Biomarkers are key to contemporary drug development. Biomarkers demonstrate target engagement, shape understanding of mechanisms, and support decisions around dose, risks, and patient selection, from first-in-human studies to late-phase trials.
Image Credit: Synexa Life Sciences
Despite their importance, a significant number of biomarker strategies fail because they are added too late, are not feasible in real clinical settings, or are poorly aligned with clinical endpoints.
This article examines the ways clinical developers can think practically about biomarkers. It looks beyond the theory of biomarkers, focusing on selecting, deploying, and interpreting them in a way that tangibly supports development decisions.
Understanding biomarkers
The simplest definition of a biomarker is a measurable characteristic that reflects biology. For example, a gene expression signature in tissue, a protein concentration in plasma, a physiological or imaging-derived signal, or a cell population measured using flow cytometry.
The FDA-NIH BEST (Biomarkers, EndpointS, and other Tools) initiative and other regulatory frameworks describe biomarkers as indicators of normal biological processes, pathogenic processes, or responses to therapeutic interventions.
The BEST framework was published in 2016 and is regularly updated, providing an essential common language in scientific communication and regulatory submissions throughout the drug development community.
The definition matters less to developers than intent. A biomarker is considered useful if it answers a specific question at a specific point in development and is able to answer this question with sufficient evidence for its intended purpose.
Understanding Context of Use (COU)
Developers must define its context of use before selecting a biomarker. This regulatory concept specifies precisely how a biomarker will be applied. For example, the COU should describe:
- The biomarker’s specific purpose and role
- The disease or condition under investigation
- The specific patient population
- The drug or intervention being assessed
- The choice of analytical method and sample type
- The interpretation framework or decision criteria
Early COU definition is more than just a regulatory formality. It drives study design, assay validation requirements, sample size calculations, and data interpretation plans. It is possible to objectively evaluate a biomarker with a well-defined COU, but one without becomes a source of ambiguity and potentially missed opportunities.
A well‑defined COU is also key to ensuring that a biomarker is anchored to a single decision, such as mechanism confirmation, dose selection, or enrichment, rather than becoming an unfocused exploratory signal.
Synexa can help translate a defined COU into assay requirements, sampling frameworks, and validation depth by aligning clinical, analytical, and regulatory needs at an early stage, helping prevent misinterpretation and ensuring biomarkers are meaningfully embedded into statistical plans, protocols, and governance decisions.
Defining the COU upfront ensures that biomarkers are actionable and generate evidence suitable for regulatory discussions.
Essential biomarker categories in practice
Biomarkers are typically grouped into discrete categories, but a single biomarker may serve different roles in practice depending on factors such as timing, context, and data quality. It is still helpful to think in terms of intent, however.
Diagnostic biomarkers
These biomarkers are used to confirm disease presence or subtype. Diagnostic biomarkers often define inclusion criteria or enable the stratification of heterogeneous populations during development. Examples include PD-L1 expression in immunotherapy trials and HER2 status in breast cancer.
Monitoring biomarkers
Biomarkers are used longitudinally to track disease activity or biological responses over time. They are especially useful in early trials in understanding variability and kinetics, and to inform treatment decisions.
The measurement of HbA1c in diabetes and viral load in HIV, for example, utilizes monitoring biomarkers.
Pharmacodynamic (PD) and response biomarkers
These biomarkers are used to show that a drug is engaging its target or modulating a pathway. PD and response biomarkers underpin dose selection, mechanism-of-action claims, and early go/no-go decisions. PD biomarkers are particularly important in first-in-human studies, where it may not be possible to evaluate clinical endpoints.
Predictive biomarkers
These biomarkers are used to identify patients who are more likely to respond to treatment. They usually begin as exploratory signals before evolving into decision-enabling or regulatory-facing tools. For example, BRCA mutations in ovarian cancer and KRAS mutations in colorectal cancer are two well-established predictive biomarkers able to guide the choice of therapy.
Prognostic biomarkers
These biomarkers are used to estimate disease trajectory independent of treatment, supporting the contextualization of outcomes and the balancing of treatment arms. Prognostic biomarkers are key to understanding whether observed treatment effects exceed the expected effects of natural disease progression.
Safety biomarkers
Safety biomarkers are used for the early detection of on-target or off-target toxicity, increasingly important for novel or potent modalities. For example, creatinine for renal function and troponin for cardiac toxicity are traditional safety biomarkers, though emerging drugs may require the implementation of newer safety monitoring strategies to match.
