A Practical Guide to Biomarkers for Clinical Development

Biomarkers sit at the centre of modern drug development. Whether you are running a first-in-human study or planning a late-phase trial, biomarkers shape how you understand mechanism, demonstrate target engagement, and make decisions about dose, patient selection, and risk. Yet despite their importance, many biomarker strategies fail because they are added too late, are poorly aligned with clinical endpoints, or are not feasible under real clinical conditions.

This guide focuses on how clinical developers can think practically about biomarkers. Not what biomarkers are in theory, but how to choose, deploy, and interpret them in a way that genuinely supports development decisions.

What do we mean by a biomarker?

At its simplest, a biomarker is a measurable characteristic that reflects biology. That might be a protein concentration in plasma, a gene expression signature in tissue, a cell population measured by flow cytometry, or a physiological or imaging-derived signal.

Regulatory frameworks such as the FDA-NIH BEST (Biomarkers, EndpointS, and other Tools) initiative describe biomarkers as indicators of normal biological processes, pathogenic processes, or responses to therapeutic interventions. The BEST framework, published in 2016 and regularly updated, provides a common language that has become essential for regulatory submissions and scientific communication across the drug development community.

For developers, however, the definition matters less than the intent. A useful biomarker is one that answers a specific question at a specific point in development- and does so with the right level of evidence for its intended purpose.

Understanding Context of Use (COU)

Before selecting any biomarker, developers must define its context of use, a regulatory concept that specifies exactly how a biomarker will be applied. The COU should describe:

• The specific purpose and role of the biomarker
• The disease or condition being studied
• The patient population
• The drug or intervention being evaluated
• The analytical method and sample type
• The decision criteria or interpretation framework

Defining COU early is not merely a regulatory formality. It drives study design, sample size calculations, assay validation requirements, and data interpretation plans. A biomarker with a well-defined COU can be evaluated objectively; one without becomes a source of ambiguity and missed opportunities.

Biomarker categories that matter in practice

Biomarkers are often grouped into discrete categories, but in practice a single biomarker may serve different roles depending on context, timing, and data quality. Thinking in terms of intent remains helpful:

Diagnostic biomarkers
Used to confirm disease presence or subtype. In development, these often define inclusion criteria or enable stratification of heterogeneous populations. Examples include HER2 status in breast cancer or PD-L1 expression in immunotherapy trials.

Monitoring biomarkers
Used longitudinally to track disease activity or biological response over time. These are particularly valuable in early trials to understand variability and kinetics. HbA1c in diabetes and viral load in HIV represent monitoring biomarkers that also inform treatment decisions.

Pharmacodynamic (PD) and response biomarkers
Used to demonstrate that a drug is engaging its target or modulating a pathway. These markers underpin mechanism-of-action claims, dose selection, and early go/no-go decisions. PD biomarkers are especially critical in first-in-human studies where clinical endpoints may not yet be evaluable.

Predictive biomarkers
Used to identify patients more likely to respond to treatment. These often start as exploratory signals and may later evolve into decision-enabling or regulatory-facing tools. KRAS mutations in colorectal cancer and BRCA mutations in ovarian cancer are well-established predictive biomarkers that guide therapy selection.

Prognostic biomarkers
Used to estimate disease trajectory independent of treatment, helping to contextualise outcomes and balance treatment arms. Prognostic biomarkers are essential for understanding whether observed treatment effects exceed what would be expected from natural disease progression.

Safety biomarkers
Used to detect on-target or off-target toxicity early, which is increasingly important for potent or novel modalities. Troponin for cardiac toxicity and creatinine for renal function are traditional safety biomarkers, while emerging modalities may require novel safety monitoring strategies.

Risk biomarkers
Used to identify individuals at increased risk of developing disease, often in prevention or early-intervention settings. Polygenic risk scores and certain imaging signatures are increasingly used in disease interception trials.

Strong programmes are selective. Not every category needs to be addressed in every study. The key is matching biomarker selection to the specific questions each development phase must answer.

Biomarker maturity and regulatory intent

Not all biomarkers are created equal, and not all require the same level of evidence. A common pitfall is treating exploratory and decision-enabling biomarkers as if they must follow the same validation pathway. This can add unnecessary complexity early in development or, conversely, insufficient rigor later on. In practice, regulatory expectations are driven by a biomarker’s intended Context of Use (COU), with evidentiary requirements applied on a fit-for-purpose basis. Rather than a single linear pathway, biomarkers are best thought of as progressing through increasing levels of regulatory reliance, reflecting how they are used across the drug development lifecycle.

