Cecilia Van Cauwenberghe from Frost & Sullivan’s TechCasting Group, lifts the lid on predictive toxicology evolving from in vivo to in vitro to in silico systems starting with a look at organoids & organ-on-chip microfluidic devices
A team of researchers working 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 Centre for the Protection of Laboratory Animals (Bf3R) from the German Federal Institute for Risk Assessment (BfR) in Berlin, Germany, and the Utrecht Institute of Pharmaceutical Sciences of the Utrecht University, Utrecht, The Netherlands, critically emphasize on the need for microphysiological systems to support the innovations in organoids & organ-on-chip microfluidic devices (Schneider et al., 2021).
According to the investigators, the strict evaluation of the potentially toxic effects of certain chemicals, including pharmaceutical compounds, on human and environmental health continues to be tough. The complexity of biological processes and the lack of accessibility to in vivo experiments exacerbate this aspect. Therefore, during the past few years, an increasing number of researchers discovered recurring model systems ranging from single cell lines to complex animal models. During the past five years, microphysiological systems mimicking human physiology on a small scale gained great attention. In particular, organoids and organ-on-chip (OoC) systems significantly enhanced biomedical research and environmental health sciences around predictive toxicology.
Computational toxicology to predict adverse outcome paths
Modern bio-analytical techniques can finely introduce computational tools for predictive toxicology assessment. A team of researchers working at the Department of Computer Science of the Swetha Institute of Technology and Science, JNTU, Tirupati, India, use computational toxicology to advise about the detrimental effects of certain chemical compounds at multiple levels, from molecular models to functional features in complex biological systems (Lalasa et al., 2021).
“During the past five years, microphysiological systems mimicking human physiology on a small scale gained great attention. In particular, organoids and organ-on-chip (OoC) systems significantly enhanced biomedical research and environmental health sciences around predictive toxicology.”
According to the investigators, these in silicon approaches dramatically improve risk assessment while interpreting the exposure of a biological system to a chemical compound. Indeed, the scientific community is building an extensive and growing range of digital resources (for example, web tools/interfaces, datasets/databases or mathematical models) to support the modeling of quantitative Adverse Outcome Pathways (qAOPs) for predictive toxicology, also following FAIR data principles of findability, accessibility, interoperability, and reusability (Paini et al., 2022).
Machine learning techniques empowering predictive toxicology research
A step further, the use of artificial intelligence approaches, such as deep neural network (DNN) and conditional generative adversarial network (cGAN) can, in an extraordinary way, help scientists to predict the toxicity of untested compounds. Researchers working at the Department of Biological Sciences and the Bioinformatics Research Center of the NC State University, Raleigh, North Carolina, United States of America (Green et al., 2021) have worked in the use of DNN and cGAN to analyze high throughout put screening (HTS) assay data and the chemical structure information to predict the toxic outcomes of untested
chemicals.
Acknowledgements
I want to thank all contributors from the industry involved with developing and delivering this article from Frost & Sullivan.
Further reading
- Green, A.J., Mohlenkamp, M.J., Das, J., Chaudhari, M., Truong, L., Tanguay, R.L. and Reif, D.M., 2021. Leveraging high-throughput screening data, deep neural networks, and conditional generative adversarial networks to advance 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, M.T., Asturiol, D., Ceriani, L., Exner, T.E., Gao, W., Gomes, C., Kruisselbrink, J., Martens, M. and Meek, M.B., 2022. Towards a qAOP framework for predictive toxicology-Linking data to decisions. Computational Toxicology, 21, p.100195.
- Schneider, M.R., Oelgeschlaeger, M., Burgdorf, T., van Meer, P., Theunissen, P., Kienhuis, A.S., Piersma, A.H. and Vandebriel, R.J., 2021. Applicability of organ-on-chip systems in toxicology and pharmacology. Critical Reviews in Toxicology, 51(6), pp.540-554.