Risk biomarkers
These biomarkers are frequently used in prevention and early-intervention settings to identify individuals at increased risk of developing disease. For example, polygenic risk scores and certain imaging signatures have seen increased use in disease interception trials.
Strong programs are selective, so each study doesn’t need to address every category. Biomarker selection must be matched to the specific questions answered in each development phase.
Biomarker maturity and regulatory intent
It is important to note that not all biomarkers are equal, and so they have varying levels of required evidence.
For example, exploratory and decision-enabling biomarkers do not always need to follow the same validation pathway, because this can add either unnecessary complexity early on in development or insufficient rigor at later stages.
Regulatory expectations are driven by a biomarker’s intended COU in practice, with required evidence applied on a fit-for-purpose basis. Biomarkers should be thought of as progressing through increasing levels of regulatory reliance, not a single linear pathway. This approach better reflects their use across the entire drug development lifecycle.
Exploratory biomarkers
Exploratory biomarkers are primarily used for learning and generating hypotheses in R&D, most often in preclinical studies or early clinical trials (Phase I/II).
Their role lies in exploration: examining relationships with disease, pharmacology, biology, or mechanisms of action, rather than in supporting regulatory claims. Assay characterization and limited analytical validation are typically sufficient at this stage, provided the acquired data is both reliable and interpretable.
These biomarkers are not used to support decision-making claims, but they may be included descriptively in regulatory submissions.
Biomarkers for internal decision-making
Biomarkers for internal decision-making support development strategy, including patient stratification hypotheses, dose selection, or go/no-go decisions.
These biomarkers must be supported by evidence clearly demonstrating they are fit-for-purpose. This means that the level of analytical and biological support should be proportional to the importance and risk of the decision being informed.
There is no need for formal FDA qualification at this stage, and these biomarkers do not need to meet the evidentiary standards for regulatory labeling.
Any underlying data and rationale should be sufficiently robust to withstand regulatory scrutiny if questioned, however, particularly in instances where these biomarkers impact patient safety or clinical trial design.
Program-specific regulatory biomarkers
There are several biomarkers used to support regulatory decision-making within a specific drug development program, for instance, assessment of clinical benefit, patient enrichment in pivotal trials, or as primary or secondary endpoints.
While these biomarkers are not formally qualified for use across programs, they do require substantial analytical and clinical validation to demonstrate a clear and credible relationship between the biomarker and the clinical concept of interest, as well as the indication and molecule in question.
The evidentiary package required will generally include analytical performance data, clinical correlation from earlier-phase studies, biological plausibility data, natural history data, or supportive external evidence. Regulatory acceptance of these biomarkers is determined on a case-by-case basis.
Qualified biomarkers
Qualified biomarkers receive formal designation via the FDA’s Biomarker Qualification Program for a specific COU, for example, safety monitoring, patient enrichment, or response assessment.
A biomarker can be used across multiple drug development programs for the specifically defined COU once it has been qualified. This can be done without requiring re-review, which can increase consistency and reduce regulatory burden.
A rigorous evidentiary package is required to achieve this qualification, and this must be commensurate with the risk associated with the intended application of the biomarker.
The FDA’s evidentiary framework requires that developers demonstrate the analytical reliability and clinical relevance of the biomarker, confirming that it can be used to accurately predict, measure, or monitor the defined clinical concept for its specific regulatory purpose.
A practical strategy allows a clear demarcation between exploratory biomarkers used for learning, biomarkers used to support internal development decisions, and biomarkers intended to support labeling or regulatory claims.
Being explicit about a biomarker’s intended use at an early stage helps developers to set appropriate expectations around data robustness, assay performance, and interpretation. This approach is also key to reducing friction at later stages when programs advance and face increased evidence thresholds.
Analytical versus clinical validation
It is also important that developers understand the distinction between analytical and clinical validation.
Analytical validation shows that an assay can reliably measure what it claims to measure, for example, demonstrating precision, accuracy, sensitivity, specificity, robustness, and reproducibility across relevant conditions.
Regulatory guidance, such as the FDA’s guidance on bioanalytical method validation and ICH M10 on bioanalytical method validation, provides detailed frameworks for this work.
Clinical validation shows that the biomarker can reliably predict, measure, or associate with a biological state or clinical outcome. This requires evidence acquired via well-designed clinical studies that confirms the biomarker changes confidently correlate with clinically meaningful endpoints.
