Transformative Reflections On The Life Science World, Post-COVID-19
“Transformative Reflections On The Life Science World, Post-COVID-19” was originally published in Life Science Leader. Click here to read the original article.
With more than 30 years of working with experts in clinical, regulatory, operational, and organizational leadership, I thought I had seen everything — until spring of 2020, when the life sciences industry suddenly changed.
I’m reflecting on life after COVID-19, a couple years out — based on the need and capacity to respond. While unorthodox, reflecting on a future possible outcome offers insight into the achievable, and the opportunity to prepare for future clinical needs using current expertise and treatments.
THE PROBABLE FUTURE
The post-pandemic response has led to drastic public health changes and new ways of creating diagnostics and treatments. COVID-19 was a turning point. The pandemic exposed weaknesses in internal processes, reliance upon traditional methodologies, and leadership gaps. It demonstrated a valuable capacity to leverage innovative technologies for prioritizing patient safety, to rapidly implement creative ways of enabling healthcare providers to continue providing care, and a new framework for solving complex medical challenges.
The industry swiftly initiated 100+ different programs for identifying or treating COVID-19 or its symptoms, which entered the clinic within a month. This generally galvanized people, especially C-suiters, to maintain forward momentum. Despite emerging from desperation, it quickly scaled to create transformative and lasting change.
Initial efforts started from the center, with site monitoring and patient visits, which, whenever possible, were conducted remotely. The U.S. FDA provided a framework for relaxing practices dictating that protocols be followed to the letter, and hospitals and physicians saw the need for expanded access, in order to get expeditious treatments and vaccines to patients. We didn’t invent a brand-new way of doing things; instead, we scaled leading-edge company practices while preserving patient safety.
Studies were reinvented with greater appreciation of how to quickly access electronic health records to aid virtual data access — a practice that accelerated with the pandemic. Meanwhile, there were companies that had assembled large clinical, medical databases empowering physicians and accelerating patient research. They had teamed up with medical associations to assemble and activate these databases in regulatory-grade data platforms — let’s call them “patient data repositories.” Anyone who opted in could elect to have their de-identified medical data in the cloud, and a life sciences company could access it to inform a study that needed to establish safety and efficacy. As medical associations realized the power of data and collaboration, more joined the effort to access these insights, which could ignite research for the vaccines and treatments needed to allow life to resume after the pandemic’s curve flattened.
Data insights improved study design, so interested subjects meeting eligibility criteria with potential positive impact were identified. Companies would partner to design better studies, simplify protocols to collect only data relevant to the study’s objectives, and send notifications to the group of physicians to confirm the study was relevant for a specific patient. Surprisingly, privacy wasn’t a major concern post-pandemic; instead, there was a greater focus on patient data protection once universal healthcare was provided to all citizens, regardless of immigration status.
Enrollment challenges were extensively mitigated through targeted outreach, and the burden of patient participation lifted through protocol simplification. This was the pivotal proof-of-concept (PoC) data for a product — one provisionally approved with evidence collected within a defined period to demonstrate broad population safety and effectiveness to expand the product label.
Disease-specific analytics, applied to real-world clinical data, enhanced evidence generation and reinvented the process known as Phases 3 and 4. New, potentially benefiting patient populations might revert to PoC data, and if enough data could be generated from disease modeling applications, there were opportunities for provisional approval to study more real-world populations using wearables and symptom trackers now broadly applied clinically. The revolutionary changes arrived in the generation of clinical trial data in PoC because studies were targeted. Often, statistical modeling generated by enhanced diagnostic criteria made those studies smaller. There were still studies where definitive or longer-term disease mitigation was needed to generate the data to get provisional approval. This addressed the largest inefficiencies with the greatest time and cost savings.
A REINVENTED OPERATING MODEL
During the crucial and uncertain COVID-19 lockdown, life sciences companies were forced to adjust decision-making — applying technology more broadly, acting decisively to shift resources or alter work schedules, and prioritizing programs, processes, and people that mattered in getting treatment to patients. We examined each stage of the clinical study from protocol design, site and vendor selection, and qualification, to patient visits, monitoring, and reporting to identify gaps in virtual capabilities that could be bridged with new technology or streamlined processes, and then we reinvented our operating model overnight. Here’s how that happened.
There were already large pharma companies working at the leading edge with resources to start the revolution; however, during the pandemic, 15 smaller, forward-thinking biotech companies united to provide purchasing power to adopt what had already been created, and then took it further.
The key to this major shift was to develop a platform used in parallel with electronic health records. To simplify the explanation, we’ll call it “eSource.” The patient was selected on criteria outlined in the patient data repositories and alerted by a message to the medical practice that there was a patient who could possibly benefit from a clinical trial. The patient would then provide phone eConsent. Video was not required, but sometimes for a complex study, it would ensure a more thorough understanding, even providing subsequent verification of that.
Once eligibility was confirmed, the physician could refer the patient to a study center or bring in a virtual trial support team to eliminate additional administrative burdens associated with setting up a clinical research practice. This became highly desirable because primary care physicians could still enroll study patients without disrupting their practices. Data collection was facilitated by using data transfers directly from eSource, which auto-populated the central clinical trial database in real time, pulling other data from laboratories and other diagnostic tests and procedures. Then, the central monitors could ensure that there were no undiagnosed issues related to investigational product using programmed alerts on study dashboards.
Wearables could detect safety issues long before a subject’s visit, enabling a physician alert that wouldn’t have been seen in regular visits, so subjects dropped out less frequently due to side effects. In a long-term study, investigators would collect safety information automatically, facilitating the follow-up needed in chronic diseases, and virtually eliminating the need for additional post-marketing studies. Monitoring included a video visit for checking in to discuss issues and updates, but the face-to-face occurred perhaps once or twice throughout the entire clinical trial, and only for troublesome situations. Once the study was completed, the database was locked, and the final report contained simple plug-ins and conclusions.
Most of the CROs that caught on early could supplement the support needed by small biotechs, so they became collaborative partners to monitor patient dashboards and extend services to research sites that were critical thought leaders but had not adopted the technology changes. Their business models were proactively impacted, and their staff were trained as data scientists. The larger CROs’ business models underwent significant disruption, but several forward-thinking companies were able to adapt into real extensions of their pharma clients to fill focused site-management roles on global trials.
While unfortunate circumstances caused this shift, they allowed us to see what the future of drug development could be — agile in action, patient-centric in design, and outcome-driven. While some of this vision is aspirational, change needs to be implemented now to address the costs of prescription drugs, with a radical change to preserve the industry and eliminate waste.