ost clinical trial teams within pharmaceutical companies want to design studies that work for patients. Trial protocols are often built on assumptions about what will reduce patients’ burden, what patients would be willing to accept, or which endpoints truly matter. These assumptions are usually well-meant, but when they are wrong, the consequences can be costly: slow recruitment, amendments, drop-outs, and frustrated participants.

Five years into this transformation, we performed a retrospective analysis of the company’s portfolio to ask the following question: Does early, structured patient input translate into measurable operational benefit?

What We Analyzed

The analysis included interventional clinical trials initiated after January 1, 2021. Trials spanned phases 1–3, with the majority in phases 2 and 3, across inflammation, cardiometabolic, oncology, respiratory, dermatology, eye health, and mental health indications. Thirty-seven trials were grouped based on whether patient input had been incorporated into the trial design or not; some trials were included in multiple analyses below.

• Recruitment speed analysis: 37 trials with completed recruitment; 24 of those trials incorporated patient input into trial design.
• Protocol amendment analysis: 17 completed trials; 9 of those trials incorporated patient input into trial design. Trials where amendments were driven by positive data and/or scope expansion were excluded.
• Drop-out rates analysis: 16 completed trials; 8 of those trials incorporated patient input into trial design.

This was a retrospective, observational analysis. Recruitment speed, amendments, and retention are influenced by many factors; patient input was assessed as one contributing factor. No individual trial, indication, or asset is identified.

How Patient Input Was Generated: Why and When It Matters

Patient input was not collected informally or late in the process. Insights were generated before protocol finalization, primarily prior to the trial design freeze, using a combination of:

• Trial simulations allowing patients to walk through draft trial concepts, followed by structured interviews.
• Structured interviews and patient advisory boards exploring burden drivers and feasibility.
• The number of insights grew substantially over time, and to avoid asking patients the same questions over and over again, an AI-powered chatbot was introduced later to summarize existing insights ahead of new patient engagements.

What the Data Showed: Patient Input Paid Off

Faster recruitment: Trials that incorporated patient input into design recruited on average 31% faster, corresponding to approximately 205 days earlier completion of recruitment per trial compared with trials without such input.

Using conservative, industry-standard estimates of daily trial costs across phases, this difference corresponds to potential estimated cost savings of approximately €24 million per trial, excluding the downstream value of earlier access to market. Since recruitment speed is influenced by many factors, the actual costs savings from patient input into trial design are likely not 100% of this estimate.

Fewer protocol amendments: Trials with early patient input had 1.6 fewer global protocol amendments on average than those without input.

Avoiding a single global amendment not only reduces direct amendment costs but also helps prevent timeline delays. Together, this translates into €11.5 million of potential savings per trial, again excluding downstream effects. Since amendments are influenced by many factors, the actual costs savings from patient input into trial design are likely not 100% of this estimate.

Lower drop-out rates: Across the analyzed portfolio, trials with patient input showed on average an 8.3% lower patient drop-out rate (9.8% for trials with patient input versus 18.1% for those without), corresponding to a relative reduction of 46.1%. Retention improvements reduce the need for overenrollment and protect data quality.

A critical insight emerged over time: Engaging patients too late limits impact. In several early experiences, patient feedback identified meaningful study challenges; however, the protocol was already frozen, and changes were no longer possible without delaying the trial start. As a result, understanding patient needs before trial design is locked became a non-negotiable requirement and a key performance indicator for all the company’s trials.

Four Lessons Learned from Moving to Systematic Patient Input

1. Don’t assume you know what patients need. In one trial, teams invested significant effort and budget into home nursing services that they believed would reduce burden, only to find that patients did not use them at all. Assumptions, even well-intentioned ones, often miss the mark.

2. Start very early. In one trial, the selected endpoint did not capture the patient experience and potential improvements in a meaningful way. However, we only asked for input three weeks before the trial started, and changes were no longer possible without delaying the trial start. Patient input has its greatest value when it informs endpoints, visit structure, and procedures before the trial design freeze—decisions that are difficult or impossible to change later.

3. Make patient centricity everyone’s responsibility. Historically, patient feedback has sometimes been gathered by specialists and handed off to busy trial planning teams, where it competed with other priorities. Embedding accountability for addressing patient needs directly within the roles that are designing and conducting the trials ensured that insights translated into decisions rather than reports.

4. Leadership commitment matters. Adoption accelerated markedly after senior leaders explicitly prioritized patient input in R&D objectives. Visible leadership support transformed engagement from an optional activity into expected practice.

Trial Examples

Several recurring design challenges illustrate how patient input translated into concrete improvements:

• Dermatology trial: Patient input revealed that prohibiting standard pain comedication created a major participation barrier. Allowing appropriate pain treatments removed this obstacle without compromising trial integrity.
• Rheumatology trial: Patient feedback identified skin imaging procedures that did not work well for patients with darker skin tones. Alternative imaging methods and targeted site training improved both inclusivity and data quality.

In each case, relatively modest design adjustments—made early—helped avoid downstream delays and rework.

Good Intentions are Not Enough

Why this matters for the industry
Recruitment challenges, protocol amendments, and patient drop-outs are widely documented as major drivers of delay and cost inefficiency in clinical development. At the same time, regulators increasingly expect sponsors to demonstrate that patient perspectives meaningfully inform trial design.

This experience suggests that patient input is imperative. When applied systematically and early, it functions as a form of risk prevention, identifying friction before it becomes expensive to fix. Importantly, these benefits were observed across therapeutic areas and trial phases, not in isolated pilots.

Turning Intent into Impact

For organizations seeking similar outcomes, three principles stand out:

1. Engage patients before trial designs are frozen, not after.
2. Use structured, repeatable methods, rather than ad hoc feedback.
3. Link patient input to operational outcomes, not just trial experience metrics. For example, feedback from pulmonary fibrosis patients stated that they face challenges reaching sites (e.g., traveling with oxygen), so solutions like concierge support or offering ePRO options can significantly reduce the burden.

The shift from assumptions to evidence does not eliminate uncertainty in drug development, but it significantly reduces avoidable friction. In an environment where speed, quality, and trust all matter, systematic patient input is increasingly becoming less a “nice to have” and more a strategic necessity.

The Convergence We Can No Longer Ignore

linical trials are growing more complex even as expectations for efficiency, inclusiveness, and transparency continue to rise. Sponsors are navigating modern clinical trials with decentralized elements, Artificial Intelligence (AI)-enabled analytics, electronic source data, digital endpoints, and global regulatory expectations, often across fragmented legacy systems that were never designed to interoperate.

Over the past five years, three structural shifts have reshaped clinical research:

1. Modern Clinical Trial Models With Decentralized Elements
   Remote visits, wearable sensors, telemedicine, and electronic informed consent (eConsent) have expanded access. The pandemic accelerated these models, but sustainability depends on regulatory compliance that includes 21 CFR Part 11, the European Union General Data Protection Regulation (GDPR), and evolving global data protection requirements. Large-scale virtual studies such as the Apple Heart Study, which enrolled more than 400,000 participants remotely and demonstrated that smartwatch-based irregular pulse notifications can safely identify atrial fibrillation, show that decentralized evidence generation is feasible at population scale.
2. AI-Enabled Decision Support
   Artificial intelligence is now embedded in protocol design, patient matching, risk-based monitoring, and data anomaly detection. The FDA’s discussion paper on AI/ML in Drug Development highlights both promise and caution. Artificial intelligence-supported central monitoring approaches have already demonstrated improved detection of atypical data patterns compared with traditional monitoring alone, as shown in multisponsor analyses published by TransCelerate BioPharma.
3. Interoperability with Healthcare Systems
   Integration with electronic health records (EHRs), claims databases, and registries is increasingly expected. However, interoperability standards such as Health Level Seven Fast Healthcare Interoperability Resources (HL7 FHIR) and the Clinical Data Interchange Standards Consortium (CDISC) models are unevenly adopted across sites and countries. The ADAPTABLE trial, which randomized 15,076 patients using the National Patient-Centered Clinical Research Network’s (PCORnet) EHR-enabled infrastructure, demonstrates that interoperable data networks support large, pragmatic trials with reduced administrative burdens and efficient, direct-to-patient recruitment. While effective in reducing site burden, the study’s virtual, direct-to-patient design resulted in challenges regarding substantial dose switching and significant missingness of patient-reported data.

Despite these advances, most clinical trials still operate on siloed systems: separate platforms for clinical trial management systems (CTMS), electronic data capture (EDC), electronic clinical outcome assessments (eCOA), eConsent, safety reporting, and monitoring. This fragmentation introduces:

• Redundant data entry
• Inconsistent audit trails
• Delayed signal detection
• Increased inspection risk
• Cybersecurity vulnerabilities.

Regulatory frameworks emphasize accountability and traceability. Yet our technical infrastructure often undermines those very principles.

The Regulatory Crossroads: Compliance in an AI-Native World

Regulatory science is evolving to accommodate innovation but not at the expense of data integrity or patient safety.

Several regulatory themes are converging:

• Transparency of Algorithms
  Agencies increasingly expect explainability in AI-driven outputs. The EMA’s 2024 Reflection Paper on AI emphasizes that opaque “black‑box” models are unlikely to be acceptable for high‑stakes decisions without clear explainability and human oversight.
• Data Provenance and Auditability
  ICH E6(R3) reinforces sponsor responsibility for oversight of computerized systems. Under ICH E6(R3), sponsors remain accountable for vendor‑managed systems, including validation, change control, and maintenance of complete, contemporaneous audit trails.
• Cybersecurity as Patient Protection
  The FDA’s growing emphasis on cybersecurity in medical devices has implications for digital trial systems. Cybersecurity frameworks such as ISO/IEC 27001:2022 and ISO/IEC 27701:2025 are increasingly referenced by regulators as benchmarks for protecting clinical trial data environments.
• Use of Real-World Data and Digital Endpoints
  While agencies support innovation, they require robust validation and pre-specification. The EMA’s 2023 qualification of the wearable‑derived stride velocity 95th centile (SV95C) as a primary endpoint in ambulant Duchenne muscular dystrophy trials demonstrates that regulators will accept digital endpoints when supported by rigorous analytical and clinical validation.

This creates a regulatory gap: Innovation is accelerating faster than harmonized guidance. Companies must design compliance into technology architecture, not retrofit it later.

Building Compliance by Design: A Unified, AI-Ready Infrastructure

Experience across large, technology-enabled research initiatives has shown that compliance is not a documentation exercise; it is an architectural discipline. Publicly available platforms such as FDA’s MyStudies illustrate how interoperable, audit-ready infrastructures can support decentralized and AI-enabled research while maintaining regulatory-grade data integrity. To responsibly enable AI and decentralized research, modern clinical trial platforms purpose-built to address the friction and fragmentation of legacy systems should incorporate:

• Role-based access controls and dynamic delegation logs
• End-to-end audit trails across all modules
• Validated AI model governance frameworks
• Integrated risk-based monitoring dashboards
• Standardized application programming interfaces (APIs) aligned with HL7 FHIR and CDISC
• Built-in electronic source documentation controls.

A unified infrastructure reduces reconciliation errors and improves inspection readiness. Risk‑based quality management principles from ICH E8(R1) and E6(R3) can be operationalized through integrated dashboards that link central monitoring signals, site performance metrics, and data-quality indicators.

When AI assistants perform data cleaning or protocol optimization, their actions should be logged, validated, and reproducible. When wearable data streams into a trial database, provenance should be preserved. When sites enter data remotely, system validation should withstand regulatory scrutiny.

This is not theoretical. It is the new operational baseline. For example, recent work underscores this shift. A 2023 study shows that decentralized elements added to trials require interoperable, audit-ready digital infrastructures to maintain data integrity and reduce quality risks, reinforcing the need for unified platforms with built-in traceability and validation.

Likewise, a 2026 HITLAB white paper demonstrates how modular, technology-enabled clinical trial platforms can streamline remote data capture, strengthen inspection readiness, and embed quality-by-design features such as centralized monitoring and structured eSource controls.

Together, these examples show that compliance by design is already being implemented through architectures that integrate interoperability, validation, and risk-based oversight into the core of modern clinical trial systems.

Implications for Sponsors, Sites, and Regulators

The shift to AI-native, interoperable platforms has wide-ranging implications.

For Sponsors:

• Greater efficiency through automation
• Earlier safety signal detection
• Reduced monitoring travel burden
• Increased responsibility for vendor oversight and AI validation

Sponsors should integrate AI and digital technology oversight into their quality management systems, including formal vendor qualification and ongoing performance monitoring.

For Sites:

• Streamlined workflows
• Reduced duplicative data entry
• Enhanced patient engagement tools
• New training requirements on digital systems and AI outputs

Digital literacy and understanding of AI‑generated alerts are becoming core competencies for site personnel.

For Regulators:

• Improved access to structured, standardized data
• More transparent audit trails
• Need for harmonized guidance on AI validation standards

Regulators are increasingly using pilot programs and regulatory sandboxes to evaluate novel digital endpoints and AI tools in controlled environments.

AI Governance: The Next Frontier in Clinical Research Compliance

Artificial intelligence introduces both operational leverage and regulatory complexity. The FDA has emphasized the importance of lifecycle management for AI/ML systems. In clinical trials, this translates into:

• Documented training data sets
• Version control of algorithms
• Ongoing performance monitoring
• Pre-specified thresholds for decision-making
• Clear delineation between automated recommendations and human oversight.

AI governance frameworks should include bias-assessment methods, revalidation triggers, and procedures for decommissioning models that no longer meet performance or fairness criteria.

Equally important, AI in clinical research must function as an augmentation tool, not a replacement for clinical judgment, with every algorithmic output remaining explainable, reviewable, and traceable to support regulatory expectations for human oversight.

The future of AI in trials will be shaped less by technical sophistication and more by trust, which is earned through transparent validation, lifecycle monitoring, and alignment with global regulatory science principles.

Regulators increasingly expect AI systems used in clinical research to demonstrate a plausible mechanism, that is, a scientifically coherent explanation for how model inputs lead to outputs, so that human reviewers can assess validity, limitations, and potential risks.

The Role of Standard Healthcare Systems

Modern trials cannot remain isolated from routine healthcare systems. Integration with EHRs and registries enables:

• Faster feasibility assessments
• Improved representation in recruitment
• Reduced site burden
• Real-time safety surveillance.

EHR‑enabled recruitment strategies have been shown to increase enrollment of underrepresented populations, as demonstrated in PCORnet‑supported cardiovascular and diabetes studies.

However, interoperability introduces new challenges:

• Data normalization
• Privacy compliance under the Health Insurance Portability and Accountability Act (HIPAA) and GDPR
• Consent management across jurisdictions
• Cross-border data transfer restrictions

Digital endpoints such as SV95C have shown greater sensitivity to short‑term functional decline than traditional measures like the six‑minute walk test, underscoring the value of continuous real‑world data streams.

A technology-enabled trial should reconcile clinical care data with research-grade data standards. For example, clinical care data in electronic medical records (EMRs) are usually re-entered in EDC systems used in clinical trials. This double data entry problem is well known and causes significant burden on site coordinators. This requires a unified digital architecture rather than a series of ad hoc integrations.

Strategic Alignment: A Path Forward

The modernization of clinical trials is no longer optional. It is an ethical and scientific necessity. But progress requires coordinated action.

Researchers and Sponsors (Next 12–24 Months):

• Invest in unified, interoperable platforms rather than layering additional point solutions.
• Implement AI governance frameworks with documented validation plans.
• Align digital endpoint strategies early with regulatory consultation.

Technology Providers:

• Design compliance into system architecture.
• Prioritize explainability in AI modules.
• Enable seamless integration with healthcare standards such as HL7 FHIR.

