Cystic fibrosis (CF) is a severe genetic disorder that impacts multiple organ systems, making it a challenging disease to manage and study. Because CF has so many system-wide effects, clinical trials to develop new treatments and investigate the disease are inherently complex. It is important to understand the complexity of these trials to predict the resources needed and the likely duration of the trials.
For instance, the Markey et al.’s study revealed that clinical trial complexity has been increasing over the years in various therapeutic areas. However, recent advances in machine learning have allowed us to predict clinical trial complexity by analysing factors such as the number of endpoints, inclusion-exclusion criteria, etc., from the trial protocol itself.
Fast Data Science’s Clinical Trial Risk Tool is an example of one such machine learning application. This tool simplifies the process of estimating complexity by assessing the trial protocol text. It leverages natural language processing and machine learning algorithms to extract features from the protocol text, such as the trial design, endpoint specifications, and patient enrolment criteria. It then uses these features to calculate a Trial Complexity Score.
On entering the CF trial protocol text into the Clinical Trial Risk Tool, the system processes it and produces a result instantaneously. The output includes the predicted trial complexity score and risk factors that might impact the trial adversely. It allows investigators to identify potential issues beforehand to improve planning and resource allocation.
As research on CF continues and evolves, the use of tools such as the Clinical Trial Risk Tool will be critical. They help in understanding and managing the escalating complexity of these trials. By harnessing the power of machine learning, we can continue to innovate and improve the process of studying new therapies for debilitating diseases such as CF.
