Clinical Development Strategy Archives -

A Look Ahead for Biopharma: Embracing Complexity in 2026

Looking back, 2025 was a year of adjustment for biotech — instability shook an already fragile biopharma sector, AI gained significant momentum, the ability to merge open-source code with commercial software matured, and the use of real-world data (RWD) continued to increase. Most companies responded responsibly: pilots, internal capability builds, exploratory regulatory dialogue, and, unfortunately, many had significant layoffs.

2026 is poised to be about embracing complexity — it’s likely many of the trends begun in 2025 will continue, and in addition we’ll see more emphasis on women’s health, an increase in discussions on external control arms, and a spotlight on biostats/clinical pharmacology. From a regulatory perspective, both the US Food and Drug Administration and the European Medicines Agency are signaling the same thing: innovation is welcome, but only when it produces decision-grade evidence.

This has real implications for how drug development companies operate.

Women’s health has expanded beyond a niche category. Regulators are increasingly focused on sex-specific evidence and generalizability across all therapeutic areas. By 2026, this is no longer optional — it’s foundational. In December 2025, the FDA published guidance on studying sex differences, with recommendations to increase female enrollment and strengthen sex-specific analyses and reporting.

Smarter trial designs are now the default. Adaptive approaches, Bayesian methods, external control arms, and decentralized elements are acceptable — expected, even — but only when the assumptions are explicit and defensible. In addition, regulators are clarifying and further exploring the use of various datasets in regulatory submissions; there’s even been a few external control arms presented to regulators.

Behind all of this is a broader shift: biostatistics and clinical pharmacology are moving from support functions to strategic capabilities. Dose justification, estimands, missing data strategy, and model-informed drug development (MIDD) are now central to regulatory credibility.

For biopharma leaders, the message is clear:

For those companies that embrace complexity as the new normal, 2026 offers a powerful opportunity: faster development, clearer regulatory paths, and greater confidence — from regulators, investors, and, ultimately, patients.

Keep an eye out for these new trends in 2026:

Women’s health expands beyond a single therapeutic area

Women’s health is no longer confined to reproductive medicine or niche pipelines. Regulators are increasingly focused on sex-specific evidence and generalizability across all therapeutic areas.

In 2026, sponsors should expect:

Greater scrutiny of female enrolment and retention
Fewer waivers for sex-specific analyses
Increased innovation in areas such as menopause, endometriosis, fertility, and autoimmune disease

Takeaway: Women’s health isn’t just a therapeutic area; it’s a generalizability requirement. If your statistical analysis plan can’t speak clearly about sex effects (or justify why not), expect pointed questions.

External control arms move from optional to strategic

External and synthetic control arms have crossed an important threshold. In 2026, they are no longer seen as novel add-ons but as intentional design choices, particularly in oncology, rare disease, and high unmet-need indications.

Regulatory acceptance is evolving and both the EMA and FDA are leaning in — carefully:

EMA is actively developing a reflection paper on external controls (including RWD-derived arms) to shape consistent scientific expectations.1
EMA is also doubling down on DARWIN EU, its federated RWE network, with plans to extend work beyond 2027 (tender activity flagged for the first half of 2026).2
FDA continues to expand its RWE framing across programs (and sponsors are expected to demonstrate data relevance, reliability, and bias management — not just “we found a database”).3

Takeaway: External controls are not a shortcut; they’re a design choice that requires 1) pre-specified causal estimands, 2) transparent matching/adjustment strategy, and 3) sensitivity analyses that are realistic.

Biostatistics becomes a strategic capability

Clinical trial design is undergoing a quiet but fundamental upgrade. In 2026, efficiency is no longer achieved by cutting corners, but by thinking better upfront. Biostatistics is no longer a downstream function; it is becoming a strategic driver of development success.

Adaptive and Bayesian designs are becoming mainstream, particularly in early and mid-stage development. Sponsors are expected to define estimands clearly and adequately to address trial objectives, to integrate biomarkers earlier, and to design trials that answer regulatory questions — not just scientific ones. Smaller trials are acceptable; ambiguous trials are not.

In 2026:

Estimands, including novel endpoints and strategies for handling intercurrent events, and missing data strategies are key for addressing primary and secondary trial objectives and are strategic topics for assessing drug product’s risk-benefit and totality of evidence.
Bayesian methods are increasingly used not just in design, but in regulatory dialogue.
Statistics, clinical pharmacology, and translational science should continue moving closer to towards seamless integration.

