Motor Neurone Disease (MND) is a rare but progressively debilitating condition impacting the brain and nerves. Despite the ongoing research for an effective and decisive treatment, the failure to end informatively in MND clinical trials remains a significant concern. Particularly, a trial is deemed to fail when it doesn’t produce sufficient, valid data to inform future treatment decisions or research directions.
With the only licensed drug in the UK being riluzole, which merely slows progression, the success of clinical trials becomes even more crucial for understanding and treating MND. Here, we highlight how Fast Data Science’s Clinical Trial Risk Tool helps enhance this understanding by estimating the risk of an MND clinical trial failure using advanced machine learning techniques.
Fast Data Science’s Clinical Trial Risk Tool is an innovative approach to mitigate the risk of clinical trial failure by applying machine learning on the protocol text. By training these algorithms on previous trial data, we can predict whether a trial is likely to end informatively or not, right from the initial stages.
A machine learning model needs to understand the factors influencing a trial’s ‘informativeness’. These factors can range from the number of participants, the length of the trial, the variables involved, and the ability to control exposure. Fast Data Science’s tool uses this data, along with the trial’s protocol text, to provide an early risk assessment.
By identifying high-risk trials at the start, precious resources can be redirected to low-risk trials or used to rectify the identified issues reducing the overall risks. Notably, a trial without a completed Statistical Analysis Plan (SAP) is considered high risk, exemplifying an easily sortable issue that could significantly reduce the trial’s failure rate.
Fast Data Science’s Clinical Trial Risk Tool is particularly pertinent to MND research. Given the scarcity of treatments and slow rate of progress, maximising the informativeness of each trial is essential. This tool can help achieve this by screening protocols for potential risk points and helping clinical researchers design more effective trials.
In conclusion, while MND remains a complex and challenging disease to tackle, advancements in machine learning provide us with the tools to enhance clinical trials’ success. Fast Data Science is at the forefront of this development, using technology to predict, mitigate and, hopefully, prevent clinical trial failures. Thus with predictive analytics on our side, the quest for a breakthrough treatment for MND looks increasingly promising.
