Biomarkers Are No Longer Optional in Drug Development
A decade ago, biomarkers were an add-on to many clinical development programmes: interesting, potentially informative, but rarely central to regulatory decision-making. That has changed. The growth of precision medicine, the expansion of companion diagnostic requirements, and the increasing use of biomarker-defined patient populations in oncology and rare disease trials have made biomarker analysis a core element of the clinical development package for many new drugs.
Regulatory agencies have responded. The FDA’s biomarker qualification programme, the EMA’s qualification of novel methodologies procedures, and the extensive biomarker content of ICH E16 all reflect an expectation that biomarker data generated in clinical trials is produced to a level of analytical rigour appropriate for the decisions it will support.
Qualification vs. Validation: Understanding the Distinction
The terms biomarker qualification and biomarker validation are used inconsistently in the literature and in industry practice. The most widely used framework, from the FDA’s 2018 biomarker qualification guidance, distinguishes between the two as follows:
| Concept | Definition | When It Applies |
| Biomarker qualification | A regulatory conclusion that a biomarker can be relied upon to have a specific interpretation in a specific context of use | When a biomarker is intended to support regulatory decisions across multiple drug development programmes |
| Biomarker assay validation (fit-for-purpose) | Demonstration that an assay is suitable for its intended use in the specific programme | The standard for most clinical trial biomarker assays; scope of validation determined by context of use |
| Full analytical validation | Complete validation to the standards applied to PK assays under ICH M10 | Required when biomarker data is used for primary regulatory endpoints or safety decisions |
For most clinical biomarker assays, a fit-for-purpose validation, with the scope of experiments calibrated to the criticality of the data, is the appropriate standard. A biomarker used to support patient stratification decisions in a pivotal Phase III trial requires more rigorous validation than the same biomarker used for exploratory characterisation in a Phase I study.
Categories of Clinical Biomarkers
Pharmacodynamic Biomarkers
Pharmacodynamic biomarkers measure biological changes that occur as a direct consequence of drug exposure. They provide evidence that the drug is engaging its intended target and producing the expected biological response. In early clinical trials, PD biomarkers are used to establish proof of mechanism, select doses for later studies, and define the dosing schedule that achieves the desired level of target engagement.
Predictive Biomarkers
Predictive biomarkers identify patient populations that are more or less likely to respond to a specific treatment. They are the foundation of precision medicine. The most commercially significant predictive biomarkers, such as HER2 amplification in breast cancer or EGFR mutation status in lung cancer, are used to define the registered indication and are assessed as companion diagnostics subject to their own regulatory requirements.
Safety Biomarkers
Safety biomarkers detect the early onset of drug-induced organ toxicity or other adverse effects. Established safety biomarkers such as alanine aminotransferase for hepatotoxicity and serum creatinine for nephrotoxicity have been used in clinical monitoring for decades. Novel safety biomarkers, such as kidney injury molecule-1 (KIM-1) for renal tubular injury, are being increasingly incorporated into clinical monitoring programmes as their qualification evidence accumulates.
Prognostic Biomarkers
Prognostic biomarkers predict the natural course of disease in the absence of treatment. They help in the design of clinical trials by identifying patient populations with the appropriate rate of clinical events to power efficacy endpoints, and in interpreting trial results by accounting for baseline differences between treatment arms.
Key Assay Performance Parameters for Biomarker Validation
| Parameter | Definition | Typical Approach for Fit-for-Purpose Validation |
| Precision | Reproducibility of repeated measurements | Within-run and between-run precision at low, mid, and high concentrations |
| Accuracy / Trueness | Agreement of measured value with true concentration | Recovery assessment using spiked samples or reference materials |
| Sensitivity (LLOQ) | Lowest concentration measurable with defined precision and accuracy | Determined during assay development; at least 5x lower than lowest expected study sample |
| Selectivity | Ability to distinguish analyte from matrix components | Tested in representative matrices including haemolysed and lipaemic samples |
| Stability | Analyte stability under relevant conditions | Bench-top, freeze-thaw, long-term frozen; specific to the matrix and storage conditions |
| Dilutional linearity | Proportional response upon sample dilution | Important for high-concentration samples exceeding the calibration range |
Ardena’s Biomarker Services at Assen
Ardena’s bioanalytical centre in Assen provides biomarker assay development, qualification, and fit-for-purpose validation services using MSD electrochemiluminescence, ELISA, flow cytometry, and qPCR platforms. The team has experience with pharmacodynamic, safety, and predictive biomarker assays across oncology, immunology, and rare disease programmes.
Ardena takes a context-of-use approach to biomarker validation, working with clients to define the appropriate scope of assay performance experiments based on the intended use of the data, the phase of development, and the regulatory context. This avoids over-validation of exploratory assays while ensuring that decision-critical biomarker data meets the standards required.
