Use of Big Data to Aid Patient Recruitment for Clinical Trials Involving Biosimilars and Rare Diseases

Patient recruitment and retention are arguably the greatest challenges to the timely execution of clinical trials. This is particularly true in the case of trials involving biosimilars and those focused on rare diseases. For biosimilars, recruitment success typically hinges on difficulty of access to reimbursement for the originator product and may be hindered by competition from studies with other biosimilars and those with new chemical entities. For rare diseases, recruitment success depends not only on finding enough patients but also on retaining them for the duration of the trial. Historical success with patient recruitment—in addition to site qualifications—needs to be considered in parallel with current market competition and results in an ever-changing patient recruitment environment. Multiple companies supporting the biopharmaceutical industry, such as Contract Research Organizations, have begun to leverage sophisticated data modeling tools and techniques to find additional patients for biosimilar and rare disease trials and/or to find patients more quickly. In addition, these companies seek to better understand the population of interest and refine their statistical assumptions when conducting clinical trials or real-world analyses. The largest and most well-established companies that support the biopharmaceutical industry now have unparalleled access to big data from clinical trials, electronic health records, medical claims, laboratory tests, and prescriptions. This paper will discuss how big data can be harnessed to aid patient recruitment with a focus on clinical trials for biosimilars and orphan rare diseases.