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Hartung, T. & Rovida, C. Chemical regulators have exceeded limits. Nature 4601080-1081 (2009).
Bopp, Sask. et al. Regulatory assessment and risk management of chemical mixtures: challenges and perspectives. Crit. Rev. Toxicol. 49174–189 (2019).
Kortenkamp, A. & Altenburger, R. Approaches to assess the combined effects of estrogenic environmental pollutants. Science. About. 233131–140 (1999).
Rajapakse, N., Silva, E. & Kortenkamp, A. The combination of xenoestrogens at levels below individual no-observed-effect concentrations significantly enhances the action of steroid hormones. About. Health perspective. 110917–921 (2002).
Walter, H., Consolaro, F., Gramatica, P., Scholze, M. & Altenburger, R. Toxicity mix of priority pollutants at no-observed-effect-concentrations (NOECs). Ecotoxicology 11299–310 (2002).
Altenburger, R., Nendza, M. & Schüürmann, G. Mixture toxicity and its modeling by quantitative structure-activity relationships. About. Toxicol. Chem. 221900-1915 (2003).
Vigi, M. et al. Water quality objectives for toxic chemical mixtures: issues and prospects. Ecotoxicol. About. Saf. 54139-150 (2003).
Lydy, M., Belden, J., Wheelock, C., Hammock, B. & Denton, D. The challenges of regulating pesticide mixtures. School. Soc. 91–15 (2004).
Google Scholar
Breitholtz, M., Nyholm, JR, Karlsson, J. & Andersson, PL Are individual NOEC levels safe for mixtures? A study on the toxicity of brominated flame retardant mixtures in the copepod Nitocra spinipes. Chemosphere 721242-1249 (2008).
de March, Toxicity indices of BGE mixtures in acute lethal toxicity tests. Camber. About. Cont. Toxicol. 1633–37 (1987).
Google Scholar
Cassee, FR, Groten, JP, Bladeren, PJ & Feron, VJ Toxicological evaluation and risk assessment of chemical mixtures. Crit. Rev. Toxicol. 2873–101 (1998).
Valerio, LG In silico toxicology for pharmaceutical sciences. Toxicol. Appl. Pharmacol. 241356–370 (2009).
Cipullo, S., Snapir, B., Prpich, G., Campo, P. & Coulon, F. Prediction of bioavailability and toxicity of complex chemical mixtures using machine learning models. Chemosphere 215388–395 (2019).
Duane, Q. et al. Machine learning for toxicity analysis of mixtures based on high throughput printing technology. Talanta 207120299 (2020).
Mayr, A., Klambauer, G., Unterthiner, T. & Hochreiter, S. DeepTox: Predicting Toxicity Using Deep Learning. Front. About. Science. 380 (2016).
Google Scholar
Peng, Y., Zhang, Z., Jiang, Q., Guan, J. & Zhou, S. TOP: A deep mixture representation learning method to improve molecular toxicity prediction. Methods 17955–64 (2020).
Kavlock, RJ et al. Reviews: computational toxicology – a mini-review of the state of the science. Toxicol. Science. 10314–27 (2008).
Kim, J., Kim, S. & Schaumann, GE Reliable predictive computational toxicology methods for the toxicity of mixtures: towards the development of innovative integrated models for environmental risk assessment. Rev. About. Science. Organic. Technology. 12235-256 (2013).
Loewe, S. & Muischnek, H. Über Kombinationswirkungen. Camber. F.Exp. Pathol. you. Pharmacol 114313–326 (1926).
Bliss, CI The toxicity of co-applied poisons. Ann. Appl. Biol. 26585–615 (1939).
Cedergreen, N. et al. A review of independent action versus concentration addition as reference models for mixtures of compounds with different molecular target sites. About. Toxicol. Chem. 271621-1632 (2008).
Howard, GJ & Webster, TF Generalized Concentration Addition: A Method for Examining Mixtures Containing Partial Agonists. J. Theor. Biol. 259469-477 (2009).
Kim, J., Kim, S. & Schaumann, GEE Development of a two-step prediction model based on QSAR to estimate the toxicity of mixtures. SAR QSAR Approx. Res. 24841–861 (2013).
Boberg, J. et al. Chemical Mixture Calculator—A new tool for risk assessment of mixtures. Food chemistry. Toxicol. 152112167 (2021).
van der Voet, H. et al. The MCRA toolbox of models and data to support the risk assessment of chemical mixtures. Food chemistry. Toxicol. 138111185 (2020).
Kim, S. et al. PubChem in 2021: new data content and improved web interfaces. Nucleic Acids Res. 49D1388–D1395 (2021).
Junghans, M., Backhaus, T., Faust, M., Scholze, M. & Grimme, LH Application and validation of approaches for predictive risk assessment of realistic pesticide mixtures. Aquat. Toxicol. 7693-110 (2006).
Corp, LOAD MSDS – CHEMLOK 144 Primer. https://www.lord.com/sites/default/files/Documents/SafetyDataSheets/_CHEMLOK_144_.pdf (2017).
Nielsen, G., Heiger-Bernays, WJ, Schlezinger, JJ, and Webster, TF Predicting the effects of mixtures of per- and polyfluoroalkyl substances on peroxisome proliferator-activated receptor alpha activity in vitro. vitr. Toxicol. 465153024 (2022).
Hobson, J. CentOS 6 Linux Server Cookbook: A Practical Guide to Installing, Configuring, and Administering the CentOS Community-Based Enterprise Server (2013).
Bergsten, H. Java Server Pages 3rd ed. (O’Reilly & Associates Inc, 2003).
Google Scholar
Team, R Core. A: A language and environment for statistical computing. MSOR connections, 1 (2014).
Zhu, XW & Chen, JY Mixtox: An R package for assessing the toxicity of mixtures. RJ. 8422–434 (2016).
Google Scholar
Jolliffe, IT Principal component analysis 2nd ed. (Springer, 2002).
Giri, V., Sivakumar, TV, Cho, KM, Kim, TY & Bhaduri, A. RxnSim: A tool for comparing biochemical reactions. Bioinformatics 313712–3714 (2015).
Godden, JW, Xue, L. & Bajorath, J. Combinatorial preferences affect molecular similarity/diversity calculations using binary fingerprints and Tanimoto coefficients. J. Chem. Inf. Calculation. Science. 40163-166 (2000).
Mauri, A. alvaDesc: A tool for calculating and analyzing molecular descriptors and fingerprints. Ecotoxicological QSARs. humane (Humana, New York, NY, 2020).
O’Boyle, New Mexico et al. Open Babel: An open chemical toolbox. J. Cheminform. 31–14 (2011).
Google Scholar
Karulin, B. & Kozhevnikov, M. Ketcher: Web-based chemical structure editor. J. Cheminform. 32011 (2011).
Google Scholar
Marin, F., Rohatgi, A. & Charlot, S. WebPlotDigitizer, a versatile and free software for extracting spectra from old astronomical publications: application to ultraviolet spectropolarimetry. preprint arXiv arXiv:1708.02025 (2017).
