Multiple sclerosis (MS) clinical trials have seen a mix of success and failure over the years. The field has undergone massive changes, with up to 25% of trials obtaining FDA approval. However, the recent surge of Phase 2 and 3 failures has led to substantial reassessment of trial strategies and outcomes. This variability in outcomes emphasizes the need for a tool to estimate the informativeness of clinical trials.
In the context of clinical trials, ‘informativeness’ refers to the capacity of a trial to inform and guide clinical, policy, or research decisions. A few of the major factors influencing the informativeness of a trial include the number of participants, length of trial, number of uncontrolled variables, exposure controls, and the presence of a completed Statistical Analysis Plan (SAP).
Fast Data Science’s Clinical Trial Risk Tool enters the frame at precisely this juncture. It aims to predict the informativeness of a clinical trial from its protocol text using Machine Learning (ML). The tool leverages computational power and the principles of ML to process and analyze vast amounts of data from diverse sources, such as trial protocols, and identify patterns or correlations that may indicate the informativeness of a trial.
The process of fast data science generally involves analyzing informative versus uninformative trials and feeding all these parameters into an ML model. This model, trained on past trials, can predict the potential informativeness of future trials based on similarities and differences in their protocols.
The Clinical Trial Risk Tool holds immense promise for improving trial design and potentially increasing the success rate in challenging areas like progressive MS. By providing insights into the informativeness of trials before initiation, the tool is poised to become an indispensable decision-making aid for researchers, aiding them in making more informed and strategic choices about trial designs and protocols.
Although the desire for a silver bullet cure for conditions like MS continues to inspire research and spur trials, the need of the hour is also to ensure that these trials are as cost-effective and efficient as possible. With this in mind, Fast Data Science’s Clinical Trial Risk Tool serves as an innovation that could transform the future of MS clinical trials.