Regulators and Policymakers:

• Continue advancing harmonized guidance on AI/ML lifecycle management.
• Encourage pilot programs for digital endpoint validation.
• Foster global dialogue on cross-border data governance.

Clinical Sites:

• Strengthen digital literacy and cybersecurity awareness.
• Participate in validation studies for digital tools.
• Advocate for simplified, integrated workflows.

The expected outcome of these coordinated efforts is not simply faster trials. It is safer, more inclusive, more reliable research.

The Future We Must Build

Clinical research is entering a defining decade. Biologics, cell and gene therapies, precision medicine, and digital health technologies are expanding rapidly. The infrastructure supporting them must evolve accordingly.

We have an opportunity to move from fragmented, reactive compliance toward unified, AI-ready systems built on regulatory alignment and patient trust.

If stakeholders align on architecture, governance, and interoperability, the next decade could deliver trials that are faster, fairer, and more reliable than any in history.

As leaders in this field, we must ensure that innovation serves integrity. The transformation of clinical trials is not about replacing humans with algorithms. It is about equipping researchers, sites, and regulators with technology that strengthens accountability, transparency, and efficiency.

If we act deliberately, architecting compliance into every layer of digital infrastructure, we can accelerate therapies while preserving the scientific rigor and ethical standards that define our profession.

The tools are here. The regulations are evolving. The responsibility is ours.

uring the past decade, regulators and researchers including the FDA, EMA, NIH, and ICH have issued guidance encouraging gender‑inclusive research. Yet in practice, development decisions continue to default to male‑centric designs and post hoc subgroup analyses. The guidance exists. So why does behavior not change?

This article reframes the female data deficit as a behavioral problem. If the healthcare industry wants consistent inclusion to move from lofty aspiration to routine practice, we need to understand the specific behavioral barriers at play and design interventions that work with—rather than against—how teams actually make decisions under pressure.

Critical Reframing: the system is behaving as designed

Clinical development teams are not irrational or indifferent to inclusion. They are operating in an environment shaped by time pressure, regulatory uncertainty, cost constraints, and accountability for delivery. Within that context, behavior is directed towards optimizing predictability and the probability of approval. Historical precedents, accepted templates, and “what worked last time” become powerful anchors.

In behavioral terms, inclusion is rarely the default option. And where inclusion is framed as additional work, additional risk, or additional cost, good intentions alone are rarely enough to change behavior. Sustained change requires altering the decision environment itself.

Key behavioral barriers and why they persist

1. Defaults and status quo bias: repeating what feels safe

The barrier
Protocol templates, statistical plans, and development playbooks often treat gender‑specific design and analysis as optional. Under time and resource pressure, teams default to familiar approaches. Status quo bias and reliance on precedent make deviation feel risky, even when guidance supports change.

What this looks like in practice

• Male‑dominant early‑phase cohorts
• Sex‑specific endpoints added late or labeled “exploratory”
• Subgroup analyses underpowered and dropped first when timelines tighten.

Actionable interventions

• Make gender‑specific design a default, not an add‑on, in protocol templates
• Require justification to opt out, reviewed alongside other core design decisions
• Use structured checklists at protocol sign‑off to prompt explicit consideration of sex differences.

The EAST framework helps policymakers to design more effective interventions. By mapping our interventions against the four EAST components (Easy, Attractive, Social, and Timely), we can align our suggestions with the ways that people naturally make decisions.

EAST lens: Make it Easy and Attractive to do the right thing by redesigning defaults so inclusion requires less effort than omission.

2. Risk perception and loss aversion: overestimating the cost of inclusion

The barrier
Risks associated with including women—particularly in early-phase studies or pregnancy‑related contexts—feel immediate, concrete, and attributable. By contrast, the risks of exclusion (incomplete evidence, post‑marketing safety issues, labeling changes) are delayed, diffuse, and often owned by someone else later in the lifecycle (see below).

What this looks like in practice

• Conservative exclusion criteria framed as “risk management”
• Pregnancy and lactation treated as blanket exclusions
• Decisions driven by fear of delay rather than quality of evidence.

Actionable interventions

• Reframe risk discussions to compare inclusion risks with the known costs of evidence gaps
• Use regulatory pre‑mortems (e.g., “If this product required a post‑approval label change, what would we wish we had done differently?”)
• Introduce staged inclusion pathways with clear governance rather than binary include/exclude decisions.

EAST lens: Make the long‑term risks of exclusion less acceptable from a Social standpoint and reduce ambiguity by providing clear, managed pathways for inclusion.

3. Incentives and feedback: optimizing for speed over evidence quality

The barrier
Clinical teams are commonly rewarded for staying on time, on budget, and aligned with precedent. High‑quality gender‑specific evidence, by contrast, is rarely visible in performance metrics or rewarded explicitly. Where regulatory feedback on inclusion is delayed or inconsistent, learning is slow.

What this looks like in practice

• Short‑term delivery metrics outweigh longer‑term evidence considerations
• Inclusion framed as a threat to timelines rather than a marker of excellence
• Repeated reliance on acceptable minimums.

Actionable interventions

• Embed evidence‑quality indicators (including gender‑specific data) into internal development metrics
• Provide consistent regulatory and ethics committee feedback when inclusion expectations are not met
• Highlight and reward “positive deviants”: programs that planned inclusively and avoided downstream corrections.

EAST lens: Make inclusion more Attractive by aligning incentives and feedback with evidence quality, not just speed. Subvert the expectation that guidelines mean delays by emphasizing the Timely downstream benefit of inclusive data.

4. Organizational dynamics and diffused accountability: when no one owns the problem

The barrier
Responsibility for inclusion is spread across sponsors, CROs, ethics committees, investigators, and regulators. When ownership is unclear, action slows. Each actor may support inclusion in principle while assuming that others will drive it in practice.

What this looks like in practice

• Inclusion discussed but not operationalized
• Assumptions that regulators or ethics committees will “catch” issues later
• Incremental drift back to familiar designs.

Actionable interventions

• Assign explicit ownership for gender‑specific design decisions at program level
• Build inclusion checkpoints into governance and decision gates, not just ethics review
• Use cross‑functional forums to surface and resolve inclusion trade‑offs early.

EAST lens: Make accountability Social and explicit, so inclusion becomes part of collective norms rather than an individual burden.

Why Information Alone Does Not Change Practice

Education and guidance are necessary but insufficient. Most teams already know what “good” looks like. Behavior is shaped by defaults, incentives, perceived consequences, and norms. Without changing these structural features, behavior predictably reverts under pressure.

This is why repeated publication of guidance, without operational reinforcement, delivers diminishing returns.

From Diagnosis to Implementation

Behavioral science frameworks, such as EAST, help bridge the gap between intention and impact. They also help teams design interventions that are more likely to work in real environments.

Crucially, behavior change does not rely solely on education. Many of the most effective interventions are discrete changes to systems, processes, and decision architectures that make inclusive choices easier, safer, and more rewarding.

What Teams Can Do Differently, Starting Now

• Redesign protocol templates and governance so inclusion is the default
• Make the risks of exclusion visible and discussable early
• Align incentives and feedback with evidence quality
• Clarify ownership for inclusion decisions
• Use behavioral frameworks consistently to diagnose and address barriers.

If behavior is the barrier, behavioral solutions are required. Sustainable progress will come not from asking teams to try harder, but from redesigning systems so that generating high‑quality, inclusive evidence becomes the natural and expected way of working.

sk any clinical operations leader what they need most when it comes to artificial intelligence (AI) and machine learning (ML), and you will hear a consistent answer: clarity. Not more solutions. Not more vendor pitches. Clarity.

In early 2026, the Partnership for Advancing Clinical Trials (PACT) consortium—facilitated by the Tufts Center for the Study of Drug Development (Tufts CSDD)—convened senior clinical operations leaders to recognize and address this challenge. What emerged was not just a framework for evaluating solutions, but a call for a fundamentally different way of thinking about AI/ML investment. The message was simple: Take a step back, apply structured analytical rigor, and start where the return on investment is clear. The taxonomy and value pyramid described in this article are grounded in that collaborative research: empirical frameworks derived from the structured analysis of real-world AI/ML investment patterns across PACT member organizations and validated against Tufts CSDD’s longitudinal benchmarking data spanning over 255 pharmaceutical and biotechnology companies.

The Decline of Monolithic SaaS—and What Comes Next

For decades, the Software-as-a-Service (SaaS) model shaped how biopharma companies bought and deployed technology. A handful of enterprise platforms—validated, compliant, and expensive—defined the landscape. That model is eroding rapidly, and the clinical space is no exception.

Coding time has collapsed exponentially. Fit-for-purpose, customized AI solutions that would have taken years and millions of dollars to build five years ago can now be prototyped in weeks. We are entering an era of use-case-led adoption where the question is no longer “Which enterprise platform do we license?” but “Which specific problem do we solve first, and how do we solve it well?”

This shift is not merely technological; it is organizational. As Tufts CSDD research has consistently shown, the adoption of transformative innovations in clinical research is painfully slow and highly inefficient. A 2023 study among 255 pharmaceutical and biotechnology companies found that recent innovations take each company an average of six to seven years to fully implement. Nearly all companies (90%) reported relying on a decentralized approach to promote adoption—meaning individual business units and functional teams independently pursued AI/ML uptake on their own timelines, without a coordinating enterprise governance structure or centralized AI Center of Excellence providing oversight and direction—and nearly eight out of 10 (78%) felt the process took much longer than expected.

A Taxonomy for Navigation

The most valuable contribution clinical operations leaders can make right now is not to identify the “best” AI solution but rather to apply a clear taxonomy for categorizing and making sense out of what is available. Through the PACT consortium’s analysis, a layered clinical AI stack has emerged that provides this structure. These five categories span the full trial lifecycle:

Protocol Intelligence and Design Optimization encompasses generative and predictive models that simulate trials before enrollment, optimize eligibility criteria, and reduce amendment rates. Companies have deployed AI feasibility engines to rank countries, sites, and principal investigators, with some reporting up to a 60% reduction in site identification cycle time.

Patient Recruitment and Enrollment covers patient matching, site forecasting, and diversity analytics. EHR-based patient matching, AI-driven outreach, and screen-fail prediction tools represent the most commercially mature investments in this category, with adoption accelerating rapidly in response to FDA diversity requirements.

Intelligent Trial Operations—ClinOps 2.0—encompasses study monitoring with agentic AI, TMF automation, risk-based monitoring, and automated medical coding. Risk-based monitoring tools are now widely deployed, with documented reductions in onsite visits of 40% to 50%. NLP-based medical coding has demonstrated 60% to 80% reductions in coding cycle time.

Data Science and Evidence Generation includes synthetic control arms, automated database lock, and radiomics. Synthetic controls carry perhaps the greatest long-term transformative potential—meaningful reductions in required patient enrollment—but remain under high regulatory scrutiny and are most viable today in rare-disease and oncology settings.

Pharmacovigilance and Regulatory Submissions is the final category, and it is moving fast. Real-time adverse event detection and generative AI for dossier drafting are gaining traction. Early adopters of LLM-assisted CSR and IND drafting report 40% to 60% time savings on first drafts, though validation frameworks remain nascent and regulatory acceptance continues to evolve.

Think in Layers, Not Platforms: The Value Pyramid

One of the most important reframes for clinical AI investment is recognizing that not all solutions operate at the same strategic level. A value pyramid model offers a practical organizing principle for sequencing and prioritizing investment.

Key benefits per layer: Infrastructure: data readiness and AI-ready workflows; Transactional: immediate ROI via cost reduction and throughput; Transformational: process reinvention and resource optimization; Strategic Data: competitive intelligence and decision support; Data Governance: regulatory compliance, audit trails, and durable enterprise value.

Above that is the Transactional Layer, where AI earns its most immediate and reliable return on investment: cost reduction and throughput improvement. Automated coding, protocol digitization, data cleaning automation, and database lock acceleration live here. These are “deploy now” investments: high readiness, high impact, with ROI that can be articulated clearly in any budget conversation.

As AI-enabled solutions become even more advanced, they move beyond processes where labor- and time-intensive activities are augmented. Solutions in the next level—the Transformational Layer—include content creation, such as documents, images, and programming that recreates and transforms legacy processes and operating practices. The ROI is even greater from these solutions as they generate streamlined processes and the reduction and replacement of capacity and resources.

The fourth tier is the Strategic Data Layer, where AI begins to enable a qualitatively different kind of intelligence: competitive benchmarks, CRO performance KPIs, delivery timelines, innovation signals, and the data that informs portfolio strategy. This is where AI moves from efficiency tool to decision-support capability.

At the apex is the Data Governance Layer: the hardest to build and the most durable in value. Cycle time monitoring, audit trail management, regulatory compliance tracking, and information control define this tier. Organizations with robust governance foundations will be better positioned as the FDA and EMA continue to develop and publish evolving guidance on AI/ML use in clinical trials.

Where Maturity and Investment Should Align

Not all solution categories are equally ready for deployment, and calibrating investment to maturity is one of the clearest differentiators between high-performing and struggling organizations in the AI/ML space.

Site performance prediction and risk-based monitoring are the most mature applications in the clinical AI landscape; in 2026, they are essentially standard industry practices. Sponsors not already leveraging these tools face a competitive disadvantage in cycle time and site performance. Automated medical coding and EHR patient matching follow closely, with meaningful adoption and documented business value.

Protocol simulation, digital endpoints, and protocol and clinical study report authoring are gaining ground but require more organizational readiness. Synthetic control arms and radiomics remain in an emerging maturity category despite their transformative potential; regulatory acceptance is the primary bottleneck, and organizations investing now are doing so as deliberate strategic bets, building expertise and audit trails in advance of broader acceptance.

A Roadmap for Cutting Through the Fog

The experience of building AI Centers of Excellence across the biopharma sector points toward a consistent set of success factors: a practical roadmap for moving from ambiguity to effective action in a way that respects both urgency and the principles of sound change management.

Start with governance before glamour. Establish a clear operating model with defined intake, prioritization, and portfolio management processes before selecting solutions. A standing AI Center of Excellence—with a leadership office for strategic oversight and a dedicated implementation team for delivery—provides the institutional backbone that individual pilots cannot. Without governance, accountability becomes diffuse, and proof-of-concept tools fail to convert into production-grade deployments.

Invest in the base of the pyramid. Data standardization, infrastructure integration, and workforce capability development are not optional prerequisites; they are the rate-limiting step. The most sophisticated AI algorithm is only as valuable as the data pipeline it relies on. Broad staff up-leveling, not just for technical teams but for clinical operations functions, dramatically improves adoption rates and the durability of AI as an enterprise capability.

Adopt a “rapid pilot” posture and resist the temptation of the “big bet.” Four-to-six-week pilots demonstrate ROI 40% faster than large-scale rollouts, and they surface integration requirements and governance needs before commitments become expensive. The clinical AI market is moving quickly enough that flexibility is itself a competitive advantage. Be especially wary of large, enterprise-wide investment decisions before pilot data exists.

Build human-in-the-loop design into every generative AI application, particularly those touching regulatory submissions. Maintaining meaningful human oversight is not a concession to risk aversion; it is a competitive differentiator. Companies building explainability and audit trails into their AI workflows now will be first movers as regulatory expectations formalize.

Finally, treat adoption—not deployment—as the primary goal. As Tufts CSDD research has made clear, the phrase “if you build it, they will come” does not hold in clinical research. Change management—communications, incentive alignment, grassroots buy-in at the function level—must be embedded in every AI/ML initiative from day one, not retrofitted after utilization falls short. Decentralization of ownership to those who bear executional risk is not a failure of central strategy; it is a necessary stage of mature deployment.

The Right Investments, Not the Most Investments

The fog of AI/ML support in clinical research will not lift on its own. It clears when leaders apply the discipline to categorize, prioritize, and sequence investments with the same rigor they bring to their clinical programs. The taxonomy exists. The maturity signals are visible. The pyramid provides a practical organizing principle that converts strategic ambiguity into a structured and effective plan.

