Introduction – A New Chapter in Drug Development
The use of biomarkers in medicine is not new. Long before the rise of modern molecular diagnostics, ancient Egyptians reportedly used a form of pregnancy testing by observing whether barley or wheat seeds sprouted when exposed to a woman’s urine. Whilst rudimentary, this early method measured a defined characteristic as an indicator of a biological process. In this sense, it can be seen as one of the earliest recorded uses of a biomarker.
By the 19th and 20th centuries, more sophisticated physiological biomarkers had emerged. Examples include blood pressure measurements to assess cardiovascular risk, blood glucose for diagnosing and managing diabetes, and imaging technologies such as X-rays, CT scans, and MRI to visualise internal disease processes. These tools transformed diagnosis and monitoring. However, they were still largely focused on the structural or physiological signs of illness.
Today, we are in the era of soluble biomarkers. These molecular indicators circulate in blood, cerebrospinal fluid, or other biofluids and reflect complex biological processes like inflammation, immune activation, tumour burden, or organ injury. Their real advantage lies in the ability to measure them non-invasively and repeatedly, often detecting changes before symptoms appear or traditional clinical endpoints are observed. They offer a real-time view into disease biology and drug action.
The clinical research landscape is evolving. Where Phase I trials once concentrated solely on safety and pharmacokinetics, they now increasingly incorporate biomarker data to build a more comprehensive understanding of a drug’s action. These molecular insights help illuminate effects well before traditional endpoints are reached. Biomarkers are no longer just supporting players. They often guide study design and decision-making from the outset.
At Agilex Biolabs, we advocate for a fit for purpose strategy when validating biomarker assays. This means ensuring each assay is designed and optimised for its intended application. Rather than applying a one-size-fits-all approach, we tailor the development to meet both the clinical and scientific objectives of each trial phase. This ensures the biomarker data we generate is both meaningful and reliable, particularly during early-stage studies when timelines are tight, and decisions carry significant weight.
Chapter 1 – The Biomarker Revolution in Early Phase Trials
To appreciate their value, consider how biomarkers contribute to Phase I trials. These studies, often the first tests of a drug in humans, benefit from biomarker data to gain early insights into pharmacological effects. Biomarkers can show whether a drug reduces inflammation, modulates immune pathways, or affects metabolic signals. This level of insight complements traditional pharmacokinetic data and offers a broader picture of drug action.
Different biomarkers serve different purposes. For example:
- Pharmacodynamic (PD) biomarkers provide evidence of biological effect
- Predictive biomarkers help identify which patients are most likely to respond
- Monitoring biomarkers allow ongoing assessment of response or safety
Understanding the role a biomarker plays, and how its data will be used within the context of a clinical study, is essential for determining the appropriate level of assay validation. This is reflected in the concept of Context of Use (COU), a framework highlighted in regulatory initiatives such as the FDA’s Biomarker Qualification Programme and the BEST glossary and further supported by industry literature and working groups including the European Bioanalysis Forum (EBF), Global CRO Council (GCC), and Workshop on Recent Issues in Bioanalysis (WRIB).
Both regulators and industry stakeholders recognise that biomarkers can serve a range of functions, including diagnostic, monitoring, pharmacodynamic or response, predictive, prognostic, safety, and risk assessment. Each of these applications carries different expectations in terms of evidence and validation. By aligning the extent of assay validation with the defined context of use, scientists can ensure a scientifically sound, fit-for-purpose approach that maintains data quality without placing undue burden on exploratory or non-decision-making biomarkers.
Reference: U.S. Food & Drug Administration. Context of Use for Biomarkers. Biomarker Qualification Program. Available at: https://www.fda.gov/drugs/biomarker-qualification-program/context-use
Chapter 2 – Same Marker, Different Story
To illustrate the importance of Context of Use, consider two separate Phase I trials evaluating different investigational drugs. Both trials use the same complement factor protein as a biomarker. However, they use it in different ways.
Case Study A – Measuring Pharmacodynamic Response
In the first trial, the complement factor protein is monitored as a pharmacodynamic biomarker. The drug being studied is designed to significantly suppress complement activity. Investigators expect to see a large drop in circulating levels of the protein, possibly as much as a 1000-fold reduction after dosing.
This expected change in concentration makes interpretation relatively straightforward. Since the effect is large, the precision of post-dose measurements is less critical. What matters more is ensuring that baseline values are accurate and consistent. That’s because the biomarker will be expressed as a percent change from the pre-dose measurement. If the starting value is poorly characterised, the resulting calculation becomes unreliable. However, after dosing, even large swings in absolute value (for example, from 5 to 27 ng/mL) will have little effect on the calculated percent change.
| Pre-dose (ng/mL) | Post-dose (ng/mL) | % Change from Pre-dose |
|---|---|---|
| 5000 | 5 | -99.90% |
| 5000 | 10 | -99.80% |
| 5000 | 20 | -99.60% |
| 5000 | 27 | -99.46% |
The data above show that even with notable variation in post-dose values, the percent change remains constant. In this case, the assay needs to be especially reliable at the predose point. Control of variability should focus on this region, ensuring baseline measurements are reproducible and accurate. The skilled biomarker scientist would recognise that the level of variability across the assay range could vary dynamically. However, this is acceptable in this context due to the large fold-change expected, the lack of decision making across the dynamic range, and the calculation being expressed as percent change from pre-dose.
