Alternative methods go green! Green toxicology as a sustainable approach for assessing chemical safety and designing safer chemicals

Main Article Content

Alexandra Maertens, Thomas Luechtefeld, Jean Knight, Thomas Hartung
[show affiliations]


Green toxicology is marching chemistry into the 21st century. This emerging framework will transform how chemical safety is evaluated by incorporating evaluation of the hazards, exposures, and risks associated with chemicals into early product development in a way that minimizes adverse impacts on human and environmental health. The goal is to minimize toxic threats across entire supply chains through smarter designs and policies. Traditional animal testing methods are replaced by faster, cutting-edge innovations like organs-on-chips and artificial intelligence predictive models that are also more cost-effective. Core principles of green toxicology include utilizing alternative test methods, applying the precautionary principle, considering lifetime impacts, and emphasizing risk prevention over reaction. This paper provides an overview of these foundational concepts and describes current initiatives and future opportunities to advance the adoption of green toxicology approaches. Chal­lenges and limitations are also discussed. Green shoots are emerging with governments offering carrots like the European Green Deal to nudge industry. Noteworthy, animal rights and environ­mental groups have different ideas about the needs for testing and their consequences for animal use. Green toxicology represents the way forward to support both these societal needs with sufficient throughput and human relevance for hazard information and minimal animal suffering. Green toxi­cology thus sets the stage to synergize human health and ecological values. Overall, the integration of green chemistry and toxicology has potential to profoundly shift how chemical risks are evaluated and managed to achieve safety goals in a more ethical, ecologically-conscious manner.

Plain language summary
Green toxicology aims to make chemicals safer by design. It focuses on preventing toxicity issues early during development instead of testing after products are developed. Green toxicology uses modern non-animal methods like computer models and lab tests with human cells to predict if chem­icals could be hazardous. Benefits are faster results, lower costs, and less animal testing. The principles of green toxicology include using alternative tests, applying caution even with uncertain data, con­sidering lifetime impacts across global supply chains, and emphasizing prevention over reaction. The article highlights European and US policy efforts to spur sustainable chemistry innovation which will necessitate greener approaches to assess new materials and drive adoption. Overall, green toxi­cology seeks to integrate safer design concepts so that human and environmental health are valued equally with functionality and profit. This alignment promises safer, ethical products but faces chal­lenges around validating new methods and overcoming institutional resistance to change.

Article Details

How to Cite
Maertens, A., Luechtefeld, T., Knight, J. and Hartung, T. (2024) “Alternative methods go green! Green toxicology as a sustainable approach for assessing chemical safety and designing safer chemicals”, ALTEX - Alternatives to animal experimentation, 41(1), pp. 3–19. doi: 10.14573/altex.2312291.
Food for Thought ...

Akhtar, A. (2015). The flaws and human harms of animal experimentation. Camb Q Healthc Ethics 24, 407-419. doi:10.1017/s0963180115000079

Ames, B. N. (2020). Preface. Toxicol Res Appl 4. doi:10.1177/2397847319897985

Anastas, P. T. and Warner, J. C. (1998). Green Chemistry: Theory and Practice. New York, USA: Oxford University Press.

Anastas, P. T. and Beach, E. S. (2007). Green chemistry: The emergence of a transformative framework. Green Chem Lett Rev 1, 9-24. doi:10.1080/17518250701882441

Anastas, P. T. and Eghbali, N. (2010). Green chemistry: Principles and practice. Chem Soc Rev 39, 301-312. doi:10.1039/b918763b

Arsène, S., Parès, Y., Tixier, E. et al. (2024). In silico clinical trials: Is it possible? Methods Mol Biol 2716, 51-99. doi:10.1007/978-1-0716-3449-3_4

Attene-Ramos, M. S., Miller, N., Huang, R. et al. (2013). The Tox21 robotic platform for the assessment of environmental chemicals – From vision to reality. Drug Discov Today 18, 716-723. doi:10.1016/j.drudis.2013.05.015

Ball, N., Cronin, M. T. D., Shen, J. et al. (2016). Toward good read-across practice (GRAP) guidance. ALTEX 33, 149-166. doi:10.14573/altex.1601251

Beilmann, M., Boonen, H., Czich, A. et al. (2019). Optimizing drug discovery by investigative toxicology: Current and future trends. ALTEX 36, 3-17. doi:10.14573/altex.1808181