It is possible for a biomarker to be analytically validated but not clinically validated, or vice versa.
Both types of validation are required for regulatory acceptance, but the depth of this validation necessary will depend on the intended use of the biomarker. For example, exploratory biomarkers may proceed with robust analytical validation and preliminary clinical data, while companion diagnostics would typically require comprehensive validation in both domains.
Building a biomarker strategy that works
Biomarker strategy begins with answering the clinical question rather than the assay.
Start with decisions
Defining a strategy should begin with questions around what decision the biomarker needs to support or help evidence. For example, proof of mechanism, patient stratification, or dose selection, among others, each require different study designs and different levels of confidence.
Adopting a decision-first approach ensures that resources are focused on biomarkers that have genuine potential to de-risk the program.
Align biomarkers with clinical endpoints early
Biomarkers should complement, not compete with, clinical readouts. Misalignment here is a common cause of late-stage issues and inconclusive results.
Ideally, biomarker strategies should be developed in parallel with endpoint selection during protocol development. This should be done with input from clinical operations, biostatistics, and regulatory affairs.
Select matrices and timing deliberately
While blood is a convenient matrix, it is not always biologically informative. Tissue, bronchoalveolar lavage (BAL), cerebrospinal fluid (CSF), or local fluids may be required, although each has its own added complexity.
It is important that sampling timepoints reflect underlying biology and not just visit schedules. This should include pharmacokinetic and pharmacodynamic considerations. For instance, target engagement biomarkers must be timed in line with expected drug exposure, while it may be necessary to assess downstream pathway markers later.
Be realistic about feasibility and scale
Assays that perform well in pilot studies may face unexpected challenges in clinical conditions, so it’s important to consider sample volume, stability, batching, site handling, and turnaround time from the outset.
Transitioning from a specialized research laboratory to a clinical or central laboratory environment typically exposes sources of variability that were previously hidden. It is best to engage with laboratory partners early on to understand any potential practical constraints.
Account for pre-analytical variability
A significant number of biomarker failures are driven by factors upstream of the assay itself. For example, site-to-site variability, freeze-thaw cycles, time-to-spin, and matrix effects can all obscure actual biology.
These risks must be actively managed rather than discovered retrospectively. Any biomarker strategy must feature comprehensive site training, sample collection and handling manuals, and pre-analytical monitoring.
Plan for data interpretation early
High-plex and multiomic approaches are increasingly reliant on panels and signatures as opposed to single analytes. These datasets are powerful, but there must be a clear analysis plan in place alongside a shared understanding of what constitutes a meaningful change.
It’s a good idea to pre-specify analytical approaches, clinical interpretation frameworks, and multiplicity adjustments prior to data lock, and while post-hoc exploration is valuable, it is important that it be clearly labeled as such.
High‑plex datasets require the use of a hypothesis‑led design to remain useful. This design should focus panel content on pathways relevant to mechanism, safety, or anticipated clinical response.
Synexa uses orthogonal confirmation, such as single‑analyte immunoassays, LC‑MS/MS peptides, or functional flow cytometry, to refine large panels down to reproducible, mechanistically interpretable signatures.
The implementation of drift monitoring, version control, and predefined analysis frameworks allows Synexa to ensure that signatures remain stable across studies and assay iterations.
This approach transforms complex data into decision‑ready biomarkers able to support patient stratification, dose selection, or regulatory justification.
Understand that biomarker strategies evolve
Early-phase biomarkers are typically exploratory by design because the goal at this stage is learning, not validation. Developing a flexible plan allows markers to be refined, dropped, or promoted as required as evidence accumulates.
An adaptive biomarker strategy recognizes that some of its markers will fail to perform while others will exceed expectations. It also considers that new opportunities will emerge as understanding deepens, allowing space for these to be explored where required.
Biomarkers must be matched to feasible matrices, realistic site capabilities, and robust pre‑analytical controls to survive real-world conditions.
Synexa regularly pilots collection, processing, and stability conditions across representative sites to identify vulnerabilities prior to scale‑up. The use of locked assay methods, cross‑site proficiency checks, and near‑real‑time QC dashboards allows Synexa to maintain data integrity as studies expand geographically.
This ensures that biomarkers in early research maintain consistency, reliability, and regulatory defensibility in later‑phase trials.
Practical considerations for implementation
Even with a well-designed strategy, biomarkers can still fail in execution, so attention to operational details is key.
