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.

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.

rtificial intelligence, particularly large language models utilizing custom prompts, can reduce the manual effort of scientific literature human review teams.

Currently, technology exists to help prioritize results from rule-based scientific literature searches and alerts. Human reviewers then conduct manual assessments to determine 1) if an abstract contains reportable adverse event information, 2) if an abstract contains pregnancy information, and 3) if the full-text article is an article of interest for signal detection.

This article provides results of an assessment of whether a commercial large language model (LLM) and custom prompts could automatically answer these questions and appropriately summarize abstracts and full-text articles for later human review.

How We Conducted Our Assessment

We collected 89 publicly available abstracts and their corresponding full text that were previously manually assessed by a pharmacovigilance scientific literature specialist. Twenty-five of these abstracts contained an AE, seven full-text articles contained pregnancy information, and 29 full-text articles contained potential safety signal information.

We developed custom prompts to answer these five questions:

1. Does the abstract contain a reportable AE?
2. Does the abstract contain pregnancy information?
3. Can a summarization be created that contains all safety-relevant and -pertinent clinical information from the abstract?
4. Can a summarization be created that contains all safety-relevant and -pertinent clinical information from the full-text article?
5. Does the full-text article contain a potential safety signal?

We then developed software code that provided each abstract and full-text article along with the relevant prompt to a commercially available LLM. We collected and reviewed the LLM’s responses for accuracy and completeness.

The system did not retain any memory or linkage between abstracts, full-text articles, or LLM prompts. We examined text only; we did not use optical character recognition to process images contained in the abstracts or full-text articles.

Our Assessment’s Outcome

Identification of reportable adverse events, pregnancy information, and safety signals was 100% accurate. Abstract summarization achieved 100% accuracy, while full-text summarization reached 94% accuracy (84/89). Challenges with text extraction from complex table structures within the full-text articles caused the remaining 6% of errors. Compared to an estimated manual review time of 890 minutes (~10 minutes/article), the LLM processed the batch (all abstracts and full-text articles across the five prompts) in 30 minutes, representing a theoretical ~97% reduction in processing time.

Discussion and Direction

Using a commercial LLM and custom prompts successfully achieved: 1) automatic analysis of abstracts for AE information; 2) automatic analysis of abstracts for pregnancy information; and 3) automatic analysis of full-text articles for interest in signal detection. We also demonstrated that an LLM with custom prompts could summarize abstracts and full-text articles.

Future work should improve the summarization capabilities while adhering to principles of trustworthy AI, including transparency, robustness, and appropriate human oversight, to ensure safe and responsible use of AI in pharmacovigilance workflows. Our work suggests a significant opportunity for future efficiency gains to enable the reduction of 100% human review of scientific literature over time.

From Intention to Execution

cross this series, we have explored how the female data deficit became embedded within drug development, why regulatory progress has not consistently translated into practice, and how behavioral and organizational dynamics continue to reinforce existing patterns. (See previous articles published in March, April, and June/July 2026.)

We also need greater focus on common women’s health conditions such as menopause, endometriosis, and polyendocrine metabolic ovarian syndrome (PMOS) to ensure that we truly understand them, that they have been studied through appropriately designed clinical studies (real-world evidence generation and clinical drug trials), and that the endpoints of these studies are meaningful and accepted by regulators. In women’s health, the problem is no longer whether we are running enough trials; it is whether those trials are capable of answering the questions that matter to women and their caregivers.

Closing the female data gap requires practical frameworks, measurable standards, and accountability mechanisms that support evidence generation across the full product lifecycle.

Endometriosis as a case study drives this point home. Despite affecting an estimated 10% of women globally, endometriosis illustrates a fundamental paradox in women’s health: the presence of numerous clinical trials without a commensurate depth of clinical understanding or therapeutic progress. Hundreds of endometriosis studies have been conducted, with large numbers of active and ongoing trials across global registries, suggesting at face value a healthy and active research ecosystem.

However, this volume masks a deeper structural issue: these trials are not generating the kind of evidence required to transform outcomes for women or understanding of the disease.

A significant limitation of the endometriosis evidence base is the historical lack of standardized, regulator-aligned clinical endpoints. Trials have used numerous different pain scales, quality-of-life instruments, and symptom measures, with no universal consensus regarding optimal outcome assessment. This heterogeneity reduces comparability between studies, complicates meta-analysis, and limits confidence in the generalizability of findings to the broader endometriosis population. While endometriosis trials provide clinically meaningful information, the absence of harmonized endpoints has constrained the development of a robust and consistent evidence base for regulatory and clinical decision-making.

A Practical Implementation Framework

To move from intention to impact, inclusion must be embedded throughout trial design, recruitment strategy, operational delivery, and evidence review processes.

This requires practical actions across three connected areas, with women at the core providing insights as part of patient and public involvement and engagement (PPIE) in trial design, recruitment and retention, and accountability and oversight.

These areas should not operate independently. Decisions made during protocol development directly influence recruitment feasibility, participant retention, evidence quality, and ultimately regulatory and clinical outcomes.

Inclusion From the Start

Inclusive trial design must be embedded in protocol development by including predefined endpoints (noting that these are not clearly defined in many women’s health conditions such as endometriosis), appropriate statistical powering strategies, and governance checkpoints that ensure inclusion remains visible throughout the program lifecycle.

Practical actions include:

• Pre-specifying gender-specific endpoints where clinically relevant
• Powering analyses appropriately to detect meaningful differences
• Building staged inclusion pathways for populations that are traditionally excluded
• Embedding flexible scheduling into protocols in order to optimize trial participation
• Requiring scientific justification for exclusion criteria
• Assigning accountability for inclusion at program-governance level.

Importantly, trial design must move beyond assumptions built around an “average patient” who, in practice, does not exist. Women experience important differences (compared to men) in pharmacokinetics, hormonal variation, comorbidity patterns, and adverse event profiles that can directly influence clinical outcomes. Certain women’s health conditions, such as endometriosis, are highly heterogenous in nature and so, even within the therapeutic area, the “average patient” may not exist.

Smarter methodologies may also help reduce operational concerns around inclusion. Staged inclusion models, physiologically based pharmacokinetic (PBPK) modeling, AI-supported simulation approaches, and hybrid trial designs can all support earlier identification of gender-specific safety and dosing considerations without significantly increasing study burden.

Designing Trials With Women’s Participation in Mind

Many barriers affecting women’s participation are operational, logistical, and behavioral, meaning that inclusion must be designed into the participant experience to overcome them.

Recruitment challenges have often been framed as participant issues rather than design limitations. However, trial designs frequently fail to reflect the realities of caregiving responsibilities, employment patterns, travel limitations, and chronic health burdens experienced by many women.

More practical recruitment and retention strategies include:

• Using the most appropriate referral pathways, including primary care networks
• Conducting feasibility searches using gender-specific metrics to identify suitable sites
• Reducing logistical barriers through childcare support, travel reimbursement, parking support, flexible appointments, and remote visits where feasible
• Incorporating decentralized or hybrid trial approaches
• Setting and monitoring recruitment targets by gender and life stage in real time
• Introducing corrective actions early when representation begins to drift.

Trust and relevance also remain critical. Partnerships with patient advocacy groups and community organizations can improve outreach, while public and patient involvement and engagement (PPIE) with women during protocol design and development of participant-facing materials can improve accessibility and trust.

Site capability is equally important. Investigators, principal investigators, and clinical staff should be trained in inclusive recruitment practices and unconscious bias awareness to help reduce disparities in enrollment and retention practices.

Monitoring participant experience continuously, rather than only at the time of enrollment, may also help identify where burden accumulates and where dropout risk increases. Feedback from female participants should inform future protocol and site improvements.

Ultimately, underenrollment should be viewed as a signal that trial design or delivery may not be functioning as intended.