Takeaway: Bringing biostatisticians to the decision-making table early and leveraging quantitative decision-making frameworks will be important for managing overall pipeline decisions.

Clinical pharmacology takes center stage

Clinical pharmacology is having a moment — and for good reason. Regulators are increasingly unwilling to forgive poorly justified dose selection, study design optimization, population enrichment, extrapolation, and/or benefit risk assessment/labelling.

MIDD is becoming the norm:

Since 2024, FDA is pushing Model-Informed Drug Development via ICH M15 draft guidance and its MIDD meeting program.4
EMA is sharpening expectations for mechanistic models (PBPK/PBBM/QSP) used in MIDD, including how they should be assessed and reported.5 A guidance is expected to be completed in 2026.
In December 2025, the FDA released a guidance that lists products that no longer need (or can reduce) 6-month non-human primate toxicity testing and, in April 2025, outlined a roadmap for reducing animal testing in preclinical safety studies.6,7

Takeaway: In 2026, there will be increased scrutiny in making sure sponsors show why the chosen dose is the best one and can justify the assumptions behind MIDD submitted, or why the approach wasn’t used.

Interested in learning more?

The Medical AI Superintelligence Test and NOHARM: A New Framework for Assessing Clinical Safety in AI Systems

Artificial intelligence has become an increasingly common tool in medical decision-making. Physicians consult large language models (LLMs) for diagnostic reasoning, documentation, and summarization; patients use them to interpret symptoms; and health systems continue to integrate them into clinical workflows. Yet a basic question remains insufficiently answered: How safe are these systems when their outputs influence real medical decisions?

A recent initiative under Arise AI, centered around the NOHARM benchmark, offers one of the most rigorous evaluations of clinical safety to date. Its findings, and the broader accountability framework behind it, have implications not only for direct patient care but also for clinical development, medical writing, pharmacovigilance, and regulatory documentation. Importantly, the study highlights patterns of AI failure that closely mirror risks encountered when using LLMs for complex scientific and regulatory work.

A benchmark designed around real patient harm

NOHARM evaluates LLMs using one hundred real physician-to-specialist consultation cases across ten specialties. Instead of relying on synthetic questions or knowledge tests, the benchmark measures whether AI-generated recommendations could expose patients to harm. More than 4,000 plausible medical actions were annotated by specialists for clinical appropriateness and potential harm, allowing the framework to assess both errors of commission (unsafe recommendations) and omission (failing to recommend necessary actions).

The benchmark sits within the broader MAST (Medical AI Superintelligence Test), initiative led by Harvard and Stanford, hosted on bench.arise-ai.org, which aims to provide ongoing public evaluation of LLMs used in healthcare settings. By publishing comparative and transparent performance metrics — including safety, completeness, precision, and harm rates — MAST serves as a standardized accountability structure for medical AI systems.

Key findings from the study

The results provide a nuanced view of current medical AI capabilities:

Harm remains a measurable risk. Some LLMs produced severely harmful recommendations in more than 20% of cases.
Omissions are the dominant failure mode. Over three-quarters of severe errors involved missing essential actions rather than giving incorrect ones.
Model “strength” does not predict safety. Size, recency, and performance on general AI benchmarks had limited correlation with clinical safety.
Top models can outperform physicians. In a subset of cases, the best LLMs demonstrated higher safety and completeness than generalist clinicians.
Hybrid systems improve outcomes. Multi-agent configurations — where one model critiques or revises another — showed materially lower harm rates.

Collectively, these findings emphasize that clinical safety must be evaluated directly; it cannot be inferred from general intelligence or linguistic fluency.

Relevance beyond clinical care: Implications for clinical development

Although NOHARM focuses on medical recommendations, its insights apply directly to workflows in clinical development, where LLMs are increasingly used for drafting protocols, summarizing analyses, generating safety narratives, and producing Clinical Study Reports (CSRs). The risk profile is different — regulators, rather than patients, are the primary audience — but the core failure mode identified in NOHARM is the same: AI systems frequently omit essential information while producing text that appears complete.

These omissions can lead to incomplete evidence packages, insufficient traceability, inconsistencies with statistical outputs, and regulatory challenges. The study therefore reinforces the need for structured validation processes when using LLMs in high-stakes regulatory environments.

