Clinical Trials Embrace AI to Tackle Failures and Enhance Drug Predictions

The market for AI in clinical trials is fueled by the significant rate of clinical trial failures, the growing demand for predicting drug pharmacokinetics, pharmacodynamics, and toxicity management. The worldwide market for artificial intelligence in clinical trials is projected to increase consistently from US$1.20 billion in 2023 to US$1.35 billion in 2024, eventually attaining a remarkable US$2.74 billion by 2030, propelled by a strong CAGR of 12.4%.

A research article released by the National Library of Medicine in February 2022 indicates that the process of drug discovery and development generally requires around 10-15 years for a new medication to receive approval for clinical use. And 90% of drug candidates do not succeed in phases I, II, and III of clinical trials and approval processes. Factors that can lead to failure include, but are not confined to, efficacy in a clinical environment, unmanageable high toxicity levels, insufficient drug-like properties, absence of strong commercial incentives, and inadequate strategic planning.

AI in clinical trials aims to address these challenges to improve the chances of drug approval and accelerate the drug trial process. A further factor is the substantial expense of clinical trials, which AI can lower by forecasting results and assisting in enhancing the design of these trials. By employing AI, businesses can model trial situations by evaluating their possible risks, reducing trial costs, and enhancing the success rates of trials. For instance, businesses such as Unlearn. AI employs digital twins, or virtual models of patients, to carry out virtual trials that predict drug efficacy prior to large-scale in-person testing.