Exploratory biomarkers

Exploratory biomarkers are used primarily for learning and hypothesis generation, typically in preclinical studies or early clinical trials (Phase I/II). Their role is to explore relationships to disease biology, pharmacology, or mechanism of action, rather than to support regulatory claims. At this stage, assay characterization and limited analytical validation are generally sufficient, provided the data are reliable and interpretable. These biomarkers may be included descriptively in regulatory submissions but are not used to support decision-making claims.

Biomarkers for internal decision-making

Biomarkers used for internal decision-making support development strategy, such as dose selection, patient stratification hypotheses, or go/no-go decisions. These biomarkers must be supported by evidence that they are fit-for-purpose, meaning the level of analytical and biological support is proportional to the importance and risk of the decision being informed. Formal FDA qualification is not required, and these biomarkers do not need to meet the evidentiary standards associated with regulatory labelling. However, if they influence clinical trial design or patient safety, the underlying data and rationale should be sufficiently robust to withstand regulatory scrutiny if questioned.

 Program-specific regulatory biomarkers

Some biomarkers are used to support regulatory decision-making within a specific drug development program, for example for patient enrichment in pivotal trials, assessment of clinical benefit, or as primary or secondary endpoints. These biomarkers are not formally qualified for use across programs but require substantial analytical and clinical validation to demonstrate a clear and credible relationship between the biomarker and the clinical concept of interest for the specific indication and molecule. The evidentiary package typically includes analytical performance data, biological plausibility, and clinical correlation from earlier-phase studies, natural history data, or supportive external evidence. Regulatory acceptance is determined on a case-by-case basis.

 Qualified biomarkers

Qualified biomarkers receive formal designation through the FDA’s Biomarker Qualification Program for a specific Context of Use (COU), such as patient enrichment, safety monitoring, or response assessment. Once qualified, a biomarker can be used across multiple drug development programs for that defined COU without requiring re-review, reducing regulatory burden and increasing consistency. Achieving qualification requires a rigorous evidentiary package commensurate with the risk associated with the biomarker’s intended application. Under the FDA’s evidentiary framework, developers must demonstrate that the biomarker is analytically reliable and clinically relevant, and that it accurately predicts, measures, or monitors the defined clinical concept for its regulatory purpose.

A practical strategy distinguishes between exploratory biomarkers used for learning, biomarkers used to support internal development decisions, and biomarkers intended to support regulatory claims or labelling. Being explicit about intended use early helps set appropriate expectations around assay performance, data robustness, and interpretation. This clarity also reduces friction later when programmes advance and evidence thresholds increase.

Analytical vs. Clinical Validation

Developers must also understand the distinction between analytical and clinical validation:

Analytical validation establishes that an assay reliably measures what it claims to measure. This includes demonstrating accuracy, precision, sensitivity, specificity, reproducibility, and robustness across relevant conditions. Regulatory guidance such as ICH M10 on bioanalytical method validation and the FDA’s guidance on bioanalytical method validation provide detailed frameworks for this work.

Clinical validation establishes that the biomarker reliably predicts, measures, or associates with a clinical outcome or biological state. This requires evidence from well-designed clinical studies demonstrating that biomarker changes correlate with clinically meaningful endpoints.

A biomarker can be analytically validated but not clinically validated, or vice versa. Both are necessary for regulatory acceptance, but the depth of validation required depends on the biomarker’s intended use. Exploratory biomarkers may proceed with robust analytical validation and preliminary clinical data, whereas companion diagnostics require comprehensive validation in both domains.

Building a biomarker strategy that works

A robust biomarker strategy starts with the clinical question, not the assay.

Start with decisions
Ask what decision the biomarker needs to support. Dose selection, proof of mechanism, patient stratification, or safety monitoring all require different levels of confidence and different study designs. This decision-first approach ensures resources are focused on biomarkers that genuinely de-risk the program.

Align biomarkers with clinical endpoints early
Biomarkers should complement, not compete with, clinical readouts. Misalignment here is a frequent cause of late-stage frustration and inconclusive results. Ideally, biomarker strategies are developed in parallel with endpoint selection during protocol development, with input from biostatistics, clinical operations, and regulatory affairs.

Choose matrices and timing deliberately
Blood is convenient, but not always biologically informative. Tissue, cerebrospinal fluid (CSF), bronchoalveolar lavage (BAL), or local fluids may be necessary despite added complexity. Sampling timepoints should reflect underlying biology—including pharmacokinetic and pharmacodynamic considerations—not just visit schedules. For example, target engagement biomarkers should be timed to expected drug exposure, while downstream pathway markers may require later assessment.

