Forgotten Biology: The Missing Link Behind Trial Outcomes

Clinical trials are often described as the science of uncertainty, and that uncertainty is expected. It is how evidence is built. However, not all uncertainty is scientific. In many cases, it arises when the biological context of an intervention is not carried through into the study population.

A product enters a trial with strong science. The mechanism is clear, the rationale is sound, and expectations are well-founded. Yet the outcome fails to support a meaningful claim. Decisions slow down, confidence weakens, and what should have been clarity begins to feel like doubt. What makes this situation particularly difficult is not just the result, but the contradiction within it. Because in many such trials, the data are not uniformly negative. A closer look often shows that some participants have responded meaningfully, in line with the expected mechanism. The product has worked, but not consistently across the population.

At this point, the question shifts.

It is no longer only about whether the product works, but whether the study preserved the biology well enough to show where it works.

Clinical studies begin with precision. Interventions are designed to act on specific biological pathways, and expected outcomes are grounded in that understanding. But as studies move from concept to execution, this precision is often diluted during population selection. Participants are recruited based on diagnosis and eligibility criteria, an approach that is necessary and aligned with regulatory expectations. However, diagnosis is a broad clinical construct. It groups individuals with similar symptoms, not necessarily similar underlying biology. Each individual carries a unique biological context shaped by genetics, physiology, and lifestyle. Within a single study, participants may differ in what is driving their condition, how it is expressed, and how they are likely to respond. These differences are not peripheral; they directly influence outcomes.

As the study progresses, this diversity becomes visible in a predictable pattern. Some participants respond strongly, some show modest improvement, and some show little or no response. When these responses are combined, the overall effect becomes less distinct. As variability increases, the true biological effect (signal) gets diluted, making it harder to detect statistically. In practice, this means a study may appear non-significant overall, even when clearly responding subgroups exist within the population.

The industry is not unaware of this pattern, but it is often addressed too late. A large proportion of trials report subgroup differences, yet very few are designed to account for this variability at the stage where it matters most. As a result, variability is analysed after the study rather than shaped before it.

This creates a structural limitation. By the time variability is understood, it is already embedded in the dataset. The population is fixed, and the statistical outcome is effectively locked. The insight explains the result, but it does not change it.

The only stage where this can be meaningfully influenced is at recruitment. This is where the study population is defined, and where the clarity of the outcome is largely determined. Today, recruitment is primarily driven by eligibility with a simple question: Does the participant meet the criteria? If yes, enroll. This ensures compliance, but it does not ensure that the study population is aligned with the biology of the intervention. There is a meaningful difference between a participant who qualifies for a study and one who is likely to demonstrate a measurable response. That difference often determines whether the signal will be clearly seen or diluted.

Advantages of Phenotype-Based Recruitment

This is where phenotype-based recruitment becomes relevant, not as a change to eligibility criteria, but as a refinement within them. Before formal enrollment, participants can be assessed through structured, low-risk pre-screening to better understand their functional state, lifestyle context, and relevance to the study endpoints.

This approach is already well demonstrated in clinical research where selecting the right biology at entry has led to clearer outcomes.

The SELECT trial (Semaglutide in Overweight/Obesity WITHOUT Diabetes- cardiovascular outcomes) focused on overweight and obese individuals with established cardiovascular disease but without diabetes, isolating a clearly defined cardiometabolic risk phenotype. This targeted selection allowed semaglutide to demonstrate a significant reduction in major cardiovascular events, which may have been less apparent in a more heterogeneous population.¹

The CANTOS Trial specifically enrolled post–myocardial infarction patients with elevated hsCRP (≥2 mg/L), thereby selecting a population with active residual inflammatory risk rather than all cardiovascular patients. This phenotype-based recruitment ensured alignment between the anti-inflammatory mechanism of canakinumab and the underlying biology, reducing heterogeneity from patients without inflammatory activation. As a result, the trial was able to clearly demonstrate that targeting inflammation, independent of lipid levels, reduces cardiovascular events, with the greatest benefit observed in participants achieving lower hsCRP levels during treatment.²

Regulatory thinking is also evolving in this direction. The US Food and Drug Administration (FDA) has emphasized enrichment strategies to improve the likelihood of detecting treatment effects by focusing on populations where an intervention is more likely to show benefit. In parallel, the updated ICH GCP E6(R3) (2025) framework places greater emphasis on how participants are selected, encouraging a more structured and risk-based approach to recruitment, including the thoughtful use of pre-screening processes, while maintaining ethical and patient-centric standards.^3,4

When this approach is applied, the study population becomes more aligned with the biological mechanism of the intervention. Variability does not disappear, but it becomes more structured. Participants become more comparable in ways that matter, and outcome measures respond more clearly. The product does not change. The study design does not change. What changes is the ability of the study to reflect the biology it was built to test.

Across studies, one pattern consistently emerges: when the population is aligned with the biology, results are clearer, more stable, and easier to interpret. When it is not, even strong products can appear inconsistent. At Vedic Lifesciences, this understanding has been translated into a structured recruitment approach under our design innovation program “Vedic Elevate,” where phenotype-based pre-screening is integrated into execution workflows. This integration is not to restrict enrollment, but to improve relevance by ensuring that the biology driving the product is meaningfully represented in the study population.

Seen through this lens, a non-significant result does not always indicate that a product is ineffective. It may simply mean that the study did not include the right population to demonstrate its effect clearly. Clinical studies begin with biology, but their success depends on whether that biology is preserved through execution. When it is not, the signal weakens. When it is, outcomes become clearer and more consistent.

In that sense, biology is not just part of science; it is the missing link that determines whether that science is ultimately seen.

References

1. Lincoff AM, Brown-Frandsen K, Colhoun HM et. al. Semaglutide and Cardiovascular Outcomes in Obesity without Diabetes. N Engl J Med. 2023;389:2221-2232. doi: 10.1056/NEJMoa2307563.
2. Ridker PM, Everett BM, Thuren T, et al. Antiinflammatory Therapy with Canakinumab for Atherosclerotic Disease. N Engl J Med. 2017;377(12):1119-1131; doi: 10.1056/NEJMoa1707914
3. US Food and Drug Administration. Enrichment Strategies for Clinical Trials to Support Approval of Human Drugs and Biological Products. March 15, 2019. Accessed May 15, 2026. https://www.fda.gov/regulatory-information/search-fda-guidance-documents/enrichment-strategies-clinical-trials-support-approval-human-drugs-and-biological-products
4. US Food and Drug Administration. E6(R3) Good Clinical Practice (GCP). September 8, 2025. Accessed May 15, 2026. https://www.fda.gov/regulatory-information/search-fda-guidance-documents/e6r3-good-clinical-practice-gcp