A computational system for Bayesian benchmark dose estimation of genomic data in BBMD. Environment International, 161, 107135. https://doi.org/10.1016/j.envint.2022.107135

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., & Mei, N. (2022). Genotoxicity evaluation of nitrosamine impurities using human TK6 cells transduced with cytochrome P450s. Archives of Toxicology, 96(11), 3077–3089. https://doi.org/10.1007/s00204-022-03347-6'

Li, X., Ni, M., Xiong, W., Tian, L., Yang, Z., Zhang, L., & 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. Ecotoxicology and Environmental Safety, 237, 113523. https://doi.org/10.1016/j.ecoenv.2022.113523

Li, Y., Zhang, Z., Jiang, S., Xu, F., Tulum, L., Li, K., Liu, S., Li, S., Chang, L., Liddell, M., Tu, F., Gu, X., Carmichael, P. L., White, A., Peng, S., Zhang, Q., Li, J., Zuo, T., Kukic, P., & Xu, P. (2022). Using NAMs to characterize chemical bioactivity at the transcriptomic, proteomic and phosphoproteomic levels. bioRxiv, 2022. https://doi.org/10.1101/2022.05.18.492410

Li, Y., Zhang, Z., Jiang, S., Xu, F., Tulum, L., Li, K., Liu, S., Li, S., Chang, L., Liddell, M., Tu, F., Gu, X., Carmichael, P. L., White, A., Peng, S., Zhang, Q., Li, J., Zuo, T., Kukic, P., & Xu, P. (2022). Using transcriptomics, proteomics and phosphoproteomics as new approach methodology (NAM) to define biological responses for chemical safety assessment. Chemosphere, 313, 137359. https://doi.org/10.1016/j.chemosphere.2022.137359

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

Moffett, D. B., Mumtaz, M. M., Sullivan, D. W., & Whittaker, M. H. (2022). Chapter 13 General considerations of dose-effect and dose-response relationships ∗. In Handbook on the Toxicology of Metals (pp. 299–317). https://doi.org/10.1016/b978-0-12-823292-7.00019-x

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., Ludwigs, J.-D., & Munns, W. R. (2022). Key challenges and developments in wildlife ecological risk assessment: Problem formulation. Integrated Environmental Assessment and Management. https://doi.org/10.1002/ieam.4710

Shao, K., Ji, C., & Chiu, W. A. (2022). Using Prior Toxicological Data to Support Dose–Response Assessment Identifying Plausible Prior Distributions for Dichotomous Dose–Response Models. Environmental Science & Technology, 56(22), 16506–16516. https://doi.org/10.1021/acs.est.2c05872

Simeone, F. C., & Costa, A. L. (2022). Quantifying uncertainty in dose–response screenings of nanoparticles: a Bayesian data analysis. Nanotoxicology, 16(2), 135–151. https://doi.org/10.1080/17435390.2022.2038298

Wang, S., Zhang, T., Liu, X., Yang, Z., Li, L., Shan, D., Gao, Y., Li, Y., Li, Y., Zhang, Y., & Wang, Q. (2022). Toxicity and toxicokinetics of the ethanol extract of Zuojin formula. BMC Complementary Medicine and Therapies, 22(1), 220. https://doi.org/10.1186/s12906-022-03684-0

Wheeler, M. W., Abrahantes, J. C., Aerts, M., Gift, J. S., & Davis, J. A. (2022). Continuous model averaging for benchmark dose analysis: Averaging over distributional forms. Environmetrics, 33(5). https://doi.org/10.1002/env.2728

Wheeler, M. W., Lim, S., House, J., Shockley, K., Bailer, A. J., Fostel, J., Yang, L., Talley, D., Raghuraman, A., Gift, J. S., Davis, J. A., Auerbach, S. S., & Motsinger-Reif, A. A. (2022). ToxicR: A computational platform in R for computational toxicology and dose–response analyses. Computational Toxicology, 25, 100259. https://doi.org/10.1016/j.comtox.2022.100259

Yasuhiko, Y., Ishigami, M., Machino, S., Fujii, T., Aoki, M., Irie, F., Kanda, Y., & 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. Regulatory Toxicology and Pharmacology, 133, 105201. https://doi.org/10.1016/j.yrtph.2022.105201

