2025 R&D and Quality Predictions: Process Excellence Removes Barriers, Speeds Connections

Process excellence will speed content and data flow across R&D

Today, a simple and vital task like turning adverse events in EDC into safety cases can run the gamut, from being an automated workflow at one company to requiring hours of manual effort across disparate systems at another. These inconsistencies slow drug development, and patients ultimately bear that burden. In 2025, biopharmas will focus on process excellence to improve the flow of content and data across R&D.

Now that sponsors are seeing the limits of disconnected, best-of-breed solutions, the industry is ready to standardize and simplify. This will be driven by investments in process standards across R&D. Some companies may focus on workflow automation to improve the flow of content and data across clinical, regulatory, safety, and quality. Others may rethink the way their organizations are structured to create dedicated process excellence teams.

Underrepresented trial populations will get more choice

Patient enrollment and retention continue to challenge sites and sponsors, with trial complexity and patient fatigue contributing to high dropout rates. In 2025, sponsors that give patients more options for onboarding and trial visits will improve their experience and expand the participant pool to previously underserved populations.

New FDA draft guidance on Diversity Action Plans urges sponsors to enroll more diverse patients and show the statistical breakdown of these groups in their trials. This will affect how data is processed and analyzed for submissions, to ensure enough clean data is available to meet statistical endpoints for each demographic subgroup.

More patient options mean more data inputs and complexity. From monitoring subgroup enrollment and retention to data management and statistical analysis, unified data at the center will enable patient participation at the edges.

Sponsors will step up to solve site capacity issues

Clinical research sites are the backbone of drug development. But, as the number of global clinical trials increases, the clinical site workforce is decreasing with high staff turnover rates. Complex protocols with multiple amendments and too many sponsor technologies per study are pulling site staff away from patients, limiting their capacity to take on more trials.

As sponsors rethink their site engagement strategies in 2025, they will prioritize consistent site technology and standardization across sponsors for all trials. With 55% of sites reporting that their top challenge is supporting a variety of technologies, sponsors will drive standardization to avoid major slowdowns in their pipelines.

The less time sites spend doing administrative work in systems, the more time they have to execute trials and help patients. This shift will help sites rebuild their capacity and ensure that drug development doesn’t stall.

Simultaneous submissions will shave years off approvals

Once a practical reality, sequential submissions should now be seen as an outdated obstacle to getting medicines to patients faster.

Regulatory veterans will remember the not-so-distant past when submissions were shipped to health authorities in trucks full of documents. Even with digitalization, the volume and complexity have persisted. Regulatory teams still take a largely sequential approach, seeking approval in core markets first and then moving to others over time. This process leaves patients in downstream markets – often smaller and underserved – waiting longer for new medicines.

New methods like active dossiers, which represent the outcomes of complex submission processes, will let teams use prior submissions faster and more easily. Submission processes that once took five years or more will take fewer as more companies and health authorities invest in closing the gap to simultaneous submissions.

These incremental changes will add up over time, for both regulatory teams and patients.

QA and QC will unify to eliminate bottlenecks

Although quality assurance (QA) and quality control (QC) business processes are connected, the legacy systems underpinning them are not. To address this challenge, leading companies are connecting QA, QC, and partners through a common technology. In 2025, this investment in digital quality will shorten testing time and accelerate time to market.

Quality organizations will unify their data and applications to significantly advance their reporting and analytics. This will deliver metrics to proactively manage risks and identify and address bottlenecks early. For example, having QA and QC data available at the batch decision point will result in faster batch release. Working from a single source of data will standardize collaboration with partners, eliminating manual work and the risk of data errors.

Automating labor-intensive tasks before commercializing will help early-stage companies scale with cost efficiencies. By bringing QA and QC together, biopharmas will establish the foundation for impactful AI use cases in years to come.

CRO data transparency will improve trial success

In 2025, sponsors will prioritize contract research organizations (CROs) offering full and continuous data transparency so they can start deriving insights immediately. As they pursue end-to-end data ownership, there will be a notable shift in dynamics when outsourcing.

Sponsors and their chosen partners will start sharing data more fluidly across their systems: for example, to inform protocol design, onboard additional sites, identify potential rare disease participants, and move study endpoints. With access to live data as the new baseline, clinical development stakeholders will be more responsive to study changes. This can improve the probability of the trial succeeding and potentially reduce the time to get new medicines to patients.

Emerging biotechs, sometimes fully outsourced, will benefit from improved oversight and be able to make decisions more nimbly. Data transparency will create greater trust, where sponsors of all sizes can collaborate with their CRO partners efficiently.

Comprehensive and reliable safety data will fuel advanced automation

Safety professionals continue to wrestle with an age-old question: how to handle growing data volumes with fewer resources while maintaining high quality. AI holds promise to do more with less, but inconsistent and disconnected data creates risk.

To effectively support AI, safety teams will strengthen their data foundations with standardized, end-to-end pharmacovigilance processes. Cross-functional workflows will eliminate manual data transfers and provide clear data traceability back to the source. By simplifying and standardizing their systems landscape, companies will lay the groundwork for accelerating automation and AI innovation.

This end-to-end data flow also opens the door for improved collaboration across organizations. For example, processes like timely reporting of SAEs from clinical EDC systems to safety can be done automatically with more complete data.

Continuous AI/ML devices will hit a new milestone

As of October 2024, the FDA has approved over 950 AI/ML-enabled medical devices. However, these approvals have been for ‘software-as-a-medical device’ (SaMD) that use ‘passive’ ML to train products. After the product is trained to process data sets and identify trends, it is locked down and no longer uses ML.

In 2025, we will see the first-ever new product submission for a medical device enabled by continuous ML. This device will take things a step further than an SaMD, addressing patient needs faster and more effectively. Rather than simply being trained using ML, the device will change as it is exposed to more patient data. Following FDA’s final guidance on postmarket updates, this will enable continuous learning during the device’s operational life cycle to help it actively respond to patient needs.

This submission will likely come from a key domain like radiology or neuromodulation, where medtech companies typically have the necessary quantities of clinical data to train AI/ML models. An example can look like a neurostimulator implant or a combination of continuous glucose monitoring and insulin delivery tech that automatically adjust to meet a patient’s dosage needs without physician intervention.