The CSR example: Completeness as a safety criterion

A clinical study report requires comprehensive reporting: methodology, protocol deviations, statistical analyses, safety findings, and linked tables, figures, and listings. While LLMs can streamline drafting and improve clarity, they do not reliably identify which elements are required for regulatory compliance. As NOHARM demonstrates, even highly capable models often omit critical actions or fail to include context necessary for safety.

This parallels the risk in clinical documentation: a well-written but incomplete CSR is not simply inconvenient — it can delay submission timelines, trigger regulatory questions, or obscure important safety signals. Ensuring completeness therefore becomes a core safety requirement.

The necessity of human-in-the-loop systems

One of the clearest insights from the NOHARM study is that hybrid systems outperform both standalone AI models and standalone human reviewers. Multi-agent architectures reduce harmful outputs, and expert human oversight further ensures contextual accuracy, completeness, and regulatory fidelity. In clinical development, this means that LLMs should support — but not replace — experienced medical writers, clinical scientists, statisticians, and safety physicians.

A well-designed workflow leverages AI for efficiency while relying on human expertise for judgment, quality control, and risk mitigation. This aligns with the MAST vision of AI systems operating under ongoing, benchmarked evaluation rather than unmonitored deployment.

A path forward: Benchmark-aligned, hybrid AI for regulated medicine

The NOHARM study and the broader Arise AI benchmarking platform represent a shift toward transparent, safety-focused evaluation of medical AI. They show that:

Safety and completeness require explicit measurement.
Omission is a primary source of AI risk in both clinical and regulatory contexts.
Multi-agent and human-in-the-loop systems materially reduce harm.
Public, standardized benchmarking supports accountability and informed adoption.

For organizations exploring or deploying AI in clinical development, the message is straightforward: LLMs can accelerate work and improve consistency, but only when embedded within systems designed to detect and mitigate the very risks NOHARM identifies. With rigorous evaluation, hybrid architectures, and expert oversight, AI can be integrated into medical and regulatory workflows in a way that advances both efficiency and safety.

Interested in learning more?

Consult the preprint by David Wu, et al., “First, do NOHARM: Towards clinically safe large language models” and access the interactive NOHARM leaderboard to see model performance.

Smart Data Strategies for Early-Stage Clinical Development

Early-stage clinical development continues to challenge teams to make high-impact decisions with limited information. With so much uncertainty — scientific, operational, and financial — the way we design and use data in early trials has never been more critical.

Today, forward-looking teams are rethinking trial design not only as a technical function, but as a strategic lever. When done well, statistical design enables smarter decisions, faster pivots, and clearer narratives for investors and internal stakeholders. And when paired with complementary tools like PK/PD modeling, it provides the foundation for early-phase programs that are not only scientifically rigorous but also capital efficient and investment-ready.

Below are three key strategies I see gaining traction — and where we’ll likely see continued momentum.

1. Quantify uncertainty, communicate risk

Early-phase development is defined by risk. Yet too often, that risk is discussed qualitatively. With the right statistical frameworks — Bayesian models, PoS simulations, scenario planning — teams can put structure around uncertainty, helping stakeholders understand the likely range of outcomes and the data needed to support each scenario. Statistical methods such as Bayesian borrowing are particularly powerful in combining external evidence — historical data, real-world data — with trial outcomes to make probabilistic statements about risk and reward.

PK/PD modeling plays a valuable role here. By clarifying the exposure-response relationship, early modeling helps teams define therapeutic windows and optimize dose selection. These insights feed directly into more credible forecasts and, ultimately, more compelling investor conversations.

2. Build adaptive designs that let you pivot

One of the clearest ways to de-risk early trials is through adaptive design. Whether re-estimating sample size, adjusting dose levels, or stopping early for futility, adaptive trials provide a framework for learning and acting in real time. More recent designs in the form of basket trials allow you to explore multiple indications, borrowing information where possible.

This flexibility is particularly powerful when supported by pharmacometric models. In dose-escalation or seamless Phase 1/2 trials, early signals from PK/PD biomarkers can trigger adaptations that reduce patient exposure to suboptimal doses — or accelerate the path to proof-of-concept. Here, statistical design and biological insight work hand in hand.