Site selection and training
Clinical sites should have the capability and infrastructure required to collect, process, and ship samples in line with protocol requirements. Thorough training and accessible reference materials should be provided to enable this.
Sample logistics
Clear chains of custody should be established, incorporating appropriate shipping conditions and backup plans able to adjust to sample failures or delays. It is also important to consider whether real-time biomarker results are required to inform dosing decisions or whether batched analysis would be acceptable.
Quality control
Ongoing QC monitoring should be implemented throughout the study, rather than solely at final analysis. The early detection of assay drift or site-specific issues allows for corrective action before there is any compromise in data quality.
Data management
Biomarker data should be integrated with clinical data systems to enable holistic analysis. Data dictionaries must be clear, units must be standardized, and lower limits of quantification must be properly handled.
Regulatory documentation
Comprehensive documentation should be maintained, featuring details of assay methods, sample handling procedures, validation data, and any protocol deviations. This is important because regulatory inspections increasingly focus on biomarker data integrity.
Emerging trends and future directions
The biomarker landscape is continuing to rapidly evolve, with several trends already shaping the future of biomarker-enabled development.
For example, liquid biopsies enabling minimally invasive disease monitoring, especially in oncology, are transitioning from research tools to regular clinical use.
Artificial intelligence and machine learning are now being implemented to help identify novel biomarker signatures and integrate complex datasets. However, regulatory frameworks for AI-derived biomarkers are still being developed.
Decentralized trial designs are creating new opportunities for remote biomarker collection, but they also introduce challenges in standardization and oversight.
Patient-centric biomarkers better align with what matters most to patients: symptoms, function, and quality of life. These biomarkers are gaining increased recognition as key complements to traditional molecular markers.
It is vital that developers keep up to date with these developments and continue to engage with regulatory guidance as it evolves. This is likely to be increasingly important for successful biomarker-enabled development.
Final thoughts
It is possible that two studies will measure the same biomarkers and reach very different conclusions. Rather than the technology itself, the difference typically lies in study design, pre-analytics, and the clarity of the defined biomarker question.
The most effective biomarker strategies for clinical developers are integrated early, designed with intent, aligned with regulatory expectations, and operationally feasible.
When this is done well, biomarkers accelerate decision-making, reduce uncertainty, and ultimately bring improved therapies to patients faster. When this is done poorly, however, biomarkers can add cost without clarity, potentially even misleading development decisions.
Planning a biomarker strategy for an upcoming study should begin with the questions that require answers. The context of use should be clearly defined, and the regulatory landscape for the intended application must be fully understood.
It is also important to engage appropriate expertise early, whether this is clinical, analytical, regulatory, or operational.
With all this in place, the assays should follow.
Biomarker science will continue to develop, but the underlying principle remains. The optimal biomarker is the one that answers the desired question with enough confidence to act.
Organizations looking for support with biomarker strategy, assay development, or regulatory submissions can benefit from working with Synexa Life Sciences. Synexa’s team offers in-depth expertise across the entire biomarker lifecycle, from early discovery to regulatory approval.
Acknowledgments
Produced from materials originally authored by Caroline Beltran (PhD) from Synexa Life Sciences.
About Synexa Life Sciences
Synexa Life Sciences is a biomarker and bioanalytical lab CRO, specializing in the development, validation, and delivery of a wide range of complex and custom-designed assays.
With a team of over 200 staff across three global laboratory locations, Manchester, Turku (Finland), and Cape Town, we provide innovative solutions to support our customers in achieving their clinical milestones.
Our main areas of expertise include biomarker identification and development, large and small molecule clinical bioanalysis, (soluble) biomarker analysis (utilizing MSD, LC-MS/MS, ELISA, RIA, fluorescence and luminescence-based technologies), cell biology (including flow cytometry, ELISpot and Fluorospot), and genomic services to support clinical trials and translational studies.
We pride ourselves on our deep scientific expertise and ability to tackle complex problems, translating them into robust and reliable assays to support clinical trial sample analysis.
Since 2019, Synexa has been backed by Gilde Healthcare, a specialized healthcare investor. Synexa, improving the quality of human health through innovative biomarker and bioanalytical solutions.
Sponsored Content Policy: News-Medical.net publishes articles and related content that may be derived from sources where we have existing commercial relationships, provided such content adds value to the core editorial ethos of News-Medical.net, which is to educate and inform site visitors interested in medical research, science, medical devices, and treatments.