Lessons learned from the COVID-19 moonshot
Society holds a long-standing expectation that scientific advancements precipitously increase with time. Apollo 11 put boots on the moon in 1969, yet the loss of funding and reprioritization of state interests has stalled the program for 50 years. Ground-breaking discoveries are not an inevitable outcome, and lasting progress is not guaranteed.
Meaningful advancements come when significant sums of money and large communities of unwavering support work relentlessly in pursuit of a shared vision, which is how pharma was able to make 10 years of progress in just a few months and develop a vaccine in less than year.
The development of COVID-19 vaccines addressed many challenges common to all clinical trials. Public and private sectors injected record-level funding with the US Dept. of Health and Human Services (HHS) kicking in 12.5 billion dollars alone. The daily headlines drove a universal understanding of clinical development that accelerated enrollment and regulatory authorities prioritized and provided rolling reviews. The pandemic has also served as a catalyst for adopting new processes and technology. Study teams that for years, have resisted changes to ways of working were quick to adapt to new processes and recognize and advocate the need for digital technology.
Clinical development organizations also engaged in significant collaboration efforts with partners and vendors to support management of billions of data points – studies with unique forms in the hundreds and edits in the thousands. Teams were working more than a hundred hours each week to monitor and manage data around the clock.
While the path to a COVID-19 vaccine is an unparalleled success story, it spotlights the many areas where clinical trials need improvement. Even before the pandemic, research has shown that between 15 and 30 percent of all clinical data collected is not used in NDA submissions, costing an additional $20 to $35 million in direct drug development costs for the average drug1.
Recent studies have also shown that less than 45% of data collected is related to primary /secondary outcomes – the data that is critical to the reliability of the study findings – the critical data that identifies key risks to patient safety and data integrity. That means that more than half of the data collected is “non-outcome” data2.
In a 2018 study conducted with one of the world’s largest pharmaceutical companies, an analysis of Phase III studies across 3 TAs disclosed that an average 12,000 hours was spent on programming and 16,000 hours on query management. That’s ~28,000 hours for study build and data cleaning alone. In another study conducted with one of the world’s leading CROs, analysis revealed that study teams spent more than 800 hours preparing for and attending meetings, protocol orientation and training, and locating documents and templates.
Additionally, the number of significant protocol amendments across the top 20 pharmaceutical companies averaged at least 4 significant amendments per study at a cost of about $2M on trials completed between 2014 and 20183,4. Collectively, the COVID vaccine trial protocols for AstraZeneca, Pfizer, Moderna and J&J contain 14 amendments3.
It is now more evident than ever that digital transformation is needed.
The unprecedented acceleration of the development of COVID-19 vaccines has been a major catalyst to the shift to decentralized trials with increasing volumes of data collected from multiple sources and multiple structures. This further reinforces the need for digital transformation to drive standardization and a “data vaccine” to provide resistance to non-critical data.
Identifying and adopting the right technology that feeds into a data fabric is imperative. The technology must be scalable and integrate with systems, processes, and most of all people to build a sustainable foundation for accelerated clinical development.
The key here is digitalization, standardization and focus on critical data i.e., data that is critical to the validity and reliability of the study findings, specifically data that supports primary and key secondary endpoints.
A digital standards-driven approach, identifies and surfaces critical data and propagates to downstream processes and systems and most importantly is fit for consumption.
The Lean Protocol™ process is an end-to-end approach to front load and parallelize clinical trial processes based on digitalized study protocols. Within the Nurocor Clinical Platform. the digital protocol embeds “critical” designation, driving behaviors that focus on critical data versus the completeness and accuracy of every data point.
Within this collaborative platform, as information about Study design, Drug formulation, Arms, Objectives and Endpoints are available early on, feasibility and other study start-up activities are triggered, including development of the Statistical Analysis Plan. As the protocol is further informed by the Digitalized Schedule of Activities and the SAP, the study-specific metadata are identified and digitally available, driving study build to begin before final protocol approval helping to compress the study build cycle time.
The common, digital, reusable components also automate and accelerate development of functional plans and documents as well as analysis and reporting requirements and specifications. This Quality by Design approach coupled with parallelized activities drive quality and consistency end to end, ensuring that final submission is consistent with the protocol.
Clearly defined objectives and endpoints are paramount to gaining timely and meaningful value through AI and ML which should be metadata driven. The same applies to data lakes and the ability to serve up data that is fit for consumption. Whether by data managers or data scientists, medical writers or statisticians, programmers, quality experts, supply managers, finance and other data consumers, the key is to focus on relevant critical data.
Further efficiencies are gained through feedback loops supported by collaborative workflows that enable changes to study design to be made and propagated before final approval, significantly decreasing the number of “unnecessary amendments” and accelerating implementation of critical amendments during study execution.
Organizations must shift from the customer-vendor relationship and look to their software providers as strategic partners positioned to provide input to the long-term strategy and portfolio decisions based on industry best practices.
The right partner should bring the expertise and framework to employ a holistic approach to implementation converging people, processes, and technology. Inserting new technology into existing processes without considering the full framework will limit efficiencies and ROI. Nurocor brings the technology with the right team with the right expertise and the right approach to implement the right solution.
Of the $1.3 trillion that was spent on Digital Transformation last year, it was estimated that $900 billion went to waste5. This is mainly due to the lack of a holistic approach to implementation that brings the stakeholders along the journey.Of the $1.3 trillion that was spent on Digital Transformation last year, it was estimated that $900 billion went to waste5. This is mainly due to the lack of a holistic approach to implementation that brings the stakeholders along the journey.
As an industry with a unified goal of bringing therapies to patients faster, we must unlearn current ways of working to break down silos, break the manual process and document paradigm and unlock the power of digital technology to accelerate the clinical development lifecycle and ensure the COVID moonshot does not fizzle out in open space.
1. (Getz, K. A. (2010b, January 1). With Clinical Data, Less is More. Retrieved July 23, 2012, from Applied Clinical Trials Online2. Crowley E, Treweek S, Banister K, Breeman S, Constable L, Cotton S, et al. Using systematic data categorisation to quantify the types of data collected in clinical trials: the DataCat project. Trials. 2020;21(1):535.3. Clintrials.gov4. Getz KA, Zuckerman R, Cropp AB, Hindle AL, Krauss R, Kaitin KI. Measuring the Incidence, Causes, and Repercussions of Protocol Amendments. Drug Information Journal 2011;45(3):265-75 doi: doi:10.1177/009286151104500307[published Online First: Epub Date]5. Behnam Tabrizi, Ed Lam, Kirk Girard, and Vernon Irvin. Harvard Business Review