3. Align milestones with financing strategy

Data is most valuable when it supports a clear decision or unlocks the next stage of development. Increasingly, companies are designing trials with planned interim readouts that align with financing tranches or partnership discussions.

Well-structured designs — especially those that integrate early markers of activity — can provide meaningful milestones before clinical endpoints are reached. These interim insights serve not only to inform go/no-go decisions but also to engage investors with data that speaks their language.

The takeaway

In today’s environment, clinical teams need to do more with less — less data, less time, less capital. Smarter statistical design is one of the most effective ways to meet that challenge. It helps you act with confidence, adapt with speed, and communicate with clarity.

Combined with the right modeling tools and a thoughtful approach to milestone planning, it turns your data into more than just evidence. It becomes a strategy.

Winning in a Budget-Constrained World: Smarter Clinical Trial Optimization

Clinical trials have become more complex and costly in recent years, driven by expanding data requirements, global regulatory demands, and increasingly specialized therapies. For sponsors and CROs, balancing quality with cost efficiency is more challenging than ever, especially when trying to streamline biometric data management across diverse geographies.

However, several proven strategies are helping organizations optimize clinical trial budgets without sacrificing quality or compliance. From flexible resourcing models to cutting-edge technology, industry leaders are rethinking traditional approaches and adopting scalable solutions to meet today’s demands.

Current trends in clinical trial cost optimization

One of the key trends in FSP biometrics is the move toward more flexible, modular engagements that allow sponsors to optimize costs while maintaining access to specialized expertise. Rather than relying on large, fixed teams, organizations are increasingly leveraging scalable FSP models to allocate resources dynamically across data management, statistical programming, and biostatistics functions based on project phases and workload intensity. This flexibility is especially valuable during high-demand periods like database lock or interim analyses, where rapid scaling is needed without long-term overhead. Additionally, sponsors are integrating global delivery models within FSP partnerships, tapping into talent pools from cost-effective regions while ensuring alignment with quality standards. The growing use of technology-driven efficiencies, such as automated data checks and AI-supported programming workflows within FSP teams, is further driving down costs and improving operational agility.

Building specialized skill sets to improve efficiency and quality

As clinical trials grow more specialized, access to niche expertise has become a critical factor in maintaining quality and managing costs. Building internal capability through focused training programs allows data management, biostatistics, and statistical programming teams to stay current with the latest methodologies and regulatory requirements.

Skilled teams reduce rework, prevent costly errors, and improve turnaround times — all of which contribute directly to budget optimization. In addition, companies that invest in continuous learning foster a culture of quality and innovation, setting themselves apart in a highly competitive market.

Global Capability Centers (GCCs): Unlocking scalability and cost savings

Global Capability Centers (GCCs) have emerged as a strategic asset for clinical trial sponsors. Located in cost-effective regions but equipped with world-class talent and infrastructure, GCCs allow organizations to scale their operations efficiently while maintaining control over quality and timelines.

By leveraging GCCs for biometric functions — including data management, programming, and biostatistics — companies can optimize labor costs without sacrificing expertise. Additionally, operating in multiple time zones supports 24/7 workflows, helping to accelerate study timelines and manage global studies more effectively.

Innovative resourcing models: FSP and just-in-time staffing

Traditional full-service outsourcing models are being supplemented — and sometimes replaced — by more agile FSP arrangements. With FSP models, sponsors retain greater control over trial oversight while benefiting from specialized services and flexible resource deployment.

Just-in-time staffing is another innovative approach that is gaining traction. This model enables organizations to quickly onboard qualified professionals only when their expertise is needed, reducing idle time and controlling personnel costs. Both models help sponsors stay nimble in response to shifting trial demands while protecting budgets.

Emerging markets and government incentives

Many emerging markets are becoming attractive hubs for clinical trial operations thanks to favorable government policies, tax incentives, and infrastructure investments. Countries across Asia, Eastern Europe, and Latin America are building sophisticated clinical research ecosystems that offer significant cost advantages.

By expanding into these regions, sponsors gain access to large, diverse patient populations and skilled professionals, creating opportunities for faster enrollment and cost-efficient trial execution.

Integrating AI and technology to streamline processes

Artificial Intelligence (AI) and machine learning are revolutionizing how clinical trial data is managed. From automating data cleaning to predictive analytics that identify risks earlier, AI-driven tools help reduce manual effort, minimize errors, and speed up decision-making.