What Gets Measured Gets Done

Accountability for inclusion has previously focused on broad participation metrics alone. However, representation without meaningful analysis does not necessarily improve evidence quality or clinical decision-making. More meaningful accountability frameworks will assess whether trials are capable of generating clinically actionable gender-specific evidence, and consider:

• Female participation relative to disease prevalence
• Whether gender-specific endpoints were prospectively defined
• Whether studies were statistically powered to detect relevant differences
• Retention rates across gender and life-stage groups
• Whether gender-specific evidence was sufficient to support product labeling and prescribing recommendations
• Post-marketing safety signals stratified by gender (whether gender-specific evidence was sufficient to support product labeling and prescribing recommendations)
• Time taken to identify gender-specific adverse reactions
• Downstream label updates or dosing changes.

Together, these measures shift the focus from participation alone towards quality evidence that drives decision-making across the product lifecycle.

Incentives must also align with the quality of the evidence. Development systems have traditionally rewarded speed, efficiency, and predictability, which can unintentionally discourage more inclusive approaches that are perceived as operationally complex.

Embedding inclusion metrics into program governance, regulatory review, and organizational performance measures can help rebalance these incentives. Feedback loops are important. Recruitment outcomes, retention patterns, protocol deviations, and evidence gaps should be reviewed continuously (“live”) rather than retrospectively.

Organizations that learn rapidly from previous trials will be better positioned to improve inclusion consistently over time.

The Role of Regulators

Over the past decade, guidance relating to gender-specific evidence generation has evolved significantly across agencies and organizations, including the US FDA and NIH, EMA, and ICH. However, variability in interpretation and implementation continues to create uncertainty around expectations in practice.

When regulatory expectations are perceived as optional, behavioral defaults tend to persist.

A more consistent regulatory approach would help shift inclusion from an aspirational objective towards a standard component of evidence generation. In many cases, clearer expectations and more predictable review signals may be as important as additional regulation itself.

Practical regulatory levers could include:

• Mandatory gender-specific analysis plans where clinically relevant
• Stronger justification requirements for exclusion criteria
• Clearer expectations for early-phase dose optimization
• Conditional evidence requirements linked to post-market commitments
• Harmonization of expectations across regions where possible.

Consistency matters because development teams optimize around what is most likely to succeed operationally and regulatorily. When regulatory agencies signal that gender-specific evidence is expected, reviewed consistently, and linked to decision-making, sponsors are more likely to embed inclusion earlier within development pathways.

Regulators globally must also acknowledge that evidence in women’s health conditions, such as endometriosis, is lacking and that research should be incentivized in this space to advance our knowledge and change clinical practice in such areas.

Making Inclusion Operational

Closing the female data gap does not require brand-new scientific frameworks. Many of these tools already exist. The challenge is applying them consistently, measuring their impact, and embedding accountability within development pathways.

Organizations that operationalize inclusion effectively will not only improve representation; they will generate more robust evidence, reduce downstream regulatory and safety risk, and improve real-world outcomes for patients.

The future of inclusive drug development will be determined not by awareness alone, but by execution.

ver the last two decades, regulatory affairs has undergone a fundamental transformation, shifting from a document-centric to an increasingly data-driven discipline. Historically, critical activities such as product registrations, submissions, and health authority interactions were managed through disparate spreadsheets or home-grown tools. This fragmentation has made collaboration, interoperability, and system integration a persistent challenge. However, as global regulatory requirements expand in both scope and complexity, these traditional methods have become unsustainable.

In response to these challenges, the DIA RIM Working Group released the DIA RIM Reference Model Version 1.0 (V1.0) in July 2022, which established an industry-aligned framework for harmonizing regulatory processes and a high-level vocabulary of data objects based on regulatory needs. While socializing the model, the working group realized that V1.0 of the model was complex and not readily usable by RIM vendors or drug sponsors due to the way it was structured as a single Excel worksheet. In April 2026, the DIA RIM Working Group released the DIA RIM Reference Model Version 2.0 (V2.0), a framework designed to harmonize terminology, data structures, and process definitions across the regulatory landscape. V2.0 significantly expands on the earlier foundation, reflecting years of industry collaboration and practical implementation experience into a more formal data model. What was in a single worksheet in V1.0 was transformed into a workbook of multiple worksheets, representing objects and relationships around RIM processes and data.

Implementing this robust, harmonized data model along with standardized processes will help support Regulatory Information Management as a strategic capability to streamline the regulatory value chain and achieve regulatory operational excellence.

What’s New in Version 2.0

V2.0 expands and operationalizes the model started with V1.0 through a structured set of 56 business entities (or objects), including attributes and relationships which mirror regulatory activities under the following domains:

Product Objects

• Product Family, Global Product, Medicinal Product, and Global Label manage information related to the product portfolio.
• Pharmaceutical Product Detail, Product Components, Ingredient, Substance, Shelf Life/Storage, Packaged Product, and Physical Characteristics manage details around a specific product.
• Clinical Particulars capture information on indications, contraindications, undesirable effects, and interactions for a specific product.

Regulatory, Commercial, and Manufacturing Objects

• Application, License-Registration, Market Status, Trade Name, and Labeling manage the lifecycle of regulatory filings and approvals for a specific product.
• Submission, Submission Content Plan, Submission Content, and Content manage data around document and submission management for a specific product.
• Manufacturing Process and Operations manage data around production activities, sites, and process steps for a specific product.

Process and Interaction Objects

• Process Objective, Process Plan, and Process Step define regulatory strategies and execution pathways for a specific product.
• HA Correspondence, HA Q&A, HA Meeting, Commitment, and Inspection cover the interactions with health authorities and scheduled tasks for a specific product.

Organizational Objects

• Organizations, Sites, and Contacts manage information on sponsors, health authorities, and operational entities.

The Figure above shows the high-level conceptual model of V2.0. The Table below shows a partial snapshot of the Application Object tab. Click on either image to view full-size.

Benefits

The practical applications of the DIA RIM Reference Model V2.0 serve as a strategic “starter kit” for organizations working to move their regulatory data from a collection of isolated documents into a structured, enterprise-level data asset, and an enabler for integrated data exchange across its regulatory ecosystem, including the clinical, manufacturing, quality, safety, and commercial functions. Its implementation provides a blueprint for consistency, automation, and cross-functional alignment and provides the backbone upon which sponsors and technology vendors can architect RIM implementations and products that support the full regulatory lifecycle, from investigational products to post-approval commitments, whether home-grown or vendor offerings.

Streamlining RIM Implementations
For organizations launching or upgrading their RIM system, the model acts as a foundational architecture to drive configuration requirements, addressing the following objectives:

• Reduced Development Time
• Best Practice Alignment
• Scalability

Accelerating Migration and M&A Efforts
During mergers and acquisitions or system migrations, V2.0 serves as a “neutral ground” for aligning disparate data sets through:

• Standardized Mapping
• Reduced Redundancy
• Harmonization Blueprint

Data Standardization and Governance
V2.0 establishes a comprehensive data dictionary that serves as the “source of truth” for regulatory data including:

• Consistent Taxonomy
• Formal Governance Structure
• Master Data Management (MDM).

Interoperability Across Applications and Functions
V2.0 is designed to facilitate seamless information exchange across the product lifecycle. The DIA RIM Reference Model V2.0 User Guide provides implementors with guidance on building systems that include the following capabilities:

• Cross-Functional Data Exchange
• International Standards Alignment
• Foundation for Automation

Comparison: DIA RIM Reference Model V1.0 vs. V2.0

The transition from the V1.0 framework described in July 2022 to the V2.0 User Guide represents a significant maturation of the model, shown in the Table below:

Summary and Invitation

The DIA RIM Reference Model V2.0 elevates regulatory information management from an administrative necessity to a strategic asset with a comprehensive and mature data model. By providing a common taxonomy and data dictionary, it is our belief that it will enable the industry to achieve compliance with greater precision and speed. We hope widespread adoption, experience, and feedback will validate our position.