Zhang, Y., Liu, Z., Wang, Z., Gao, H., Wang, Y., Cui, M., Peng, H., Xiao, Y., Jin, Y., Yu, D., Chen, W., & Wang, Q. (2022). Health risk assessment of cadmium exposure by integration of an in silico physiologically based toxicokinetic model and in vitro tests. Journal of Hazardous Materials, 443(Pt A), 130191. https://doi.org/10.1016/j.jhazmat.2022.130191

Zou, X., Wang, R., Yang, Z., Wang, Q., Fu, W., Huo, Z., Ge, F., Zhong, R., Jiang, Y., Li, J., Xiong, S., Hong, W., & Liang, W. (2022). Family Socioeconomic Position and Lung Cancer Risk: A Meta-Analysis and a Mendelian Randomization Study. Frontiers in Public Health, 10, 780538. https://doi.org/10.3389/fpubh.2022.780538

Alamri, F. S., Boone, E. L., & Edwards, D. J. (2021). A Bayesian Monotonic Non-parametric Dose-Response Model. Human and Ecological Risk Assessment: An International Journal, 27(8), 2104–2123. https://doi.org/10.1080/10807039.2021.1956298

Edler, L. (2021). Benchmark Dose Approach in Regulatory Toxicology. In Regulatory Toxicology (pp. 339–374). https://doi.org/10.1007/978-3-030-57499-4_93

Green, J. W., Foudoulakis, M., Fredricks, T., Carro, T., Maul, J., Plautz, S., Valverde, P., Schapaugh, A., Sopko, X., & Gao, Z. (2021). Statistical Analysis of Avian Reproduction Studies. Research Square. https://doi.org/10.21203/rs.3.rs-1054353/v1

Hsieh, N.-H., Chen, Z., Rusyn, I., & 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. Environmental Health Perspectives, 129(1), 17004. https://doi.org/10.1289/ehp7600

Jo, S., Park, B., Chung, Y., Kim, J., Lee, E., Lee, J., & Choi, T. (2021). Bayesian semiparametric mixed effects models for meta‐analysis of the literature data : An application to cadmium toxicity studies. Statistics in Medicine, 40(16), 3762–3778. https://doi.org/10.1002/sim.8996

Mikkonen, A. T., Martin, J., Dourson, M. L., Hinwood, A., & Johnson, M. S. (2021). Suggestions for Improving the Characterization of Risk from Exposures to Per and Polyfluorinated Alkyl Substances. Environmental Toxicology and Chemistry, 40(3), 883–898. https://doi.org/10.1002/etc.4931

Shao, K., Zhou, Z., Xun, P., & 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. https://doi.org/10.1016/j.tox.2021.152752

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

Yao, M., Zeng, Q., Luo, P., Sun, B., Liang, B., Wei, S., Xu, Y., Wang, Q., Liu, Q., & Zhang, A. (2021). Assessing the risk of coal-burning arsenic-induced liver damage: a population-based study on hair arsenic and cumulative arsenic. Environmental Science and Pollution Research, 28(36), 50489–50499. https://doi.org/10.1007/s11356-021-14273-y

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

Chiu, W. A., & Paoli, G. M. (2020). Recent Advances in Probabilistic Dose–Response Assessment to Inform Risk‐Based Decision Making. Risk Analysis, 41(4), 596–609. https://doi.org/10.1111/risa.13595

Chou, W.-C., & 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. Environment International, 137, 105581. https://doi.org/10.1016/j.envint.2020.105581

Edler, L. (2020). Benchmark Dose Approach in Regulatory Toxicology. In Regulatory Toxicology (pp. 1–36). https://doi.org/10.1007/978-3-642-36206-4_93-2

Hatherell, S., Baltazar, M. T., Reynolds, J., Carmichael, P. L., Dent, M., Li, H., Ryder, S., White, A., Walker, P., & Middleton, A. M. (2020). Identifying and characterizing stress pathways of concern for consumer safety in next generation risk assessment. Toxicological Sciences, 176(1), kfaa054–. https://doi.org/10.1093/toxsci/kfaa054

