Running a clinical trial can prove to be an exorbitant affair, shaped by a myriad of factors such as protocol design choices, interventions used, size of the subjects, number of site visits, and the country where the trial is conducted. A case in point is Multiple Sclerosis (MS) clinical trials. Over the past 3-4 years, the MS field has witnessed numerous phase 2 and phase 3 trial failures, contributing to massive re-assessments of trial protocols, significant shareholder losses, and even entire companies having to shut shop.
More often than not, trials fail due to lack of innovation in study outcomes, use of superiority vs non-inferiority designs, and the impact of global recruitment efforts on timely completion. The situation appears graver in the field of progressive MS or remyelinating therapeutics, where there’s barely ever been a success story, with most molecules failing at the phase 2 stage and several large phase 3 trials also facing a similar fate.
Given the high cost and the risk involved, accurately estimating the cost of MS clinical trials is crucial to effective planning and execution. This is where Fast Data Science’s Clinical Trial Risk Tool comes into the picture.
Fast Data Science’s Clinical Trial Risk Tool uses advanced machine learning to predict the cost of a trial from the protocol text. The tool works by examining the trial protocol, which includes details about the number of interventions, type of intervention, number of subjects, sites, and visits, and even the specific country where the trial will be conducted. By analyzing this information, the tool can make an estimation of the overall trial cost.
By leveraging this technology, stakeholders can predict costs and potential hurdles before a trial commences, allowing for better resource allocation, potential protocol revisions, or decision-making about the viability of the trial itself. The key here is to make a calculated decision, underpinned by data science, that mitigates risks and optimizes returns.
In a nutshell, understanding the cost of a clinical trial for Multiple Sclerosis is an intricate process involving numerous variables. Fast Data Science’s Clinical Trial Risk Tool simplifies this process by employing machine learning algorithms trained on historical trial data. The end result? A more predictable and cost-effective clinical trial in the field of Multiple Sclerosis.
