ABSTRACT
Appropriately validated biomarkers are important tools that can benefit drug development and regulatory assessments. For drug development purposes, biomarker development involves identifying a drug development need and context of use (COU) for the biomarker, analytically validating assays, clinically validating the biomarker for the COU, and determining if the biomarker provides benefits over current methods. This perspective provides information on determining a biomarker's COU, fit‐for‐purpose validation, and regulatory acceptance of biomarkers for drug development.
Keywords: biomarkers, drug development, regulatory
1. Biomarker Use
Biomarkers are an integral component of both the practice of medicine and the development and approval of new therapies. Whether evaluating a patient's risk for cardiovascular disease in a doctor's office or trying to determine if an investigational drug causes kidney damage, biomarkers are a key part of the assessment. Historically, new biomarkers have been discovered and evaluated in nonclinical experiments, epidemiological studies, and clinical trials. As their role in a given disease becomes better understood through additional studies or through clinical use by early adopters, they slowly become integrated into clinical care. Eventually, clinical practice guidelines written by professional societies may incorporate biomarkers in the assessment of patient risk or response to therapy, further expanding their use in clinical practice. In drug development, biomarkers often play an important role in the selection of the patient population to be studied, dose selection, and safety and efficacy assessments. As such, biomarkers are often critical to the Food and Drug Administration's (FDA's) determination of which patient populations benefit from the drug, how the drug is used, and can contribute to the benefit–risk assessment. Therefore, it is important to have a thorough understanding of the biomarker and robust data supporting its use for a specific assessment during drug development.
2. Biomarker Category and Context of Use
FDA defines a biomarker's COU as a concise description of the biomarker's specified use in drug development; it includes the BEST biomarker category and the biomarker's intended use in drug development. The BEST (Biomarkers, EndpointS, and other Tools) Resource is an online glossary that was created by an FDA‐NIH joint working group and defines multiple categories of biomarkers, such as diagnostic, monitoring, predictive, response, and safety, among others [1]. As described in Table 1, FDA categorizes biomarkers into several types, including susceptibility/risk, diagnostic, monitoring, prognostic, predictive, pharmacodynamic/response, and safety biomarkers (Table 1). An individual biomarker might fall into more than one category, depending on how it is being used. For example, Hemoglobin A1c is used to identify patients with diabetes (diagnostic biomarker) and to monitor long‐term glycemic control (response biomarker) in individuals with diabetes.
TABLE 1.
BEST (Biomarkers, EndpointS, and other Tools) classification.
| Biomarker category | Biomarker use | Example |
|---|---|---|
| Susceptibility/risk | Identify individuals with an increased risk of developing breast or ovarian cancer | BRCA1 and BRCA2 genetic mutations for breast and ovarian cancer |
| Diagnostic | Diagnose diabetes and pre‐diabetes in adults | Hemoglobin A1c for diabetes mellitus |
| Prognostic | Define higher risk disease population, enhancing trial efficiency | Total kidney volume for autosomal dominant polycystic kidney disease |
| Monitoring | Monitor response to antiviral therapy in patients with chronic Hepatitis C | HCV RNA viral load for Hepatitis C infection |
| Predictive | Predict response to EGFR tyrosine kinase inhibitors in patients with NSCLC | EGFR mutation status in nonsmall cell lung cancer |
| Pharmacodynamic/response/surrogate | Surrogate for clinical benefit in HIV drug trials | HIV RNA (viral load) in HIV treatment |
| Safety | Monitor renal function and potential nephrotoxicity during drug treatment | Serum creatinine for acute kidney injury |
Abbreviations: BRCA1, breast cancer gene 1; BRCA2, breast cancer gene 2; EGFR, epidermal growth factor receptor; HIV, human immunodeficiency virus; NSCLC, nonsmall cell lung cancer; RNA, ribonucleic acid.
There are many challenges in drug development that can be addressed, at least in part, by use of an appropriately validated biomarker. Key examples include safety biomarkers that detect organ injury earlier than traditional clinical signs or symptoms, potentially before significant irreversible damage occurs, pharmacodynamic response biomarkers to aid in dose selection, response biomarkers used as reasonably likely or validated surrogate endpoints, and predictive or prognostic biomarkers for patient selection in clinical trials. The development of a biomarker for a COU should carefully consider (1) whether the COU represents an important drug development challenge, (2) whether the biomarker has the potential to improve upon the standard assessments used in drug development, and (3) what studies or data are needed to validate the biomarker for the proposed COU. Different drug development issues may require different categories of biomarkers to address the need, and it is important to consider not only the type of biomarker that is needed, but the feasibility of measurement within a drug development program, the frequency that the assessment is needed, whether or not the biomarker will need to be assessed in routine clinical care if the drug is approved, and other practical considerations.
3. Fit‐For‐Purpose Biomarker Validation
The validation of the biomarkers is a complex process and the level of evidence needed to support the use of a biomarker depends on the COU and the purpose for which a biomarker is applied [2]. This principle underscores the importance of a fit‐for‐purpose approach to biomarker validation. Different biomarker types require varying validation approaches, focusing on specific evidence characteristics based on their intended COU.