The following deliverables (or artifacts) related to V2.0 can be found on the DIA Website under Tools and Downloads along with testimonials from two RIM product vendors:

• DIA RIM Reference Model V2.0 Conceptual Model (PDF download)
• DIA RIM Reference Model V2.0 Workbook (Excel download)
• DIA RIM Reference Model V2.0 User Guide (PDF download).

As organizations continue to transform, modernize, and expand the scope of their regulatory operations, adoption of the DIA RIM Reference Model Version 2.0 offers a clear and strategic pathway toward standardized data, efficient processes, and integrated digital regulatory ecosystems, enabling faster, more compliant, and more informed delivery of therapies to patients worldwide.

Organizations and individuals downloading these artifacts for RIM requirements analysis or implementation efforts can reach out to the authors and representatives of the DIA RIM Working Group to further understand the model or to provide feedback. The DIA RIM Working Group continues to invite sponsors and technology vendors to adopt, validate, and contribute to the continued evolution of the model.

oday, data managers report spending nearly 12 hours per week, per study to complete data review, cleaning, and reconciliation. These processes often rely on spreadsheets, manual work, and siloed tools that slow execution. With growing data volumes and increasing regulatory requirements, maintaining exhaustive data reviews isn’t sustainable. The clinical data review process must change.

Risk-based data management (RBDM) is the data-specific contribution to RBQM that identifies and evaluates risk for key data for a study, enables proportionality, and supports compliance. RBDM provides clinical data teams with an advanced, faster data review process; even so, confusion remains about application and execution. Even as regulation drives more risk-based strategies, these three factors challenge the successful adoption and implementation of RBDM:

1. RBDM versus RBQM: Lack of clarity about the discipline of RBDM and how it fits within the larger RBQM strategy.
2. Legacy clinical data management systems: Reliance on legacy electronic data capture (EDC)-centric architectures that can’t support targeted, risk-based workflows.
3. Gaps in change management: No organizational commitment to advance legacy processes, upskill data teams, or redefine roles.

Any disconnect in understanding how to implement RBDM within an RBQM framework can deter adoption of risk-based data review. Defining the role of each within the clinical organization can help teams get started.

Understanding the Difference between RBQM and RBDM

The overarching discipline that directs risk-based approaches for the trial from concept to close-out is RBQM. ICH E6(R3) defines risk-based as two fundamental principles that span the planning and execution phases: Proportionality (Principle 7) and QbD, defined in ICH Q8(R2). These principles are the groundwork for risk-based processes across trial design and conduct. RBQM applies these principles to the overarching cross-functional activities of the trial, while RBDM applies them to data-specific activities.

Clinical Application of RBQM and RBDM

QbD guidelines state that clinical data management involvement should be upstream from protocol design to proactively define CtQ factors, identify data risks, and collaborate with other functions to derisk the protocol. When considering study conduct, applying the principle of proportionality means that the time spent on reviewing data must be proportionate to the risk and criticality of the data collected. Proportionality places increased effort on reviewing primary and key secondary data while reducing the emphasis on resource-intensive noncritical data points, such as reconciling patient-reported adverse events (AEs) with concomitant medication dates.

Confusion within clinical teams around RBQM and RBDM often causes delays in execution. Typically, RBDM initiatives are tied into broader RBQM projects before gaining traction. This pushes data teams to put work on hold as they wait for the start of larger risk-based implementations before actioning RBDM. By prioritizing RBDM as a separate but related component of RBQM, clinical data teams can gain meaningful traction in adopting risk-based strategies.

Moving from Standalone Tools to Centralized Clinical Data Hub

The increased urgency in deploying RBDM, in part, is due to the increasing number of data sources in clinical trials. The volume of data sources used in a typical study is significantly more than it was 10 years ago, shifting EDC away from its primary data management role towards managing many data sources. To effectively execute risk-based workflows, clinical data teams can’t rely on legacy EDC-centric architectures that lack real-time integration and automation. RBDM calls for a new centralized hub for clinical data: the workbench.

A clinical data workbench ingests and manages all clinical data sources, including EDC, labs, electronic clinical outcome assessment (eCOA), and eSource. A workbench can also automate manual processes and unlock data flow across the clinical ecosystem. For data managers, the workbench frees up time for high-value tasks by flagging when data review is needed for new or changed data with automated documentation and real-time reporting.

These three pillars define the core capabilities a workbench needs to execute RBDM:

Classification: Recognizing critical data as defined by data managers. With defined data priorities, a workbench can understand the data’s meaning and purpose. This allows the system to centralize the data with an understanding of its use, allowing data managers to prioritize actions on the data that matter most. The workbench classifies critical data, endpoints, and clinical concepts, helping data managers move their focus from reviewing all data towards a more targeted, risk-based review. Classification of data in this way is in the early stages, as organizations begin exploring its opportunity and benefits.

Automation: Industry can automate approximately half of manual data management queries today, comparing collected data across sources or against expected values. If the workbench understands the data’s purpose, clinical teams can automate cleaning and reconciliation with minimal human intervention and scale it across studies. Taking this approach also requires connectivity across clinical systems to automate data flows and actions downstream. A connected clinical ecosystem provides the foundation for the next wave of automation: AI-enabled data aggregation, standardization, cleaning, and transformation.

One top 20 biopharmaceutical company prioritized refining its change-management process to reduce the administrative burden on its staff. One area of focus was reconciliation of serious adverse events (SAEs) across the safety database and EDC, which traditionally is a complex and manual process. Automation removed the need for tracking spreadsheets and provided clean data access to all stakeholders. Overall, the company reduced the time it takes to clean and monitor data by 20%.

Orchestration: Risk-based data review will be a central pillar in clinical data activity. A workbench can help a data manager ensure that data reaches the right stakeholders at the right time, assign and prioritize tasks to review data, and monitor progress all in one place.

Another top 20 biopharmaceutical company, in its shift toward RBDM, is using EDC and a clinical database to clean and visualize patient data. Going from individualized bespoke tools to a simplified, orchestrated approach has helped the company achieve first patient activated (FPA) to go-live in 9 weeks on average, and 80% standardization across studies. This level of data orchestration frees time for data managers’ other tasks and supports faster database lock.

A workbench functions as the central point of access across teams, allowing cross-functional collaboration on real-time data. For example, unexpected lab results for a patient submitted with no AEs can be flagged by a medical reviewer, making observations directly on the data and assigning it for action to a data manager by querying the site to confirm whether there are any reported AEs.

With the growing acceptance, trust, and scaling of agentic AI capabilities, the data manager’s orchestration responsibilities will expand from managing cross-functional data reviews to the more strategic role of data surveillance. As AI advances, regulatory bodies continue to provide guidelines for expectations, oversight, and good machine learning practice (GMLP) principles. The US FDA’s Guiding Principles of Good AI Practice in Drug Development, for example, provides 10 principles focused on human-centric designs, risk-based approaches, data governance, lifecycle management, and more. To successfully implement agentic AI for data management, understanding regulatory guidance and considerations are key to remaining compliant.

Establishing RBDM Strategy for Speed

Clinical data teams that prioritize RBDM and establish positive traction applying its principles to risk-based prioritized key data can drive faster and more compliant trials. There is a significant opportunity for improvement, yet on average, only 57% of a company’s clinical trials implement risk-based strategies. The key is a focused approach to RBDM, with clinical data aligning to RBQM programs in the future.

A clinical data workbench can streamline RBDM execution by centralizing data, understanding what it means, and automating processes. Data managers can then become data stewards for the collection, quality, and delivery of clinical data. This advances data managers’ roles from reacting and processing data toward focusing on proactive risk-based execution.