Bessems, J. G., Loizou, G., Krishnan, K. et al. (2014). PBTK modelling platforms and parameter estimation tools to enable animal-free risk assessment. Regul Toxicol Pharmacol 68, 119-139. doi:10.1016/j.yrtph.2013.11.008

Bhatia, S. N. and Ingber, D. E. (2014). Microfluidic organs-on-chips. Nat Biotechnol 32, 760-772. doi:10.1038/nbt.2989

Bowes, J., Brown, A., Hamon, J. et al. (2012). Reducing safety-related drug attrition: The use of in vitro pharmacological profiling. Nat Rev Drug Discov 11, 909-922. doi:10.1038/nrd3845

Broom, D. M. (2019). Animal welfare complementing or conflicting with other sustainability issues. Appl Anim Behav Sci 219, 104829. doi:10.1016/j.applanim.2019.06.010

Caloni, F., De Angelis, I. and Hartung, T. (2022). Replacement of animal testing by integrated approaches to testing and assessment (IATA): A call for in vivitrosi. Arch Toxicol 96, 1935-1950. doi:10.1007/s00204-022-03299-x

Campbell, I. J. (2018). Animal welfare and environmental ethics: It’s complicated. Ethics Environ 23, 49-69. doi:10.2979/ethicsenviro.23.1.04

Cherkasov, A., Muratov, E. N., Fourches, D. et al. (2014). QSAR modeling: Where have you been? Where are you going to? J Med Chem 57, 4977-5010. doi:10.1021/jm4004285

Clark, J. H. et al. (2008). Green chemistry: Today (and tomorrow). Green Chem 10, 268-278. doi:10.1039/b516637n

Corradi, M. P. F., de Haan, A. M., Staumont, B. et al. (2022). Natural language processing in toxicology: Delineating adverse outcome pathways and guiding the application of new approach methodologies. Biomater Biosys 7, 100061. doi:10.1016/j.bbiosy.2022.100061

Crawford, S. E., Hartung, T., Hollert, H. et al. (2017). Green toxicology: A strategy for sustainable chemical and material development. Environ Sci Eur 29, 16. doi:10.1186/s12302-017-0115-z

Deng, S., Li, C., Cao, J. et al. (2023). Organ-on-a-chip meets artificial intelligence in drug evaluation. Theranostics 13, 4526-4558. doi:10.7150/thno.87266

Edington, C. D., Chen, W. L. K., Geishecker, E. et al. (2018). Interconnected microphysiological systems for quantitative biology and pharmacology studies. Sci Rep 8, 4530. doi:10.1038/s41598-018-22749-0

Eladak, S., Grisin, T., Moison, D. et al. (2015). A new chapter in the bisphenol A story: Bisphenol S and bisphenol F are not safe alternatives to this compound. Fertil Steril 103, 11-21. doi:10.1016/j.fertnstert.2014.11.005

Essen, E. V., Lindsjö, J., Berg, C. (2020). Instagranimal: Animal welfare and animal ethics challenges of animal-based tourism. Animals 10, 1830. doi:10.3390/ani10101830

Ewart, L., Dehne, E. M., Fabre, K. et al. (2018). Application of microphysiological systems to enhance safety assessment in drug discovery. Annu Rev Pharmacol Toxicol 58, 65-82. doi:10.1146/annurev-pharmtox-010617-052722

Faria, C. and Paez, E. (2019). It’s Splitsville: Why animal ethics and environmental ethics are incompatible. Am Behav Sci 63, 1047-1060. doi:10.1177/00027642198304

Ferreira Matos, L., Mendes Cabral, L., Correa Matos, G. et al. (2022). Application of in silico methods in clinical research and development of drugs and their formulation: A scoping review. J Appl Pharm Sci 13, 1-10. doi:10.7324/japs.2023.87792

Geiser, K. (2015). Chemicals Without Harm. MIT Press.