Smart technology adoption also enhances resource allocation, allowing biometric teams to focus on high-value tasks while repetitive work is automated. This balance leads to better quality data, faster insights, and meaningful cost reductions across the trial lifecycle.

Shaping the future of cost-efficient clinical trials

Rising clinical trial costs are a reality — but they don’t have to derail your development pipeline. By embracing scalable solutions, investing in talent, and exploring emerging technologies, organizations can navigate today’s challenges while safeguarding both budgets and data quality.

Agile leadership, a global mindset, and a willingness to innovate will be key to succeeding in this new landscape. Whether it’s tapping into global capability centers, leveraging just-in-time staffing, or integrating AI tools, the path to more efficient and effective clinical trials is within reach.

Staying informed and adaptable ensures your clinical development strategies remain competitive, cost-effective, and ready for the future of healthcare innovation.

Interested in learning more? Watch our on-demand webinar, “Winning in a Budget-Constrained World: Smarter Clinical Trial Optimization”:

Understanding Clinical Pharmacology: From Strategic Studies to Product Labeling

In drug development, clinical pharmacology studies are crucial to gather information to mitigate risks for the end user. Several studies are needed to determine how to safely administer the product to diverse patient populations. This data ultimately supports the recommendations found in the product label/SmPC. Examples of factors that need to be described include the impact of renal and hepatic impairment, age, drug-drug interactions, and food on the effect and safety of a drug.

Here, I delve into the fundamentals of clinical pharmacology studies, emphasizing their importance, and provide insights on conducting them effectively to optimize drug development outcomes.

Patient Journey-Centric Study Designs in Clinical Trials

Contract Research Organizations (CROs) play a crucial role in the execution and management of clinical trials. As intermediaries between sponsors and research sites, CROs have a unique opportunity to champion patient journey-centric study designs. By prioritizing patient experience, CROs can enhance trial efficiency, improve data quality, and foster greater patient engagement and retention. Here, we share some key points from our perspective on integrating patient journey-centric study designs into clinical trials.

Embracing Evolution: Welcome to the New Cytel

At Cytel, evolution is in our DNA. We dare to evolve because we must — innovation is the heartbeat of our mission to advance the future of human health through the power of data science. Recently, we unveiled our newly redesigned website and refreshed brand, showcasing not just a new look, but a renewed commitment to driving success in drug development and commercialization, and improving patient outcomes. Let’s explore the reasons behind our transformation and the key elements of our new brand. Stay with me until the end for interesting insights on industry trends and how Cytel is uniquely equipped to support you in realizing the full potential of therapies.

Pediatric Development Plans: Key Considerations

Historically, many drugs have been prescribed to children even though this patient population have largely been excluded from clinical trials. Authorities worldwide have, therefore, implemented regulations to address the gap in drug research involving children and to promote efforts that can lead to increased knowledge of pediatric pharmaceutical use.

There is an obvious logic. If medicines are to be used in children, they need to be studied in pediatric populations to ensure they are safe and effective. Here, we share important considerations for your pediatric development plan, including the US pediatric study plan (PSP) and the EU pediatric investigation plan (PIP).

When do sponsors need to conduct pediatric studies and when are they exempt?

Whether you need to include children in your clinical studies will partly depend on which disease you are targeting and what type of medicine you are studying. If you have a drug that targets a condition that does not affect children, such as Alzheimer’s disease, you will be granted a waiver. A waiver may also be given for specific age groups based on safety or lack of efficacy, the condition not occurring in the specific age group or other specific age-related reasons. Sometimes, a deferral from the requirement to study the drug in the pediatric population may be granted which means that the studies can be postponed until after you have shown that the drug is safe and effective in adults. However, outlining a PIP/PSP for your drug is mandatory, regardless of whether you expect to receive a waiver or deferral for the pediatric studies.

The challenge of harmonizing across national borders

Harmonizing pediatric study plans for different parts of the world is a complex task due to authorities in different regions having varying recommendations about when to initiate the development of pediatric study plans and what they should include. For example, in the EU, it’s preferred to submit a PIP early in the development process, when pharmacokinetic data are available, whereas in the US, the FDA requests a PSP after the completion of Phase II trials. These differences in timing make it challenging to coordinate pediatric studies globally. To manage this effectively, the best practice is to set a strategy for the global pediatric plan early in the development process. Without this proactive approach, the pediatric plans could delay the entire development project.