The companies that will look back on this period as a turning point are not those that invested most aggressively. They are those that invested most thoughtfully: starting at the base, capturing near-term wins in the transactional layer, building toward strategic intelligence, and governing it all with clarity and accountability.

The fog clears one layer at a time.

References available upon request.

ringing a new drug to market remains a long, expensive process, and clinical trials account for much of that burden. Artificial intelligence (AI) now offers a credible way to compress parts of this cycle across scientific and operational work: trial simulation and synthetic controls, protocol design and feasibility, safety review, data cleaning and reconciliation, and drafting submission-ready documentation. In controlled settings, these applications have produced measurable productivity and quality gains. Yet a persistent gap remains between what AI can do in a pilot and what organizations can deploy in regulated, inspection-facing workflows.

This article synthesizes structured discovery interviews with 15 leaders across Clinical Development, Clinical Operations, Quality, and Data/IT Security at biotech and large companies, alongside evidence from the AI in Clinical Research track at SCOPE 2026. The picture that emerges is that these barriers are interlocking, not sequential: Trust, data readiness, and workflow integration must be addressed in parallel. Organizations that solve one while neglecting the others find deployment stalled for reasons their governance framework was never designed to address.

Trust Splits in Two: Operational versus Scientific Trust

Trust was the first concern raised in nearly every discovery interview, but not for the reasons most frameworks emphasize. IT and security teams block external AI tools because trial materials—patient-level safety narratives, proprietary program information, and unblinded analyses—cannot risk being exposed outside corporate controls. In parallel, hallucinations have created lasting skepticism: Multiple interviewees described direct incidents in which models generated confident, fabricated clinical content with no reliable mechanism for detection.

The key point is that “trust” is not a single requirement; it separates by function. For operations leaders—those responsible for data management, monitoring, and regulatory submissions—trust means audit trails, document provenance, and the ability to answer “who changed what, when, and why” during inspections. For R&D and scientific leaders—those designing trials, selecting endpoints, and making go/no-go decisions—trust means something fundamentally different: validated, disease-relevant models with transparent performance characteristics and interpretable evidence that a clinician (not an AI expert) can evaluate.

One hematology clinical development leader was blunt: The bottleneck is not access to large language models or document tools; it is the lack of validated models and fit-for-purpose data sets to build and test them. This leader relies on internal AI colleagues to translate model behavior into clinically actionable judgments of credibility, a dependency that does not scale.

This distinction has a practical implication. A governance program optimized for provenance and audit trails will not satisfy the scientific side of the house. And a validation program designed for predictive performance will not satisfy operational inspection requirements. In regulated deployment, both trust requirements must be met simultaneously.

Siloed Data Block Reliable AI

AI performance depends on data quality, consistency, and access. Yet clinical development data are typically distributed across dozens of systems—electronic data capture, trial management, safety databases, laboratory systems, and patient-reported outcomes—each with its own definitions, structures, and permissioning. Before organizations can evaluate whether to trust an AI tool, they often cannot even provide it reliable access to the data it needs.

At SCOPE 2026, one large biopharma company reported building a production AI assistant that consolidated 60+ terabytes from 50+ source systems. Their AI-powered analytic tool reached ~95% accuracy on natural-language queries—but only after they collapsed hundreds of fragmented tables into a small set of standardized, domain-specific tables with consistent definitions and role-based controls. Without that foundation, even high-performing models produced confident but incorrect answers—not because “AI was unreliable,” but because the underlying data relationships were ambiguous or inconsistent.

This is also why data standards are re-emerging as an AI enabler, not a compliance exercise. CDISC’s Digital Data Flow work has been modernizing long-standing standards toward more connected, interoperable models—an approach that makes clinical data more computable and, in turn, makes AI outputs more reliable. The implication is consistent across our research: Data readiness is not a step that follows governance. It is a prerequisite that must be built in parallel with governance if AI is to move from pilots into production.

Workflow Fit Determines Whether AI Scales

Even the most capable AI tool delivers little value if it cannot fit into how teams already work. And where AI does add efficiency, it is rarely “push-button.” Multiple interviewees emphasized that AI-assisted work still requires human oversight, iteration, and domain expertise—particularly in regulated contexts. Organizations that promise instant automation often end up creating the opposite outcome: frustration when the workflow remains iterative, followed by skepticism about whether AI is worth the effort.

This “workflow embedding” gap was a consistent theme in the AI in Clinical Research track at SCOPE 2026. Both our interviews and conference evidence pointed to a repeatable pattern: Small, cross-functional teams (often one technical lead paired with one clinical operations lead) moved faster than large, centralized AI initiatives. Broad “chat with everything” approaches—applying conversational AI across every system and document type—consistently disappointed, while targeted, high-value use cases succeeded. In one such deployment, an AI-powered protocol optimization tool was introduced to a small cross-functional team to flag design risks such as overly complex eligibility criteria or endpoint selection issues before they became costly amendments.

Adoption also tends to follow a bottom-up path. The tool only became mandatory after early users demonstrated impact and recruited peers organically. Mandates imposed before proof of value reliably stalled.

Perhaps the most revealing finding came from this deployment: More than half of the AI’s protocol optimization recommendations did not introduce new insights. Instead, they validated concerns teams already had and helped convert uncertainty into decision by providing a structured, documented rationale. The value was not in confirming a preferred answer; it was in providing source-linked, auditable evidence that could withstand regulatory scrutiny, turning informal consensus into inspection-ready documentation, AI’s most scalable role in clinical development may not be generating novel answers; it may be in giving teams source-linked, auditable evidence (and therefore the confidence) to act on what they already suspect, turning informal consensus into inspection-ready documentation.

What This Means for the Industry

The Manual State Is the Actual Trust Crisis
The industry’s instinct is to ask whether AI can be trusted. The more urgent question is whether today’s manual, duplicate-entry workflows deserve the trust they receive by default. In a multisite cancer clinical trials study of EHR-to-EDC workflows, manual entry produced a 5.8% field-level error rate; using an EHR-to-EDC application with human oversight reduced errors to 1.2%—a 79% reduction. Notably, the remaining errors were largely attributable to human entry, underscoring a practical point: The dominant risk in many workflows is not “AI hallucination” but routine transcription.

This matters because manual transfer is still common.

A 2025 eSource survey reported that 72% of sites continue to transfer data from eSource to EDC manually. Meanwhile, avoidable protocol churn remains expensive: Tufts CSDD has estimated the median direct cost of substantial protocol amendments at $141k (phase 2) and $535k (phase 3). These are not abstract inefficiencies. Every day a trial is delayed is a day patients wait for therapies that may already exist in a sponsor’s pipeline.

Overlay Beats Replacement
A consistent finding from both our discovery interviews and SCOPE 2026 conference evidence: Organizations cannot rip out and replace their existing infrastructure. As one former pharmaceutical executive noted, replacing a global trial infrastructure is not flipping a switch—it is a change-management project measured in hundreds of millions of dollars. Companies hesitate to abandon systems that work well enough, even when more efficient alternatives exist.

The AI deployments that succeed are those that overlay existing systems: reading data where it lives, integrating with current tools, fitting within established governance gates. They do not ask teams to learn new systems or abandon familiar workflows. They make existing processes faster and more reliable without requiring organizational transformation as a prerequisite.

A Readiness Checklist

Based on the evidence from our discovery interviews and SCOPE 2026 conference, teams preparing to move AI from pilot to production should assess readiness across all three barrier domains simultaneously:

Trust and governance:

• Provenance, auditability, and accountability: Can every AI output be traced to its source data with timestamps, user actions, and model versions? Is a named human owner responsible for each final output, documented in SOPs?
• Scientific validation: For predictive or scientific applications, is there a validation summary defining intended use, evaluation data sets, performance characteristics, error modes, and known limitations?
• In practice: One enterprise deployment linked every AI-generated protocol recommendation to its source data, model version, and the named clinical lead who approved it, creating an inspection-ready audit trail.

Data infrastructure:

• Standardization: Are critical sources standardized with consistent definitions, or is the model forced to navigate fragmented, inconsistent systems and table semantics?
• Access controls: Does clinical data remain within the sponsor’s controlled environment, with explicit governance over AI access, permissions, and any model training or fine-tuning?
• Data lineage: Can teams document where the data came from, how it was transformed, and which version was used for a given output?
• In practice: One large biopharma company achieved ~95% query accuracy only after collapsing hundreds of fragmented tables into standardized, domain-specific definitions with consistent role-based controls.

Workflow integration:

• Embedded gates: Is AI embedded into existing decision gates and governance processes (rather than operating as a standalone “side tool”)?
• Realistic operating model: Are expectations calibrated? Does the organization plan for human oversight, iteration, and exception handling (instead of full automation)?
• Adoption pathway: Is adoption designed to be bottom-up first (pilots with clear wins, project champions, and voluntary adoption by colleagues), before any mandate is imposed?
• In practice: A protocol optimization tool gained traction only after a small cross-functional team demonstrated impact; it became mandatory through voluntary colleague adoption, not top-down mandate.

Looking Ahead

The path from AI pilot to production in clinical development does not run through better models alone. It runs through solving three problems at once: earning trust through transparency and validation, building data infrastructure that AI can actually reason from, and embedding tools into workflows without demanding organizational upheaval as a prerequisite.

What is needed now is not more pilots. It is the unglamorous work of building trust infrastructure, standardizing data foundations, and redesigning workflows to include AI as a governed participant rather than a bolt-on experiment. A fully integrated approach remains ahead of current practice; to our knowledge, no organization has yet fully operationalized all three in parallel. Early enterprise deployments described in this article demonstrate progress on individual dimensions; organizations that begin this work in parallel, rather than sequentially, will be the first to move AI from conference demos to inspection-ready production.

For patients waiting for therapies already in development pipelines, the cost of that delay is not measured in dollars. It is measured in time they may not have.

rust in science is no longer assumed; it must be actively earned. In today’s information-rich environment, patients, caregivers, and healthcare professionals (HCPs) increasingly seek information that is not only accurate but also transparent, relevant, and easy to understand. At the same time, medical affairs, field medical, and medical writing teams are navigating rapid advances in artificial intelligence (AI), digital engagement, and evolving regulatory expectations. This convergence is reshaping how scientific information is generated, communicated, and applied.

Trust Is Built Through Clarity and Cocreation

Credibility in medical communication is shifting. Expertise still matters, but it is no longer enough. Trust now depends on transparency, authenticity, and accountability. Patients expect to understand how evidence is generated, where uncertainty exists, and how decisions affect them directly.

This shift places patient centricity at the core of Medical Affairs strategy. Leading organizations are moving beyond one-time engagement toward continuous cocreation with patients and caregivers. When patient perspectives are integrated early across trial design, endpoint selection, and medical communications, outcomes improve. Trials become more relevant, recruitment is more effective, and scientific information is more accessible.

However, gaps remain. Traditional endpoints often fail to capture lived experience. To close this gap, organizations must redefine success by incorporating patient-prioritized outcomes alongside clinical measures. Plain language, accessibility, and inclusive communication are no longer optional; they are essential to ensuring that information is understood and actionable.

From Channels to Impact

Medical Affairs is evolving from fragmented communication to a strategic, omnichannel model centered on audience needs. Success is no longer measured by volume, such as email opens or content views, but by meaningful impact. This includes changes in clinical behavior, depth of engagement, and the ability to influence decision-making.

A unified scientific narrative is key. When medical, commercial, clinical, and compliance teams align around a shared story, organizations can deliver consistent, high-value content across channels. Modular content strategies further enhance this approach, allowing teams to tailor messages without compromising scientific integrity, while also allowing the opportunity to update information in a timely manner.

From Insights to Strategy

Field medical teams, including Medical Science Liaisons (MSLs), are increasingly expected to translate frontline insights into actionable strategies. Medical insights are meaningful observations gathered from scientific exchange that help inform medical strategy. These insights inform not only engagement plans but also research and development (R&D) decisions and beyond. Rather than relying on traditional key performance indicators (KPIs), such as number of engagements, organizations are focusing on the quality and relevance of pre-identified insight topics gathered from engagement with HCPs, investigators, and patients. Medical insights transform information derived from scientific engagements into actionable strategy.

While there is no single industry-wide standard for defining “meaningful impact” or “depth of engagement,” these constructs are increasingly operationalized through framework-based approaches that assess behavioral, scientific, and decision-making outcomes.

For example, some organizations are moving beyond traditional activity metrics to evaluate whether scientific exchange led to changes in clinician understanding, follow-up scientific inquiries, identification of evidence gaps, participation in research initiatives, or adoption of educational resources. Others are implementing omnichannel engagement models that integrate insights gathered across in-person interactions, virtual meetings, medical information requests, and digital engagement platforms to better understand stakeholder needs and preferences over time. Additionally, Medical Affairs teams are increasingly using structured insight taxonomies, analytics dashboards, and cross-functional review processes to prioritize and translate frontline observations into strategic actions.

These models are evolving across organizations and are informed by emerging industry guidance on omnichannel engagement and Medical Affairs value measurement.

At the same time, collaboration across medical and commercial team leadership is essential to ensure that insights are effectively communicated and acted upon. Medical communication is becoming more personalized and interactive. Tools such as enhanced Scientific Response Letters (SRLs) and digital platforms allow for tailored, visually engaging content.

From Drafting to Leading Strategic Messaging

Medical writing is likewise transforming from a deliverable-focused service to a strategic capability that shapes the scientific narrative. The role now goes beyond describing outcomes to translating complex data into clear, accessible, inclusive language. In doing so, it influences how science is understood, trusted, and acted upon by regulators, HCPs, and patients across the product lifecycle. This change elevates patient-centric and plain-language communication from a “nice to have” to a core competency. And the focus shifts from communicating about patients to communicating with them through health-literate design, cocreation, and sustained engagement.

At the same time, medical writers are stepping beyond the document. They anticipate regulatory expectations, align cross-functional teams around a shared strategic message, and enable regulatory agility in a faster, more iterative review environment.

Leveraging Technology as an Enabler

Artificial intelligence (AI) is accelerating transformation across the medical affairs and scientific communication fields. AI enables faster synthesis, scalable content generation, and more personalized engagement. Tasks that once took weeks, such as literature reviews or content drafting, can now be completed in minutes. AI-driven analytics can help identify patterns and trends, enabling more informed decision-making. Yet, AI adoption alone does not create value. Organizations must align AI tools with clear use cases and redesign workflows accordingly.

Randomized studies tell us whether a medicine works under controlled conditions. Once a product enters routine practice, Medical Affairs and Value & Access teams face a different set of questions. But trust remains critical at all stages of product development. As AI accelerates drafting and synthesis, the differentiator becomes effective AI implementation through strong governance and human judgment, accuracy, transparency, and traceability, so that what is communicated is not only compliant and consistent, but meaningful and trusted by those who rely on it most. “Human-in-control” models ensure that outputs are transparent, traceable, and aligned with regulatory standards. In this context, AI acts as augmented intelligence, enhancing human expertise rather than replacing it.

However, these innovations must operate within increasingly complex regulatory environments. Differences across regulatory jurisdictions, particularly in Europe, require careful localization while maintaining global consistency and compliance.

In 2024, the European Medicines Agency (EMA) issued its first reflection paper to help medicine developers and marketing authorization applicants use AI and machine learning in a safe and effective way at the different stages of a medicine lifecycle. In 2025, the US FDA published its draft Considerations for the Use of Artificial Intelligence to Support Regulatory Decision Making for Drug and Biological Products. And in early 2026, EMA and FDA jointly identified and issued 10 guiding principles for good AI practice in the product lifecycle.

