At a time when trials are becoming more complex, evidence expectations are rising, and pressure to move faster has never been greater, what does meaningful progress in clinical development look like today?
To mark International Clinical Trials Day 2026, we spoke with Esther Kitto, VP Clinical Operations at Resolution Therapeutics; Duncan McHale, Founder of Weatherden; and Stephan Schann, CSO of Kainova Therapeutics, about how thinking is changing across the clinical trial landscape. Together, they discuss the early signals they trust, the growing importance of translational and adaptive trial design, shifting regulatory expectations, and why trust, inclusion, and operational discipline are now central to ethical research and high-quality data.
Beyond “success vs failure,” what does meaningful progress in clinical trials look like today?
McHale: Meaningful progress is an increase in the probability of making the right decision, quickly. There is growing emphasis on speed in clinical development, but speed without direction is just lost time. Patients are waiting, so moving faster only matters if you are moving in the right direction.
We focus on velocity, integrating clinical, biological, and statistical data to make the best possible decision on the direction of travel, then executing rapidly with confidence. Early studies should reduce uncertainty around whether a therapy works, for whom, and why. If they don’t, they haven’t progressed the program.
Kitto: Confirming that preclinical biology directly translates into a functioning clinical protocol that demonstrates safety, whilst giving a clear signal of efficacy, is an essential step in early-stage clinical studies. Operationally, this may involve a range of functions and key activities spanning regulatory approval and site activation, IMP manufacture and QP release, to patient enrolment and a final clinical study report. However, meaningful progress is only observed when trial-related activities are executed in a coordinated and timely manner, ensuring high-quality data generation, efficient patient recruitment, and seamless oversight – ultimately allowing confident decisions on dose, safety, efficacy and the overall viability of the program.
What kinds of early signals do you trust most when deciding whether to advance, pivot, or stop a program – and why?
Schann: For GPCR-modulating programs, the most important early signals actually begin before the clinic. We design candidates with the complexity of GPCR pharmacology in mind, including receptor biology, signaling bias, and selective pathway modulation, because this increases the likelihood that early clinical signals can be understood and acted on, and reduces the risk of misleading readouts. Once in patients, the signals we trust most are mechanism-linked biomarkers such as target engagement and early biological shifts that show the drug is engaged and modulate the intended pathway. We look for alignment between pharmacology, biomarker shifts, emerging activity, and dose-dependent effects.
McHale: In early development, I would not trust any single endpoint in isolation. These studies, if well designed, are rich in clinical and biomarker data, the key is to integrate the full evidence set and look for consistency across measures to build confidence we are making a medicine. A standalone significant result is often misleading. Endpoints should be treated as estimates of treatment effect, not simple pass-fail tests, so each study delivers as much learning as possible. Understanding how likely early signals are to translate to regulatory endpoints and larger patient populations is critical, as is building confidence in asset differentiation.
How has closer integration between preclinical and clinical teams changed the way trials are designed, analyzed, or interpreted in recent years?
McHale: Integration has improved, but the real shift is toward treating development as a continuous learning system. We can now measure biology in far greater depth, linking preclinical hypotheses to clinical data more directly. This should allow us to distinguish between a failure of mechanism and a failure of execution. Too often, those distinctions are blurred. The organizations that perform best are those that design trials in a loop, integrating every decision across functions, including the operational team. Following a traditional cascade or hierarchy of decision, rather than grounding them in the risks of that program, hugely increases the likelihood of attrition.
Kitto: As development progresses from preclinical to clinical stages, it is no longer a simple handoff but a continuous, integrated process. Preclinical work has evolved from being purely foundational to becoming an active, iterative component of trial design, analysis, and interpretation. Mechanistic hypotheses generated in the laboratory are now prospectively built into clinical protocols and tested directly in participants through biomarker strategies, translational endpoints, and adaptive designs. Importantly, preclinical insight does not stop at trial initiation; it continues alongside the clinical program, informing ongoing decision-making, cohort expansion, and dose refinement. This creates a dynamic feedback loop, where emerging clinical data also reshapes preclinical understanding, ensuring that both disciplines remain aligned and mutually informative throughout the study.
Schann: Closer preclinical-clinical integration has made clinical development more hypothesis-driven and mechanism-informed. For GPCR programs, connecting receptor pharmacology, downstream signaling and clinical readouts helps avoid overinterpreting signals that could be misleading without the right biological context, and ensures that early trials are designed to test the right questions. Preclinical insights now guide biomarker prioritization and dose selection, while clinical observations feed back into candidate optimization and backup strategies. This continuous loop strengthens decision-making and increases the likelihood that the candidates entering the clinic are aligned with the biology most likely to matter for patients.
Have regulatory expectations around evidence – particularly for early‑stage or novel modalities – changed in ways that meaningfully affect how trials are designed today?
Kitto: Yes – and largely for the better. Regulators now engage earlier and more constructively, helping sponsors address ATMP challenges, including limited validated animal models, manufacturing complexity, limited feasibility of placebo control, and the ethical need for sentinel dosing in first-in-human studies. That said, real constraints remain.
