The ability to predict organ toxicity directly from a chemical structure would support many applications throughout the product life cycle, from screening candidates to formulating testing strategies and assessing non-genotoxic impurities.

A recent cross-industry working group, as part of the in silico toxicology protocol project1, was initiated to understand the needs and challenges for in silico prediction of organ toxicity, focusing on the liver, heart, lung, and kidney, with a parallel effort focusing on neurotoxicity. This includes an assessment of current experimental approaches (such as off-target panels from secondary pharmacology batteries), the state-of-the-art in in silico modelling (including sources of training data), as well as how to potentially combine both experiment and in silico results as part of a defendable hazard assessment framework.

Although significant progress has been made in understanding the mechanistic basis for many of these adverse effects, there are still gaps that may hamper the development of robust in silico models. In addition, many in silico approaches predict the presence or absence of specific adverse effects, whereas an indication of the safe dose would be more valuable in many applications. Additional effort is also needed to better define how to follow-up from any in silico signal for the different specific contexts of use (e.g. regulatory frameworks).

This information has been summarized in three cross-industry publications that we are getting ready to submit. We hope these papers will help drive the development of the next generation of in silico models to accelerate the development of new chemical products as well as to support the 3Rs.

If you would like more information on these initiatives, please contact me (glenn.myatt@instem.com).

References

  1. Myatt, G.J., Ahlberg, E., Akahori, Y., Allen, D., Amberg, A., Anger, L.T., Aptula, A., Auerbach, S., Beilke, L., Bellion, P., Benigni, R., Bercu, J., Booth, E.D., Bower, D., Brigo, A., Burden, N., Cammerer, Z., Cronin, M.T.D., Cross, K.P., Custer, L., Dettwiler, M., Dobo, K., Ford, K.A., Fortin, M.C., Gad-McDonald, S.E., Gellatly, N., Gervais, V., Glover, K.P., Glowienke, S., Van Gompel, J., Gutsell, S., Hardy, B., Harvey, J.S., Hillegass, J., Honma, M., Hsieh, J.-H., Hsu, C.-W., Hughes, K., Johnson, C., Jolly, R., Jones, D., Kemper, R., Kenyon, M.O., Kim, M.T., Kruhlak, N.L., Kulkarni, S.A., Kümmerer, K., Leavitt, P., Majer, B., Masten, S., Miller, S., Moser, J., Mumtaz, M., Muster, W., Neilson, L., Oprea, T.I., Patlewicz, G., Paulino, A., Lo Piparo, E., Powley, M., Quigley, D.P., Reddy, M.V., Richarz, A.-N., Ruiz, P., Schilter, B., Serafimova, R., Simpson, W., Stavitskaya, L., Stidl, R., Suarez-Rodriguez, D., Szabo, D.T., Teasdale, A., Trejo-Martin, A., Valentin, J.-P., Vuorinen, A., Wall, B.A., Watts, P., White, A.T., Wichard, J., Witt, K.L., Woolley, A., Woolley, D., Zwickl, C., Hasselgren, C., 2018. In silico toxicology protocols. Regulatory Toxicology and Pharmacology 96, 1–17. https://doi.org/10.1016/j.yrtph.2018.04.014

Published by Glenn Myatt

Glenn J. Myatt is the co-founder and currently head of Leadscope (An Instem company) with over 25 years’ experience in computational chemistry/toxicology. He holds a Bachelor of Science degree in Computing from Oxford Brookes University, a Master of Science degree in Artificial Intelligence from Heriot-Watt University and a Ph.D. in Chemoinformatics from the University of Leeds. He has published 27 papers, 6 book chapters and three books.

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