Impact of Computational Structure-Based Predictive Toxicology in Drug Discovery

Abstract

Computational tools for predicting toxicity have been envisioned to have the potential to broadly impact up on the attrition rate of compounds in pre-clinical drug discovery and development. An integrated approach of computer-assisted, predictive, and physico-chemical properties of a compound, along with its in vitro and in vivo analysis, needs to be routinely exercised in the lead identification and lead optimization processes. Starting with a good lead can save a lot of money and it can significantly reduce the entire drug discovery process. The journey towards triple R's- reduce, replace and refine, further proves to be successful in predicting adverse drug reactions in patients (or animals) enrolled in clinical trials. However, the impact of predictive toxicity analysis was modest and relatively narrow in scope, due to the limited domain knowledge in this field. It is important to note that advances within medical science and newer approaches in drug development will require predictive toxicology applications to be viable. The field of computational toxicology has been heading in a direction more relevant to human diseases by reducing the adverse drug reactions. Therefore, efforts must be directed to integrating these tools relevant to the goal of preventing undesired toxicity in pre-clinical trials followed by different phases of clinical trials.