Preparing for the Next Pandemic: Lessons Learned from COVID-19
Until about a year ago, pandemics were largely confined to history books. But now, even as we take steps to recover from the reality of COVID-19, in no small part due to the heroic efforts of pharma companies that developed and rolled out multiple vaccines in under a year, it’s never too early to begin planning for another epidemic. That starts by taking stock of which drug development and clinical trial processes worked over the past year. What’s exciting is that we can build on that momentum and leverage these successes to combat a far wider range of serious illnesses.
Let’s start with a fact that most scientists agree on: it may have been 100 years between the Spanish flu and COVID-19, but it’s not going to be a century until the next pandemic hits. There are a lot of reasons for this, but a prominent New Zealand epidemiologist says that environmental changes and the loss of natural habitat for wild animals are among many factors that will make an outbreak come sooner rather than later. The good news is that we can look at some real wins from the last year and adapt these best practices moving forward.
Speed, Speed, Speed
The first thing that we need to look at is how we were able to develop several COVID-19 vaccines so quickly. Many experts pointed out that no vaccine had ever successfully been developed for a coronavirus. What changed? Something remarkable happened: major pharma companies, clinical researchers, and regulatory bodies joined efforts and focused on the singular goal of finding a vaccine for Covid-19. For the first time that I can remember, the entire industry came together with a sense of purpose to research and distribute a vaccine. If we are going to stave off a new pandemic successfully in the next few decades, this is going to have to become the norm. Now is the time to build processes and protocols to allow companies to share information freely and openly with each other.
More Data, More Solutions
Focusing on this exchange of information is critically important. At the foundation of all the insights we are able to use to develop treatments successfully is data. That data are growing in volume and diversity every day. Consider the use of continuous sensor devices that monitor important markers in our health, like glucose levels for diabetics, or the use of other types of wearables that track the number of steps you take, how well you sleep, and other fitness and wellness metrics. This increase in data from all of these sources has many benefits, but it also comes with pitfalls. On the one hand, having a wealth of information is extremely valuable for clinical researchers when it comes to analyzing data for trends and capturing insights. On the other hand, as new data types emerge, and, as data increases in volume, clinical teams must cut through more information and put more effort in cleaning, integrating, and understanding all of the valuable data that they’ve collected. This data complexity has resulted in delays in clinical studies, where data cleaning cycle times over the last two years have increased by 40%.
We've seen with the COVID-19 vaccine that speed in drug development is essential. In order to achieve that efficiency for other new treatments, we must manage data complexity first. One way to do this is by building modern data infrastructures that can handle more data types intelligently. Innovative technology, like centralized clinical data platforms, can become a vital part of this new ecosystem, where data are harmonized from many sources. This technology can also simplify the collection and analysis processes so that they're more accessible to a wider range of stakeholders and clinical data team members. The volume and variety of data that is now used by the life sciences industry will only grow, but, with the right technology and tools, disparate data sources can be standardized and analyzed quickly, enabling faster drug submissions and approvals.
Improving the Way We Reach Patients
A common barrier in progressing clinical studies is patient participation, and making clinical trials more accessible for participants is another important lesson from the pandemic that can be carried forward. A decentralized trial model, where technology and processes are implemented to enable patients to participate in studies outside of traditional clinical settings, and the way in which this approach was widely adopted last year were key factors in the successful development of the COVID-19 vaccines. As the industry looks ahead, there has been a shared sense of dedication in building on the decentralized trial model, as seen with the founding of patient-focused associations, such as the Decentralized Trials & Research Alliance (DTRA). With the adoption of digital processes and technology, this new, improved way of engaging patients in clinical studies can help overcome the challenge of finding more representative patient pools and adapting the clinical trials product so that it's more convenient for people to use.
Beyond Pandemics
Imagine an intensive global 12-month program to eliminate strokes or to come up with an effective cure for diabetes. Does it sound impossible? If it does, don’t forget that a year ago there were serious doubts as to whether or not the pandemic could be ended by vaccines at all. As an industry, we’ve proven that the impossible is within reach. We have a unique and unprecedented opportunity to improve our clinical trial processes and make a difference for millions of people around the world.
It’s rare that we are given a chance to fundamentally reimagine everything that we know to be true. This is one of those times. The life sciences industry just slayed a giant because we really didn’t have another choice. It started with collaboration and grew from there, but now that we’ve won the battle we need to shift to the next big challenge: doing all that we can to eliminate other major diseases. By putting patients first and modernizing the clinical data ecosystem, we can realize the possibility of turning our valuable clinical research into real-time insights that lead to new therapies and treatments.