However, none of these efforts explicitly address the impact and use of AI in Medical Affairs and scientific communication. Future updates must specifically mention these fields/functions within the product lifecycle.

To support this evolution, teams must develop new capabilities. Cross-training, data literacy, and strategic thinking are essential. AI can reduce administrative burden, but human expertise remains critical for interpretation, storytelling, and ensuring scientific integrity.

Broader Implications

These shifts have far-reaching implications for the healthcare ecosystem. For patients, clearer and more inclusive communication improves understanding and supports informed decision-making. For HCPs, personalized and timely information enhances clinical practice.

For organizations, the stakes are higher. Trust is now a differentiator. Companies that fail to communicate clearly and in a timely manner risk losing credibility, while those that embrace transparency and cocreation can strengthen relationships across stakeholders.

At the same time, technology introduces both opportunity and responsibility. AI can improve efficiency and scale, but it also raises questions around bias, accuracy, and accountability. Organizations must balance innovation with governance, ensuring that technological advancements do not compromise scientific rigor or patient safety.

Finally, these changes redefine the role of medical affairs and scientific communications. No longer limited to information delivery, medical affairs, field medical, and medical writing teams are becoming strategic partners, driving insight, shaping decisions, and influencing outcomes across the product lifecycle.

Conclusion and Call to Action

The path forward is clear but requires deliberate action. Leaders in Medical Affairs and Scientific Communications must prioritize trust as a strategic objective, embedding transparency, patient voice, and clear communication into every aspect of their work. They must invest in the right capabilities, align teams around a shared scientific narrative, and adopt AI thoughtfully, with strong governance and human oversight.

Equally important, leaders must foster cultures that encourage curiosity, collaboration, and responsible risk-taking. Innovation will not come from tools alone, but from people empowered to use them effectively.

In a rapidly evolving healthcare landscape, the organizations that succeed will be those that connect science to real-world impact, delivering information that is not only accurate but meaningful, accessible, and trusted by those who rely on it most.

sk a clinical operations professional at a global sponsor company what community engagement means, and their answer will likely center on one word—recruitment—or on the related concepts of patient enrollment rates or site activation timelines. This is not wrong (community engagement does support recruitment), but this dramatically undersells what community engagement actually is and what it can do for a clinical trial from protocol conception through data lock to acceptance and implementation of trial outcomes.

Community engagement, practiced well, is a Quality by Design discipline in clinical research. It must be architected into a clinical trial from the earliest stages of protocol development, not activated as an afterthought when recruitment falls short. And the global sponsor community—navigating new expectations under ICH E6(R3) and an evolving landscape of decentralized and participant-centered trials—has a great deal to learn from how it has been done in these settings for decades and from specific experiences.

Several years ago, we both worked in quality oversight and clinical operations roles supporting the BOHEMIA trial (Broad One Health Endectocide-based Malaria Intervention in Africa), a large-scale, decentralized, cluster-randomized clinical trial conducted first in Mozambique and then in Kenya.

We came to that work with nearly six decades of combined experience from two different professions: one a GCP quality professional focused on quality system design, risk-based oversight, and inspection readiness, and the other a clinical operations leader with extensive experience managing trials across CRO, biotech, and pharmaceutical settings.

In Mozambique, community engagement activities were embedded within broader demography, social science, and field implementation efforts, including engagement with community leaders, rumor identification and mitigation, and ongoing dialogue within participating communities; these activities, however, were not yet as visible to operational oversight teams as they later became during the Kenya implementation.

In the Kenya arm, we encountered something neither of us had seen operationalized in quite this way before: a community engagement function that was not peripheral to the trial but was woven into its fabric. This reframed how we each think about community engagement—and how our two disciplines (GCP/quality and clinical operations) must work together to realize its full value.

To expand beyond our experience, we conducted semistructured interviews with community engagement leaders from three research institutions across four countries: Noni Mumba, Head of Engagement at the KEMRI-Wellcome Trust Research Programme in Kilifi, Kenya; Simon Peter Asiimwe, the Public Engagement Specialist at the Infectious Diseases Institute (IDI) in Uganda; and Professor Richard Adegbola, Chair of Nigeria’s National Health Research Ethics Committee (NHREC), whose career arc—from 19 years conducting community engagement in Gambian villages as part of the UK Medical Research Council program, to funder, to industry, to national regulator—gives his observations a depth that is rare in any conversation about clinical research governance.

We do not claim to speak for community engagement practice across Africa. We speak only to what we observed, and what these three experienced practitioners described from their specific institutional contexts in Kenya, Uganda, Nigeria, and The Gambia.

What Community Engagement Is—and Is Not

Community engagement in clinical research is frequently defined by what it facilitates rather than by what it does. But Professor Adegbola drew a sharp distinction that the global research community often blurs:

“It is not the community engagement team that provides input; it is the community. Community engagement facilitates that input into the protocol and the ethical review process.”

This is more than semantics. It means that the community engagement function does not speak for communities; it creates the structured conditions under which communities can speak for themselves and ensures that those voices reach the people making decisions about protocol design, feasibility, and conduct. The distinction matters because it defines where accountability sits and challenges the common assumption that having a community liaison on staff satisfies the requirement. Just as quality cannot be delegated to a single function and declared resolved, community engagement cannot be delegated to a single liaison and declared complete. It requires a system, which means defined roles, documented processes, trained personnel, and feedback loops that reach decision-makers.

Noni Mumba described the primary role of community engagement at KEMRI as fundamentally about relationship building and trust, and explained why trust is not a soft or supplementary goal but is an operational prerequisite:

“In our context, research is not always a well-understood activity in many LMIC settings, compared to high-income countries that have been conducting research for many decades. In our settings, research is relatively new, and hence building trust in science is very important. The main role is trust: enabling community members to understand what researchers are doing, and enabling researchers to understand local culture, social norms, and lived experiences so that even as they develop their research protocols, they are informed about the population they are going to recruit from.”

A clinical operations professional directly translates this into study feasibility. Protocols written without that understanding of the community tend to generate start-up delays, consent challenges, and retention failures—problems that are far more expensive to fix than to prevent. For a GCP quality professional, protocol development is the earliest possible point at which critical-to-quality factors can be identified and designed into the trial instead of being discovered during monitoring.

Simon at IDI Uganda added the dimension of scope, noting that what he does is more accurately described as public engagement than community engagement because the relevant stakeholders extend well beyond the communities in which research takes place:

“Stakeholders are much more than the community. There is mainstream media engagement, stakeholders at district and national levels, academia, other research institutions, the Ministry of Health, international partners—and most importantly, the identification of stakeholders for a particular study.”

What does this look like on the ground, in practice? KEMRI-Wellcome Trust does not organize community engagement as an informal outreach activity but as a dedicated institutional function embedded within the research program itself. Community engagement personnel participate in protocol and participant-facing material review, liaise with study teams and community leadership structures, and contribute directly to study planning and implementation oversight.

In addition, community engagement personnel also participate in systematic stakeholder identification and mapping across multiple levels (national health authorities, district health management teams, local government administrators, cultural and religious leaders, community health workers, and community members); the formation and ongoing support of community advisory boards (CABs) with clearly defined roles, training, and terms of reference (e.g., CAB charters); development and distribution of culturally appropriate information, education, and communication (IEC) materials in appropriate languages, adapted for communities with varying literacy levels; and continuous feedback mechanisms that allow the research team to identify and respond to concerns throughout the life of the study, not only at the start.

This was directly operationalized in the Kenya arm of BOHEMIA. Community engagement liaisons worked alongside site staff to develop the educational and engagement strategy and materials collaboratively, not in isolation. Before any information reached the broader community, presentations were first shared with community leaders, whose questions were addressed and whose concerns were formally relayed back to the study team. Materials were then revised as needed before being presented to the community at large; community questions were similarly collected and transmitted back to the study team. This iterative, multi-directional process—resembling the functional equivalent of a community advisory structure even where no formal CAB charter existed—was not improvised. The engagement strategy and materials were shared with both the local and national institutional ethics committees as part of the regulatory process.

Simon described IDI’s approach to community engagement as governed by a suite of strategies and standard operating procedures (SOPs) covering community entry, CAB coordination, ethical social media engagement, and co-creation of communication materials. SOPs create institutional continuity so that knowledge does not walk out the door when a practitioner moves on, which a quality professional immediately recognizes as the difference between a function and a dependency on individuals, and an operations professional recognizes as the difference between a study that can be scaled and replicated and one that cannot.

Required, Not Optional: The Regulatory and Institutional Mandate

One of the most important findings, and one that will likely surprise sponsors whose experience is primarily in North American or European trial settings, is that in each country represented here, community engagement in clinical research is not a best practice. It is a requirement.

In Uganda, a public engagement strategy is mandatory for national ethics approval by the research institution research ethics committee and the Uganda National Council for Science and Technology (UNCST). UNCST together with research institutions hosts annual national cross-CAB meetings that convene representatives from research organizations across the country to review study performance and refine national guidance. A protocol that arrives without evidence of community engagement is not allowed to proceed.

At KEMRI in Kenya, community engagement is embedded at the institutional review board (IRB) level before a protocol even reaches the national ethics committee. Mumba detailed the sequencing clearly: The engagement strategy must be documented in the protocol itself, reviewed by the IRB, which includes engagement staff charged specifically with reviewing protocols for adequate community engagement, and approved at that level before national review begins. Without that documentation, the protocol does not advance. The national ethics committee has also tightened its own expectations in recent years, consistent with changes to global research ethics guidance including the revised Declaration of Helsinki.

“About five years ago, they may not have asked any questions if a protocol landed on their desk without community engagement. Now they would—because the global ethics guidance has changed and is requiring community engagement,” Mumba explained.

In Nigeria, Professor Adegbola described NHREC’s review of approximately 354 protocols in 2025, with review timelines now targeting 60 days. Protocols without evidence of community engagement or CAB formation trigger queries from the committee and can delay or block approval. As Professor Adegbola noted, the regulatory and ethical landscape globally is moving in one direction: toward mandatory community engagement, not away from it.

Funders have arrived at the same position. Both the UK’s National Institute for Health and Care Research (NIHR) and the Wellcome Trust treat community engagement as a funded line item, not an unfunded expectation. Mumba noted that without a community engagement component, funding applications to NIHR face a much harder time during funding assessments. This is not altruism; it reflects hard-won institutional learning that studies without community engagement are operationally fragile.

For global sponsors, the implication is practical and unambiguous: Community engagement must be in the budget, in the protocol, and in the timeline from day one. It is not a contingency. For those accustomed to ICH E6(R3)’s emphasis on sponsor oversight and risk-proportionate monitoring, the framing will be familiar: Community engagement is not a compliance checkbox; it is a foundational element of a quality management system for any trial conducted in these settings.

he BOHEMIA trial (Broad One Health Endectocide-based Malaria Intervention in Africa; ClinicalTrials.gov: NCT04966702) was a cluster-randomized controlled trial evaluating mass drug administration (MDA) with ivermectin and albendazole for malaria transmission reduction. It enrolled approximately 28,932 adults and 2,871 children aged 5–15 across 84 clusters in 69 villages in Kwale County, Kenya, with three rounds of MDA conducted between 2021 and 2023. The trial achieved a 26% reduction in malaria incidence in the intervention arm.

In Kenya, this visibility was partly structural: Community engagement liaisons were not outsiders brought in to interface with the community, but recognized community members—health workers already known, trusted, and embedded in the communities they served. Their presence did not require introduction; it was already legible to the people they engaged. This made community engagement not just visible to the trial team, but visible as the trial to community members—seamlessly integrated rather than announced.

The result was a trial that could achieve a 26% reduction in malaria incidence partly because the communities it depended on were already engaged, already informed, and already invested.

The lesson, visible from both our perspectives, is the same: Community engagement that is documented, structured, and integrated into the trial’s quality and operational architecture produces results that neither monitoring infrastructure nor community goodwill can produce alone.

More Than Trials: Clinical Research as a Care Option

One dimension of community engagement in these settings that has no real equivalent in the experience of most global sponsors is the role that research institutions play in the broader healthcare ecosystem of the communities they work with, and the trust relationships that develop over time when community engagement is sustained rather than project-specific.

At IDI in Uganda, Simon described a public engagement infrastructure that includes peer mothers: individuals with lived experience in maternal health who support participants in ways that clinical staff often cannot.

“For some clinical studies, participants actually trust peer mothers more than they trust a doctor or research nurse. They confide in them because they know this is a person who has similar experience. If I explain to them the challenge I’m facing, they will easily understand what I’m going through.”

This is not a recruitment strategy. It is a care relationship. The peer mother is not trying to enroll anyone. She is providing continuity of human connection to someone navigating a health system that may otherwise feel impersonal, inaccessible, or intimidating. Home visitors trained in counseling perform similar functions, such as following up with participants between visits, providing guidance, and sometimes identifying safety concerns before they escalate. From a clinical operations standpoint, this is a retention mechanism that no site visit schedule can replicate. From a quality standpoint, it is an additional node in the safety signal detection network, one that reaches participants in the spaces between formal data collection windows.

At KEMRI in Kenya, Mumba described what two decades of sustained community engagement produces: a population that understands what research is, knows its rights as participants, and in some cases actively seeks out participation. “We do community engagement for the sake of building relationships, such that researchers can come into our research institute and find communities that are not research-naïve. For us, one of the successes is that we’ve been able to continue carrying on research successfully for almost four decades in the same community,” Mumba explained.

In the Kenya arm of BOHEMIA, this was observable in a specific and meaningful way: Given the general low literacy levels of the population, participant rights—including the right to decline—were identified as a critical-to-quality factor during study design. Significant effort was invested in ensuring that participants genuinely understood the research and what their consent indicated.

The results of that investment were visible in practice: Although community leaders and the community at large had agreed to the trial’s presence in their communities, some individual families declined to participate. Rather than representing a failure of engagement, the study team recognized this as evidence of success: Participants understood not only what they had agreed to, but more importantly, that they retained the right to say no.

In settings where community trust in the research institution has been built over time, participation in a clinical trial is not experienced as an experiment being done to a community. It is understood as a relationship of mutual benefit, in which the community gains access to monitoring, support, and potentially therapeutic benefit, and the research institution gains the access and cooperation that make science possible: Clinical Research As A Care Option (CRAACO).

Professor Adegbola raised a further question sitting at the ethical frontier of this concept: What happens to communities after the trial ends? In his view, informed by his 19 years working in Gambian villages and his current role reviewing trial protocols at the national level, to conduct a study without guaranteeing that the people who participated will have access to the intervention if it proves effective approaches unethical:

“I think we are moving to a stage where it would be unethical to do a study where you cannot guarantee that the people participating will have access to the outcome. That is what the whole idea of community engagement is to avoid: people being used. It is about respecting the individual, and the idea of justice.”

This is a challenge that community engagement alone cannot solve; it implicates pricing, access agreements, and the fundamental economics of drug development. But it surfaces through community engagement, because community engagement creates the conditions under which communities can articulate what they need and expect from research. And it is increasingly on the agenda of regulators and ethics committees as a criterion of ethical review, as Professor Adegbola noted.

What Makes Community Engagement a Critical Success Factor

Several specific mechanisms explain why community engagement functions as a critical success factor rather than a supplementary activity, and why those mechanisms are visible from both a quality and a clinical operations perspective:

It enables protocol feasibility, not just recruitment. When community engagement begins at protocol development (as it does at KEMRI and IDI), the protocol is written with genuine knowledge of the population it will involve. Investigators learn about local social structures, health-seeking behaviors, cultural norms, and potential barriers before those barriers become protocol amendments or recruitment crises. From an operations standpoint, it means the study is scoped and resourced for the community it will actually, not theoretically, enroll. From a quality standpoint, it is the earliest possible intervention point; community-related risks identified before protocol IRB submission won’t become monitoring findings.

