Summary:
UCLA researchers have developed a software to accelerate vaccine clinical trial processes by modeling disease behavior and targeting future trial participants.
Background:
Vaccine clinical trials are time-intensive processes; ones that may be necessary during active pandemics in which time is of short supply. One of the primary factors contributing to the slow progression of trials is the recruitment of relevant participants. Recruitment processes often rely on random sampling and this step requires a diverse group of individuals. These processes can be hindered by lack of trial awareness, willingness to participate, and limited knowledge of disease-related health history. Advancing from large-scale population infection to vaccine rollout often proves difficult as well; it requires extensive data collection to evaluate real-world performance. These stages in vaccine clinical trials are critical for safeguarding public health, but they heavily contribute to the lengthiness of the vaccine development and deployment process There is a demonstrated need for an improved and efficient vaccine clinical trial process that would facilitate rapid rollout in the face of future pandemics and public health crises.
Innovation:
UCLA researchers in the Department of Epidemiology have developed a computer program to decrease overall vaccine clinical trial times by optimizing the population recruitment and data collection stages as the disease spreads. The software utilizes epidemiological data gathered from participants who are tentatively recruited for a potential clinical trial. The barriers that often hinder participation are addressed on an individual level before the clinical trial even starts. Infection models are mathematically modeled, allowing population-level public health predictions. The model ranks participant pools in order of probability of exposure and likelihood of clinical trial participation, improving vaccine trial by giving priority to those with a higher risk factor. The predictive model and the participation are used in conjunction to derive a probability-of-infection heuristic to give specific enrollees priority in the ensuing clinical trial. The group of longitudinal participants will ensure that analysis derived from this software’s data is more relevant to an expedited vaccine clinical trial.
Potential Applications:
• Vaccine clinical trial acceleration
• Population recruitment
• Public health monitoring & analysis
• Pandemic management
Advantages:
• Shortens duration of clinical trial.
• Low adoption cost to trial institution.
State of Development:
The algorithm and accompanying protocol have been developed by the inventor. The software was compared to standard clinical trial procedures in simulation and found to be faster in silico.
Reference:
UCLA Case No. 2024-148
Lead Inventor:
Akihiro Nishi, UCLA Professor of Epidemiology.