As the COVID-19 pandemic moves into an endemic phase, the major impacts on drug research, collaboration, and use of new technologies due to the spread of the virus warrant recognition.
Five years ago, the spread of COVID-19 was considered widespread enough to be declared a global pandemic, marking the official onset of a health care crisis that has since claimed more than 7 million lives with an impact that continues to resonate today. Initial containment efforts for the evasive and evolving SARS-CoV-2 virus required unprecedented collaboration between nations, health care agencies, and industry innovators. This pandemic has become a pivotal moment in history, not only for its impact on human lives, but for fundamentally transforming modern drug discovery approaches.1,2
The “new normal”—an expression so often heard in the throes of the crisis—now necessitates deeper collaboration and strategic use of data, automation, and artificial intelligence (AI) to deliver rapid innovation. Agility, flexibility, and collaboration were paramount to the world’s pandemic response, and these key tenets remain essential to both driving progress against other hard-to-treat diseases and preparing for future public health emergencies.
COVID-19 disrupted traditional R&D paradigms, compelling research teams to become more agile, flexible, and collaborative. Saving lives meant shattering traditional timelines, sharing data for the greater good, and leveraging established knowledge to rapidly deliver breakthroughs.
Building upon established knowledge and leveraging advanced technologies, particularly AI, were key to quickly delivering COVID-19 breakthroughs. For example, AI-based protein modeling helped researchers quickly identify treatment targets, and machine learning was used to accelerate clinical trial data analysis.3,4
Following the emergency phase of the COVID-19 pandemic, innovators are increasingly looking for ways to strategically integrate AI into their R&D workflows in order to reduce timelines and manage costs across the board. Today, AI applications include modeling proteins and molecular interactions, optimizing new drug candidates, and guiding drug repurposing initiatives.
Departing from conventional clinical trial design proved essential when evaluating urgently needed vaccine candidates throughout the pandemic. Accelerated trial phases and adaptive designs with interim analyses enabled researchers to quickly identify effective interventions. In this new stage of the pandemic, adaptive trial design software has gained recognition as an innovative and effective tool for designing clinical trials, particularly for therapeutics targeting complex conditions such as cancer, Ebola, Alzheimer disease, and other challenging medical conditions.5,6
Researchers embraced nontraditional vaccine development, pursuing mRNA vaccines by leveraging existing mRNA platforms. The rapid vaccine turnaround comes on the coattails of platforms that were developed over the course of 20 years. The distinctive value of these platforms lies in their flexibility and adaptability. Researchers were able to build off decades of work on mRNA vaccines and quickly adapt solutions for COVID-19.7
This success has stimulated broader interest in mRNA vaccine applications, such as for personalized immunotherapy, as well other alternative vaccine platforms, such as those based on adenovirus-vectors and DNA monoclonal antibodies, which aim to serve as flexible immunization options for conditions such as influenza, Ebola, HIV, malaria, and more.8
These efforts reflect an even larger research trend of embracing flexibility within the world of drug discovery, most notably through a multimodal approach to R&D. Innovators are looking to flexibly address hard to reach targets through the best means possible, whether that be small molecules, biologics, or conjugates. Biotech companies are responding with new R&D tools to support this growing need for research diversity and flexibility.
During the pandemic, we have witnessed an unprecedented level of open-source work, with data about the virus and potential treatments widely shared in hopes of speeding up drug discovery. COVID Moonshot, for example, saw cross-discipline scientists worldwide collaborating on the development of antiviral candidates. The distributed computing initiative, Folding@Home, enabled those researchers to band together their computing resources to power simulations to study proteins and drug targets. Project Discovery enlisted the help of online gamers to analyze huge volumes of flow cytometry results data, not only helping researchers better understand how the human immune system responds to COVID-19 but also letting them collect training data for AI models.9-11
Phil Mounteney is the regional vice president of science & technology, North America at Dotmatics, the global leader in R&D scientific software that connects science, data, and decision-making. Phil has worked in a variety of leadership roles at Domatics since 2009.
These collaborative scientific initiatives have continued to gain traction in this endemic phase of COVID-19. In late 2024, Google DeepMind’s AlphaFold 3, an AI program for predicting protein structures, was made open source to academic researchers. This will help researchers more quickly grow their understanding of biological targets and potential drug candidates.12,13
A critical lesson from the pandemic was that expanded cooperation across pharma, biotech, and government agencies is needed to tackle global health challenges. ACTIV exemplified this ideal. Spearheaded by the US National Institutes for Health (NIH), ACTIV, or Accelerating COVID-19 Therapeutic Interventions and Vaccines, was a public-private collaboration that aimed to accelerate the development of COVID-19 vaccines and treatments by aligning goals and sharing resources.14
Those efforts continue today to prepare for future pandemics. The Research and Development of Vaccines and Monoclonal Antibodies for Pandemic Preparedness Network (ReVAMPP) is bringing together researchers, public health officials, and pharmaceutical companies to explore the adaptability of mRNA and monoclonal antibody technologies for other high-priority virus families.15
These types of collaborative efforts aren’t limited to pandemic preparedness. For example, OneHealthTrust is leading a number of initiatives to tackle another global health threat: antimicrobial resistance. And, in the area of oncology, Partnership for Accelerating Cancer Therapies (PACT) is bringing together public and private organizations to help advance immune therapies for cancer.16,17
While the pandemic undeniably caused significant hardship and loss worldwide, we can now begin to appreciate the opportunities that emerged in its aftermath. COVID-19 not only advanced scientific boundaries—it transformed research methodologies.
The longstanding goal in drug discovery has been to deliver life-saving therapeutics to patients more rapidly and affordably, reducing development costs from billions to millions of dollars. In this new research landscape, AI-driven tools, automation, cloud-based collaboration, and multimodal discovery approaches have become fundamental to R&D, making the process faster, more efficient, and more cost-effective.