It creates an early-warning system that monitoring alone cannot replicate. In the BOHEMIA trial, community-facing personnel were equipped with structured tools to collect participant-reported information, including adverse events, and established channels to transmit that information to the study team, creating an additional pathway for safety-related signals to reach medical monitoring alongside formal safety reporting processes. As our understanding of the community engagement liaison responsibilities deepened and it became clear that these individuals would be interacting directly with participants, families, and caregivers (and potentially hearing about adverse or serious adverse events before any formal report reached the investigator), the study team recognized and rectified this structural gap. Community engagement liaisons were trained on the protocol and purpose-built data collection tools were developed so that, in the event a liaison became aware of an adverse event or serious adverse event (AE/SAE), there was a defined, structured method for capturing and transmitting that information through appropriate channels to the study team.

From a GCP quality perspective, this is a recognizable risk control: a supplementary signal detection mechanism that captures what formal data collection windows miss. From an operations perspective, it is the difference between learning about a problem at the next scheduled monitoring visit and learning about it in time to act. Communities talk to people they trust before they talk to investigators. A well-functioning community engagement infrastructure captures that conversation.

It supports retention, not just enrollment. In these communities, participation in a clinical trial is not typically an individual decision. It is a family decision, sometimes a community decision. When families and community members understand the study, they support their loved ones to remain in it. When they do not, participants often find themselves under significant social pressure to withdraw regardless of their own wishes. For operations teams managing dropout rates and protocol deviations attributable to early withdrawal, this is one of the most consequential dynamics in trial conduct, and yet one of the least visible in standard monitoring reports. From a quality standpoint, retention directly affects data completeness and reliability; community engagement is a data quality intervention as much as an operational one.

It builds the infrastructure that makes future research possible. The KEMRI model demonstrates what sustained community engagement produces over time: a community that understands informed consent, knows its rights, and actively participates in shaping how research is conducted—and an institution that carries that relationship forward across studies and decades. Professor Adegbola described a parallel dynamic from his Gambia experience: communities that would ask between studies, “When are we doing something again?” This is the opposite of the skepticism, resistance, and misinformation that characterize community-research relationships where engagement has been transactional or absent. It also produces inspectability: Community engagement governed by SOPs, trained personnel, and defined roles becomes an auditable element of the trial record, which is directly relevant to GCP inspection readiness and, in the authors’ view, a meaningful differentiator in the quality of sponsor oversight.

Lessons for the Global Sponsor Community

These experiences are specific to Kenya, Uganda, Nigeria, and The Gambia. We do not broadly extrapolate them to Africa or claim that every element of these models translates directly to every trial setting.

We do propose that the underlying design principles—community engagement built in from the start, resourced adequately, governed by clear roles and documented processes, and evaluated for its contribution to study outcomes—are transferable across sponsor organizations and trial settings. They are, in fact, precisely what ICH E6(R3) points toward; specifically, the guidance’s emphasis on stakeholder engagement, participant-centered trial design, and reducing unnecessary burden on sites and participants.

All the above collectively offer the following best practices framework:

• Budget for community engagement as a named line item in both the protocol and the study budget, not as an unfunded expectation that is delegated to the site. Adequate, dedicated resourcing is the most reliable predictor of whether community engagement is sustained or abandoned under operational pressure. It also signals to sites and communities that they are valued, not merely tolerated.
• Engage community engagement professionals during protocol development, before feasibility assessment and well before initiating recruitment. Protocol design is the earliest and most consequential point of intervention for identifying community-specific quality risks and for ensuring that the operational plan reflects the real conditions in which the study will run.
• Require context-specific community engagement strategies that reflect local social structures, cultural norms, and stakeholder landscape, not generic templates. What works in one community may not translate to another; the same logic that argues against one-size-fits-all monitoring plans applies here.
• Establish clear roles and boundaries between community engagement, informed consent, clinical monitoring, and safety reporting to prevent confusion, duplication, or gaps. These functions are distinct and complementary, and clarity in their responsibilities and boundaries is a quality management requirement and an operational necessity.
• Integrate community engagement into quality and risk management frameworks as a formal, recognized source of information about participant experience and emerging safety signals prospectively built into the study at the protocol stage rather than discovering them retrospectively during oversight reviews.
• Define meaningful success metrics. Study completion rates, retention rates, and community trust indicators are the primary outputs of effective community engagement, alongside the critical-to-quality factors established for community engagement at the protocol stage and monitored throughout.
• Support capacity building at investigator sites including investment in CAB infrastructure, community engagement SOPs, and trained personnel. Sites cannot deliver what they have not been resourced and trained to deliver.
• Engage the institutions that have been doing this for decades as genuine partners, not merely as access points to patient populations. KEMRI, IDI, and NHREC hold expertise and institutional memory from which the global sponsor community would greatly benefit. This engagement begins at study design, not at contract negotiation.

Conclusion

One phrase in quality management applies here with direct relevance: Quality cannot be inspected in; it must be designed in. The same is true of community engagement. It cannot be activated when recruitment falters, retrofitted when communities raise concerns, or satisfied by a checkbox at protocol submission. It is built into the architecture of the research settings described above from the moment a study concept is conceived because these practitioners have learned through experience, through failure, and through decades of relationship building that there is no other way to do it well.

From a GCP quality standpoint, this is a “culture of quality” argument: These organizations in these settings have embedded community engagement into their quality management systems, not bolted it on as an appendage. From a clinical operations standpoint, it is a “feasibility and execution” argument: Trials in these settings succeed not (only) because they are staffed by exceptional individuals but because the systems those individuals work within are designed for the specific environments in which they operate.

Global sponsors navigating new regulatory expectations, evolving trial models, and increasing pressure to demonstrate ethical and equitable research conduct do not need to build these systems from scratch. The knowledge exists. The practitioners who hold it are not asking to be discovered. They are asking to be taken seriously.

Community engagement, done well, is community engagement by design. The question is whether the global sponsor community—including GCP professionals and clinical operations leaders—is ready to design it in.

he TRACE (Trial Regulation and Clinical Ethics Optimization) Project is a multi-country African initiative launched in 2025 to strengthen and harmonize clinical trial ethics and regulatory oversight. The below Q&A with Elly Kourany-Lefoll and Collins Mitambo of the TRACE Project team details its work with national ethics committees, regulators, and research institutions to make review systems more transparent, predictable, and efficient through capacity building, policy and standard operating procedures (SOP) harmonization, digitalization, sustainable financing models, and stronger coordination with regional and continental frameworks. TRACE has been active in Nigeria, Rwanda, Tanzania, and Zimbabwe, and has recently expanded into Kenya.

Framework and Structure

Where and how does/will the TRACE project align with the African Medicines Agency framework?

The Trial Regulation and Clinical Ethics Optimization (TRACE) Project in Africa strengthens national clinical trial ethics and regulatory systems in ways that directly support the objectives of the African Medicines Agency (AMA). While AMA operates at the continental level to promote regulatory harmonization and reliance, TRACE focuses on building the local national foundations for clinical trials in terms of governance, transparency, predictability, digital infrastructure, and reviewer capacity, to enable effective participation in regional convergence frameworks.

TRACE operations include five technical subteams. Would you briefly explain the short- and long-term goals for each team?

The five TRACE technical subteams, and their short- and long-term goals, are:

• Harmonization
  Short-term: Align SOPs, clarify roles, and streamline review pathways within countries for clinical trials.
  Long-term: Position national systems to engage confidently in regional reliance and AMA-aligned processes.
• Clinical Trial Capacity Building
  Short-term: Deliver structured training, strengthen reviewer competence, and support formal registration and accreditation processes for Institutional Review Boards (IRBs).
  Long-term: Institutionalize continuous professional development for ethics committee members and advance IRB accreditation, reinforcing quality standards and institutional credibility for IRBs and National Ethics Committees (NECs).
• Digitalization
  Short-term: Upgrade ethics and regulatory platforms to improve traceability and end-to-end oversight of a clinical trial, from initial submission through review and approval, implementation, amendments, safety reporting, inspections, and trial closure/reporting.
  Long-term: Develop interoperable systems that support transparency and future cross-border collaboration.
• Financing Models
  Short-term: Address critical funding gaps and stabilize the core operational capacity of ethics committees, including staffing, review processes, and administrative functions.
  Long-term: Establish financially sustainable oversight institutions supported by predictable and diversified funding mechanisms that ensure continuity of operations and reduce reliance on external funding.
• Communication
  Short-term: Improve transparency and visibility of TRACE activities and outputs through clearer public communication, including newsletters, website content, and stakeholder engagement.
  Long-term: Build institutional credibility and trust with both local investigators and global sponsors through consistent, transparent, accessible communication. The communication workstream focuses on transparency and visibility of TRACE activities as a whole including system reforms, tools, and country progress, not only on clinical trials. Clinical trial oversight is central, but the objective is to strengthen communication and engagement across the wider clinical research ecosystem.

TRACE’s monitoring and evaluation framework tracks measurable outputs, such as adoption of harmonized SOPs, digital system functionality, training completion, and reduced variability in regulator and ethics committee clinical trial application review timelines, to progressively align national reforms with AMA principles of regulatory convergence, transparency, and predictability.

In essence, TRACE strengthens the national pillars of clinical trials oversight and, more broadly, clinical research governance, upon which effective continental harmonization depends.

System Reform and Harmonization

TRACE is working across countries with very different legal, regulatory, and ethics review traditions. What has been the hardest element to harmonize so far: processes, timelines, standards, or institutional incentives? And why?

The most difficult element to harmonize has been institutional mandates embedded in different legal and regulatory traditions.

Across TRACE countries, workflows can be mapped and timelines optimized. However, authority is distributed differently between national regulatory authorities and ethics committees, often in ways defined explicitly by law. Where mandates overlap or are ambiguously defined, reform requires more than procedural alignment; it requires governance clarification and, in some cases, legislative reform.

For example, in Zimbabwe, modernization efforts intersected directly with drafting a new Medical Research Bill to replace a legal framework dating back to the 1970s. The bill governs all operational and clinical research involving human participants in Zimbabwe. Without an updated legislative foundation, digital reforms and streamlined review pathways could not be sustainably anchored.

In Nigeria, the ongoing revision of the National Code of Health Research Ethics is addressing the role of the National Health Research Ethics Committee (NHREC) in relation to institutional ethics committees, with the aim of strengthening national coherence and clarifying oversight responsibilities. This process illustrates how harmonization often depends on parallel legal and policy reform.

At the continental level, TRACE has worked with the WHO-African Vaccine Regulatory Forum (AVAREF), which plays a critical role in promoting joint reviews, regulatory collaboration, and alignment of clinical trial standards across countries. However, through the harmonization workstream, AVAREF also faces challenges such as varying levels of institutional capacity, differences in national legal mandates that limit reliance models, and uneven digital maturity across authorities. These experiences demonstrate that harmonization is less about synchronizing timelines and more about aligning governance structures within diverse legal frameworks. Without clarity of institutional authority, even well-designed operational reforms struggle to scale nationally.

Much of the reform narrative focuses on reducing delays and duplication in clinical trial application review processes. How do you ensure that gains in efficiency do not come at the expense of ethical rigor or contextual sensitivity?

Much of the reform narrative focuses on reducing delays and duplication of clinical trial application (CTA) review and approval processes, but TRACE is explicit that efficiency must not come at the expense of ethical rigor or contextual sensitivity.

Process optimization targets redundancy, not scientific scrutiny. In Rwanda, for example, SOP reviews streamlined workflow steps while preserving ethics committee quorum requirements (i.e., maintaining the minimum number and composition of members required for valid decision-making), independent scientific assessment, and documentation standards. The objective was clarity and predictability, not dilution, of clinical trial protocol review.

Similarly, in Kenya and Rwanda, parallel submission pathways are being developed to allow ethics and regulatory review to proceed concurrently rather than sequentially. This removes unnecessary waiting periods but maintains independent decision-making and full technical assessment by each authority.

Capacity building further safeguards rigor. Training on complex trials, safety oversight, and emerging issues such as AI ensures that faster workflows are supported by stronger expertise. Digital platforms enhance traceability and accountability through audit trails and structured templates.

Several countries are piloting joint or better-aligned ethics and regulatory workflows. From your perspective, what signals indicate that harmonization is improving review quality rather than just speed?

While several countries are piloting more aligned or parallel ethics and regulatory workflows (such as Tanzania and Kenya, for example), formal cross-country joint protocol review is not within the core scope of TRACE. That mandate sits more directly with initiatives such as AVAREF. TRACE focuses instead on strengthening national systems including governance, process alignment within countries, digitalization, and capacity building.

Harmonization improves quality, not just speed, when it results in clearer, risk-proportionate standards, stronger and more consistent scientific queries, better-documented decisions, and enhanced reviewer capacity.

However, TRACE integrates capacity building of EC members (both NECs and IRBs) as a core pillar of harmonization. Through targeted technical assistance and training on regulatory science and research ethics, TRACE strengthens the competence and confidence of reviewers; Tanzania and Zimbabwe, for example, have recently completed member training for several IRBs. Through the Capacity Building workstream, TRACE will ensure that harmonization does not dilute standards but instead strengthens their consistent and rigorous application, aligned with international best practices.

Digital Transformation

Digital ethics platforms are a major pillar of TRACE’s approach (e.g., Nigeria’s NHREC e-Portal, Zimbabwe’s ethics committee new portal). What design or governance choices have proven most critical to adoption by ethics committees and regulators?

Digital platforms are a major pillar of TRACE, but adoption has depended far more on governance and collaboration than on the technology itself.

In Nigeria, for example, rollout of the NHREC e-Portal, which supports the submission, review, and tracking of research protocols, was accompanied by structured training for ethics committees and secretariats. Hands-on training sessions, user guidance, and phased onboarding helped demystify the system and build confidence. Adoption improved because committees were not simply given a platform; they were supported through the transition.

In Zimbabwe, the development of the new Medical Research Council of Zimbabwe (MRCZ) ethics portal was characterized by very strong collaboration between MRCZ and the Medicines Control Authority of Zimbabwe (MCAZ). Early alignment between the ethics and regulatory authorities ensured that key workflows (including protocol submission, document management, completeness screening, reviewer evaluation, and overall application processing) were coherent and that institutional roles were respected. This collaboration increased trust in the system and reduced resistance to change.

Across countries, several governance choices have proven critical to strengthening clinical research oversight systems, including clinical trials:

• Early and continuous stakeholder engagement
• Clear institutional ownership
• Alignment of digital workflows with national SOPs and legal mandates
• Investment in training and change management
• Cross-institutional collaboration where mandates intersect.

Across countries, what recurring digital challenges (connectivity, payments, interoperability, user training) could become bottlenecks, and how is TRACE addressing them early?

Recurring digital challenges across countries include interoperability between institutions, system optimization to accommodate new functionalities (such as adverse event monitoring), and sustained user capacity. While clinical trials are a primary focus, these challenges apply more broadly to clinical research oversight systems, which must also support other types of research—including behavioral and social research—reviewed by ethics committees and which must also be captured, managed, and routed within digital systems. Therefore, interoperability, system functionality, and user capacity must be designed to support the full research ecosystem, not just clinical trials.

As platforms mature, many authorities are expanding their purpose beyond protocol submission to support end-to-end lifecycle oversight of clinical research, including review, amendments, safety reporting, and study closure. This requires upgrading existing systems that were not originally designed for modular expansion. Without early technical and governance alignment, such upgrades can strain legacy architecture and institutional workflows.

Tanzania provides a useful example. Under TRACE, upgrades to the National Health Research Management Information System’s (NIMR) Research Ethics Information Management System (REIMS) include interoperability with the Tanzanian Medicines & Medical Devices Agency’s (TMDA) Regulatory Information Management System (RIMS) for real-time clinical trial status access, automated turnaround-time calculations, red-flag alert systems, and integration with national institutions such as the Tanzania Commission for Science and Technology (COSTECH). These enhancements illustrate the shift from static submission portals to active oversight tools.