Glielmo, A., Husic, B. E., Rodriguez, A. (2021). Unsupervised learning methods for molecular simulation data. Chem Rev 121, 9722-9758. doi:10.1021/acs.chemrev.0c01195

Graff, D. E., Shakhnovich, E. I. and Coley, C. W. (2021). Accelerating high-throughput virtual screening through molecular pool-based active learning. Chem Sci 12, 7866-7881. doi:10.1039/d0sc06805e

Greene, N. (2002). Computer systems for the prediction of toxicity: An update. Adv Drug Deliv Rev 54, 417-431. doi:10.1016/s0169-409x(02)00012-1

Groff, K., Bachli, E., Lansdowne, M. et al. (2014). Review of evidence of environmental impacts of animal research and testing. Environments 1, 14-30. doi:10.3390/environments1010014

Hartung, T. and Hoffmann, S. (2009). Food for thought on … in silico methods in toxicology. ALTEX 26, 155-166. doi:10.14573/altex.2009.3.155

Hartung, T. and Rovida, C. (2009a). That which must not, cannot be... a reply to the EChA and EDF responses to the REACH analysis of animal use and costs. ALTEX 26, 307-311. doi:10.14573/altex.2009.4.307

Hartung, T. and Rovida, C. (2009b). Chemical regulators have overreached. Nature 460, 1080-1081. doi:10.1038/4601080a

Hartung T. (2010a). Evidence based-toxicology – The toolbox of validation for the 21st century? ALTEX 27, 241-251. doi:10.14573/altex.2010.4.253

Hartung, T. (2010b). Food for thought ... on alternative methods for chemical safety testing. ALTEX 27, 3-14. doi:10.14573/altex.2010.1.3

Hartung, T., Luechtefeld, T., Maertens, A. et al. (2013). Integrated testing strategies for safety assessments. ALTEX 30, 3-18. doi:10.14573/altex.2013.1.003

Hartung, T. (2016). Making big sense from big data in toxicology by read-across. ALTEX 33, 83-93. doi:10.14573/altex.1603091

Hartung, T. (2017). Utility of the adverse outcome pathway concept in drug development. Expert Opin Drug Metab Toxicol 13, 1-3. doi:10.1080/17425255.2017.1246535

Hartung, T. (2023a). Arifical intelligence as the new frontier in chemical risk assessment. Front Artif Intell 6, 1269932. doi:10.3389/frai.2023.1269932

Hartung, T. (2023b). ToxAIcology – The evolving role of artificial intelligence in advancing toxicology and modernizing regulatory science. ALTEX 40, 559-570. doi:10.14573/altex.2309191

Huang, R., Xia, M., Sakamuru, S. et al. (2016). Modelling the Tox21 10 K chemical profiles for in vivo toxicity prediction and mechanism characterization. Nat Commun 7, 10425. doi:10.1038/ncomms10425

Hughes, J. P., Rees, S., Kalindjian, S. B. et al. (2011). Principles of early drug discovery. Br J Pharmacol 162, 1239-1249. doi:10.1111/j.1476-5381.2010.01127.x

Hemmerich, J. and Ecker, G. F. (2020). In silico toxicology: From structure-activity relationships towards deep learning and adverse outcome pathways. WIREs Comput Mol Sci 10, e1475. doi:10.1002/wcms.1475

Hunt, R. G. and Franklin, W. E. (1996). LCA – How it came about. Int J LCA 1, 4-7. doi:10.1007/bf02978624

Jacobs, M. M., Malloy, T. F., Tickner, J. A. et al. (2016). Alternatives assessment frameworks: Research needs for the informed substitution of hazardous chemicals. Environ Health Perspect 124, 265-280. doi:10.1289/ehp.1409581

Judson, R., Kavlock, R., Martin, M. et al. (2013). Perspectives on validation of high-throughput pathway-based assays supporting the 21st century toxicity testing vision. ALTEX 30, 51-66. doi:10.14573/altex.2013.1.051

Keeling, L., Tunón, H., Olmos Antillón, G. et al. (2019). Animal welfare and the United Nations sustainable development goals. Front Vet Sci 6, 336. doi:10.3389/fvets.2019.00336

Keserű, G. M. and Makara, G. M. (2006). Hit discovery and hit-to-lead approaches. Drug Discov Today 11, 741-748. doi:10.1016/j.drudis.2006.06.016

Kiani, A. K., Pheby, D., Henehan, G. et al. (2022). Ethical considerations regarding animal experimentation. J Prev Med Hyg 63, Suppl 3, E255-E266. doi:10.15167/2421-4248/jpmh2022.63.2S3.2768

Kleinstreuer, N. and Hartung, T. (2023). Artificial Intelligence (AI) – It’s the end of the tox as we know it (and I feel fine) – AI for predictive toxicology. Arch Toxicol, in press.

