In the past few years, the volume of patient data created through clinical trials and electronic medical records (EMR) has soared, creating huge data lakes. The ability to store, process and analyze this data has also expanded, with technological advances in AI and analytics, cloud utilization and computing power enabling more ambitious use of patient data. These advances are occurring at a time when pharmaceutical companies are still using highly time-intensive and costly means to obtain data in the clinical trial phase of drug development. A key driver of the cost and duration of clinical development is the number of participants needed for each trial. This is particularly an issue for indications with high unmet medical need, such as those within oncology, or in rare diseases, where the limited number of patients make it even more difficult to recruit patients without delay.
Today, a synthetic control arm can be generated from historical data. Patients who have received standard of care in the past have electronic medical records that track their outcomes. Data on trial protocols and results from completed clinical trials, observational studies and data from registries can all be incorporated into a robust synthetic control arm or used to reduce the size of the comparator arm. Using synthetic data for the control arm substantially reduces the demand for patient recruitment, saving time and resources.
In addition to minimizing the need for placebo patient enrollment, synthetic patient data can be used to model target patient populations and to define the boundaries of a trial. This can optimize clinical trial design and feasibility to positively impact operational success.