TRACE addresses these bottlenecks early through co-design, joint technical planning across institutions, phased implementation, and broad, structured user training on digital systems across clinical research workflows (not just one protocol or trial). Digital reform is therefore approached not as a one-time build, but as an iterative governance and capacity-strengthening process.

Scoping to Implementation to Scale

As TRACE moves from scoping to implementation, what early indicators are you watching most closely to decide whether a country is ready to scale clinical trial reforms nationally?

As TRACE moves from scoping to implementation, we focus less on activity completion and more on institutional readiness signals. Key early indicators include:

• National regulatory authority ownership of (and leadership commitment to) reforms; revision of the National Code of Health Research Ethics in Nigeria, for example, signals institutional willingness to anchor reforms in updated governance frameworks.
• Legal and regulatory alignment, such as in Zimbabwe, where legislative updates are underway to strengthen oversight of research involving human participants, including clinical trials. Scaling is unlikely without this foundation.
• Operational use of new systems, not just developing digital platforms but generating pilot evidence of their successful use in CTA reviews and decision-making.
• Cross-institutional coordination, particularly between ethics committees and regulatory authorities to reduce duplication and fragmented oversight in clinical trial review.
• Sustainability planning including budget allocation, IT support structures, and training plans that pick up where TRACE funding ends.

Scale becomes realistic when reforms are embedded into routine practice, supported by governance structures, and internally owned. This also includes ensuring that sustainable financing mechanisms are in place to support clinical trial oversight beyond TRACE funding. Without these signals, national expansion risks replicating tools rather than strengthening systems.

Additional TRACE examples illustrate these readiness signals in practice. In Tanzania, interoperability between NIMR’s research ethics system and TMDA’s regulatory platform is being developed to support real-time tracking of clinical trial status and improved coordination across institutions. In Nigeria, TRACE is supporting the strengthening and optimization of the NHREC e-portal, including user training and system enhancements to improve functionality and adoption in clinical trial review processes.

With Kenya now included in TRACE, what lessons from the first four countries are shaping how TRACE approaches expansion?

As TRACE has expanded to include Kenyan institutions, several lessons from the first four countries (Rwanda, Tanzania, Nigeria, and Zimbabwe) are shaping our approach.

First, clearly defined objectives supported by a robust monitoring and evaluation framework are important. Early experience demonstrated that broad reform ambitions must be translated into concrete outputs, measurable outcomes, defined timelines, and accountable functional leads; for example, designated focal points within national regulatory authorities, ethics committees, and implementing partners responsible for delivering specific activities. Expansion is therefore anchored in structured workplans and KPI frameworks from the outset.

Second, early and deliberate cross-institutional collaboration is essential. In Kenya, four institutions are engaged in CTA review from the very beginning: the Pharmacy and Poisons Board (PPB), the National Commission for Science, Technology and Innovation (NACOSTI), the Kenya Medical Research Institute (KEMRI) Ethics Committee, and the Moi Teaching & Referral Hospital/Moi University – Institutional Scientific & Ethical Review Committee (MTRH/MU-ISERC). A central priority is enabling parallel review of clinical trial applications between ethics committees and PPB, alongside strengthening digital systems to improve workflows within each institution. Another key objective is to ensure appropriate alignment of information flows between IRBs and NACOSTI, without requiring full system integration. Experience from other countries has shown that digital implementation and parallel review mechanisms can become fragmented if governance arrangements, data flows, and institutional roles are not explicitly aligned early.

Third, digital reform is approached as a joint governance exercise, not merely a technical deployment. Interoperability, coordinated SOP development, and harmonized parallel review pathways are prioritized to prevent duplication and siloed oversight structures.

In short, expansion is no longer about replicating tools. It is about embedding structured objectives, measurable outcomes, and institutional coordination mechanisms from the outset to ensure that reforms are coherent, scalable, and nationally owned.

Your March 2026 newsletter provides updates on several national initiatives; would you please recap a few highlights from these or other national initiatives?

The March 2026 TRACE Newsletter reflects a clear shift from scoping to implementation across participating countries.

The TRACE project in Rwanda moved beyond system assessment to concrete system design. This includes the validation of revised SOPs for administrative screening, initial review, and monitoring, which are set to be featured on the recently revamped Rwanda National Ethics Committee (RNEC) website. The team has finalized a technical roadmap to integrate a new locally owned digital ethics review system with the Rwanda Food and Drugs Authority’s Integrated Regulatory Information Management System (IRIMS) platform, which is expected to directly address critical vulnerabilities identified during a digital needs assessment of the previous third-party system solution.

In Nigeria, nationwide rollout of the NHREC e-Portal, accompanied by user training of more than 60 ethics committee members, demonstrates that digital reform is not merely conceptual but is operational. Importantly, discussions on sustainable fee models and the ongoing review of the National Code of Health Research Ethics indicate that governance and quality improvement are advancing alongside digitalization.

In Tanzania, strengthened coordination between the National Health Research Management Information System (NIMR) and the Tanzanian Medicines & Medical Devices Agency (TMDA), and plans to enhance its National Research Ethics Information Management System (NREIMS) to manage amendments and serious adverse events, illustrate a move toward lifecycle oversight and regulatory-ethics synergy. Tanzania also achieved a landmark milestone with the development of the National Traditional Medicine Research Guidelines, a move that diversifies the clinical trial ecosystem by bridging regulatory gaps for traditional medicine research.

In Zimbabwe, modernization efforts combine legislative reform, through drafting a new Medical Research Bill, with the launch of a revamped website for the Medical Research Council of Zimbabwe (MRCZ) and development of an integrated digital review system linking the MRCZ and the Medicines Control Authority of Zimbabwe (MCAZ). This dual approach addresses both structural and operational bottlenecks.

Kenya has transitioned into the project following a successful prioritization meeting in Nairobi, focusing on a costed concept note to enable parallel review pathways and sustainable financing.

Additionally, TRACE’s benchmarking visit to the Uganda National Council for Science and Technology (UNCST) highlights a forward-looking commitment to learning from established digital platforms, particularly regarding interoperability and joint review models. Finally, the project reached a collaborative milestone with the successful kick-off of the TRACE Bioethics Forum webinar series, led by the Multi-Regional Clinical Trials (MRCT) Center of Brigham and Women’s Hospital and Harvard. The inaugural session, hosted by Rwanda, focused on the complex ethical considerations of the Marburg Virus Outbreak clinical trials. By sharing lessons learned from the outbreak, the forum has established a vital platform for institutional growth and ethical preparedness across the continent.

Long-Term Vision and Impact

Looking ahead five to 10 years, what would success look like for TRACE engagement in Africa: stronger institutions, a larger pipeline of locally led trials, or a different relationship between African regulators and global sponsors?

Success for TRACE would be evidence of system-level transformation anchored in transparency, efficiency, and predictability related to clinical research.

First, success would mean stronger, self-sustaining institutions responsible for clinical research oversight; i.e., ethics committees and regulatory authorities operating under stable funding/financial models, clear mandates, updated/current legal frameworks, and interoperable digital systems. Transparent processes, publicly available guidelines, and traceable digital workflows would make decision-making by ethics committees and regulatory authorities clearer and more accountable. Predictable timelines and standardized procedures would reduce uncertainty for investigators and sponsors alike.

Second, we would expect to see a larger and more diverse pipeline of locally led clinical trials. When review systems are transparent and predictable, local investigators are better positioned to initiate research aligned with national health priorities because they can be confident that oversight processes are rigorous but navigable.

Third, success would reshape the relationship between African regulators and global sponsors. Transparent standards and predictable processes foster trust. Instead of being perceived as opaque or slow, regulatory and ethics authorities would be recognized as reliable, technically competent partners who are fully integrated into the global research ecosystem and yet retain contextual authority.

Ultimately, TRACE’s long-term impact would be measured not only in shorter regulatory timelines but in institutional credibility, research sovereignty, and a culture of accountable, high-quality oversight grounded in transparency and predictability.

How can regulatory and ethics professionals interested in your work learn more about or perhaps even support/contribute to your work?

Regulatory and ethics professionals interested in TRACE can engage with the project in several ways.

First, we encourage colleagues to visit the TRACE website to access newsletters, country updates, and technical insights. Professionals can also connect directly via connect@traceclinicalethics.com or follow TRACE on LinkedIn and X (@traceinafrica on both platforms) for ongoing updates.

Second, TRACE welcomes technical collaboration and peer learning. Regulatory authorities, ethics committees, academic institutions, and digital health experts may contribute through benchmarking exchanges, harmonization dialogues, capacity-building initiatives, and knowledge sharing on topics such as digital oversight systems, IRB accreditation, reliance models, and sustainable financing frameworks.

Third, institutions interested in strengthening their own ethics and regulatory systems may explore partnership discussions, particularly in areas aligned with TRACE’s five technical workstreams.

TRACE is fundamentally collaborative. Strengthening clinical trial oversight across Africa requires a growing network of professionals committed to transparency, predictability, and high-quality review. We welcome engagement from those who share this vision.

On Sustainability and Financing

How is TRACE addressing financial sustainability of clinical trial oversight systems?

Financial sustainability is a core component of TRACE, as effective clinical trial oversight depends on reliable and predictable resources. Across countries, key challenges include limited and fragmented funding, reliance on donor support, and constrained financial autonomy of ethics committees and regulatory authorities. TRACE supports countries in addressing these gaps by developing practical financing approaches, including structured review fee models for clinical trial applications, financial planning tools, and revenue projections aligned with national systems. The objective is to enable oversight institutions to operate sustainably, support essential functions such as review and monitoring, and progressively strengthen their independence and readiness for higher levels of regulatory maturity.

rug discovery, clinical research, therapeutic product development, and their regulation continue to evolve and expand throughout Africa. But are the manufacturing and supply chains in these product pipelines keeping up with this progress? Where can companies invest, take advantage of these growing opportunities, and deliver safe and efficacious products to patients there? Learn more in this Q&A with IFPMA Director, Scientific and Regulatory Affairs, Janis Bernat.

Janis Bernat (JB): The pharmaceutical industry is a key driver of clinical research, with clinical trials underpinning the development of safe, effective medicines and strengthening healthcare systems through high-quality evidence generation. In addition to scientific expertise, the industry brings unique operational readiness built on extensive experience conducting thousands of clinical trials involving large and diverse patient populations under Good Clinical Practice (GCP) conditions. This deep, practical experience in trial design, execution, data integrity, and regulatory compliance represents a distinctive capability that the industry contributes to the clinical research ecosystem.

Across Africa, drug discovery, clinical research, therapeutic development, manufacturing, and patient access are moving in a forward-looking direction. This progress is reflected in the emergence of centers of research excellence, such as the H3D Centre for Drug Discovery, which demonstrate the continent’s growing scientific and operational capacity. More broadly, research activity is expanding across priority areas such as infectious diseases, noncommunicable diseases, vaccines, and oncology. This is supported by growing multistakeholder initiatives, involving patients, multilateral organizations, development banks, government stakeholders, and the biopharmaceutical industry.

Investments in clinical trial site capacity and regulatory strengthening are contributing to a more mature, ethical, and scientifically robust ecosystem, laying the foundation for sustainable innovation and improved patient access over time.

The continent’s untapped potential is evident in its growing clinical trial capacity, maturing regulatory frameworks, and developing network of partners. To name a few, these include the Africa Clinical Research Network (ACRN), EDCTP, and AdVAncIng Clinical TRials Excellence in Africa (AVAREF).

GF: What are some of the major challenges in turning clinical discoveries into clinical care products that can be manufactured by companies in Africa for patient access in Africa?

JB: Turning clinical discoveries into clinical care requires robust clinical trials, efficient regulatory and ethics review, and health-system readiness to deliver innovative therapies safely. While scientific capability and clinical expertise are growing across Africa, progress is often slowed by fragmented and sequential regulatory and ethics approval processes, limited use of risk-based approaches, and insufficient reliance and work-sharing mechanisms.

These challenges can result in duplicative, costly, and time-consuming approval pathways, particularly for multicountry trials, delaying evidence generation and patient access to innovative treatments and discouraging further research investment.

Greater harmonization with international standards, predictable timelines, parallel regulatory and ethics reviews, and wider use of reliance frameworks would significantly accelerate clinical development while maintaining patient safety and data integrity. Recognizing these challenges, we hosted the Africa Regulatory Conference from September through November 2025, creating a dedicated platform for open dialogue among regulators, ethics committees, industry, and other stakeholders to advance regulatory convergence, reliance, and practical, implementable solutions.

GF: Where/what is the biggest opportunity for pharmaceutical/biotech manufacturers in Africa right now?

JB: The biggest opportunity for pharmaceutical and biotech manufacturers in Africa lies in the emergence of a more harmonized and enabling policy environment for scientific innovation, notably through the African Medicines Agency (AMA) and the African Continental Free Trade Area (AfCFTA). Together, they create the conditions for reduced regulatory fragmentation, greater predictability, and scale across markets, enabling Africa’s growing scientific capability to translate more rapidly into patient access and health system impact. There is opportunity to upgrade requirements, digital systems, and skills according to international standards.

Another opportunity is in regulatory reliance. By allowing regulators to leverage scientific assessments from trusted authorities, reliance can reduce duplicative reviews, shorten approval timelines, and make better use of limited regulatory resources, while maintaining high standards of patient safety, quality, and data integrity. IFPMA recently explored a series of principles for effective reliance in clinical trial reviews and consideration for implementing reliance in these reviews.

At the same time, Africa’s pharmaceutical manufacturing potential depends on creating the right conditions for geographically diversified production to succeed. This requires three mutually reinforcing elements. First, a demand-driven market that makes investment viable. Second, the infrastructure to operate—from healthcare systems that can absorb and deliver products to the supply chains and skilled workforce that keep production running. Third, policy coherence: the regulatory frameworks, intellectual property protection, open trade, and legal certainty that give companies the confidence to commit for the long term.

GF: Where and how can IFPMA support the work to overcome these challenges and advance opportunities for manufacturing growth and patient access in Africa?

JB: For those working to improve the clinical research and trials ecosystem in Africa, enhancing the visibility of capacity, fostering trust between researchers, regulators, sponsors, and the community, and increasing investments in ethical clinical trials in Africa all remain critical goals.

IFPMA and its members are already supporting ecosystem development through initiatives such as the ATOM Coalition, Access Accelerated, the African Medicines Agency Treaty Alliance (AMATA), engagement with the AfCFTA Secretariat and AMA, Fight the Fakes, and the H3D Foundation to promote innovation driven in Africa.

With the African Medicines Agency moving towards operationalization, the continent has a chance to accelerate approval timelines and strengthen regulatory processes.

A key priority is better integrating clinical trials into the care continuum and strengthening meaningful engagement with patients and patient advocates. Despite growing research activity, in Africa fewer than 3% of patients participate in clinical trials, highlighting an opportunity to improve inclusion. Industry is increasingly applying diversity by design as a scientific imperative—selecting sites based on disease burden and population relevance, engaging communities to build trust, ensuring culturally appropriate consent processes, and working with patients to define meaningful endpoints. We are contributing to training initiatives supporting operational readiness with our global clinical trialist expert experience.

These efforts help ensure that trials are scientifically robust, ethically responsible, and executed efficiently and safely.

frica is increasingly at the center of global clinical research, not only as a site for trials but as a critical partner in advancing new interventions for infectious diseases. This is particularly important for tuberculosis (TB), which continues to place a disproportionate burden on many African communities, driving high morbidity and mortality, especially among adolescents, adults, and people living with HIV.

