VALENCIA, SPAIN - Clinical trials are the lifeline of modern medicine, bridging the gap between lab-borne scientific discoveries and the availability of life-saving treatments for patients. 

Unfortunately, the success rate of clinical trials - particularly in the field of oncology - is not as robust as one might hope. A 2019 study of 7,455 interventional phase trials in oncology conducted from 2000 to 2015 suggests only 3.4 percent of cancer drugs that enter Phase I clinical trials ultimately receive approval from the United States Food and Drug Administration (FDA). Many of these failures are attributable to obstacles such as poor trial design and inefficient patient selection, which often result from a lack of biomarker-driven insights. As a result, drug development can be laborious and expensive, with the estimated cost of bringing a new drug to market around USD2.6 billion. As the world grapples with these challenges, calls for more efficient and targeted methods of drug development become more urgent. The answer for achieving this may lie in the utilization of novel actionable biomarkers and the power of artificial intelligence (AI) to plan and execute clinical trials. 

IQVIA's AI-based platform shows how this works using its real-world data to enable more precise targeting of patients and healthcare professionals. Compared to prior traditional methods, this has doubled the number of identified eligible patients, found 30 percent more with uncontrolled symptoms, predicted 81 percent of those likely to quit treatment early, pinpointed events linked to early discontinuation, and boosted treatment transition success rates fivefold.