Knight, J., Hartung, T. and Rovida, C. (2023). 4.2 million and counting… the animal toll for REACH systemic toxicity studies. ALTEX 40, 389-407. doi:10.14573/altex.2303201

Komura, H., Watanabe, R. and Mizuguchi, K. (2023). The trends and future prospective of in silico models from the viewpoint of ADME evaluation in drug discovery. Pharmaceutics 15, 2619. doi:10.3390/pharmaceutics15112619

Krebs, J. and McKeague, M. (2020). Green toxicology: Connecting green chemistry and modern toxicology. Chem Res Toxicol 33, 2919-2931. doi:10.1021/acs.chemrestox.0c00260

Kriebel, D., Tickner, J., Epstein, P. et al. (2001). The precautionary principle in environmental science. Environ Health Perspect 109, 871-876. doi:10.1289/ehp.01109871

Lackmann, C., Brendt, J. and Seiler, T.-B. (2021). The Green toxicology approach: Insight towards the eco-toxicologically safe development of benign catalysts. J Hazard Mater 416, 125889. doi:10.1016/j.jhazmat.2021.125889

Leeson, P. and Springthorpe, B. (2007). The influence of drug-like concepts on decision-making in medicinal chemistry. Nat Rev Drug Discov 6, 881-890. doi:10.1038/nrd2445

Leist, M., Ghallab, A., Graepel, R. et al. (2017). Adverse outcome pathways: Opportunities, limitations and open questions. Arch Toxicol 31, 221-229. doi:10.1007/s00204-017-2045-3

Leung, C. M., de Haan, P., Ronaldson-Bouchard, K. et al. (2022). A guide to the organ-on-a-chip. Nat Rev Methods Primers 2, 33. doi:10.1038/s43586-022-00118-6

Limban, C., Nuţă, D. C., Chiriţă, C. et al. (2018). The use of structural alerts to avoid the toxicity of pharmaceuticals. Toxicol Rep 5, 943-953. doi:10.1016/j.toxrep.2018.08.017

Lopez, J. (2007). Animal welfare: Global issues, trends and challenges. Can Vet J 48, 1163-1164.

Lorenzo, M., Campo, J., Farré, M. et al. (2016). Perfluoroalkyl substances in the Ebro and Guadalquivir river basins (Spain). Sci Total Environ 540, 191-199. doi:10.1016/j.scitotenv.2015.07.045

Low, L. A. and Tagle, D. A. (2017). Microphysiological systems (“organs-on-chips”) for drug efficacy and toxicity testing. Clin Transl Sci 10, 237-239. doi:10.1111/cts.12444

Luechtefeld, T. and Hartung, T. (2017). Computational approaches to chemical hazard assessment. ALTEX 34, 459-478. doi:10.14573/altex.1710141

Luechtefeld, T., Rowlands, C. and Hartung, T. (2018a). Big-data and machine learning to revamp computational toxicology and its use in risk assessment. Toxicol Res 7, 732-744. doi:10.1039/c8tx00051d

Luechtefeld, T., Marsh, D., Rowlands, C. et al. (2018b). Machine learning of toxicological big data enables read-across structure activity relationships (RASAR) outperforming animal test reproducibility. Toxicol Sci 165, 198-212. doi:10.1093/toxsci/kfy152

Lyu, J., Irwin, J. J. and Shoichet, B. K. (2023). Modeling the expansion of virtual screening libraries. Nat Chem Biol 19, 712-718. doi:10.1038/s41589-022-01234-w

Maertens, A., Anastas, N., Spencer, P. J. et al. (2014). Green toxicology. ALTEX 31, 243-249. doi:10.14573/altex.1406181

Maertens, A. and Hartung, T. (2018). Green toxicology – Know early about and avoid toxic product liabilities. Toxicol Sci 161, 285-289. doi:10.1093/toxsci/kfx243

Maertens, A., Golden, E. and Hartung, T. (2021). Avoiding regrettable substitutions: Green toxicology for sustainable chemistry. ACS Sustain Chem Eng 9, 7749-7758. doi:10.1021/acssuschemeng.0c09435

Maertens, A. (2022). Green Toxicology: Making Chemicals Benign by Design. The Royal Society of Chemistry. doi:10.1039/9781839164392

Malloy, T. F., Zaunbrecher, V. M., Batteate, C. M. et al. (2017). Advancing alternative analysis: Integration of decision science. Environ Health Perspect 125, 066001. doi:10.1289/ehp483

Marx, U., Walles, H., Hoffmann, S. et al. (2012). “Human-on-a-chip” developments: A translational cutting-edge alternative to systemic safety assessment and efficiency evaluation of substances in laboratory animals and man? Altern Lab Anim 40, 235-257. doi:10.1177/026119291204000504