To automate clinical execution and eliminate the manual reconciliation of data, establish a connection for the workbench with other core systems that support clinical trials, including clinical trial management systems (CTMS), randomization and trial supply management (RTSM), and patient safety systems. Compliance with ICH E6(R3) computerized system expectations and FDA 21 CFR Part 11 for electronic signatures and records present additional considerations. Compliant systems that enable end-to-end processes and real-time data flow empower data managers to optimize their time and increase the potential value of their analysis.

AI Trust Gap Slows Down Innovation in Clinical Research

rtifical Intelligence is poised to confer significant benefits in all areas of clinical research: protocol design, site selection, patient recruitment and enrollment and engagement, insights and decision making, monitoring, and medical writing.

There is an AI trust gap in the clinical research industry, as we work to demonstrate the utility of new and non-deterministic models in a highly regulated industry. The AI trust gap will hold back the realization of this value.

Trust erosion manifests as operational consequences of premature deployment: sites refusing tools, ethics committees delaying approvals, and sponsors quietly reverting to manual workarounds. As negative experiences by early adopters are more prominently publicized, trust erodes, and development interest and adoption wanes.

AI offers incredible potential to improve outcomes in clinical trials. As an industry, we need to partner to build trust in responsible use of AI.

A Tradition of Publication and Transparency

The life science industry has established mature and trusted publication and transparency frameworks (see below). Our rigorous existing norms should apply to AI development and deployment. This process can accelerate trust and adoption, to leverage the power of AI to improve outcomes in clinical trials, faster. Given the adoption of transparency frameworks in all aspects of biomedical research, we should apply similar principles to sharing experiences gained with AI initiatives geared toward the greater good of getting medical advancements to patients quicker, cheaper, and with higher quality. This publication and transparency framework requires us to report both positive and negative findings in our use of AI in clinical research.

We ask the clinical research industry to create a registry of AI outcomes in clinical trials to enable our industry to report on their use of AI to accelerate positive outcomes.

Apply Existing Trust Frameworks to AI

Clinical research is bound by formal, and in many cases legally enforceable, frameworks that require researchers to report both positive and negative outcomes: Good Clinical Practice (GCP), trial registration and results-reporting mandates such as FDAAA 801, International Committee of Medical Journal Editors Guidelines (ICMJE), and CONSORT Guidelines. This is what distinguishes clinical research from most other stages of science: the reporting of both positive and negative outcomes isn’t just encouraged, it’s required. Additionally, under the Declaration of Helsinki, researchers have an ethical obligation to make positive, inconclusive, and negative results publicly available.

When publishing research, we not only focus on positive outcomes but also strive to provide a fuller account, including negative or unexpected effects, protocol deviations, and self-critique of research design. We do this because healthcare providers, patients, payers, and policy makers deserve a complete picture for critical decisions affecting millions of patients. Disclosing a complete picture, rather than a selectively favorable narrative, is not only the right thing to do, but it also builds trust in our products and services. When bad actors violate this trust, it negatively affects good actors.

As an industry, we should follow these same principles to report AI outcomes, both positive and negative. Scientific publications and data sharing come with the understanding that the information may also be used by competitors to achieve the greater good in a better and faster way. Under these principles, we can accelerate the use of AI that helps us advance medicine.

Apply Existing Norms and Principles to AI

Publish Results to Benefit the Clinical Research Industry
The dissemination of clinical findings, both positive and negative, regardless of commercial intent, results in healthier and happier lives and is considered an ethical responsibility. While there is no shortage of press releases and conference presentations publicizing AI successes, shared results must adhere to our industry’s standard of rigor.

The Coalition for Health AI defines accountability as the responsibility of those involved in AI development and deployment to maintain auditability, minimize harm, report negative impact, and communicate design trade-offs. This transparency-driven accountability builds trust with key stakeholders, helps others understand the full context of findings for more responsible use, and helps others learn from mistakes to minimize or prevent the prolonging of human suffering.

Publish More AI Outcomes in Clinical Trials
The clinical research industry needs more knowledge of both AI successes and failures. We call upon our industry to create a registry of AI outcomes in clinical trials to enable our industry to report on their use of AI to accelerate innovation.

There may be hesitancy in sharing AI outcomes because the regulatory treatment of AI in clinical trials is still unsettled. Sponsors may worry that voluntarily disclosing an AI-related failure could be misread as noncompliance or invite scrutiny from regulators who haven’t yet clarified how they’ll treat AI use in trials. And currently many organizations hesitate to share true success stories (or failure stories) publicly, to preserve competitive advantage.

Life science professionals recognize the disclosure of shortcomings not as a negative, but as an opportunity for growth, intellectual honesty, and building public trust. Common shortcomings in clinical research are directly analogous to those in AI implementations: limitations in data sampled, selection bias, methodological flaws, and constraints on generalizability. If we are serious about accelerating trust, we must name the successes and failures people are already experiencing but not sharing.

Call to Action

Transparency in publishing clinical research results is not simply a matter of goodwill. Trial registration and results reporting are mandated under previously mentioned frameworks like FDAAA 801, ICMJE, and GCP, which is why negative and inconclusive findings reach the public record even when they are commercially inconvenient. That mandate does not extend upstream. In preclinical and basic research, no comparable reporting requirement exists, and negative results are known to go unpublished. AI in clinical trials sits in this same unregulated space today; there is no requirement to report what fails. If we want AI development to earn the trust that clinical research has earned, we need to build the equivalent of that mandate voluntarily, starting with a registry of AI outcomes, positive and negative.

This registry cannot credibly sit inside any single sponsor, technology vendor, or service provider. The same conflict-of-interest logic that makes precompetitive data-sharing valuable also applies to who is trusted to hold it. What it needs is a steward that meets three criteria. It must have standing across sponsors, CROs, sites, and technology vendors—not allegiance to one. Its governance must be built for transparency and accountability, along the framework that the Coalition for Health AI has already broadly outlined for AI development. And it must have no commercial stake in the outcomes being reported. Our industry already has precompetitive infrastructure built for exactly this kind of shared, noncompetitive problem. Bodies like the Clinical Trials Transformation Initiative, TransCelerate BioPharma, and the Clinical Data Interchange Standards Consortium (CDISC) exist because competitors recognized that trial methodology, data standards, and biomarker science aren’t worth guarding competitively. That’s the logical foundation to build on.

Our interdependency in this is embodied in Rudyard Kipling’s observation that “the strength of the pack is the wolf, and the strength of the wolf is the pack.” By applying transparency norms, we can accelerate trust in AI and raise the tide that lifts all life science boats.

round the world, regulators and payers are looking for ways to use real-world data (RWD) to make decisions without sacrificing trust, transparency, or rigor. In China, the answer is increasingly being built—not just debated—through new policies, standards, and data sharing infrastructure. A three-agency flywheel—comprising data infrastructure (National Data Administration [NDA]), regulatory standards (Center for Drug Evaluation [CDE]), and reimbursement incentives (National Healthcare Security Administration [NHSA])—is now in motion.

For clarity, in the context of this article RWD is defined as data relating to patient health status and/or the delivery of healthcare routinely collected from a variety of sources (e.g., electronic health records, medical claims data, product or disease registries, etc.) that can inform on health status, and RWE is defined as the clinical evidence about the usage and potential benefits or risks of a medical product derived from analysis of RWD. RWD becomes RWE when it is analyzed using appropriate scientific methods to generate clinical evidence.

Why Trusted Data Spaces Matter for Real-World Data in China Now

Since the second half of 2024, China’s national data agenda has shifted from positioning data as one of the “factors of production” to building practical mechanisms that enable safe and compliant use. One visible example is the rise of trusted data spaces: governed environments where multiple parties can analyze data together without transferring raw data sets.