The contents of a PSP or PIP

The purpose of a PIP/PSP is to gather comprehensive information about the use of a drug in pediatric populations. Below are examples of what it should contain:

An overview of the disease, diagnosis, and treatment, highlighting differences between children and adults.
An assessment of the need for the drug in children across all age groups from birth to adolescence.
A summary of available chemical, preclinical, and clinical data on the drug.
A proposed strategy for any required preclinical studies and measures to adapt the drug’s formulation for use in children.
A proposed plan for potential clinical studies in children, including the timing of these studies in relation to those conducted in adults.

Financial benefits of conducting pediatric studies

Conducting pediatric studies not only ensures the safety and efficacy of a medicine in children but may also introduce new market opportunities in the pediatric population. In addition, following your pediatric plan can yield significant financial benefits in the form of a six-month patent extension (additional protection). It may seem short, but a six-month extension provides valuable exclusivity on the market and helps developers maximize the commercial lifespan of their product.

Regulatory incentives for pediatric oncology drugs: The RACE for Children Act

The Research to Accelerate Cures and Equity (RACE) for Children Act, passed by the U.S. Congress in 2017 and implemented in August 2020, significantly reformed the landscape of pediatric oncology drug development. The Act mandates that new cancer drugs developed for adults must also be evaluated for pediatric use if the molecular target of the drug is relevant to pediatric cancers. This requirement includes drugs with orphan drug designation, previously exempt from such studies. Prior to the RACE Act, pharmaceutical companies were not obligated to conduct pediatric studies for oncology drugs developed for adult cancers, leading to a significant gap in treatment options for children.

Early findings are promising, showing a clear rise in the number of oncology drugs being studied for pediatric use. Between August 2020 and August 2022, 32 initial pediatric study plans were submitted to the FDA due to the RACE Act, indicating a promising shift towards more inclusive drug development practices. [1]

Key Takeaways

Integrating pediatric patients into clinical trials can help ensure the safe and effective use of medicines for children. This is emphasized by global regulatory requirements and incentivized initiatives. However, navigating diverse sets of regulatory guidelines across countries and regions presents challenges in harmonizing and coordinating pediatric development plans on a global scale. With careful planning and considerations of the key factors outlined here, sponsors can minimize delays and expedite the approval process, ensuring timely access to safe and effective drugs for both adults and children.

Have questions? Get in touch with our experts: Erika Spens, Director Regulatory, Affairs; Sofie Broberg, Senior Consultant, Regulatory Affairs; Anna Törner, VP, Strategic Regulatory Affairs; and Linda Nord, Senior Consultant Regulatory Affairs: Contact Our Strategic Consulting Team

Notes

[1] Children’s Cancer Cause. (2023, February 8). First Two Years of the RACE Act Evaluated in New GAO Report. https://www.childrenscancercause.org/blog/race-act-gao-report

Planning Strategies for Externally Controlled Trials: Insights from ISPOR US 2024

External Control Arms (ECAs) provide comparative evidence when recruiting patients is difficult or unethical in randomized controlled trials. ECAs have significant potential to save resources and accelerate access to innovative treatment. In a previous blog, our experts took a deep dive into the concept of ECAs, their acceptable use cases, and the current regulatory guidance.

Existing guidance on the design and conduct of externally controlled trials emphasizes the importance of early engagement with regulatory and HTA bodies to justify using an ECA and discuss the preliminary study design and statistical analyses. With the increasing use of ECAs in regulatory and HTA submissions, the acceptable use cases for ECAs and important design and analytical considerations are becoming clearer. However, sponsors still face key questions about the optimal timing to plan for an ECA and how to prepare for early interactions to address differing regulatory and HTA perspectives.

In the ISPOR US 2024 HEOR Theatre session, Jason Simeone, Evie Merinopoulou, and Grace Hsu delved into these questions, discussing how regulatory and HTA stakeholders appraise ECAs, common issues from both perspectives and proposed practical solutions. In this blog, we ask Evie follow-up questions, highlighting insights from their ISPOR HEOR Theater session.

Your ISPOR US 2024 presentation was about early planning strategies for ECA. So, what is the optimal timing to start planning for an ECA?

