We have developed a machine learning and rule-based tool using natural language processing which allows a user to upload a trial protocol, and which categorises the protocol as high, medium or low risk of ending uninformatively. The tool is at https://clinicaltrialrisk.org/tool and is open-sourced on Github. You can read an explanation of how the tool works here, and a description of how we validated its accuracy here.
There are several indicators of high risk of uninformativeness which can be identified in a protocol, such as a lack of and or an inadequate statistical analysis plan, use of non-standard endpoints, or the use of cluster randomisation. One of the most common causes of a trial ending uninformatively is underpowering. Low-risk trials are often run by well-known institutions with external funding and an international or intercontinental array of sites. These indicators can be referred to as features or parameters.
This project is an initial Proof of Concept (POC) which to showcase what is possible with natural language processing, with a view to moving towards a more comprehensive main project which may identify a more complete set of cost, complexity, or uninformativeness risk factors.
The tool is designed with a feedback form so that inaccurate data extractions can be reported back to the developers.
In addition the MIT License means that you are free to add features or extend the scope of the tool.
We hope that researchers who are considering submitting a protocol of a trial to a prospective source of funding will be able to use the tool as a kind of checklist to ensure that their trial is designed to reduce risk and increase the prospects of being funded.
Guest post by Safeer Khan, Lecturer at Department of Pharmaceutical Sciences, Government College University, Lahore, Pakistan Introduction The success of clinical studies relies heavily on proper financial planning and budgeting. These processes directly impact key factors such as project timelines, resource allocation, and compliance with regulatory requirements. The accurate forecasting of costs for clinical trials, however, is a highly complex and resource-intensive process. A study by the Tufts Center for the Study of Drug Development found that the average cost of developing a new drug is approximately $2.
Guest post by Safeer Khan, Lecturer at Department of Pharmaceutical Sciences, Government College University, Lahore, Pakistan Introduction Recent years have seen a substantial rise in oncology clinical trials, with annual growth exceeding 260 studies on average [1]. Despite this increase, these studies continue to be some of the most demanding and resource-intensive in clinical research. The combination of intensive monitoring, detailed assessment schedules, and highly specific eligibility criteria creates substantial operational challenges.
Guest post by Safeer Khan, Lecturer at Department of Pharmaceutical Sciences, Government College University, Lahore, Pakistan Introduction In recent years, the cost of conducting clinical trials has risen dramatically. This increase has posed a significant challenge for pharmaceutical companies, researchers, and healthcare innovators. According to a study published in Nature Reviews in 2017, the median cost of conducting a study was $3.4 million for phase I trials, $8.6 million for phase II trials, and $21.
