Advancing Drug Safety in Drug Development: Bridging Computational Predictions for Enhanced Toxicity Prediction

Abstract

The attrition rate of drugs in clinical trials is generally quite high, with estimates suggesting that approximately 90% of drugs fail to make it through the process. The identification of unexpected toxicity issues during preclinical stages is a significant factor contributing to this high rate of failure. These issues can have a major impact on the success of a drug and must be carefully considered throughout the development process. These late-stage rejections or withdrawals of drug candidates significantly increase the costs associated with drug development, particularly when toxicity is detected during clinical trials or after market release. Understanding drug-biological target interactions is essential for evaluating compound toxicity and safety, as well as predicting therapeutic effects and potential off-target effects that could lead to toxicity. This will enable scientists to predict and assess the safety profiles of drug candidates more accurately. Evaluation of toxicity and safety is a critical aspect of drug development, and biomolecules, particularly proteins, play vital roles in complex biological networks and often serve as targets for various chemicals. Therefore, a better understanding of these interactions is crucial for the advancement of drug development. The development of computational methods for evaluating protein-ligand interactions and predicting toxicity is emerging as a promising approach that adheres to the 3Rs principles (replace, reduce, and refine) and has garnered significant attention in recent years. In this review, we present a thorough examination of the latest breakthroughs in drug toxicity prediction, highlighting the significance of drug-target binding affinity in anticipating and mitigating possible adverse effects. In doing so, we aim to contribute to the development of more effective and secure drugs.

Figure 1

AI-driven drug development and toxicity forecasting, highlighting the use of AI in enhancing each stage of the drug development pipeline from target validation to approval and underscores its pivotal role in improving efficacy and safety by predicting toxicological risks.

Figure 2

Confluence of drug development and predictive technologies, illustrating the key aspects of drug development, highlighting (1) the role of proteomic data, (2) applications of artificial intelligence (AI) in drug discovery, (3) the Significance, limitations, and role of AI in toxicity prediction, and (4) the significance and challenges of in vivo reproducibility.

Figure 3

General and specific models in toxicity prediction, encompassing the model’s generality or specificity regarding predicted toxicity end points (lethal dose 50% (LD50), drug-induced liver injury (DILI), human ether-a-go-go-related gene (hERG inhibition), carcinogenesis, and Ames mutagenicity), along with its advantages and disadvantages.