Marx, U., Andersson, T. B., Bahinski, A. et al. (2016). Biology-inspired microphysiological system approaches to solve the prediction dilemma of substance testing using animals. ALTEX 33, 272-321. doi:10.14573/altex.1603161

Marx, U., Akabane, T., Andersson, T. B. et al. (2020). Biology-inspired microphysiological systems to advance medicines for patient benefit and animal welfare. ALTEX 37, 364-394. doi:10.14573/altex.2001241

Matus, K. J. M., Xiao, X. and Zimmerman, J. B. (2012). Green chemistry and green engineering in China: Drivers, policies and barriers to innovation. J Clean Prod 32, 193-203. doi:10.1016/j.jclepro.2012.03.033

Mayr, A., Klambauer, G., Unterthiner, T. et al. (2016). DeepTox: Toxicity prediction using deep learning. Front Environ Sci 3. doi:10.3389/fenvs.2015.00080

McKim, J. M. Jr. (2010). Building a tiered approach to in vitro predictive toxicity screening: A focus on assays with in vivo relevance. Comb Chem High Throughput Screen 13, 188-206. doi:10.2174/138620710790596736

Meigs, L., Smirnova, L., Rovida, C. et al. (2018). Animal testing and its alternatives – The most important omics is economics. ALTEX 35, 275-305. doi:10.14573/altex.1807041

Meng, Q. and Zhou, X. (2023). Enhancing the value of comparative exposure assessment in alternatives assessment. Front Sustain 4, 983218. doi:10.3389/frsus.2023.983218

Mitchell, J. (2022). Sorting out animal policy: Ideas, problems, and solutions. Int Rev Public Policy 4, 340-355. doi:10.4000/irpp.2952

Neves, B. J., Braga, R. C, Melo-Filho, C. C. et al. (2018). QSAR-based virtual screening: Advances and applications in drug discovery. Front Pharmacol 9, 1275. doi:10.3389/fphar.2018.01275

Niazi, S. K. and Mariam, Z. (2023). Recent advances in machine-learning-based chemoinformatics: A comprehensive review. Int J Mol Sci 24,11488. doi:10.3390/ijms241411488

Owens, R. D. (1991). Environmentalism and animal welfare: Cornerstones of wildlife damage management. Great Plains Wildlife Damage Control Workshop Proceedings 14.

Pamies, D. and Hartung, T. (2017). 21st century cell culture for 21st century toxicology. Chem Res Toxicol 30, 43-52. doi:10.1021/acs.chemrestox.6b00269

Paul, D., Sanap, G., Shenoy, S. et al. (2021). Artificial intelligence in drug discovery and development. Drug Discov Today 26, 80-93. doi:10.1016/j.drudis.2020.10.010

Phillips, K. A., Yau, A., Favela, K. A. et al. (2018). Suspect screening analysis of chemicals in consumer products. Environ Sci Technol 52, 3125-3135. doi:10.1021/acs.est.7b04781

Polishchuk, P. (2017). Interpretation of quantitative structure-activity relationship models: Past, present, and future. J Chem Inf Model 57, 2618-2639. doi:10.1021/acs.jcim.7b00274

Putin, E., Asadulaev, A., Vanhaelen, Q. et al. (2018). Adversarial threshold neural computer for molecular de novo design. Mol Pharm 15, 4386-4397. doi:10.1021/acs.molpharmaceut.7b01137

Quignot, N. (2013). Modeling bioavailability to organs protected by biological barriers. In Silico Pharmacol 1, 8. doi:10.1186/2193-9616-1-8

Roth, A. and MPS-WS Berlin 2019 (2021). Human microphysiological systems for drug development. Science 373, 1304-1306. doi:10.1126/science.abc3734

Rovida, C. and Hartung, T. (2009). Re-evaluation of animal numbers and costs for in vivo tests to accomplish REACH legislation requirements for chemicals. ALTEX 26, 187-208. doi:10.14573/altex.2009.3.187

Rovida, C., Longo, F. and Rabbit, R. R. [Hartung, T.] (2011). How are reproductive toxicity and developmental toxicity addressed in REACH dossiers? ALTEX 28, 273-294. doi:10.14573/altex.2011.4.273

Rovida, C., Alépée, N., Api, A. M. et al. (2015). Integrated testing strategies (ITS) for safety assessment. ALTEX 32, 171-181. doi:10.14573/altex.1506201

