Animal testing has been used extensively for the prediction of human toxicity. In addition to the cost and time involved, ethical considerations and increasing restrictions on animal testing (e.g., Europe’s 2013 prohibition on animal testing for cosmetic ingredients) have made the development of alternative methods to reduce and replace the number of animals used in toxicological tests, imperative.

Moreover, the assumption that animal models can accurately predict toxicity has been acknowledged as unreliable. A review of 221 animal experiments found that they agreed with human outcomes only ~50% of the time. The failure rate of clinical trials due to unanticipated toxicity also raises concerns about the reliability of in vivo testing [1]. In this article, we discuss how Syngene’s Computational & Data Sciences unit offers toxicity assessment across various levels of complexity to enable a holistic, mechanism-driven approach to toxicity assessment.