With rapid digital transformation occurring in healthcare today, it is understandable that the Food and Drug Administration (FDA) is calling for new, decentralized clinical trial models, both to improve the efficiency and impact of studies, and to reduce overall costs. Quest supports this position, acknowledging that we are in a transformational time, not only related to therapeutics but also to today’s data and computing environment.
As demonstrated by the accelerated development of COVID-19 vaccines, we now have the digital capabilities and know-how to scope, define, recruit, test, and bring to market broad-spectrum medications for large populations, as well as biologics and cell-based therapies, putting patients at the center of the process.
Instead of just establishing more sites and recruiting more patients, which requires both enormous budgets and long cycle times, the opportunity now exists to connect to the right patients, as well as care teams, labs, health plans, and the pharma and clinical trial industry overall, catalyzing a different approach. Downstream, the benefits to pharma are enormous, as every day of patent life is worth an estimated million dollars.
Shifting the focus
While today’s medicines are more complex, many clinical trials are still conducted for the broad population versus the focused populations they are intended to benefit. This approach is both inefficient and expensive, with 60% of patients who express interest in a trial failing at prescreen. Given that approximately 50% of a trial’s budget has already been spent for patient outreach prior to prescreening, this underscores the inefficiency of the traditional approach.
Additionally, only two percent of physicians in the country participate in clinical trials, with half one-and-done. Most never participate again because they consider the process too cumbersome and difficult to navigate.
Leveraging the power of analytics
To cut through this inertia requires an infrastructure built around data analytics that adheres to privacy and regulatory requirements. At Quest, we recognize the need to standardize and normalize data, pulling it together into a secure cloud and translating it into a de-identified, secure, linkable format. This platform unlocks the power of lab data, especially when linked to other kinds of information, such as claims, diagnostic history, procedures, etc.
Related to clinical trials, analytics also supports and enables more complex and precise inclusion/exclusion criteria, as well as prior diagnostic and therapeutic exposure. In this spirit, it is also important to partner with other data suppliers to create a robust infrastructure that meets these demands.
At Quest, our robust database, which includes 58+ billion clinical lab results from 2,250 Patient Service Centers, can be parsed to reflect the size of targeted populations, the kinds of physicians participating patients see, how their disease has evolved over time, the impact of one therapeutic over another on underlying biology, etc. All of this is served up through cohort analytics, enabling researchers to garner a rich depth of insights to inform their efforts.
Today, Pharma and CROs can access this level of data through our Quest self-service platform via a subscription service. Quest data can also be leveraged to help with digital outreach campaigns.
In a very real sense, it is all about looking at a population of patients and being able to predict who is going to enroll and who is most likely to stay in the trial, both from a clinical standpoint and related to social determinants of health, which factor in demographics, psychographics and other elements of the patient’s profile.
In this context, it is important for investigators to think about the therapeutic design and the patient population they are reaching out to when deciding whether to go with a traditional anchor site trial, a hybrid model with sub-PIs, or a direct-to-patient/virtual trial. Understanding how patient populations or therapies fit these scenarios is key to the trial’s success.
Don’t underestimate the power of convenience, relevance
Ultimately, some of the biggest justifications for a decentralized clinical trial model are convenience and relevance. People want trials that are both relevant to them personally and close to home, otherwise trials wouldn’t have as much as a 30% dropout rate. The days of moving the patient to the PI or a clinical trial institute are largely over.
In a decentralized model, any number of touchpoints can become part of the network for a trial, putting more and more capabilities into service centers. In these environments, health care professionals can also help identify and pre-screen patients as they do standard-of-care visits. It is only when a patient has a more invasive, complex issue that they need to go to a hospital to participate in an onsite clinical trial.