In November 2024, the NDA issued the Trusted Data Space Development Action Plan (2024–2028). It emphasizes rule-based access, usage control, and accountability—elements that can enable compliant multi‑institution analyses while keeping raw data secure.

This infrastructure advancement creates the conditions for decision‑grade RWD and RWE to inform not only regulators, but also payers. Here, “decision‑grade” refers to data and analyses with clear provenance, fit‑for‑purpose quality controls, auditability, reproducibility, and governance that can withstand external scrutiny.

This infrastructure aligns with China’s national “Data Element ×” program (2024–2026), which highlights medical and health data as a prioritized scenario for application of data elements in key sectors such as the digital economy and public services, with healthcare identified as a prioritized domain. In early 2025, the NDA and the National Development and Reform Commission also issued a trial specification for authorized operation of public data resources, clarifying how government-held data can be accessed by qualified operators under defined safeguards.

Together, trusted data spaces and clearer authorization pathways create practical “guardrails” for scaling the generation and use of the solid evidence generated based on the RWD. This foundational framework enables the secure and governed flow of data, which supports a convergence between regulatory guidance and payer pilots.

How China’s Regulator is Making RWE More Usable

In parallel, the CDE is making RWE more reviewable in practice while aligning with International Council for Harmonisation (ICH) standards.

A recent anchor is ICH M14, a framework for noninterventional studies using RWD for medicines safety assessment. In January 2026, CDE released the official Chinese translation and implementation guidance, supporting more post‑authorization safety studies in China.

China has also contributed to emerging global guidance on using RWE for effectiveness evaluation. ICH’s E23 workstream (initiated in 2025) is expected to clarify when RWE can support effectiveness-related regulatory decisions.

Domestically, CDE has issued practical guidance on fit-for-purpose RWD and real-world study (RWS) design. A key 2024 addition focuses on using disease registries as a regulated RWD source. For example, multihospital registries can generate external control arms or post-approval safety and effectiveness evidence to support drug registration and, in some cases, reimbursement negotiation (e.g., National Reimbursement Drug List, NRDL) in both drug and medical devices.

How China’s Payer is Building an RWD Engine for Reimbursement

Between 2025 and 2026, China took concrete steps to formalize the use of RWE in healthcare insurance decision-making. NHSA launched pilot programs and released draft guidelines on real-world medical insurance comprehensive value evaluation, signaling a shift toward continuous, verifiable assessment based on full lifecycle evidence rather than one-time negotiation dossiers.

To support this approach, NHSA established a network of “trusted evaluation centers,” initially involving 79 hospitals, to pilot shared conventions, analytical methods, and governance frameworks. In parallel, commercial insurers have begun referencing RWE to inform differentiated coverage and value-based arrangements.

These developments are further reinforced by the State Council Office’s recent drug pricing policy, which explicitly highlights the use of RWE to support post-launch price refinement. Together, they reflect China’s growing emphasis on aligning evidence generation across the product lifecycle with evolving health policy and reimbursement needs.

In this framework, RWE helps characterize value across multiple dimensions; for example, real‑world clinical outcomes, healthcare resource use and budget impact, and patient‑reported experiences such as quality of life, treatment adherence, and preferences.

The direction is clear: Reimbursement evaluations are being set up to evolve as evidence accumulates, which raises expectations for data quality, study design, and transparent governance.

For global stakeholders, China’s trajectory matters because it links evidence generation to concrete decision points—regulatory review, reimbursement evaluation, and price refinement—within a governed, auditable environment.

Methodology is Catching Up

Academia and industry are also moving from “RWD availability” to decision‑grade RWE. Academic groups are advancing methods to address bias and confounding, while companies invest in standard definitions, privacy‑preserving analytics, reproducible pipelines, and governance that can be inspected by external stakeholders.

Implications

• Regulators: Trusted data spaces make provenance, audit trails, and protocol-driven analyses easier to verify—if standards for fit-for-purpose data quality and reproducibility are enforced consistently.
• Payers: “Trusted evaluation centers” create a route to comparable, lifecycle evidence updates. This supports risk-sharing and reassessment but also requires establishing clearer expectations and higher standards on data quality and study methodologies (for example, endpoints, confounding control, and transparency).

Industry: Competitive advantage will shift to those who can deliver reproducible, auditable analytics that simultaneously meet evolving regulatory (CDE) and value assessment (NHSA) standards. Building standardized, reusable data pipelines and demonstrating robust data governance will be essential.

Looking ahead, the opportunity is to move beyond enabling access to RWD toward the routine use of decision-grade RWE in regulatory, reimbursement, and healthcare decision-making. Achieving this will require continued alignment among regulators, payers, academia, and industry on data quality, methodological rigor, transparency, and governance. With these foundations in place, China is well positioned to demonstrate how trusted data ecosystems can translate real-world data into evidence that is both credible and actionable.

he DIA GCP & QA Community and the DIA Clinical Research Community have been merged to form the DIA Clinical Research, Compliance, and Quality Community under a new Charter.

In December 2025, the longstanding Chair of the DIA GCP & QA (Good Clinical Practice and Quality Assurance) Community, Terry Katz, announced his upcoming retirement. Under Terry’s leadership, this Community formed several standing Working Groups (including Decentralized Trials [DCT], Risk-Based Quality Management [RBQM], and System Validation) as well as shorter-term workstreams, such as the GCP (R2) workstream that developed a toolkit to help small organizations adopt risk-based approaches. The Community also published articles and presented Content Hubs, Roundtables, and posters at numerous DIA Annual Meetings.

We are grateful for Terry’s contributions and thank him for his commitment and inspiring leadership!

Since the Core Committee members’ workload limited their capacity to replace Terry as Community Chair, and the GCP & QA Community has a history of collaboration with the DIA Clinical Research Community, the Core Committee members proposed merging both Communities and their Core Committees. The idea was approved by the Core Committee members’ vote in March 2026. The new merged Community chose the name Clinical Research, Compliance, and Quality and developed its new Charter (below).

Mission Statement

The Clinical Research, Compliance, and Quality Community provides information exchange and a discussion forum for topics related to clinical research, including best practices, clinical trial trends, quality expectations and practices, and regulatory compliance. We foster global communication among industry sponsors, government agencies and regulators, healthcare delivery systems, and academia to promote ethical and responsible clinical research, improve compliance and quality in clinical research and healthcare in general, and contribute to articulating best practices.

Deliverables

The Clinical Research, Compliance, and Quality Community shall pursue our mission by exploring a broad range of topics and work as a team to share expertise with and disseminate information to DIA members and other professionals in the field. We aim to:

1. Develop and deliver educational DIA DIRECT webinars and shared learning sessions.
2. Write articles for Global Forum and for Therapeutic Innovation & Regulatory Science (TIRS).
3. Submit abstracts for DIA Annual Meeting roundtables, panel discussions, content hubs, and posters.

Core Committee

Core Committee members currently include Kamila Novak (Chair), Richie Siconolfi, Paula Horowitz, Maryrose Petrizzo, Douglas Drake, Sanjay Bagani, John (Jack) Arthur, Robert Arbeit, Hazel Lai, Nimita Limaye, Jorge Ferreira, Marianne Gabra, and Terry Katz (until June 2026). We hold monthly meetings on the second Wednesday of each month at 2pm ET.

Past and Upcoming Events

On July 7, 2026, at 12pm EDT, we delivered a DIA DIRECT Webinar The Power of Influence: How can we influence others for better outcomes? The recording is available in our Community Library.

We are working on several more topics to present to the DIA members and nonmembers in the second half of this year. Announcements will be posted on our Connect page and on LinkedIn.

