The evolution of predictive toxicology from in vivo to in vitro to in silico systems

Cecilia Van Cauwenberghe of Frost & Sullivan’s TechCasting Group lifts the lid on predictive toxicology
Development from in vivo to in vitro to in silico systems, starting with a look at organoids and organ-on-chip microfluidic devices

A team of researchers is working out at the Laboratory for Health Protection of the National Institute of Public Health and the Environment in Bilthoven, the Netherlands, in collaboration with the German Center for the Protection of Laboratory Animals (Bf3R).
the German Federal Institute for Risk Assessment (BfR) in Berlin, Germany, and the Utrecht Institute of Pharmaceutical Sciences at the University of Utrecht, Utrecht, Netherlands, critically emphasize the need for microphysiological systems to support the innovations in organoids and organon-chip microfluidic devices (Schneider et al ., 2021).

Rigorous assessments of the potentially toxic effects of certain chemicals, including pharmaceutical compounds, on human and environmental health remain difficult, according to investigators. The complexity of biological processes and the lack of accessibility to in vivo experiments exacerbate this aspect. Therefore, in recent years, a
More and more researchers are discovering recurring model systems ranging from single cell lines to complex animal models. In the last five years, microphysiological systems that mimic human physiology on a small scale have attracted a great deal of attention. In particular, organoids and organ-on-chip (OoC) systems have significantly improved biomedical research and environmental health science around predictive toxicology.

Computational toxicology to predict adverse outcome pathways

Modern bioanalytical techniques can finely introduce computational tools for predictive toxicological assessment. A team of researchers working at the Department of Computer Science, Swetha Institute of Technology and Science, JNTU, Tirupati, India, uses computational toxicology to advise on the harmful effects of specific chemical compounds at multiple levels, from molecular models to functional traits complex biological systems (Lalasa et al., 2021).

“In the last five years, microphysiological systems that mimic human physiology on a small scale have received a great deal of attention. In particular, organoids and organ-on-chip (OoC) systems have greatly enhanced biomedical research and environmental health science in the field of predictive toxicology.”

According to the researchers, these approaches in silicon greatly improve risk assessment by interpreting a biological system’s exposure to a chemical compound. In fact, the scientific community is building an extensive and growing range of digital resources (e.g. web tools/interfaces, datasets/databases or mathematical models) to support the modeling of quantitative adverse outcome pathways (qAOPs) for predictive toxicology, even after FAIR to support data principles of discoverability, accessibility, interoperability and reusability (Paini et al., 2022).

Machine learning techniques to strengthen predictive toxicology research

Taking it a step further, the use of artificial intelligence approaches such as deep neural network (DNN) and conditional generative adversarial network (cGAN) can exceptionally help scientists predict the toxicity of untested compounds. Researchers working at NC State’s Department of Biological Sciences and Bioinformatics Research Center
University, Raleigh, North Carolina, United States of America (Green et al., 2021) have worked on using DNN and cGAN to provide high-through-put screening (HTS) assay data and chemical structure information analyze to predict toxic outcomes untested
chemicals.

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Green, AJ, Mohlenkamp, ​​MJ, Das, J., Chaudhari, M., Truong, L., Tanguay, RL, and Reif, DM, 2021. Leveraging High-Throughput Screening Data, Deep Neural Networks, and Conditional Generative Adversarial Networks Predictive Toxicology. PLoS Computational Biology, 17(7), p.e1009135.

Lalasa, M., Nithya, S., Nagalakshmamma, K., Suvarnalatha, A., and Nageshwar Rao, P., 2021. In silico platforms for systems toxicology. In Proceedings of the 2nd International Conference on Computational and Bio Engineering (pp. 25-31). Springer, Singapore.

Paini, A., Campia, I., Cronin, MT, Asturiol, D., Ceriani, L., Exner, TE, Gao, W., Gomes, C., Kruisselbrink, J., Martens, M., and Meek, MB, 2022. Towards a qAOP framework for predictive toxicology – linking data to decisions. Computational Toxicology, 21, p. 100195.

Schneider, MR, Oelschlaeger, M., Burgdorf, T., van Meer, P., Theunissen, P., Kienhuis, AS, Piersma, AH, and Vandebriel, RJ, 2021. Applicability of organ-on-chip systems in the Toxicology and Pharmacology. Critical Reviews in Toxicology, 51(6), pp. 540-554.