Be realistic about feasibility and scale
Assays that perform well in pilot studies may struggle under clinical conditions. Sample volume, stability, site handling, batching, and turnaround time should all be considered from the outset. The transition from a specialised research laboratory to a clinical or central laboratory environment often exposes previously hidden sources of variability. Engage with laboratory partners early to understand practical constraints.

Account for pre-analytical variability
Many biomarker failures are driven by factors upstream of the assay itself. Site-to-site variability, time-to-spin, freeze–thaw cycles, and matrix effects can all obscure true biology. These risks should be actively managed, not discovered retrospectively. Comprehensive sample collection and handling manuals, site training, and pre-analytical monitoring are essential components of any biomarker strategy.

Plan for data interpretation early

High-plex and multi-omic approaches increasingly rely on panels and signatures rather than single analytes. These datasets are powerful, but only if there is a clear analysis plan in place, and a shared understanding of what constitutes a meaningful change. Pre-specify analytical approaches, multiplicity adjustments, and clinical interpretation frameworks before data lock. Post-hoc exploration is valuable but should be clearly labelled as such.

Accept that biomarker strategies evolve
Early-phase biomarkers are often exploratory by design. The goal is learning, not validation. Building flexibility into the plan allows markers to be refined, dropped, or promoted as evidence accumulates. An adaptive biomarker strategy recognises that some markers will fail to perform, others will exceed expectations, and new opportunities will emerge as understanding deepens.

Practical considerations for implementation

Even a well-designed biomarker strategy can fail in execution. Attention to operational details is essential:

• Site selection and training: Ensure clinical sites have the capability and infrastructure to collect, process, and ship samples according to protocol requirements. Provide thorough training and accessible reference materials.
• Sample logistics: Establish clear chains of custody, appropriate shipping conditions, and backup plans for sample failures or delays. Consider whether real-time biomarker results are needed to inform dosing decisions or if batched analysis is acceptable.
• Quality control: Implement ongoing QC monitoring throughout the study, not just at final analysis. Detecting assay drift or site-specific issues early allows for corrective action before data quality is compromised.
• Data management: Integrate biomarker data with clinical data systems to enable holistic analysis. Ensure data dictionaries are clear, units are standardised, and lower limits of quantification are properly handled.
• Regulatory documentation: Maintain comprehensive documentation of assay methods, validation data, sample handling procedures, and any protocol deviations. Regulatory inspections increasingly focus on biomarker data integrity.

Emerging Trends and Future Directions

The biomarker landscape continues to evolve rapidly. Several trends are shaping the future of biomarker-enabled development:

• Liquid biopsies for minimally invasive disease monitoring, particularly in oncology, are moving from research tools to routine clinical use.
• Artificial intelligence and machine learning are being applied to identify novel biomarker signatures and integrate complex datasets, though regulatory frameworks for AI-derived biomarkers are still maturing.
• Decentralised trial designs are creating new opportunities for remote biomarker collection, but also new challenges around standardisation and oversight.
• Patient-centric biomarkers that align with what matters most to patients, symptoms, function, quality of life, are gaining recognition as essential complements to traditional molecular markers.

Staying current with these developments and engaging with regulatory guidance as it evolves will be increasingly important for successful biomarker-enabled development.

Final Thoughts

Two studies can measure the same biomarkers and reach very different conclusions. Often the difference lies not in the technology, but in study design, pre-analytics, and how clearly the biomarker question was defined upfront.

For clinical developers, the most effective biomarker strategies are those that are integrated early, designed with intent, grounded in what is operationally achievable, and aligned with regulatory expectations. When done well, biomarkers reduce uncertainty, accelerate decision-making, and ultimately bring better therapies to patients faster. When done poorly, they add cost without clarity and may even mislead development decisions.

If you are planning a biomarker strategy for an upcoming study, start with the questions you need answered. Define your context of use. Understand the regulatory landscape for your intended application. Engage the right expertise early; clinical, analytical, regulatory, and operational. The assays should follow from there.

Biomarker science will continue to advance, but the fundamental principle remains unchanged: the best biomarker is the one that answers your question with sufficient confidence to act.

For organisations seeking support with biomarker strategy, assay development, or regulatory submissions, our team brings deep expertise across the full biomarker lifecycle from early discovery through regulatory approval.

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