Abstract
Nowadays, like exposure to chemicals has been increased in several ways, toxicity testing is important, to know their effect on plants, animals, and humans. Also, toxicity testing is one of the crucial steps in the new drug development process. Previously in vivo testing methods were used, which is a tedious process consuming more time and money with ethical concern. Hence, it is the need of the hour to use alternative methods. Computational toxicology serves the purpose. Computational toxicology amalgamates information from various sources to develop mathematical and computer-based models to better recognize risk and forecast adverse or toxic effects caused by chemicals such as pollutants and drugs. It transforms in vivo toxicity testing to in vitro methods, providing great accessibility to toxicological databases reducing animal testing. It is a novel, practical and economical approach to assess the safety of chemical molecules more rapidly and effectively. The latest analytical techniques provide information-rich data. The data can be analyzed productively by combining new bio-statistical, mathematical methods and computational tools. The present paper focuses on different computational tools and applications of it in different disciplines of science.
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Nithya, S., Lalasa, M., Nagalakshmamma, K., Archana, S. (2020). Computational Approaches in Toxicity Testing: An Overview. In: Jyothi, S., Mamatha, D., Satapathy, S., Raju, K., Favorskaya, M. (eds) Advances in Computational and Bio-Engineering. CBE 2019. Learning and Analytics in Intelligent Systems, vol 16. Springer, Cham. https://doi.org/10.1007/978-3-030-46943-6_29
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DOI: https://doi.org/10.1007/978-3-030-46943-6_29
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