Rovida, C., Busquet, F., Leist, M. et al. (2023). REACH out-numbered! The future of REACH and animal numbers. ALTEX 40, 367-388. doi:10.14573/altex.2307121

Schmeisser, S., Miccoli, A., von Bergen, M. et al. (2023). New approach methodologies in human regulatory toxicology – Not if, but how and when! Environ Int 178, 108082. doi:10.1016/j.envint.2023.108082

Schmidt, C. W. (2009). TOX 21: New dimensions of toxicity testing. Environ Health Perspect 117, A348-A353. doi:10.1289/ehp.117-a348

Schneider, G. (2018). Automating drug discovery. Nat Rev Drug Discov 17, 97-113. doi:10.1038/nrd.2017.232

Sewell, F., Edwards, J., Prior, H. et al. (2016). Opportunities to apply the 3Rs in safety assessment programs. ILAR J 57, 234-245. doi:10.1093/ilar/ilw024

Tandon, A., Howard, B. Ramaiahgari, S. et al. (2022). Deep learning image analysis of high-throughput toxicology assay images. SLAS Discov 27, 29-38, doi:10.1016/j.slasd.2021.10.014

Taylor, K., Gordon, N., Langley, G. et al. (2008). Estimates for worldwide laboratory animal use in 2005. Altern Lab Anim 36, 327-342. doi:10.1177/026119290803600310

Thomas, R. S., Cheung, R., Westphal, M. et al. (2017). Risk science in the 21st century: A data-driven framework for incorporating new technologies into chemical safety assessment. Int J Risk Assess Manag 20, 88-108. doi:10.1504/ijram.2017.082560

Tickner, J. A., Schifano, J. N., Blake, A. et al. (2015). Environ Sci Technol 49, 742-749. doi:10.1021/es503328m

Tran, T. T. V., Surya Wibowo, A., Tayara, H. et al. (2023). Artificial intelligence in drug toxicity prediction: Recent advances, challenges, and future perspectives. J Chem Inf Model 63, 2628-2643. doi:10.1021/acs.jcim.3c00200

Tsaioun, K., Blaauboer, B. J. and Hartung, T. (2016). Evidence-based absorption, distribution, metabolism, excretion and toxicity (ADMET) and the role of alternative methods. ALTEX 33, 343-358. doi:10.14573/altex.1610101

van Vliet, E., Daneshian, M., Beilmann, M. et al. (2014). Current approaches and future role of high content imaging in safety sciences and drug discovery. ALTEX 31, 479-493. doi:10.14573/altex.1405271

Velders, G. J. M., Andersen, S. O., Daniel, J. S. et al. (2007). The importance of the Montreal protocol in protecting climate. Proc Natl Acad Sci U S A 104, 4814-4819. doi:10.1073/pnas.0610328104

Vernetti, L., Gough, A., Baetz, N. et al. (2017). Functional coupling of human microphysiology systems: Intestine, liver, kidney proximal tubule, blood-brain barrier and skeletal muscle. Sci Rep 7, 42296. doi:10.1038/srep42296

Viegas, S., Zare Jeddi, M. B., Hopf, N. et al. (2020). Biomonitoring as an underused exposure assessment tool in occupational safety and health context-challenges and way forward. Int J Environ Res Public Health 17, 5884. doi:10.3390/ijerph17165884

Vinken, M., Benfenati, E., Busquet, F. et al. (2021). Safer chemicals using less animals: Kick-off of the European ONTOX project. Toxicology 458, 152846. doi:10.1016/j.tox.2021.152846

von Aulock, S., Busquet, F., Locke, P. et al. (2022). Engagement of scientists with the public and policymakers to promote alternative methods. ALTEX 39, 543-559. doi:10.14573/altex.2209261

Wikswo, J. P. (2014). The relevance and potential roles of microphysiological systems in biology and medicine. Exp Biol Med 239, 1061-1072. doi:10.1177/1535370214542068

Xia, M., Huang, R., Witt, K. L. et al. (2008). Compound cytotoxicity profiling using quantitative high-throughput screening. Environ Health Perspect 116, 284-291. doi:10.1289/ehp.10727

Zaunbrecher, V., Beryt, E., Parodi, D. et al. (2017). Has toxicity testing moved into the 21st century? A survey and analysis of perceptions in the field of toxicology. Environ Health Perspect 125, 087024. doi:10.1289/ehp1435

Most read articles by the same author(s)

1 2 3 4 5 6 7 8 9 10 > >>