Advancing predictive toxicology: overcoming hurdles and shaping the future

Abstract

Modern drug discovery projects are plagued with high failure rates, many of which have safety as the underlying cause. The drug discovery process involves selecting the right compounds from a pool of possible candidates to satisfy some pre-set requirements. As this process is costly and time consuming, finding toxicities at later stages can result in project failure. In this context, the use of existing data from previous projects can help develop computational models (e.g. QSARs) and algorithms to speed up the identification of compound toxicity. While clinical and in vivo data continues to be fundamental, data originating from organ-on-a-chip models, cell lines and previous studies can accelerate the drug discovery process allowing for faster identification of toxicities and thus saving time and resources.