Clinical Researcher—December 2024 (Volume 38, Number 6)

SITES & SPONSORS

Manny Vazquez, CCDM

 

 

 

Every year, clinical trials reach new levels of innovation. From the explosion of biomarker use in precision medicine, such as Genomics England’s 100,000 Genomes Project that supports patients affected by rare diseases and cancer,{1} to the expanded use of real-world and digital device data.{2} The industry continues to evolve and increase in complexity.

A large-scale analysis of protocols and other data sources from more than 16,000 studies found that clinical trials across all the indications evaluated are becoming more complex.{3} Complex science often undermines operational efficiency. Over the past seven years, the average number of amendments per protocol increased by 60%, while the typical time to implement an amendment has almost tripled.{4}

Applying simple adjustments to a gene therapy trial, such as expanding the number of participants, can make or break a study by causing costs to escalate sharply. Over-hyped technologies (like decentralized clinical trial solutions) haven’t delivered on their potential, leading to lower operational efficiency rather than sought-after improvements. To avoid a tug-of-war between scientific rigor and operational efficiency, we must prioritize the user and data journeys of sites, patients, and sponsors. Simpler everyday experiences and connected data are the basis for delivering the trials we need rather than what the technology allows.

Unblocking Barriers for Sites

Sites have been voicing their concerns for years about the growing technology burden. Common pain points include navigating more than 15 portals per study, organizing password changes every six to eight weeks, and accommodating each sponsor’s unique definitions, standards, and database setups. Not only do disconnected tools take site staff away from patient care and absorb their time in training, but they also undermine data quality by forcing repeated data entry. Viviënne van de Walle, medical director and founder of PT&R, likens the site experience to being stuck “in a really bad escape room.”

Thankfully, we are turning the corner as an industry on delivering better site support. The aspiration of fewer systems will reduce the site administrative burden and positively affect patient recruitment and engagement. Hopefully, a better patient experience would widen access to life-enhancing treatments, particularly in rare diseases.

Reflecting on her experiences and needs as a rare disease patient, Helen Shaw, co-founder of the virtual site VCTC, observes: “I see how hard it is to take part in a clinical trial. But patients do want that opportunity to be offered—something that they wouldn’t get in their standard care—whether additional MRIs or new medicines.”

New Dialogue Needed

Simplifying at a time when science is becoming more complex can feel counterintuitive. However, when sites and sponsors shed the legacy systems holding them back, they can finally determine which processes they need to run the trials they want.

Sponsors and contract research organizations increasingly focus on alleviating sites’ concerns when introducing new systems, even when those systems are ultimately designed to simplify processes. This involves aligning on shared objectives and working together closely.

All sites are unique, bringing varying levels of technological experience. A “one-size-fits-all” interaction style is one of the most cited challenges sites face in their partnerships with sponsors.{5} One clinical trial management software leader notes the impact of this mindset on sites: “Every site can have a different starting point or place of comfort when it comes to implementing technology. The ideal is to remove some administrative burden, but sites can have mixed feelings about new technology.”

They add, “Simplifying is a big win. It shows that we’re moving to a mindset of fixing problems instead of just adding more functionality.”

Connected Data as the Pillar for Smart Automation

With cell and gene therapies accounting for a more significant share of the drug development pipeline,{6} we can expect a changing research profile: more studies with relatively small patient populations and rolling regulatory approvals, leading (hopefully) to compressed timelines. Yet, paradoxically, even a study of 30 to 40 patients can still ingest and generate huge volumes of relevant data (e.g., DNA-related, molecular information), because each person is treated as an individual rather than a study average. These data are then used to develop highly personalized and effective treatments.

As we transition into a non-electronic data capture-centric world, we will need more flexible data management so that sponsors can drive science forward while delivering complex studies efficiently. Rather than a one-size-fits-all approach, systems and technology must be able to support many protocols with enough flexibility for niche trial requirements.

Artificial intelligence (AI) and machine learning (ML) will have an essential role in transforming raw data so that they are clean and usable: Andy Cooper, CEO of CluePoints, a risk tracking and analytics provider, observes that ML is already taking the noise out of edit checks, for example.

AI and ML are not yet ready to solve all our data challenges. In the meantime, automation can generate value at multiple points during clinical data management. Commenting on its impact within their organization, a senior data science leader at a top global healthcare company says: “The growing number and complexity of trials means that we should be working at scale, not just in production and facilities but also in our clinical setup. These functions must be able to work together and to scale.”

Once all study data are connected, advances in clinical trial efficiency become feasible. For instance, automation is one of the main four pillars that the author’s healthcare company is optimizing for growth. Its data science leader explains the importance of another pillar—a strong data foundation—to their team’s success: “It’s common for data infrastructure set up to be horribly patchworked. You can’t introduce meaningful end-to-end automation on poor data and without seamless processes. We need to get rid of the patchwork.”

Complexity Meets Efficiency

The time is right to interrogate and leave behind old habits, including excessive data collecting, cleaning, and querying. Too much time, money, and effort are spent today on the latest technology, generating surplus data—all in the name of “innovation.” A pragmatic, no-nonsense approach focuses on the value first to innovate sustainably. Regulators are already moving in the right direction by encouraging us to apply a risk-based approach to trial design and focus on the data and processes that safeguard trial quality.{7}

If we aim to bring all data together with the right processes and trained people, clinical teams can focus on the science, while data management can help systematically find patterns within (and across) studies. The benefits of a centralized approach go beyond operational efficiency and, over time, could change the economics of clinical trials. Having medical, investigators, and site coordinators on one connected platform will make it easier to meet ambitious patient recruitment timelines and tackle emerging drug development challenges sooner.

We must end the tug-of-war between scientific rigor and trial efficiency. By prioritizing simpler everyday experiences for sites, clean and connected data, and a pragmatic approach to innovation, we will advance science together.

References

1. Genomics England, 100,000 Genomes Project. https://www.genomicsengland.co.uk/initiatives/100000-genomes-project
2. Diabetes Research Institute, “The Expanding Role of Real-World Evidence Trials In Health Care Decision Making.” J Diabetes Sci Technol.
3. Boston Consulting Group, “Clinical Trials are Becoming More Complex: A Machine Learning Analysis of Data from over 16,000 Trials.” Scientific Reports.
4. Tufts University Center for the Study of Drug Development, “New Benchmarks on Protocol Amendment Practices, Trends and their Impact on Clinical Trial Performance.” Therapeutic Innovation and Regulatory Science.
5. Tufts University Center for the Study of Drug Development, “Comprehensive Summary of Site Engagement Literature.” https://csdd.tufts.edu/white-papers
6. Pink Sheet, “Orphans Account for More Than a Third Of EU New Drug Approvals in 2022.”
7. European Medicines Agency, ICH guideline E8(R1) on general considerations for clinical studies.

Manny Vazquez, CCDM, Veeva Clinical Data Strategy, has spent almost 20 years in clinical data management, starting in a small oncology biotech before moving to the CRO space for 10 years. In early 2022, he joined Veeva Systems to support strategy for the Clinical Data suite of applications. He can be reached at manny.vazquez@veeva.com.