Requirements for objective, reliable outcomes, and the lack of widely accepted surrogate endpoints, means that sponsors, especially small biotechs, face a sustained challenge: to demonstrate strong efficacy evidence within small patient populations and compressed timelines. In this context, regulatory progress in endpoint qualification could be as transformative for development speed as advances in trial design or operational execution.
Schann: Yes, particularly for novel GPCR modalities such as biased ligands and allosteric modulators. Current regulatory frameworks place greater emphasis on a strong rationale linking mechanism and study design. This expectation applies across modalities, but innovation makes the rationale more integrated because early evidence showing that the drug engages the intended biology, modulates the relevant pathway, and supports the proposed clinical endpoints must fit together more tightly to support the study design. This drives more rigorous translational planning and ultimately leads to better trials, but it also requires closer preclinical-clinical alignment from day one.
How should trial design evolve to increase confidence and interpretability of results, especially in earlier phases?
McHale: One major step forward is moving from underpowered “classical” trial designs to Bayesian adaptive designs, focused on maximizing learning. Whilst common in biopharma, biotech companies are lagging in adoption of adaptive trials. Investors should become more comfortable with complex trial designs, shifting the focus from whether a p-value is below 0.05 to whether a drug is likely to meet unmet need and offer meaningful differentiation based on more sophisticated probability statements. The entire success or failure of a trial depends on the ability to separate signal from noise, and crude trial designs increase the risk of losing a signal significantly.
Schann: Early-phase trials benefit most from hypothesis-driven, adaptive frameworks that integrate translational biomarkers for sharper interpretability. That begins with biomarker selection, choosing readouts that reflect the underlying hypothesis and can be linked to pathway selectivity rather than broad clinical outcomes. Dose-finding should capture target engagement, pathway modulation, and the dose-response relationships that connect them. Reducing patient-population heterogeneity further strengthens interpretability, making early studies far more informative and allowing us to determine whether the drug is hitting the intended biology and whether the program should advance.
How can companies build trust with trial participants and sites when studies are becoming more complex – and why does that matter for data quality as well as ethics?
Kitto: Early participant and public involvement – through focus groups and transparent dialog – ensures that trial endpoints are clinically meaningful to participants and that the overall burden placed on participants is carefully considered and proportionate. This is particularly critical in cell therapy, where treatments are novel, irreversible, and often based on limited clinical data – all requiring significant participant trust. That trust is built through clear, upfront communication, robust informed consent, and strict manufacturing quality and traceability.
Sites must also trust the processes and systems in place, supported by strong training and operational discipline to manage complexity without error. Ultimately therefore, data quality is a direct consequence of this shared trust.
What needs to change to improve inclusion and real-world relevance – without compromising data quality?
Kitto: A shift in mindset for those designing trials to actively seek bolder solutions in considering how groups of patients that have historically been excluded from trials, such as those linked to socioeconomic disadvantage, which represent real-world patients, can be included. Thus, early engagement with patient groups in study design is essential.
For advanced therapies, access is usually constrained by the intricacies of cell manufacturing, cryogenic preservation infrastructure, and the need for specialized infusion and leukapheresis centers, which are often limited in availability. Enhancing inclusion therefore requires designing clinical operations and manufacturing logistics around patient vulnerability, rather than expecting patients to adapt to system constraints.
Clinical trials are expanding in scope and complexity – what change would make the biggest difference to development speed and participant access over the next few years?
McHale: Better decisions earlier. Much of the cost and delay in development is self-inflicted, driven by imprecise patient populations, unclear hypotheses, and poorly aligned teams. These issues are rarely fixed later, they compound. Improving development speed is less about adding complexity and more about removing avoidable uncertainty upfront. That requires discipline, integration, and a willingness to stop programs that do not meet a meaningful threshold of confidence. Faster access for patients comes from better decisions, not more activity.
Schann: As trials become more complex, greater data interoperability and collaboration across participating centers, CROs, and regulators would have the biggest impact. For smaller clinical-stage companies, operational bottlenecks can slow progress even when science is advancing quickly. Improving data flow enables faster regulatory alignment, more efficient site operations, and real-time access to high-quality clinical and biomarker data, ultimately streamlining trial execution and shortening timelines from the first sign the drug is working to full approval without compromising quality. Advances in AI-enabled analytics can help integrate diverse datasets, but their impact depends on the quality and structure of the underlying information.
Kitto: From a development perspective, the most significant acceleration in timelines may be driven by deeper integration of participant-centric digital trial solutions. Seamless connectivity with electronic healthcare e-systems can support remote consent and automated eligibility assessment, while real-time data capture via ePRO and connected devices minimizes delays and reduces the burden of data cleaning. At the same time, decentralized trial components – such as telemedicine visits, home nursing, and wearable technologies will expand access to rural, mobility-limited, and historically underrepresented populations, and thus reduce dependence on large academic sites. Together, these advances will enable faster decision-making through real-time data, streamlined workflows, and remote oversight, while strengthening recruitment, retention, and patient experience.
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