Validation of susceptibility/risk biomarkers requires epidemiological evidence and may also be supported by genetic evidence, biological plausibility, and establishing causality. Depending on the drug development or clinical use, diagnostic biomarkers may prioritize sensitivity and/or specificity and require proof of accurate disease identification across diverse populations. Prognostic biomarkers require robust clinical data showing consistent correlation with disease outcomes, while monitoring biomarkers require validation of their ability to reflect disease status changes over time. Predictive biomarkers prioritize sensitivity, specificity, and often causality, with emphasis on a mechanistic link to treatment response. Pharmacodynamic/response biomarkers also emphasize biological plausibility, requiring evidence of a direct relationship between drug action and biomarker changes. Lastly, safety biomarkers need to demonstrate consistent indication of potential adverse effects across different populations and drug classes. This tailored approach ensures rigorous assessment of each biomarker type according to its COU in drug development, specific role within a drug development program, or use in clinical decision‐making [2]. For example, the same biomarker may require less extensive validation for use as a pharmacodynamic biomarker to help identify a safe and effective dosing regimen for a treatment, but more extensive mechanistic and/or epidemiologic data to be used as a reasonably likely surrogate endpoint to support accelerated approval, and additional clinical validation to be used as a validated surrogate endpoint to support traditional approval [1, 3].
Analytical validation is a critical component of the biomarker validation process. It involves assessing the performance characteristics of the biomarker measurement tool; the appropriate performance characteristics are dependent on the method of detection and the analyte of interest, and may include accuracy, precision, analytical sensitivity, analytical specificity, reportable range, and reference range [4, 5]. Clinical validation, on the other hand, demonstrates that the biomarker accurately identifies or predicts the clinical outcome of interest. This may involve assessing sensitivity and specificity, determining positive and negative predictive values, and evaluating the biomarker's performance in the intended population. FDA also considers the potential benefits and risks of using a biomarker. This benefit/risk assessment includes the consequences of false positive or false negative results, the availability of alternative tools and evaluating the impact on the patient population that the biomarker is being developed for.
4. Regulatory Acceptance of Biomarkers
The COU and supportive evidence, including fit‐for‐purpose validation, are important considerations in the regulatory acceptance of a biomarker (Figure 1). There are several pathways for regulatory acceptance of biomarkers:
Early engagement: Drug developers and biomarker developers can engage with the FDA early in the drug development process to discuss biomarker validation plans via paths such as Critical Path Innovation Meetings (CPIM) [6]. Drug developers can also engage with the FDA early in the drug development process to discuss biomarker validation plans via the pre‐Investigational New Drug (IND) process.
IND process: Drug developers can engage with the FDA through the IND application process to discuss and pursue clinical validation and regulatory acceptance of biomarkers within the context of specific drug development programs. A Type C surrogate endpoint meeting is an example of a formal FDA consultation within the IND process where drug developers seek regulatory guidance on the use of surrogate endpoints as endpoints in clinical trials for supporting efficacy claims in marketing applications [7].
Biomarker qualification program (BQP): The FDA's BQP [8] provides a structured framework for the development and regulatory acceptance of biomarkers for a specific COU. This program involves three stages: the Letter of Intent, the Qualification Plan, and the Full Qualification Package.
FIGURE 1.
The drug development need informs the selection of candidate biomarkers and the COU. The COU subsequently drives the extent of evidence needed for validation, potential qualification under the BQP, and ultimately regulatory acceptance of using the biomarker in drug development. Note that the regulatory engagement pathways are independent, but do not necessarily occur in isolation; all are data driven and involve regulatory assessment and outcomes based on the available data.
The ideal pathway may depend on several factors. Engaging with FDA through the IND application process may be an efficient pathway for specific drug development programs in many cases, including for well‐established biomarkers with data available supporting their use within the drug development program. The BQP offers a pathway for broader acceptance of biomarkers across multiple drug development programs. While the BQP may take longer and require more supporting evidence, once qualified, a biomarker can be used by any drug developer in their drug development program without requiring FDA re‐review of its suitability, provided it is used within the specified COU. The biomarker qualification process promotes consistency across the industry, reduces duplication of efforts, and helps streamline the development of safe and effective therapies. It's important to note that in this context, we are discussing regulatory acceptance or qualification for use in drug development, not the approval process for biomarker tests or for the approval of the drug's marketing application.
In conclusion, fit‐for‐purpose validation of biomarkers depends on the biomarker type and its intended COU. FDA emphasizes the importance of early engagement with drug developers and biomarker developers to discuss biomarker use, validation strategies, and regulatory acceptance. By considering the biomarker type, COU, analytical and clinical validation, and benefit/risk assessment, FDA aims to ensure that biomarkers contribute meaningfully to drug development and patient care.
Disclosure
Disclaimer: This article reflects the views of the authors and should not be construed to represent FDA's views or policies.
Conflicts of Interest
The authors declare no conflicts of interest.
Acknowledgments
The authors would like to acknowledge Dr. Michael Pacanowski and Dr. Jeff Siegel for their helpful comments on the manuscript.
Schuck R. N. and Sekar V., “Use of Biomarkers in Drug Development for Regulatory Purposes,” Clinical and Translational Science 18, no. 10 (2025): e70377, 10.1111/cts.70377.
Funding: The authors received no specific funding for this work.
References
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