DIA Communities are a benefit of DIA membership. Please visit our Community Connect page to join us!

he adoption of biosimilars for cancer and autoimmune conditions, favorable regulatory policies, technological advancements in biomanufacturing, and improved healthcare reach in developing regions are accelerating the global biosimilar market growth trajectory. That market is projected to experience significant growth in the coming years, driven by factors such as patent expirations, more cost-effective therapies, and expanding biologics accessibility. Today, the pharmaceutical industry is at a turning point: the wave of biologic drug patent expirations is creating an unprecedented landscape—a “golden era”—for biosimilars.

What is a Biosimilar?

In the US, a biosimilar is a biological product that is highly similar to, and has no clinically meaningful differences from, an existing FDA-approved reference product. Biologics, or biological products, are large, complex molecules—often proteins—derived from living organisms like bacteria or mammalian cells.

Key Growth Drivers of the Global Biosimilar Market

Recent analyses indicate that the global biosimilar market size was USD 46.30 billion in 2025, will rise to USD 51.93 billion in 2026, USD 58.25 billion in 2027, and ultimately grow to USD 145.83 billion by 2035. This robust expansion reflects a CAGR forecast of 12.16% from 2026 to 2035, and an even more expansive CAGR of 13.8% from 2026 to 2032.

• Increasing patent expirations of blockbuster biologics are opening the market to biosimilar competition.
• Rising prevalence of chronic and autoimmune diseases is boosting demand for cost-effective biologic alternatives, and the successful use of biosimilars is simultaneously increasing clinician and patient confidence in them.

Global Biosimilar Trends

Key Approved Biosimilars by Drug Class or Modality

FDA has approved 90 biosimilars within the product classes of 1) insulin; 2) granulocyte colony-stimulating factor; 3) monoclonal antibodies; 4) tumor necrosis factor-alpha (TNF-α); and 5) vascular endothelial growth factor (VEGF) inhibitor. Monoclonal antibodies dominated 2025 with a 32.4% biosimilars market share and accounted for the largest revenue share.

EMA has recommended the approval of 144 biosimilars within the product classes of 1) human growth hormone; 2) granulocyte colony-stimulating factor; 3) erythropoiesis stimulating agent; 4) insulin; 5) follicle-stimulating hormone (FSH); 6) parathyroid hormone; 7) tumor necrosis factor (TNF)-inhibitor; 8) vascular endothelial growth factor (VEGF) inhibitor; and 9) monoclonal antibody. Here, the targeted therapeutic precision of monoclonal antibodies also leads in market share.

Biosimilars Market Growth by Indication

The growth of the biosimilars market is primarily driven by three core therapeutic areas: oncology, immunology, and diabetes, which accounted for more than 70% of the innovative biologic market in 2024. Oncology applications led with 54.30% of 2025 revenue, whereas autoimmune and chronic inflammatory indications grew the quickest from 2024 to 2025 at a 22.10% CAGR.

Biosimilar Market Growth by Region

The Global Biosimilars Market Share, by Country (2025) report explicitly states that North America leads the global biosimilars market with a 42.06% share in 2025, followed by Europe at 33.31% and Asia Pacific (APAC) at 16.48%, while the Middle East & Africa (MEA) and South America account for 5.01% and 3.13%, respectively. Forecasts predict that North America will remain the largest market by value and Europe will see steady growth, whereas APAC is the fastest-growing region, followed by Latin America and MEA.

Patent Cliff Driving Biosimilar Growth, Especially in the US Market

In the world of pharmaceuticals, patents matter. They grant drugmakers exclusive rights to sell a medicine for a defined period, allowing companies to recover massive research and development costs and, in many cases, generate profits to fund future innovation. When patents expire, the competitive landscape shifts dramatically as cheaper generics or biosimilars enter, prices drop, and brand revenues typically fall sharply.

Small-molecule drugs typically lose 90% of their market share within months of patent expiration; due to more complex production and slower adoption, market share for biologics declines by 30% to 70% in that first year.

What is the patent cliff?

Patent cliff refers to a sharp decline in revenue or profitability when a firm’s patents expire, opening these original products up to competition from biosimilars, and opening the door to less costly, more accessible and sustainable treatments for healthcare systems worldwide.

• An IQVIA report found that between 2025 and 2034, 118 biologic medicines will lose patent protection, representing a $232 billion opportunity for biosimilar competition.
• DrugPatentWatch noted: “Industry-wide analyses project that between 2025 and 2030, nearly 70 high-revenue products will face patent expiration, putting a colossal $236 billion in annual revenue at risk.” Unlike the patent cliffs of the early 2010s, which primarily affected primary care small molecules, this cliff targets complex biologics and specialized therapies.

The period between 2025 and 2030, the patent cliff for the biopharma industry, is critical: A massive wave of exclusivity losses for blockbuster biologic drugs, often termed “Patent Cliff 2.0,” as it targets the industry’s most sophisticated biologic assets and specialized therapies. Between 2025 and 2030, $300 billion in pharmaceutical revenue (one-sixth of the industry) will lose patent exclusivity.

Unlike the previous patent cliff in 2008, this period features a higher proportion of biologics, which face slower but substantial erosion from biosimilars. This shift is being accelerated by streamlined regulatory approvals from both the FDA and EMA, creating a more challenging landscape for established pharmaceutical companies.

While this presents substantial revenue challenges for originator companies, it simultaneously heralds a new era of increased patient access and significant cost savings for healthcare systems globally thanks to the introduction of biosimilars.

Blockbuster Biologics Losing Exclusivity: Focus on US Market

Conclusion

The pharmaceutical industry is entering what analysts describe as a “super-cliff.” Between 2026 and 2030, a cluster of mega-blockbuster drugs will lose market exclusivity.

The “patent cliff” for biologics fundamentally shifts the roles and incentives for all healthcare stakeholders by introducing competition through biosimilars. This will redefine the next decade by expanding affordability and access:

• Financial relief to patients: The HHS 2025 fact sheet reports that the typical biosimilar launch price is 50% less than the reference brand biologic’s price; this 2026 study states that biosimilars typically cost 40% less than the reference biologics. Biosimilars generated more than $56 billion in US healthcare savings since their introduction, with continued growth expected as additional biologics lose exclusivity.
• Total Patient Therapy and Expanded Patient Access: “Total Patient Therapy,” often cited as “patient days of therapy,” refers to the cumulative increased volume of treatment days provided to patients, whereas “Expanded Patient Access,” or “incremental patient days,” refers specifically to the increase in the number of patients receiving care, as explained with data in this Association for Accessible Medicines (AAM) report.

In March 2015, FDA issued its landmark decision approving the first biosimilar, to the reference product filgrastim to support white blood cell creation for patients with cancer receiving chemotherapy and radiation.

This biosimilar was approved for all five indications included in the reference product label: patients receiving myelosuppressive chemotherapy, patients with acute myeloid leukemia receiving chemotherapy, patients with cancer undergoing bone marrow transplantation, patients receiving autologous peripheral blood progenitor cell collection and therapy, and patients with neutropenia.

Since this first biosimilar entry, total savings from biosimilars in the US was $12.4 billion in 2023 (AAM 2024 report), increased to $20.2 billion in 2024, and $56.2 billion in total, per the 2025 US Generic & Biosimilar Medicines Savings Report. Since 2015, cumulative “patient days of therapy” has grown to 3.3 billion, with 460 million “incremental days of patent therapy” in the 2025 report.

The growing adoption of biosimilars can be significantly accelerated by the upcoming patent cliff, but realizing this potential will require timely policy action. Policymakers can help unlock broader use of biosimilars by removing unnecessary regulatory barriers and addressing key challenges such as patent thickets and litigation, IRA (Inflation Reduction Act) price controls, and adoption barriers associated with the pharmacy benefit manager (PBM) system. Uptake can be further strengthened by promoting appropriate education and strengthening confidence in biosimilars among patients, providers, and payers.

iologic medicines have reshaped care for cancer, autoimmune, inflammatory, and rare disorders. Yet for many people in both developed and low- and middle-income countries (LMICs), these therapies remain out of reach, in part due to challenges regulators face in reviewing biologic and biosimilar applications, which include lack of common benchmarks for analytical assessment and need for training and technical support for modern analytical tools such as quantitative NMR, MAM, and digital PCR, particularly for LMICs.