Case Study B – Using the Marker to Stratify Patients
In a second trial, the same biomarker is used differently. It helps to stratify patients before they receive treatment. Based on previous studies, patients with higher baseline levels of the complement factor protein are more likely to respond to the drug. The sponsor uses this information to include only those with suitable concentrations.
Here, the story changes. The biomarker is no longer about measuring response but instead about selecting patients. The assay must be precise across a narrower spectrum, related to clinical decision points. Small differences in measured concentration may determine whether a patient qualifies for the study. In this case, both analytical precision and reproducibility become critical.
The stakes are different. False positives might include patients unlikely to benefit. False negatives might exclude those who could. The assay must reliably distinguish between patient groups clustered closely around specific thresholds.
The key takeaway is that the biomarker remains the same. However, how it is used, and therefore how its assay must be validated, depends entirely on the clinical context. This is why defining Context of Use early is so important. It helps align validation activities to what really matters and ensures the assay is fit for its intended role.
Chapter 3 – Why Biomarker and PK Assays Follow Different Validation Paths
The FDA recently released a guidance document on bioanalytical method validation (BMV) for biomarker assays, which has since been removed from its website. While the guidance reflects the agency’s continued efforts to bring clarity and consistency to evolving areas of bioanalysis, it also highlights an ongoing dialogue within the scientific community: biomarker assays present fundamentally different challenges from pharmacokinetic (PK) assays, and applying PK-based validation principles uniformly may not always align with the scientific or clinical context.
This view has been echoed by several global bioanalytical consortia, for example, the European Bioanalysis Forum (EBF), the Global CRO Council (GCC), and participants at the Workshop on Recent Issues in Bioanalysis (WRIB). These groups have long advocated for a fit-for-purpose approach to biomarker assay validation and recognise that biomarker assays often serve diverse roles, from exploratory research tools to decision-making endpoints. Thus requiring validation strategies tailored to their specific context-of-use.
Unlike PK assays, which are highly standardized and focus on quantifying exogenous drug compounds using well-defined validation criteria (e.g., ICH M10), biomarker assays typically measure endogenous molecules with natural physiological variability. This variability introduces unique challenges around calibration, matrix effects, and precision targets, especially in the absence of true blank matrices or reference standards.
| Aspect | PK Assays | Biomarker Assays |
|---|---|---|
| Analyte | Exogenous drug | Endogenous molecule |
| Matrix | Defined blank matrix | May vary depending on biomarker and population |
| Calibration | Accurate standards available | Often relative; may use surrogate or spiked matrix |
| Precision target | Strict (e.g., ≤15% CV) | Fit-for-purpose |
| Validation level | As defined in ICH M10 | Depending on Context-of-Use |
Global stakeholders agree that aligning biomarker validation with scientific purpose and regulatory expectations is essential. For example, exploratory biomarkers may require less stringent validation than those used to support safety or efficacy claims. A flexible, context-sensitive framework—grounded in scientific rigor—can help ensure both assay reliability and continued innovation in biomarker science.
As the field evolves, open dialogue between regulators, industry, and scientific working groups remains critical to developing practical, science-based guidance that supports innovation while ensuring data quality.
Chapter 4 – The Evolving Nature of Context of Use
Setting a Context of Use is not the end of the journey for a biomarker. In fact, it is only the beginning. As clinical development progresses, the way a biomarker is used often changes. What begins as a pharmacodynamic marker in Phase I may later be repurposed as a predictive marker in Phase II or even as a surrogate endpoint in Phase III.
These evolving roles require continual reassessment of the biomarker’s validation status. A fit for purpose strategy must remain dynamic and flexible. What was once sufficient in early-phase development might need refinement or full revalidation in later stages. This is particularly true when the biomarker’s influence on clinical decisions increases, or when it becomes integral to trial endpoints or regulatory submissions.
By regularly reviewing the COU and aligning the assay’s performance characteristics with the new demands, sponsors can ensure that the data remains credible, decision-ready, and aligned with current regulatory expectations.
At Agilex Biolabs, we support our partners through this evolving journey. Our ongoing assay lifecycle management ensures that as the role of your biomarker grows, the science behind it remains robust, reproducible, and ready for what comes next.
Final Chapter – Partnering for Precision and Progress
At Agilex Biolabs, we bring expertise in bioanalytical assay development, validation, and strategy tailored to the lifecycle of your biomarker. From early discovery through to regulatory submission, we work with you to ensure your assay matches its role, clinical purpose, and regulatory expectations.
With state-of-the-art platforms including LC-MS/MS, ligand binding assays, flow cytometry, and polymerase chain reaction (PCR), we offer deep scientific understanding and practical experience across diverse biomarker types. Our team ensures that fit-for-purpose validation is more than a concept, it is a cornerstone of successful clinical development.
Get in touch to learn how we can support your biomarker strategy and deliver results that are scientifically sound, clinically meaningful, and regulator ready.