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

Korchevskiy, A. (2020). Using benchmark dose modeling for the quantitative risk assessment: Carbon nanotubes, asbestos, glyphosate. Journal of Applied Toxicology, 41(1), 148–160. https://doi.org/10.1002/jat.4063

Lent, E. M., Leach, G., & Johnson, M. S. (2020). Development of health-based environmental screening levels for insensitive munitions constituents. Human and Ecological Risk Assessment: An International Journal, 27(6), 1543–1567. https://doi.org/10.1080/10807039.2020.1859352

Lent, E. M., Sussan, T. E., Leach, G. J., & Johnson, M. S. (2020). Using Evidence Integration Techniques in the Development of Health-Based Occupational Exposure Levels. International Journal of Toxicology, 40(2), 178–195. https://doi.org/10.1177/1091581820970494

Li, X., He, X., Chen, S., Guo, X., Bryant, M. S., Guo, L., Manjanatha, M. G., Zhou, T., Witt, K. L., & Mei, N. (2020). Evaluation of pyrrolizidine alkaloid-induced genotoxicity using metabolically competent TK6 cell lines. Food and Chemical Toxicology, 145, 111662. https://doi.org/10.1016/j.fct.2020.111662
Mohapatra, A. (2020). Chapter 72 Software tools for toxicology and risk assessment. In Information Resources in Toxicology (pp. 791–812). https://doi.org/10.1016/b978-0-12-813724-6.00072-4

Wang, T., Tu, Y., Zhang, G., Gong, S., Wang, K., Zhang, Y., Meng, Y., Wang, T., Li, A., Christiani, D. C., Au, W., Zhu, Y., & Xia, Z.-L. (2020). Development of a benchmark dose for lead-exposure based on its induction of micronuclei, telomere length changes and hematological toxicity. Environment International, 145, 106129. https://doi.org/10.1016/j.envint.2020.106129

Wheeler, M. W., Blessinger, T., Shao, K., Allen, B. C., Olszyk, L., Davis, J. A., & Gift, J. S. (2020). Quantitative Risk Assessment: Developing a Bayesian Approach to Dichotomous Dose–Response Uncertainty. Risk Analysis, 40(9), 1706–1722. https://doi.org/10.1111/risa.13537

De Pretis, F., & Osimani, B. (2019). New Insights in Computational Methods for Pharmacovigilance: E-Synthesis, a Bayesian Framework for Causal Assessment. International Journal of Environmental Research and Public Health, 16(12), 2221. https://doi.org/10.3390/ijerph16122221

De Pretis, F., Landes, J., & Osimani, B. (2019). E-Synthesis: A Bayesian Framework for Causal Assessment in Pharmacosurveillance. Frontiers in Pharmacology, 10, 1317. https://doi.org/10.3389/fphar.2019.01317

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

Kvasnicka, J., Stylianou, K. S., Nguyen, V. K., Huang, L., Chiu, W. A., Burton, G. A., Semrau, J., & Jolliet, O. (2019). Human Health Benefits from Fish Consumption vs. Risks from Inhalation Exposures Associated with Contaminated Sediment Remediation: Dredging of the Hudson River. Environmental Health Perspectives, 127(12), 127004. https://doi.org/10.1289/ehp5034

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

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

Chiu, W. A., Axelrad, D. A., Dalaijamts, C., Dockins, C., Shao, K., Shapiro, A. J., & Paoli, G. (2018). Beyond the RfD: Broad Application of a Probabilistic Approach to Improve Chemical Dose–Response Assessments for Noncancer Effects. Environmental Health Perspectives, 126(6), 67009. https://doi.org/10.1289/ehp3368

Wheeler, M. W., Piegorsch, W. W., & Bailer, A. J. (2018). 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 Analysis, 39(3), 616–629. https://doi.org/10.1111/risa.13218

Yang, G., Li, J., Wang, Y., Chen, C., Zhao, H., & Shao, K. (2018). Quantitative ecotoxicity analysis for pesticide mixtures using benchmark dose methodology. Ecotoxicology and Environmental Safety, 159, 94–101. https://doi.org/10.1016/j.ecoenv.2018.04.055


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.