In the US, this deficit in biologics and biosimilars uptake is due in large measure to health plan formularies and payment policies that create market access barriers. But in low- and middle-income countries, the access gap is different and is significantly affected by lack of scientific tools that can support regulators in assessing the safety, efficacy, and quality of products; this ultimately slows or prevents approvals. These various challenges have made investment in new biosimilars less compelling in the US and many other countries. Consequently, there is a growing “biosimilar void,” where for many biologics nearing loss of exclusivity there are no known biosimilars in development. The lack of biosimilar options for these therapies restricts patient access to affordable options, on average more than 50% less than the innovator biologic, and limits overall healthcare savings.

Numerous reasons underly this development and access gap. Biologics are inherently challenging to develop because they are produced in living systems and are more complex and variable than traditional chemical drugs. Even for biosimilars—follow-on versions developed after innovator biologic patent expiry—upfront investment remains high and risky. When reimbursement is low, the prospect of low or no return on investment may block product development.

The Growing Centrality of Analytics

Over the past decade, biosimilar evaluation and understanding of how certain attributes impact quality, safety, and efficacy have evolved, increasing reliance on analytical comparability testing. Techniques such as mass spectrometry, capillary electrophoresis, LC-MS peptide mapping, higher-order structure assessment, and cell-based bioassays allow deeper insight into structure-function relationships. But the more sensitive our tools become, the more the biosimilars field needs shared benchmarks for what “good” looks like: how methods should perform and whether the entire analytical system, not just the instrument, is performing acceptably.

In addition, expectations for analytical data in regulatory submissions are changing as scientific understanding of biologics increases. Specifically, now that science has a much better understanding of which analytical characteristics of a drug substance are likely to affect clinical performance, regulators worldwide are moving toward granting approval based on rigorous analytical comparison and less clinical data than in the past. This is a significant advancement because clinical trials drive development timelines and costs. However, regulatory expectations still vary across jurisdictions, even when regulators share the same principles, due in part to the lack of consensus across the range of accepted analytical methods.

The consequence is a “biosimilars policy-practice gap”: strong intent to expand access, but uneven availability on the ground. If biosimilars are to fulfill their promise, developers require a shared scientific foundation that supports predictable development and efficient regulatory review without constraining innovation or replacing regulatory judgment. That is where public, product-specific quality benchmarks can help. Without these shared anchors, equally rigorous data sets can lead to different questions and divergent regulatory outcomes, slowing time to market and clouding investment decisions.

USP Consultations with International Regulators Confirm Need for Standards

To better understand where standards for biosimilars can reduce uncertainty, the USP consulted 30 organizations, including 14 National Regulatory Agencies, 11 biologics manufacturers, and several scientific and trade groups across Asia Pacific, Latin America, India, and the Middle East and North Africa.

A few clear themes emerged:

Trusted reference points can foster efficient review. Regulators in these regions want neutral tools—validated methods, acceptance criteria for system suitability, and reference materials—that anchor analytical interpretation and streamline dialogue across laboratories and agencies.

Flexibility is essential. Stakeholders requested that standards include multiple method options where applicable and allow for new/alternative approaches. Because of the complexity of biologics/biosimilars, USP also anticipates that manufacturers will need to consult with local regulatory authorities to establish product specifications.

TABLE: Key Takeaways from Consultations

Product-Specific Standards Can Streamline Development and Support Broader Access

Patient access improves when regulators and manufacturers share a common scientific foundation early in biosimilar development. Public, product-specific standards can contribute to that foundation by providing guidance on key product quality attributes and appropriate testing methods, while improving efficiency through validated methods and reducing the need for manufacturer-led method development and validation that can lead to fragmentation and overall inefficiencies and delays in development, review, and approval.

Clearer reference points can reduce avoidable rework and analytical uncertainty during development and assessment, streamlining review timelines and supporting earlier market entry. More predictable review pathways can also accelerate patient access while improving returns on the significant investment required to bring a biosimilar to market.

These tools do not replace regulatory judgment. They serve as consistent reference points that can support method performance. The resulting efficiency is particularly consequential at a time when the business case for biosimilars is increasingly challenged by downstream formulary and payment dynamics.

A Collaborative, Early Engagement Pathway Through USP’s Emerging Standards Platform

The pathway: flexible, iterative, and collaborative. USP’s Emerging Standards Platform provides a structured yet flexible way to introduce these product-specific tools early, before any consideration of compendial adoption. The platform is designed for flexibility and faster iteration as science and technology evolve and enable structured, transparent engagement with stakeholders while concepts are still forming.

The steps in the emerging standards process will include:

• Draft an emerging standard. Define key product quality attributes, identify appropriate methods, and articulate system suitability and assay performance characteristics.
• Solicit feedback. Encourage laboratories to apply the suggested methods of analysis in the draft, share alternative methods, or propose refinements.
• Iterate. Update to reflect new evidence, method advances, and real-world feedback across regions.
• Decide on next steps. If broad value is demonstrated, consider moving toward formal monograph development.

Standards Support Quality Across the Full Pharmaceutical Lifecycle

USP’s compendial and non-compendial standards are designed to be modular and mutually reinforcing:

• Documentary Standards (monographs and general chapters)
  • Monographs articulate the quality expectations for a specific product. They also describe the tests to validate its quality.
  • General Chapters address common quality considerations across a platform or class of molecules (e.g., host cell protein, residual DNA, endotoxin, bioassay), reducing duplication across programs.

  The combination of monographs and general chapters reflects accumulated consensus and scientific maturity, providing durable reference points for quality assessment.

• Reference Standards and Analytical Reference Materials enable calibration, method validation, and quantitative assays; paired application notes support appropriate use and comparability across labs.
• Emerging Standards: Concepts in early development that are not official compendial standards and contain methods for analytical development to support method development and foster scientific dialogue.

From Shared Science to Real-World Benefit

As biosimilars move from policy priority to real-world availability in developed countries as well as LMICs, predictable development pathways, regulatory efficiency, and consistent quality all influence how and when patients benefit from them. USP is working to enable earlier engagement and scientific alignment across development and review.

On June 1, 2026, USP made emerging standards for biologics available for comment on its Emerging Standards Platform. By commenting on them, the global community can accelerate development, streamline review, and promote regulatory convergence. Stakeholders can also volunteer to serve on Expert Committees, join ad hoc Expert Panels, contribute methods through the Donations Program, or engage in Stakeholder Forums. These collaborations help set standards that ensure the quality of biotherapeutics.

Emerging standards are one part of an interconnected portfolio that includes traditional monographs, cross-cutting general chapters, reference materials, and other tools. Together, these elements support quality across the full pharmaceutical lifecycle, helping ensure that advances in biologics translate into cost savings for health plans and governments, and meaningful benefits for people around the world.

n December 2025, just ahead of the new year, the US FDA published a Questions and Answers Guidance for Industry, addressing questions manufacturers, packers, and distributors (“firms”) may encounter when developing FDA-regulated promotional labeling and advertisements (i.e., promotional communications).

