P

hase 3 clinical trials now collect an average of nearly 6 million data points per protocol. That figure has grown by more than 280% over the past decade, increasing 11% annually since 2020. Within that volume is data that determines whether a drug works, whether it’s safe, and whether it earns approval. The rest—a substantial portion of it—is gathered from study participants, administered and processed by site staff, tracked across patient visits, and reported in documents, but is not relevant (see below) in supporting assessment of primary and key secondary efficacy and safety endpoints.

A collaborative study between TransCelerate BioPharma and the Tufts Center for the Study of Drug Development (Tufts CSDD), recently published in Therapeutic Innovation & Regulatory Science, quantified the distribution and rationale of protocol data volume to better understand this problem. The findings are difficult to set aside, and they have prompted TransCelerate’s Optimizing Data Collection (ODC) initiative team to move from diagnosis to action.

What the Data Tells Us

The study analyzed 105 phase 2 and 3 protocols from 14 biopharmaceutical companies, categorizing procedures by their relationship to study endpoints and their contribution to participant and site burden. Procedures fell into three groups: core procedures, which generate data supporting primary or key secondary endpoints; standard and required procedures, such as informed consent and adverse event reporting; and noncore procedures, which serve exploratory or secondary purposes.

The categorization was developed for Tufts’ first protocol complexity working group study in 2008. Protocol authors and clinical teams from participating companies categorized all procedures, and this method has since been used for six subsequent working group studies. The categorization is not biased since it is procedure-based. It includes all procedures and it relates to the associated datapoints that each procedure generates.

Noncore procedures represented 17.8% of total phase 2 procedures and 16.2% of total phase 3 procedures in the sample. The study also examined a category the research team termed “nonessential” data: data collected more frequently than necessary to demonstrate a primary or key secondary endpoint or to fulfill a regulatory requirement. The clinical teams and the protocol authors determined what was essential and what was deemed excessive; i.e., beyond what was necessary to demonstrate a primary or key secondary endpoints or to fulfill a regulatory requirement. When both noncore and nonessential data were accounted for, between 25% and 30% of participant and site burden traced back to procedures not directly supporting primary trial objectives.

What happens to this data downstream compounds the problem. While 74% of noncore data appears in Clinical Study Reports (CSRs), much of it is exploratory or flagged for potential future use and is not relevant to demonstrating the primary and key secondary endpoints in the study. Data that travels through an entire trial—from site operations and patient visits to sponsor review and regulatory reporting—without being put to substantial use represents a cost that accumulates across every stakeholder in the system.

These findings align with the recently finalized International Council for Harmonisation (ICH) E6(R3) guidelines, which call for fit-for-purpose protocol designs that weigh participant and site burden and seek to optimize data volume. Regulatory direction and research evidence are pointing to the same conclusion: The industry has the opportunity to be more deliberate about what gets collected and why.

Building Tools That Change Behavior

Identifying the problem is the easy part. The harder work involves building structures that help sponsors change habits earlier in the development process, before undue complexity becomes embedded in a study.

The TransCelerate ODC initiative has developed two new resources designed to support that earlier intervention, each targeting a distinct phase of planning. Both documents are publicly available here.

The Clinical Development Plan (CDP) Framework and Considerations for Data Optimization works at the program level, before individual protocols are written. A CDP reflects the integrated strategy guiding the registrational pathway for a given asset-indication toward an aligned target product profile (TPP). It is the stage where endpoint strategy, participant populations, and the overall shape of a development program take form, and where data optimization conversations should begin.

The CDP framework traces a clear line from TPP through objectives, endpoints, and schedule of assessments (SoA) to regulatory submission, and includes considerations for endpoint-guided data collection, development-phase alignment, participant population definitions, and site and participant burden. A CDP-to-protocol alignment checklist carries decisions forward into individual study design. The framework also prompts teams to consider, at the program level, which endpoints and procedures are expected to contribute to primary CSR narratives and conclusions, using that lens to identify noncore or nonessential data before it is collected and accumulates unnecessarily.

The Protocol Considerations for Data Optimization Resource operates at the study level, supporting cross-functional teams during the design and planning phases of individual protocols, including amendments. Its core tool is a structured planning table that walks teams through six sequential steps:

• Situating the study within the CDP
• Evaluating the endpoint-to-procedure-to-data chain
• Mapping procedures to category types
• Defining the visit schedule and collection frequency
• Assessing burden across participants, sites, and sponsors
• Identifying mitigation opportunities.

The resource also incorporates input from a Site Advocacy Group (SAG), integrating site-level perspectives directly into planning guidance. Too often, sponsors make protocol decisions without a full picture of what they look like in practice; sites must then manage the downstream consequences. Building site input into the framework—on schedule design, visit flexibility, procedural sequencing, and SoA readability—helps close that gap during design rather than during, or after, study conduct.

Both resources are designed to complement, rather than replace, existing regulatory guidance and sponsor-specific procedures. They are practical tools for the kinds of cross-functional conversations that shape protocols: grounded in structured questions, documented rationale, and the commitment to fit-for-purpose data collection practices.

Who Bears the Cost

The burden of unnecessary data collection impacts all stakeholders. Participants may be asked to undergo repeated assessments or comply with procedures that ultimately may not be necessary. Sites absorb the operational weight of data that may not inform primary or secondary objectives. And sponsors carry the downstream costs of that complexity, often without recognizing how much of it could have been avoided upstream.

The ODC initiative’s goal is not to reduce the scientific ambition of clinical research. Every data point that supports a primary safety or efficacy endpoint, that meets a regulatory requirement, or genuinely serves participants in the near term, belongs in a protocol. The aim is to ensure that sponsors have the tools early on to identify what else may truly warrant collection, with input from the full range of stakeholders who live with and carry the burden.

A Foundation for More Intentional Design

The evidence is clear enough. What the industry needs now is not more research confirming that most protocols are too complex; it is learning and practicing the habit of asking harder questions earlier and using tools to make that questioning rational and easier. The CDP Framework and Protocol Considerations resources are a starting point. How far sponsors take them is the real test.