Using Technology to Build Patient-Centric Clinical Development Strategies
Researchers from the MIT Sloan School of Management recently published a breakthrough study on the success rate of clinical trials. It found that only 14% of all drugs in clinical trials eventually win approval from the FDA. Believe it or not, that was considered good news. Clinical trials are notoriously fraught with uncertainty and challenges, such as low patient participation and high cost, and the results were widely hailed as progress for the life sciences industry.
But what would it mean for patients if 20 percent of trials succeeded? Or 30 percent? Or 50 percent? Any uptick would mean millions of saved lives through more available therapies, better procedures, or new devices to improve health.
Today, roughly 80% of clinical trials fail to meet enrollment timelines and requirements. Of those that do make it to Phase 2, about 70% never make it to Phase III. There are many reasons for this: study design flaws, lack of funding, and inadequate efficacy are among the most common. But there is one big problem that cuts across all of these and creates an unnecessary barrier to new drug development: structural inefficiency.
The physical process of running a clinical trial—from site selection to physician engagement to patient recruitment—has been a largely manual process, ripe for reinvention.
Disrupting Patient Recruitment
Take patient recruitment as an example. The conventional method of identifying and selecting patients for a trial through a mix of EHR data, patient surveys, and relationships with providers and advocacy groups often misses eligible patient populations due to the inherent lag time in the data and the limited scope of data sources used. In the case of rare diseases or quickly progressing, high mortality diseases like cancer, this process is tantamount to finding needles in a haystack. Data is often incomplete, inaccurate and out-of-date, and the process of actually getting from that data to recruiting enough patients for a trial consumes a significant portion of the total cost of a trial, which can range from $1.4 million to $6.6 million for a Phase I study.
New technology has made it possible to quickly identify much larger and more precise patient populations faster. Today, it is possible to create a payer-complete map of individual patient journeys at scale, capturing real-time data and real-world touchpoints with the healthcare system to understand the full spectrum of patient experience as it’s happening.
This enables life science companies to identify specific cohorts of patients at key stages of progression, such as newly tested, newly diagnosed, starting therapy, or procedure completion. This visibility into specific care pathways and phases of disease progression allows a clinical trial sponsor to zero in on the discreet patient populations most relevant to a specific trial. Most importantly, the granularity provided by this technology allows life sciences companies to put real patients at the center of their drug development strategies.
Fine-Tuning Site Selection
Site selection is another source of significant structural inefficiency in the clinical development process. For the last several decades, life sciences companies have centered their trials around the physicians with the highest level of clinical engagement and scientific publication on a particular condition. That has meant deploying research teams and consultants to scour Google and PubMed for highly cited authors, then approaching those key opinion leaders (KOLs) to consult on the drug development process.
That creates a couple of problems. First, it focuses disproportionately on the handful of physician academics who are publishing regularly on a specific topic and are often already spread thin across a deluge of clinical trials. Which means another clinical trial may not get the attention it deserves. Second, the process ignores the vast majority of in-the-trenches providers who are treating patients every day but may not be publishing as frequently.
Today, it is possible to flip that legacy approach by using real-time data to identify physicians who are actively seeing the largest segment of patients with a particular condition(s) and building a site selection strategy that targets those physicians. By expanding the calculus of physician outreach to include real-world patient volume as well as industry payment data, clinical development teams are able to surface entirely new groups of providers that have more time to focus on their trials and more active patients being treated for a specific condition.
This ability to quickly surface new centers of influence and new patient populations is particularly important in an era in which life sciences companies are competing for access to the large academic teaching hospitals and widely published KOLs. It is not uncommon for major KOLs to receive significant financial incentives from the industry, which can influence the prioritization of patient recruitment. By seeking alternative patient populations outside the most obvious choices, sponsors are able to dramatically expand the pool of eligible patients that might fit into their trails.
Getting Life Saving Therapies to Patients Faster
A new JAMA study recently put the total cost of bringing a new drug to market at just under $1 billion. The estimate accounted for projects that failed during clinical trials. On average, the entire clinical trial process can take anywhere from six to seven years or more. Those numbers should put in perspective just how much is at stake for life sciences companies navigating the clinical trial process and how much patients stand to gain by any mechanism that speeds the process of getting life-saving therapies to those who need it most.
The entire process is in dire need of a methodological overhaul, and, thankfully, class-leading solutions exist today in the form of real-time longitudinal patient data and the platforms and software required to leverage that data for actionable insights. The days of poring over bibliographic references and wading through ancient EHR data to power the patient recruitment and site selection process are over. It’s time the industry adopted a clinical trial recruitment process that is as scientifically robust and patient-driven as the trials themselves. There are no longer any excuses to do anything less.