• Prescription biological reference products licensed under section 351(a) of the Public Health Service (PHS) Act (42 U.S.C. § 262(a)), and
• Prescription biosimilar and interchangeable biosimilar products licensed under section 351(k) of the PHS Act (42 U.S.C. § 262(k))

The guidance outlines considerations for how data and information about reference products and biosimilar products should be rendered in promotional communications. For purposes of the guidance, a biosimilar product is one licensed under section 351(k) of the PHS Act as either biosimilar to, or interchangeable with, a reference product. The guidance seeks to reduce likely confusion among healthcare practitioners and patients—particularly around biosimilarity, interchangeability, and appropriate product use—by outlining how data and comparative information should be presented as “accurate, truthful, and non-misleading.” Note that recommendations for nonprescription products are not included in this guidance.

The global biosimilars market value is expected to more than double from $35.04 billion in 2025 to approximately $72.29 billion by 2035. The US and Europe together account for the bulk of this market. While market estimates vary by vendor, the market situation highlighted in the December guidance remains unchanged: increasing approvals and competitive entry are driving both opportunity and regulatory focus.

There are three ways in which this December guidance—applicable to all FDA-regulated promotional labeling and advertising, regardless of communication medium—is intended to support manufacturers: 1. to help firms develop compliant promotional materials; 2. to increase confidence in the FDA’s biosimilar framework; and 3. to underpin informed prescribing and substitution decisions.

Regulatory Context

This all traces back to section 351(k) of the PHS Act, which created an abbreviated licensure pathway for biosimilar and interchangeable biological products. To count as a biosimilar, a product must be highly similar to a reference product, with no clinically meaningful differences in safety, purity, or potency. To have interchangeable status under section 351(k)(4), the product must produce the same clinical result in any patient, and switching or alternating with the reference product can’t increase the risk to safety or efficacy. Only then can it be substituted at the pharmacy without the prescriber’s sign-off, subject to state law. FDA laid out its original expectations for meeting this standard in its May 2019 guidance, “Considerations in Demonstrating Interchangeability With a Reference Product,” which generally called for switching-study data. FDA’s thinking has since shifted. A 2024 draft update allows applicants to instead submit an assessment explaining why their existing analytical and clinical data already meets the standard without dedicated switching studies—and FDA has signaled plans to finalize this shift in 2026.

Once licensed, biosimilar and interchangeable products may be prescribed to both treatment‑naïve and treatment‑experienced patients and are held to the same rigorous standards as reference products, providing the context for the FDA to issue this guidance addressing questions related to FDA‑regulated promotional communications.

FDA’s activities in early 2026 have reinforced that the agency continues to refine its thinking and expectations regarding biosimilar development and acceptable comparative evidence. In March 2026, the agency announced further steps to improve biosimilar development, including the publication of draft Q&As and the withdrawal of certain previous scientific guidance—part of the same broader push that includes the interchangeability update discussed above, with FDA officials signaling that both are on track for finalization in the first half of 2026. Separately, FDA’s legislative materials (April 2026) included a proposal to eliminate the statutory distinction between “biosimilar” and “interchangeable”; if adopted, this policy could materially change how firms communicate about interchangeability and substitution. Since this distinction is written into the PHS Act itself, only Congress can eliminate it; the FDA does not have authority to make this change unilaterally through guidance or rulemaking. Right now, this is still just a budget proposal, not law, and therefore it will need congressional action before anything changes.

At the DIA Global Annual Meeting 2026, Peter Richardson of the European Medicines Agency (EMA) raised a useful point: the EU has largely moved away from comparative clinical efficacy studies, leaning instead on analytical and quality data to establish biosimilarity, and EU biosimilars are treated as interchangeable by default, with no separate designation required. FDA is already moving toward the switching-study side of that picture on its own, as noted above. What Congress alone can still do is erase the legal category itself—which is exactly what FDA’s own proposal is now asking it to do. The EU is functionally already there; the US still needs an act of Congress to fully catch up.

Still Applicable: Core Promotional Standards

According to the Q&A publication, reference, biosimilar, and interchangeable biological products are still subject to longstanding promotional standards, and promotional communications are still subject to existing misbranding provisions (including requirements that claims be truthful, nonmisleading, and presented with an appropriate balance between benefits and risks). Whether a communication is misleading depends on a number of factors, including: (1) the quality of the data relied upon; (2) the manner in which information is framed; (3) whether sufficient context and disclosures are provided; and (4) express and implied claims made. Firms also need to keep their promotional materials current with FDA-approved labeling changes, and to meet post-marketing submission requirements (21 CFR 601.12(f)(4); 21 CFR 314.81(b)(3)(i)).

Relevance of Precise Product Identification

FDA advises firms to clearly and specifically identify whether a promotional communication refers to a reference product, a biosimilar product, or multiple products, using appropriate nomenclature to avoid misattribution of data or confusion among audiences. This will matter most when presenting safety or effectiveness information that actually applies to more than one product; for example, a biosimilar whose FDA-approved labeling uses the reference product’s core name followed by the word “products” (e.g., “replicamab products”) to show that a risk applies to both the reference product and the biosimilar. If that is the case, it is appropriate to use the same convention in promotional communications for the biosimilar.

Presenting Reference-Product Data in Biosimilar Promotion

When data from studies supporting licensure of the reference product appear in both the reference product’s and the biosimilar’s FDA-approved labeling, FDA recommends that biosimilar promotional communications draw from the biosimilar’s own FDA-approved prescribing information (its package insert), rather than pulling from the reference product’s label directly, since the biosimilar’s own labeling includes only the data relevant to its licensed conditions of use. This means relying on the CLINICAL STUDIES section and related clinical pharmacology, immunogenicity, and toxicity studies within that document. FDA also acknowledges that firms may wish to communicate additional data not found in that prescribing information (e.g., biosimilarity studies not included in the labeling) but affirms that these communications must be consistent with FDA-required labeling, supported by appropriate scientific evidence, and presented with appropriate context.

FDA-Identified Risk: Comparative Promotion

The guidance makes an important point: a statement can be completely accurate but still be misleading when presented without the appropriate context or set up as a comparison that suggests a clinically meaningful difference in, for example, safety, purity, or potency, where none genuinely exists. That is exactly the risk with any claims implying superiority or inferiority, based on small differences, approval pathways, or dosage forms: FDA’s promotional standards prohibit implying clinical superiority devoid of substantial evidence (21 U.S.C. § 352(a)).

Enforcement Trends and Practicalities

In March 2026, FDA announced that it had issued 30 warning letters to telehealth companies for misleading promotional claims about compounded GLP-1 products marketed as equivalent to FDA-approved medications. That authority came from FDA’s oversight of compounded drugs and the FD&C Act’s misbranding provisions, not from any biosimilar-specific power under section 351(k). Still, the action shows the FDA is willing to use its enforcement tools against promotional claims it considers false or misleading. These letters were not about biosimilars, but firms developing biosimilar promotion should take heed: interpret the Q&A conservatively, document comparative claims carefully, and keep a close eye on digital channels before any promotional communication goes out.

FDA’s Q&A publication and subsequent agency actions reinforced the expectation of a heightened standard: promotional communications for reference, biosimilar, and interchangeable products need to reflect the scientific and regulatory basis underlying their licensure. Confidence in biosimilars can be undermined by ambiguity regarding biosimilarity and interchangeability (due to inconsistent or unclear messaging), which can ultimately affect prescribing and substitution decisions by healthcare providers. Avoiding that kind of ambiguity comes down to a few practical habits:

• Marketing should work with Medical, Legal, and Regulatory (MLR) review to name products precisely and frame any comparisons carefully; everything needs to remain tied to the approved labeling, so the message stays clear and does not mislead.
• Marketing and digital teams should route every digital asset (e.g., owned channels, search, paid media) through an MLR review so that labeling and product characterization remain consistent across all platforms.
• Regulatory affairs and policy teams should keep an eye on future FDA developments, including final or draft guidance, since a shift in policy on interchangeability could change what is permissible tomorrow.
• Medical affairs and regulatory teams should document evidentiary support and context for any comparative statements, particularly numerical comparisons or cross-product references.