Ideally, sponsors that need to perform an ECA to support their development program should start planning for the ECA alongside the clinical trial design. This allows them to gain experience with current real-world data (RWD) and make any necessary investment decisions for data improvements, such as additional data collection or infrastructure upgrades. Further, considering an ECA during the trial design provides the opportunity to incorporate real-world endpoints into the clinical trial. This is particularly valuable because defining clinical endpoints in real-world databases can often be challenging, especially when they are not measured consistently between routine practice and clinical trials.

Further, we showed in our presentation how although formal guidance from regulatory and HTA bodies on ECAs is consistent, final decisions when appraising ECAs may differ. This divergence in regulatory vs HTA acceptance reflects differing requirements for ECAs. When planning ECAs, both perspectives and requirements should be considered. Therefore, within sponsor organizations, early planning is key for cross-functional alignment (between HEOR/Market Access and Medical Affairs teams) on ECA study objectives and design, leading to more efficient evidence planning. With regards to external engagements with regulators and payers, the optimal timing is very contextual, but generally, sponsors should engage with decision makers via available routes like early advice programs, early enough to have the time to incorporate feedback and adjust their RWD strategy and study design—before protocol and SAP finalization.

Is early planning necessary for all cases? For instance, if a product is being developed for an indication with a rapidly changing treatment landscape and the appropriate comparators may not yet be known, would these early planning activities still be useful?

Yes, absolutely. During the early feasibility assessments that we discussed in our ISPOR presentation, we should evaluate a range of elements to determine the feasibility of an ECA—ranging from the identification of target populations to the reliable capture of confounders and study endpoints, among other factors. Identifying relevant comparators is only one element of those assessments. Even if comparators change over time, becoming familiar with RWD and current gaps helps inform discussions about the appropriate data strategy and design, which should be flexible enough to reflect some of the changes in the treatment landscape. Perhaps now, we would want to know if treatments are well captured and elements like patient count on a relevant comparator will need to be refreshed. It is important to ask questions during the early planning stages that are specific yet broad enough to inform ECA feasibility, even if the research question evolves, particularly concerning the RWD strategy.

You’ve recommended that study sponsors should be prepared to discuss certain topics during early engagement meetings, such as the ECA rationale, data source, early design considerations, and feasibility assessment. In a resource-constrained environment, sponsors may not want to invest so much money in these activities before the very first meeting, only to receive a negative response. What topics should be prioritized for that first engagement with an HTA or regulatory agency vs. subsequent meetings?

This is an important point. Ultimately, the most crucial aspect is to clarify the justification for an ECA and assess whether the agencies are open to considering evidence from an ECA. Working with the right experts who understand agency requirements from prior experience is important. Beyond the justification for an ECA being clear, we see that most critiques of ECAs stem from data issues. So, in my opinion, presenting external data source options and discussing anticipated challenges can facilitate a more productive discussion in those early engagements. If resources are constrained, a more targeted review is sufficient rather than a full-blown data landscaping exercise.

During your presentation, you emphasized the importance of identifying fit-for-purpose data. However, in some cases, a sponsor may have to submit to an HTA body in a region where such data is not readily available. For instance, if a detailed data landscaping assessment reveals that most fit-for-purpose data is in the US, but the submission is for a European HTA agency, how can sponsors address this challenge in their submission?

First and foremost, sponsors need to present to the local agency that they have thoroughly attempted to identify a data source accurately representing the local (target) population of interest. Local agencies are usually quite understanding if sponsors can demonstrate that they made the necessary effort and did not cherry-pick data sources, but instead selected a source with the highest quality data available for the research question, in a transparent and systematic manner. However, this means that there might be some potential external validity bias that could concern a local decision-maker. For example, a UK or German payer might be concerned that evidence submitted from a US data-derived ECA may not be generalizable to the target population of the decision problem.

At Cytel, we have been engaged in some very interesting work to understand how we could adjust for this potential external validity bias using transportability methods. These are quantitative methods, similar to those adjusting for confounding, and can be reliably used to extend conclusions from one study population to an external target population. Essentially, if core evidence comes from a US-data derived ECA, transportability methods can be applied to adjust the study findings to measurable patient characteristics in the target population of interest, accounting for prognostic factors or effect modifiers. We recently published a demonstration project on this topic [1 ]. Additionally, NICE recently updated its RWE framework [2] to include transportability analysis methods.

