Multiple sclerosis (MS) is a critical area for clinical trials. It has seen both a boom in research focus over the past 30 years and a recent string of disappointments. A surprising number of phase 2 and phase 3 trials have failed, causing major investments to collapse and sending a shockwave through the field.
The risk of failure, or more precisely, the failure to end informatively, is a key concern. When a trial fails to provide valuable insights to guide clinical, policy, or research decisions, it deprives researchers, patients, and investors of the information needed to advance the field.
Fast Data Science’s Clinical Trial Risk Tool offers a unique approach to this issue. This tool harnesses the power of machine learning to predict the risk of a trial based on the protocol text. Here’s how it works:
The Risk Tool uses a machine learning algorithm fed with data from past trials, both successful and not. It analyzes a multitude of factors, such as the number of trial participants, length of the trial, and uncontrolled variables, among others.
The protocol text of each trial is studied in detail to detect patterns and indications of risk. For instance, the text could reveal an association between certain terms or phrases and the subsequent outcome of the trial. Through machine learning, the tool can identify these textual signals and use them to predict the trial’s potential for success.
A key aspect of the Risk Tool is its use of retrospective analysis. This means it doesn’t just predict the outcome of new trials; it also learns from the experiences of past trials.
Did a trial with a similar design fail in the past? Are there shared terms or patterns in protocol texts for uninformative trials? By answering these kinds of questions, the tool continually ‘learns’ which factors influence a trial’s likelihood to end informatively.
By providing a risk estimate directly from the protocol text, Fast Data Science’s Clinical Trial Risk Tool empowers researchers, policymakers, and investors to consider the pitfalls and potential areas of improvement before the trial is underway. It holds significant potential for improving the efficiency and efficacy of future MS clinical trials, and ultimately, boosting the informativeness of trial outcomes.
The end goal is to assist the industry in learning from earlier, unsuccessful trials and adopt more effective strategies for innovative therapy development. With enhanced preparation and insight, the risk of failure can be decreased, leading to more informative and successful clinical trials.