Global Forum spoke with Devika Zachariah and Dakshina Reddy of the Gates Medical Research Institute (Gates MRI) about these efforts, which aim to overcome long-standing challenges in TB prevention and treatment.

Would you explain the background, purpose, and progress to date of the M72/AS01E TB vaccine trial across Africa?

The Gates Medical Research Institute (Gates MRI) is sponsoring the phase 3 clinical trial of the M72/AS01E tuberculosis vaccine, a vaccine candidate originally developed by GSK and licensed to Gates MRI for late-stage development. The trial is designed to evaluate whether M72/AS01E can prevent pulmonary TB in adolescents and adults, particularly those with latent TB infection, a population that accounts for a large share of TB transmission globally.

This phase 3 study is among the largest infectious disease vaccine trials ever conducted, enrolling approximately 20,000 participants across 54 trial sites in five countries (South Africa, Kenya, Zambia, Malawi, and Indonesia). It includes cohorts of IGRA positive, IGRA negative, and people living with HIV.

As of April 2025, the trial reached full enrollment roughly 11 months ahead of schedule, a major operational milestone. The study remains ongoing, with primary completion anticipated later in the decade and potential product availability around 2029, if efficacy and safety are confirmed.

What operational or regulatory inflection points made it possible to reach full enrollment nearly a year early, and what does that signal about the maturity of African trial ecosystems?

Multiple factors contributed to early full enrollment, which demonstrates the readiness and scale of the African trial infrastructure including experienced investigators, established high-incidence catchment areas, and strong collaboration with national regulatory authorities.

The trial leveraged existing high-performing research centers with prior TB and HIV trial experience, reducing potential site start-up delays. In addition, community advisory boards (CABs) and long-standing engagement structures supported rapid recruitment without compromising ethical standards.

Reaching full enrollment early signals a maturing African clinical research ecosystem capable of supporting complex, late-stage trials at a global scale.

A robust and mature clinical trial infrastructure in Indonesia also contributed to reaching the full enrollment milestone early.

Market-shaping efforts play an important role prior to wide-scale rollout of a new medicine. At what stage in product development does Gates MRI now integrate access planning?

Making vaccines accessible and affordable for communities in areas of high disease burden is a priority for all trial partners. The M72 partners Gates MRI, GSK, Wellcome, and the Gates Foundation are actively working alongside regulators and governments to understand the potential demand for the vaccine and build an end-to-end plan to ensure long-term sustainable access, if and when it is approved. This includes supporting research and building an evidence base for the potential impact of the vaccine and community requirements for uptake, as well as collaborating with multilateral, regional, and country partners required to introduce the vaccine.

Are there similar initiatives underway in Africa that you’d also like to explain/describe?

Beyond vaccines, Gates MRI is engaged in TB drug development in Africa. Gates MRI is currently conducting phase 2 trials of novel, oral TB treatment regimens in drug-susceptible TB in South Africa. The ultimate aim of Gates MRI’s TB drug program is to develop a shorter, simpler TB treatment regimen to treat both drug-susceptible and drug-resistant TB.

Additionally, Gates MRI is conducting a phase 1 trial of a novel monoclonal antibody to prevent malaria. After a first-in-human clinical trial in adults was conducted in the United States, the current trial is being conducted in adults and children in Uganda.

Gates MRI is running early-phase and late-stage trials in TB and malaria simultaneously in multiple African countries. How do you avoid overconcentrating demand on high-performing sites while still accelerating development timelines?

Gates MRI uses a networked, strategic approach to site selection and trial management, not limited to only a few research institutes.

• Where appropriate, trials are distributed across many sites and countries, like the phase 3 M72 TB vaccine trial, which is being conducted at 54 sites across Kenya, Indonesia, Malawi, South Africa, and Zambia. This spreads out workload and speeds up timelines.
• Gates MRI leverages proven African trial research centers with their affiliated sites while also expanding into new areas, focusing on high-incidence communities experienced in relevant trials to maintain data quality and reduce overreliance on certain centers.
• The institute invests in the selection process and qualifying sites before large trials, as seen in the M72 program’s preparatory studies that identified sites with the highest incidence of TB to ensure that more sites could participate.
• Early and late-stage trials are run in parallel but often at different sites: Experienced centers handle advanced studies, while newer sites take on early-phase work, keeping operations efficient.
• Long-term partnerships and community engagement support recruitment, maintaining enrollment rates without overburdening specific populations or institutions.

From a sponsor’s perspective, where are the biggest remaining friction points in multicountry African trials today: Contracting, import/export logistics, pharmacovigilance systems, digital interoperability, or something else?

From Gates MRI’s perspective, the biggest remaining challenge points are not scientific or site capability related, but rather system-level operational and regulatory challenges which are many and varied across numerous countries.

Challenges include import/export logistics where there are delays related to customs clearance, variable interpretation of regulations, and country-specific documentation requirements, all of which can introduce unpredictable start-up and resupply timelines, even when sites themselves are fully ready to operate.

Differences in pharmacovigilance requirements are another area of friction, particularly in late-stage trials enrolling large populations (divergent safety reporting formats and reporting timelines), while multiple interfaces between sponsors, sites, and national authorities create additional operational complexity.

While local systems are strengthening, harmonized pharmacovigilance workflows across countries remain limited, increasing the coordination burden for multicountry trials. Regulatory capacity has improved substantially in many African countries, but regulatory duplication across countries remains a major challenge. Even when dossiers are aligned, sponsors must often navigate separate national reviews with different timelines, requirements, and sequencing, which can slow regional trial activation.

The African Medicines Agency (AMA) has been established to strengthen clinical development in Africa by building regulatory capacity in national authorities and harmonizing standards, guidelines, and reliance-based pathways, enabling more consistent, efficient, coordinated multicountry approval of clinical trials and products.

What lessons learned from these trials will improve clinical research and care for patients in Africa?

These trials demonstrate that large-scale, high-quality research can be conducted in African settings, which simultaneously strengthens local health systems, surveillance, and laboratory capacity. They also normalize adult and adolescent vaccination and prevention paradigms, which historically received less investment than pediatric programs.

Looking ahead 5-10 years, what would signal that Africa has moved from being a potential trial location to a coequal innovation partner in global product development?

Indicators would include African investigators leading global trials, earlier inclusion of African regulators in development planning, and routine codevelopment, rather than downstream participation. The M72 program already reflects movement in this direction through African leadership at national and site levels.

AMA aims to build the infrastructure, expertise, and systems needed to operate as a fully autonomous continental regulator—on par with agencies like the EMA and FDA—capable of independently overseeing and harmonizing medicines regulation across Africa.

There is growing emphasis on harmonization through regional bodies and strengthened national systems. Have you seen measurable reductions in review timelines, duplication, or unpredictability, and how does that compare to global benchmarks?

From Gates MRI’s perspective, the trend has been positive:

• Review timelines are shortening for harmonized pathways
• Duplication is decreasing where reliance is fully used
• Predictability is improving in a growing number of countries such as South Africa, Tanzania, and Zimbabwe among others where they have achieved WHO Maturity Level 3 regulatory systems with stable, well-functioning, integrated regulatory frameworks capable of supporting clinical development and reliance models.

However, although progress has been made, the system has not yet fully crossed the threshold where regional review outcomes reliably translate into rapid, synchronized national decisions across all participating countries.

Large-scale vaccine and prevention trials depend heavily on community trust. How is Gates MRI measuring meaningful engagement beyond enrollment numbers, particularly in settings with historical research inequities?

Gates MRI measures meaningful engagement not just by enrollment, but by sustained community advisory involvement, early and continuous engagement before recruitment, long‑term site-community partnerships, and evidence that community input actively shapes how trials are designed and conducted. For example, study sites host World TB Day events to provide touchpoints for participants and keep them updated and engaged.

Gates MRI-sponsored trials collaborate with CABs as part of clinical development programs. These bodies provide ongoing, structured input into trial design, consent processes, recruitment approaches, and issue escalation.

From Gates MRI’s standpoint, CABs are crucial partners in a trial’s success. CABs provide input that can lead to concrete protocol or operational adjustments.

For the phase 3 M72 TB vaccine trial, community engagement began during the preparatory epidemiologic study, well before participant recruitment for the vaccine trial. These preparatory activities were used to understand local disease burden, feasibility, and community concerns, and helped to build trust before pivotal clinical trials launched. This sequencing allows Gates MRI to view community readiness and build trust.

How can clinical research professionals interested in your work learn more about or perhaps even support/contribute to your work?

Clinical research professionals can engage through academic partnerships, trial site collaboration, and global health organizations involved in TB, HIV, and malaria research, including Gates MRI and its partners mentioned above.

ith the January 2025 implementation of the Health Technology Regulation, the European Union (EU) has introduced the Joint Clinical Assessment (JCA) as a parallel process to the Marketing Authorization review process. This leads to a much earlier evaluation of clinical comparative evidence regarding the relative effects of a new health technology—whether a medicinal product or medical device—and the earlier identification of uncertainties that may impact subsequent national or regional access decisions. Health technology developers need to overcome these emerging challenges.

Developers are also adapting to new health authority standards and requirements concerning health data quality and methodological rigor, striving to optimize use of available data sources and study designs to accelerate clinical development, regulatory approval, and patient access.

Moving forward, it will be essential to also integrate healthcare data sources and evidence into unified evidence generation plans and the adoption of integrated approaches to analyze such data. There is one example of a data integration and evidence generation plan for a specific disease/product area at one specific point in time during the product’s lifecycle. This article will explore other approaches for achieving such integration.

Comparative evidence at the time of Marketing Authorization Application/Joint Clinical Assessment (JCA) remains limited.

Choosing whether to invest in pharmaceutical innovation in Europe has become a pivotal decision for companies developing health technologies worldwide. While EU legislators signal support for innovation, current United States (US) pricing policies tend to discourage clinical development and early product launches in the EU. The introduction of the EU Joint Clinical Assessment (JCA) requires developers to plan and present comparative clinical evidence on health outcomes at the time of Marketing Authorization Application (MAA). Fulfilling all expectations and requirements of EU Regulators, national Health Technology Assessment bodies (HTAb), and reimbursement systems through the conduct of traditional randomized controlled trials (RCT) increases costs and lengthens the development, marketing authorization, and introduction of health technologies and, in many cases, raises expected return on investment (ROI) concerns to investors. Additionally, at times such RCTs may not be feasible, may not target the most relevant populations, or may not be ethical.

EU Health Technology Assessment (HTA) bodies will evaluate the certainty of comparative clinical evidence before products reach any EU market. This early assessment is likely to reveal more uncertainties than what would be identified at a later stage, when additional evidence from real-world exposure will have been accumulated.

Recent experience with products approved early under Priority Medicines Status (PRIME)/conditional marketing authorization (CMA) as reported by Cerreta et al. (publication in progress) indicate higher uncertainties for healthcare decision makers (as compared to standard approvals) at time of approval. This relates to the extent to which clinical trial evidence can support access to the approved indications, which comparators are relevant, whether the trial duration is sufficient, and the value of available health-related quality-of-life data or other relevant patient experience data (PED).

To overcome challenges in comparative clinical evidence and increase the certainty and availability of comparative clinical evidence to inform stakeholder decisions early in, very early integration of all available data and evidence into a comprehensive Integrated Evidence Generation plan will be of key relevance and value to health technology developers and healthcare decision-making bodies.

Many developers consider integrated evidence planning primarily as a means of generating RWE beyond RCTs or as a way to generate evidence that fills the gaps left by those RCTs.

However, this perspective overlooks the potential to connect data and evidence early in the process, which optimizes opportunities for health technology development by complementing RCTs and by addressing knowledge gaps throughout the development lifecycle.

Additionally, developers often have internal structures that hinder comprehensive data integration, evidence generation planning, and study execution. Planning for activities such as generating evidence from different data sources and populations on standard of care, complementing single-arm trials with RWD, or generating RWE for an external control arm within a RCT must be done early on by a multidisciplinary team of experts designing the RCTs relevant for the health technology under development. However, expertise and objectives are frequently siloed with teams of experts based on different, not interconnected, structures. Processes for the design and conduct of different types of clinical studies are also often governed by separate committees or functions within a developer’s organization.

Key Elements of a Comprehensive Integrated Data and Evidence Generation Strategy and Plan

A comprehensive Integrated Data and Evidence Generation Plan for a specific health technology should systematically incorporate the perspectives and needs of different stakeholders such as patients, clinicians, regulatory bodies, payers, HTA bodies, and other policymakers. It should also identify and address all evidence gaps; leverage, map, and select multiple high-quality, fit-for-use health data sources; and use advanced research methodologies and innovative technologies to generate robust, reliable evidence supporting health technology development throughout its entire lifecycle (Figure 1).

Closely aligned functions within the developer company should contribute to the early strategic conceptualization and planning of the Integrated Data and Evidence Generation plan, including the assessment of evidence gaps, the identification, mapping, and selection of the most appropriate health data sources through a comprehensive data plan, and the deployment of advanced methods and innovative technologies, as well as the execution of required studies (Figure 2).

Novel Concepts with Potential to Increase Certainty of Evidence

With the increasing availability of fit-for-use health data sources complemented by advanced methodological approaches and improved computational competences—including emerging applications of AI in clinical research—it is now possible to leverage multiple and diverse health data sources with sophisticated statistical methods and innovative analytical tools. These advances enable more robust and reliable evidence generation for the development and access of new health technologies. New techniques for collecting, curating, analyzing, and integrating health data from multiple sources are also enhancing extrapolation and modeling capabilities. Table 1 below outlines key methodological and technological developments pertinent for the utilization and harmonization of health data in clinical research. The adoption of these approaches depends on strong expertise, partnerships, and collaboration. It also depends on establishing trust among healthcare decision makers. Facilitating stakeholder dialogue is essential to fostering confidence in these methodological and technological developments, thereby supporting efforts to increase the certainty of evidence at the time of MAA and JCA submissions.

How we respond now will shape future patient access to health technology innovations in the EU.

Enhancing evidentiary standards in light of scientific advancements requires a renewed approach on how we access, use, combine, and analyze fit-for-use health data and on how we generate robust, reliable evidence to accelerate clinical development and regulatory approval and maximize patient access to new health technologies within the EU.

Trust from healthcare decision makers in the suitability of fit-for-use health care data and methodological and technological innovations is fundamental.

A dialogue on evidentiary standards between EU regulators and EU HTA bodies was initiated in 2025. Article 6 of the HTR implementing act on cooperation of HTA bodies and EMA mandates opportunities for exchanging views on scientific or technical matters across products relevant to JCA and joint scientific consultation. Such discussions—informed by expert insights—contribute to clarifying acceptance criteria for developers and promote broader adoption. They address minimum requirements for acceptance, and whether these standards are relevant for particular diseases or therapeutic areas.

To meet the expectations of healthcare decision-making bodies, developers should formulate comprehensive Integrated Data and Evidence Generation strategies and plans that are tailored to individual health technologies. The active involvement and leadership of the essential functions—including experts in methodological and technological domains such as statisticians, epidemiologists, and data scientists—as well as policy experts are vital to success. Developers must incorporate these elements to establish robust, reliable comparative evidence needs at an early stage and maximize opportunities for patient access to health technologies in Europe.

ith the Medical Research Act (Medizinforschungsgesetz, MFG), Germany is not merely shortening administrative timelines; it is repositioning its regulatory ecosystem as an active enabler of clinical development. By combining early regulatory dialogue, central coordination between BfArM and Paul-Ehrlich-Institut (PEI), harmonized ethics review, integrated radiation-protection procedures, and more predictable contracting, Germany is creating a clearer pathway from scientific idea to first patient access. This article details these changes and how they will support medical product developers in Germany in the future. The challenge for this Act was to strike a balance between streamlining procedures and maintaining high safety standards.

