Clinical trials are the cornerstone of advancing medical knowledge and patient care. However, the risk of clinical trial failure looms large, especially when dealing with complex diseases like cystic fibrosis.
Cystic Fibrosis (CF) trials come with a unique set of challenges. For a chronic, troublesome ailment such as CF, trials are complex, time-consuming, and expensive. The risk of failure, either due to unsuccessful results or the inability to prove the drug’s efficacy, can have serious implications, both financially and, more importantly, for the patients who have been waiting for an effective treatment.
One of the significant issues affecting CF clinical trial outcomes is their informativeness - the ability of a trial to guide clinical, policy, or research decisions. A study by Hutchinson et al, investigating trial informativeness, found only 26.4% of trials met four conditions for informativeness, adding a significant factor to the risk profile.
A pivotal question that arises is this: Is there a way to predict or estimate the risk associated with a CF clinical trial from its protocol? The answer is yes, and the solution lies in sophisticated machine learning algorithms.
Fast Data Science’s Clinical Trial Risk Tool hopes to provide this estimate. The tool combines the lessons learned from past trials with state-of-the-art machine learning algorithms to predict trial risk from protocol text.
This approach allows us to identify potential risks early in the trial design process and adjust accordingly, increasing the trial’s chances of informative completion.
The development of such a risk prediction tool becomes increasingly important given the cost and implications of CF clinical trials. Each trial, potentially costing more than £100,000 per patient per year, represents a significant investment. Furthermore, the existence of modulator therapies that require altering clinical trial strategies throws another wrench into the calculus.
Machine learning offers us the opportunity not just to cut costs and time in CF clinical trials, but to significantly increase our chances of success. It enables us to make better-informed decisions right at the start and control the risk parameters throughout.
Fast Data Science’s Clinical Trial Risk Tool is not just a handy tool; for many CF patients and their families, it could represent a beacon of hope. It signals a potential future where CF research can spring up more quickly, more efficiently and with a higher chance of success - bringing a brighter future for those living with cystic fibrosis.