Alternatively, sponsors could consider designing a prospective study, though this approach requires much higher costs and extensive timelines. If you’re taking this route, you should design data collection with the ECA in mind, aligning patient selection criteria, endpoint definitions, etc., which is why planning early is important.

Overall, at Cytel we encourage sponsors to approach data selection in a transparent and systematic way, as recommended across all existing ECA formal guidance documents, and leverage available analytical approaches to address potential external validity concerns when using non-local data if additional data collection is not feasible.

Which internal stakeholders should be involved in this process of early planning for ECAs, and what should sponsors consider when partnering externally?

Typically, in sponsor organizations, there are clinical development and medical affairs teams that understand regulatory requirements and processes very well. In addition, there are Market Access and HEOR/RWE teams that know RWD and real-world evidence methods very well. These teams may not always work closely together, but in our presentation, we talked about the importance of bringing these two teams together early on in planning for ECAs to align differing regulatory vs payer requirements. When selecting external partners, it’s important to work with organizations that have important methodological and technical expertise. They should also have a thorough understanding of the evolving guidance and acceptance criteria of decision-making agencies and be able to provide strategic guidance on important study design decisions and early stakeholder engagements.

Interested in exploring further? Download the slides from the ISPOR HEOR Theatre Session presented by Cytel here.

Notes

[1] Ramagopalan SV, Popat S, Gupta A, et al. Transportability of Overall Survival Estimates From US to Canadian Patients With Advanced Non–Small Cell Lung Cancer With Implications for Regulatory and Health Technology Assessment. JAMA Netw Open. 2022;5(11):e2239874. doi:10.1001/jamanetworkopen.2022.39874

[2] https://www.nice.org.uk/corporate/ecd9/resources/nice-realworld-evidence-framework-pdf-1124020816837

What’s Ahead for Clinical Trial Design?

The past two years have been transformative for Cytel. Most notably, the global COVID-19 pandemic unleashed an industry-wide tidal wave of challenges in clinical trial conduct, the ripple effects of which were felt across workstreams, project timelines, and other deliverables. In response, our company expanded considerably during this time, meeting the challenges with new scientific and product offerings. They include Solara, a new SaaS platform that harnesses massive cloud computational power to assist teams in selecting the best design for their studies; innovative Bayesian trial designs only found in East Bayes; LiveSLR, a web software product that defines a new standard for human-machine partnership to expedite and standardize HTA submissions; and over 200 scientists worldwide working to leverage real-world data and create value evidence across the pipeline from natural history studies to market access.

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Women’s health expands beyond a single therapeutic area

External control arms move from optional to strategic

Biostatistics becomes a strategic capability

Clinical pharmacology takes center stage

Interested in learning more?

A benchmark designed around real patient harm

Key findings from the study

Relevance beyond clinical care: Implications for clinical development

The CSR example: Completeness as a safety criterion

The necessity of human-in-the-loop systems

A path forward: Benchmark-aligned, hybrid AI for regulated medicine

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1. Quantify uncertainty, communicate risk

2. Build adaptive designs that let you pivot

3. Align milestones with financing strategy

The takeaway

Current trends in clinical trial cost optimization

Building specialized skill sets to improve efficiency and quality

Global Capability Centers (GCCs): Unlocking scalability and cost savings

Innovative resourcing models: FSP and just-in-time staffing

Emerging markets and government incentives

Integrating AI and technology to streamline processes

Shaping the future of cost-efficient clinical trials

Interested in learning more? Watch our on-demand webinar, “Winning in a Budget-Constrained World: Smarter Clinical Trial Optimization”:

When do sponsors need to conduct pediatric studies and when are they exempt?

The challenge of harmonizing across national borders

The contents of a PSP or PIP

Financial benefits of conducting pediatric studies

Regulatory incentives for pediatric oncology drugs: The RACE for Children Act

Key Takeaways

Your ISPOR US 2024 presentation was about early planning strategies for ECA. So, what is the optimal timing to start planning for an ECA?

Is early planning necessary for all cases? For instance, if a product is being developed for an indication with a rapidly changing treatment landscape and the appropriate comparators may not yet be known, would these early planning activities still be useful?

Which internal stakeholders should be involved in this process of early planning for ECAs, and what should sponsors consider when partnering externally?