Focus of the MFG

The MFG came into force on 30 October 2024. It was developed within the context of the federal government’s pharmaceutical strategy. The legislative process was characterized by broad engagement of relevant stakeholders from the pharmaceutical industry, academic research, clinical trial centers, and the two federal national competent authorities (NCAs), the Federal Institute for Drugs and Medical Devices (BfArM) and the Paul‑Ehrlich‑Institute, which contributed their perspectives through a series of consultation processes.

The primary focus of the MFG is on shortening the time to trial initiation and thereby increasing the number of clinical trials in Germany. More efficient and standardized approval procedures and faster contract negotiations between trial sites and sponsors should significantly reduce the time between application submission and the initiation of clinical trials.

Ultimately, the MFG aims to increase efficiency in administration and regulation. Through digitalization, standardization, and clearer structures of responsibility, resources on the part of authorities, ethics committees, and applicants are to be better utilized.

Measures to Shorten Time to Trial Initiation

For mono‑national clinical trials, no coordinated review with other EU Member States is required, as the substantive assessment—covering both Part I of the assessment report, carried out jointly by the relevant NCA (either the BfArM or the PEI, depending on the product) and the respective ethics committee; and Part II, assessed solely by the competent ethics committee—is conducted entirely at the national level. Nevertheless, longer processing times were observed in the past, as the EU Clinical Trials Regulation (CTR) does not provide for adjusted timelines for mono‑national clinical trials. The MFG addressed this issue at the national level by shortening the substantive review period by 19 days to 26 days and enshrining this new deadline in the German Medicines Act. Although the law does not extend this reduction to include substantial modifications to approved clinical trials, both the German Working Group of Medical Ethics Committees (AKEK) (German text only) and the two NCAs have implemented a similar reduction in these procedures through a voluntary self-commitment.

In addition to the sometimes lengthy approval periods, analyses by industry associations showed that the lengthy contract negotiations between sponsors and individual trial sites in Germany took significantly longer than in comparable Member States. This was addressed by the authorization of a national ordinance to establish standard contractual clauses for such contracts. This new ordinance provides for pre-formulated standard clauses for contracts between the parties, unless both parties mutually agree to deviate from any of these clauses.

Harmonizing the Work of Ethics Committees

Although the AKEK has been attempting for years to harmonize the working methods of the ethics committees involved in clinical trial review in Germany, this effort has been only partially successful, as the AKEK previously lacked the authority to issue guidelines with the necessary binding force. This was corrected by the MFG, and the AKEK was granted the authority to issue and publish such guidelines, which should be followed by all ethics committees assessing clinical trial applications in Germany.

Specialized Ethics Committee for Special Procedures

For clinical trials requiring special expertise, the MFG additionally established a separate “Specialised Ethics Committee for Special Procedures” organized under federal law. This committee, whose secretariat was established at the BfArM, is composed of external scientists from various disciplines and evaluates the following clinical trials: first-in-human clinical trials, clinical trials involving advanced therapy medicinal products (ATMPs), clinical trials following complex master protocols, and clinical trials that have received scientific advice from the Emergency Task Force of the European Medicines Agency.

The registered ethics committees of the German States (Länder) may issue a special joint schedule of responsibilities for them for specific procedures requiring particular medical expertise (for example, clinical trials with minors as participants), in addition to the general joint schedule of responsibilities for the other registered ethics committees of the Länder.

Radiation Protection in Clinical Trials

Another Germany-specific issue in clinical trials involving the research‑related use of ionizing radiation was the insufficient integration of the clinical trial assessment with the additional radiation protection assessment. This sometimes led to duplicate reviews and, because the Federal Office for Radiation Protection (BfS) had to be involved in all procedures with its own submission and approval processes, it was very time‑consuming and often caused significant delays to the start of studies.

The MFG simplified the processes both for the notification procedure—which applies to most clinical trials and no longer requires the involvement of the BfS—and for the approval procedure with the BfS, which remains necessary for certain uses of radiation. Both procedures now require the submission of documents in parallel with the submission of the actual clinical trial application via the CTIS portal. For clinical trials subject to the radiation notification procedure, the radiation protection assessment is integrated into the scientific review of the clinical trial application, does not prolong the procedure, and is carried out exclusively by the concerned ethics committee. For clinical trials requiring a radiation protection approval procedure with the BfS, the approval procedure assessment timelines have been aligned with the CTR deadlines so that, as a rule, the start of the study is not delayed by this additional procedure either.

Single-Gate Approach for German NCAs

In addition to these fundamental adjustments to processes and responsibilities, the MFG optimized various other aspects of clinical trials and regulatory cooperation between the two national NCAs. In addition to increasing the flexibility of the two NCAs’ jurisdictions, a coordination office was established at the BfArM to coordinate all processes related to scientific advice, clinical trials, and licensing of medicinal products for both NCAs. This single-gate approach ensures that all applications and inquiries regarding these matters are processed through a single point of contact, eliminating the need for sponsors and pharmaceutical companies to conduct complex jurisdictional checks.

Clinical Trials Investigating Medicinal Products and in vitro Diagnostic Medical Devices

Although combined clinical trials involving medicinal products and in vitro diagnostic medical devices are becoming increasingly common in the context of personalized medicine, the legal approval procedures are not harmonized at either the European or national level and sometimes result in differing jurisdictions among ethics committees. The MFG has at least clarified at the national level that the ethics committee responsible for the CTR authorization procedure together with the NCA is also responsible for the required authorization procedure under medical device law, to avoid overlapping responsibilities between ethics committees.

Further Simplifications

In addition to the above changes, the MFG has introduced various other simplifications in the practical conduct of clinical trials. For instance, the national requirements for the labeling of investigational and auxiliary medicinal products have been simplified and now permit the use of English labeling in many cases, provided these products are not directly provided to trial participants (in which case the labeling must be in German). Furthermore, the distribution channel for investigational and auxiliary products for clinical trials with decentralized elements has now been legally adapted to allow for direct delivery to trial participants in their home environment.

Early Dialogue and Innovation Support

Beyond formal authorization procedures, Germany offers early dialogue formats that help innovators navigate regulatory expectations before pivotal procedural decisions are taken. The BfArM Innovation Office supports orientation meetings and advice for digital health applications, while the PEI Innovation Office has long supported early development of advanced and complex medicinal products, including ATMPs. Together with the central advice application portal introduced by BfArM and PEI, these formats strengthen Germany’s role as a regulatory partner for academic groups, startups, and pharmaceutical developers at an early stage of product development.

Enabling Clinical Development and Early Access to Innovation in Germany

This initiative was driven by the decline in Germany’s attractiveness for conducting clinical trials, a trend observed for years, particularly when compared to countries with more efficient regulatory processes and more centralized structures. The MFG marks a strategic step in strengthening Germany’s role as a predictable, science-driven, patient-oriented location for clinical research. In an increasingly competitive European clinical trial landscape, sponsors choose trial locations based on speed, predictability, regulatory clarity, and the ability to activate studies efficiently. The MFG addresses these factors through a coherent set of measures designed to make clinical research in Germany faster, more coordinated, and more attractive for both commercial and academic sponsors.

Rather than merely responding to past procedural complexity, the reform establishes a more enabling framework for clinical development. More centralized procedures, harmonized ethics review, standardized contractual elements, and clearer regulatory interfaces are intended to reduce avoidable administrative burdens and increase planning certainty for sponsors, investigators, and trial sites.

This enabling approach is further strengthened by early regulatory dialogue and innovation support. The innovation offices at BfArM and PEI provide an important entry point for developers seeking early orientation on regulatory expectations, procedural pathways, and authority responsibilities, particularly in the case of novel technologies, complex products, digital health applications, ATMPs, and other innovative development programs. These formats are especially valuable for academic research groups, biotech companies, and startups that may not have extensive in-house regulatory infrastructures.

By combining early scientific and regulatory interaction with coordinated assessment procedures and practical simplification measures, Germany can position itself not only as a country addressing previous inefficiencies, but as an active enabler of high-quality clinical development. If implemented consistently, the MFG can contribute to earlier patient access to innovation while reinforcing Germany’s role as a leading clinical research and innovation hub in Europe.

Where We Are Today: Implementation Ongoing

The MFG should be understood as more than a legislative package to reduce timelines. It establishes an enabling architecture for clinical development in Germany: early regulatory dialogue, central coordination, specialized ethics expertise, integrated interfaces for radiation protection and combined studies, predictable contracting, and incentives for local evidence generation. Its success will ultimately depend on implementation discipline, transparent performance metrics (yet to be determined), and the willingness of developers, academic sponsors, and trial sites to use the new pathways. If implemented consistently, the MFG can reposition Germany as a predictable, science-driven, patient-oriented location for clinical research in Europe.

As the law only came into force on 30 October 2024, most of its measures are still in early implementation. The new reimbursement incentives for drugs with ≥5% German study participation have been active since January 2025, while the 28-day approval deadline for mono-national clinical trials was one of the first steps implemented. Initial analyses indicate that the time to trial initiation has already been reduced and that the number of clinical trials in Germany has increased by approximately 6% in 2025 compared to 2024.

The overall impact will only become quantifiable during the coming years. It is now up to developers to embark on clinical trials in Germany and test the new ways of working. And there is more to come: Further changes in the German regulatory environment are expected through the implementation of the General Pharma Legislation once it is adopted. In December 2025, the European Parliament and the Council of the EU reached an agreement on the reform of the EU pharmaceutical legislation. During the transition period from 2026 to 2028, Member States will be required to adapt their national legal frameworks accordingly. The new pharmaceutical legislation is expected to apply from 2028 onwards.

To bridge the gap until the faster procedures under the EU Biotech Act are implemented, Germany is actively participating in the FAST-EU initiative to speed up the authorization of multinational clinical trials.

apan has long been recognized for its strong fundamental research capabilities, yet it still lacks a robust ecosystem that connects scientific achievements to social implementation and commercialization. The trend toward Open Innovation (OI) could change that.

Japan’s Current Challenges in Open Innovation:

Open innovation (OI) in drug development often refers to a framework in which companies, universities, research institutes, government agencies, and investors share knowledge, technology, data, and capital across organizational and national boundaries. Unlike conventional joint development, OI requires sustained multistakeholder collaboration supported by institutional and financial mechanisms that enable joint risk-taking and data utilization beyond individual projects. Japan’s research infrastructure and international competitiveness are respected globally, but these strengths have not translated into globally competitive pipelines or a steady stream of innovative drug products. Despite Japan’s scientific capabilities, this form of OI has not yet become a pervasive operating norm.

The central bottleneck is the limited mobility of professionals with commercialization expertise, particularly talent moving from pharmaceutical companies to start ups, academia, and venture capital firms. This constrains the emergence of “OI producers” who can integrate scientific, regulatory, and business perspectives to orchestrate OI across organizations.

Recent policy measures in Japan, such as the Startup Development Five-Year Plan and AMED’s (Japan Agency for Medical Research and Development’s) Certified Venture Capital Program, have strengthened public-private collaboration and improved access to funding. These initiatives are establishing co-investment structures and linking academic seeds with industrial partners across Japan and Asia. Yet a deeply rooted culture of cautious decision-making and risk aversion persists among both investors and academia and makes it difficult to embrace the uncertainty and long timelines inherent in drug development. Fostering a culture that accepts calculated risk-taking and external collaboration is essential for embedding OI as a genuine operating principle.

The Growing Momentum of Japan-Asia Open Innovation

Japan-Asia OI in drug development is entering a new phase. Regulatory reforms under the Japan Pharmaceuticals and Medical Devices Act, combined with Japan’s predictable business environment and substantial public funding, have made Japan an attractive partner for Asian start ups. Many start ups from South Korea and Taiwan now choose Japan as their first overseas destination to access the Japanese market, quality standards, clinical development know-how, and pharmaceutical networks.

Government programs such as JETRO’s Japan Entry Acceleration Program are actively positioning Japan as a gateway and hub for overseas life-sciences start ups. These programs support foreign start ups in building OI relationships with Japanese academia, clinical research institutions, and industry, through proof of concept (PoC) studies and co-development projects tailored to Asian disease profiles. As a result, Japan is increasingly functioning as a coordination hub through initiatives such as the MASTER KEY Project, a platform trial that integrates multiple investigational therapies and stakeholders under a unified protocol, demonstrating Japan’s capability to coordinate complex clinical development and jointly design and test new drug development models with other Asian partners.

Asia, home to about 60% of the world’s population and one of the fastest-growing pharmaceutical markets, offers enormous benefits for Japan. Many Asian countries have established efficient drug development infrastructures including skillful clinical data management organizations (CDMOs), agile clinical research organizations (CROs), and attractive tax incentives. From an OI perspective, these assets form a complementary “incubation base” where Japanese and Asian partners can jointly advance promising seeds through preclinical development, early-phase clinical trials, and scalable manufacturing while sharing risks and leveraging each country’s comparative advantages.

For Japan, engaging in OI with Asia is not merely incremental but is a strategic response to demographic constraints, rising development costs, and the need for diverse patient populations. By building Japan-Asia OI networks, Japan can evolve from a domestic-oriented market into an open innovation platform for cocreating globally relevant therapeutic innovations.

A Path Forward: From Concept to Implementation through Open Innovation:

Japan’s strategic vision can be summarized as “Japan Innovates, Asia Scales, the World Benefits.” This concept envisions that Japan leverages its scientific and regulatory strength through collaborating with Asian partners and premises that Japan should function as a hub that designs, coordinates, and quality-assures joint development programs under shared frameworks.

Realizing this vision requires three mutually reinforcing pillars:

• People and knowledge: Systemic frameworks like Japan’s Mobile Software Competition Act (MSCA) and the European Institute of Innovation and Technology’s Cross-KIC Global Outreach (EIT KIC) program should promote cross-border talent exchange and knowledge sharing across academia, industry, government, and investors. Joint training programs, cross-appointments, secondments to start ups and regulatory bodies, and fellowships between Japan and Asian hubs can cultivate “OI producers” who combine scientific expertise, curiosity, initiative, and a business mindset, and who can structure and execute collaborative projects.
• Capital and incentives: Public-private partnerships should enhance financial circulation through co-investment vehicles such as Japan-Asia life science funds that bring together Japanese corporate venture capital, regional venture capital, government-backed funds, and international financial institutions. These platforms can align incentives by providing risk capital specifically for Japan-Asia joint projects and by serving as forums for sharing pipeline information, regulatory trends, and partnering opportunities. Examples from other regions, such as the EU’s Innovative Health Initiative (IHI) and the Center for the Biomedical Advanced Research and Development Authority (BARDA) in the US, illustrate how public-private partnerships can effectively mobilize cross-sector funding and collaboration, and may provide useful reference models for Japan-Asia initiatives.
• Regulation, data, and technology: Regulatory harmonization across Asia and internationalizing clinical development environments are indispensable to OI. Work-sharing arrangements, mutual reliance in regulatory review, and aligned clinical requirements can reduce duplication and enable efficient multicountry studies that share data, sites, and patient pools. Such collaboration can generate large-scale clinical and real-world data from Asian populations, an essential foundation for understanding disease heterogeneity and improving the generalizability of global development programs. Digital and AI technologies should serve as core enablers of OI. Examples from other regions include the EMA’s centralized procedure and Project Orbis, led by the US FDA Oncology Center of Excellence, which enable regulatory work-sharing and concurrent multicountry review.

By advancing these initiatives coherently, Japan can solidify its role as a gateway linking conceptual innovation to practical implementation while aligning with Asia’s incubation ecosystems. This entails building pipelines where seeds generated in Japanese academia and start ups are codeveloped with Asian partners, ultimately delivering innovative therapies to patients across Asia and beyond. Scaling successful Japan-Asia OI case studies will further clarify the pathways for this model to mature into a regionally integrated innovation ecosystem, with Japan serving as its orchestrator.