Running a clinical trial is no small feat—it involves a myriad of moving pieces, a vast range of variables, and, inevitably, significant cost. In particular, clinical trials concerning HIV, a virus that attacks the body’s immune system, pose unique challenges in regard to both complexity and financial demands.
Thomas Moore, a senior scientist for drug safety and policy, and his colleagues have reported that the median cost of a clinical trial between 2015 and 2016 was $19 million. And trials a scale up to 100 times this in cost. Particularly, later-stage trials, which involve larger patient cohorts, can cost around $30 million each to implement.
HIV clinical trials are especially expensive, entailing comprehensive and effective HIV prevention, testing, and treatment services. The process includes testing with rapid diagnostic tests, antiretroviral treatment (ART) monitoring, periodic testing of virus amount in the blood (viral load), and CD4 cell count. These procedures provide indicators of disease progression and treatment response, but they can be time-consuming and costly.
One of the challenges is accurately predicting these costs upfront to ensure trials are financially feasible and properly budgeted for. Thankfully, advances in technology can now aid in this crucial estimation process.
Fast Data Science’s Clinical Trial Risk Tool uses machine learning—an application of artificial intelligence—to predict the cost of a trial from the protocol text. This revolutionary tool analyses the planned schedule of events and interventions in the trial’s protocol, then calculates the expected cost. Factors such as the number of subjects, sites, visits, and the location of the trial are taken into account, providing a comprehensive picture of the potential expense.
In addition to these factors, the type of intervention—whether it be gene therapy or cell therapy, for instance—is considered, as some interventions are significantly more expensive.
Despite the diversity in clinical trial protocols, Clinical Trial Risk Tool’s predictive algorithms move beyond standard statistical analyses to cover the nuances of cost estimation. It is a game-changer for planning and implementing HIV clinical trials.
Predicting clinical trial costs is a crucial step in the fight against HIV. With tools like Fast Data Science’s Clinical Trial Risk Tool, researchers and organizations can more effectively plan their trials, making the most of their budgets and resources, and advancing our overall understanding and treatment of HIV.
