M&A was already on the upswing in 2024, and the new Trump administration may support that trend. But if data aren’t handled properly, acquisitions won’t reach their full potential.
The biopharma industry has always been driven by mergers and acquisitions, but the last few years have been slightly less predictable. After a post-pandemic investment slowdown fueled by uncertainty, high cost of capital and market downturns, acquisitions are making a comeback—and are increasingly crucial to maintaining innovation pipelines. The success of these transactions hinges on more than securing intellectual property and new therapies; it also depends heavily on seamless data sharing and strong data foundations.
As the science behind drug development has changed, so have the priorities driving M&A strategies. Data is now as valuable an asset as drugs themselves. But data are only as valuable as the ability to extract value from them. Management of the data is therefore key.
This is not easy, particularly in a time when regulatory oversight has grown more intense, for example regarding data privacy and security with GDPR and HIPAA, data integrity and traceability with 21 CFR Part 11, and required standards for data submission to regulatory authorities. As the data landscape expands, compliance becomes more difficult. Without robust data foundations that can centralize and standardize clinical data in a regulated environment, integrating a newly acquired company becomes a massive challenge.
Over 12 years ago when I co-founded eClinical Solutions, the clinical data infrastructure and services company I still lead today, the space was different—the concept of centralizing data in the cloud was in its infancy. Now, the industry has accepted cloud tech, and the data complexity of clinical trials has been validated. Life sciences companies increasingly recognize the need for technology that can integrate data in compliance with evolving regulations—something I see as an essential component of M&A integration success.
M&A’s Promise and Peril
As access to capital has tightened significantly in biopharma, many emerging or midsize companies are now exploring acquisition opportunities to ensure their therapies or treatments reach patients.
Larger companies, meanwhile, are navigating challenges including regulatory pressures, the Inflation Reduction Act and patent cliffs. As such, they are actively seeking opportunities to acquire innovative firms to help protect their revenue streams. Key acquisitions of late 2023, such as Bristol Myers Squibb’s purchase of Karuna Therapeutics and Pfizer’s acquisition of Seagen, set the M&A upswing in motion. But 2024 saw more moderate M&A deals in comparison, with Roche, GSK, Genmab and others buying biotech innovators.
However, newly merged firms face data and operational challenges, including misaligned systems and disparate data locations, standards and formats, making it difficult to bring data—and the teams who manage it—together efficiently. In drug development, effective data management is a critical factor in the success of a program and of an organization, making it imperative that we address these challenges if M&A deals are to achieve their intended outcomes.
The difficulty of data management in biopharma is compounded by several factors. The cost of clinical trials remains astronomical and continues to rise. In response, modern trials have grown more complex, often layered to achieve multiple outcomes.
Data inundation is another challenge, as the volume, velocity and variability of data have accelerated with new data capture solutions beyond traditional electronic data capture (EDC) systems, such as wearables, sensors and apps, in addition to electronic patient-reported outcomes, a type of electronic clinical outcome assessment focused on patient-reported symptoms, quality of life and treatment adherence. Just a few years ago, the Tufts Center for the Study of Drug Development found that Phase III clinical trials generated 3.6 million data points on average, three times the data collected by late stage trials a decade before. Additionally, obsolete technology and operations plague the industry, with clinical trials still modeled around outdated, manual processes with redundant roles and too many handoffs. Many clinical trial technologies remain inflexible, and legacy systems dominate. Interoperability challenges are also significant, as fragmented tools and point solutions make it increasingly difficult to integrate trial data across digital ecosystems.
No single company can solve the problem of efficiency across the entire R&D value chain. Numerous players offer different solutions to R&D inefficiencies in the life sciences sector, but this fragmentation also means that consolidation across point solutions is inevitable. To achieve desired efficiency, the industry must prioritize collaboration and an interoperable ecosystem of “right-sized” technological solutions.
We’ve already started to see this trend in life sciences tech and expect it to accelerate in the coming years. More and more, we’ll see tech firms merging to offer comprehensive services that address a broader range of challenges, particularly as larger pharma companies seek fewer, but more capable, partners to help them achieve their outcomes faster.
Looking ahead, M&A will continue to be a core strategy for biopharmas seeking to stay competitive and profitable, and it remains to be seen if changing macroeconomic conditions will fuel that further. To thrive in an M&A strategy means treating data infrastructure and analytics technologies as a core component. Pharma deal-making is largely about bets on the promise of the next breakthrough, but it’s also about how well companies can efficiently and effectively integrate data, harness value and adapt to a regulatory environment that grows more complex every year. In this new era, the right technological partnerships are crucial for realizing the return on investment of integrating with another company.