A Guide to Biomarker Validation

A Guide to Biomarker Validation

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A Guide to Biomarker Validation

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Author: Joy Kavanagh, Diagnostic Programme Manager | Reading time: 16 Minutes
Joy Kavanagh

Joy Kavanagh

Diagnostic Programme Manager

Joy is the clinical diagnostic program leader at Sonrai. The program is comprised at present of three CE-IVD development projects. She is responsible for the management of the program and for ensuring the resulting products are in line with the executive leadership strategy. Joy also advises on biomarker validation and assay development for Sonrai's commercial client projects, leveraging her prior experience to support Sonrai's clients to achieve their goals.

In this article, we will explore:

What does ‘Biomarker Validation’ actually mean?
Validation planning
The Biomarker Validation process

The potential for the use of biomarkers in early disease detection, drug development and precision medicine is undeniable. It’s no surprise that the area has generated a rapidly-growing market, with more than a million scientific papers, over half of them published in the last decade, and a projected market worth of $147 Billion by 2028.

Much biomedical research is now focused on biomarker discovery and validation, searching for predictive, prognostic, or safety biomarkers across various biological sample types and analytical approaches. Many candidates have been identified that can be used for both early diagnosis and therapeutic guidance in cancer, heart disease, Alzheimer's disease, rheumatoid arthritis, and asthma.

Perhaps the most notable recent success story in precision medicine is in relation to the biomarker human epidermal growth factor receptor 2 (HER2). Results from the DESTINY study highlighted just how groundbreaking and potentially life-saving advances in this field could be.

HER2 is associated with a poor prognosis, such as earlier recurrence and metastatic disease in human breast cancer. Patients expressing high levels of HER2, categorized as ‘HER2-positive’, have had HER2-targeted therapy available for over two decades. However, results from the recent DESTINY-Breast04 study (NCT03734029) showed patients with low levels of the HER2 receptor treated with the antibody-drug conjugate fam-trastuzumab deruxtecan-nxki (T-DXd) had a two-fold increase in their progression-free survival and had an extended overall survival, regardless of their hormone receptor status.

These results imply a shift in categorizing HER2 status. Incorporating a ‘HER2-low’ category means many more patients will benefit from HER2-directed therapy, whereas previously, they may have been considered HER2-negative and unsuitable for this type of treatment. 

To see more great successes such as this, we need to speed up the translation rate into clinical use and reduce the failure rate associated with biomarkers while maintaining ethical and efficient procedures. One way of doing this is ensuring we tackle validation correctly. 

This article outlines the key validation stages and considerations for risk mitigation in the validation process. It aims to provide you with strategies for moving forward confidently in the product development life cycle. 

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What does ‘Biomarker Validation’ actually mean?

In its simplest form, biomarker validation is the determination that the performance of the discovered biomarker is credible. It is a process to determine how well it measures, represents and/or predicts something else. There are many different aspects to consider when attempting to validate - there isn't a ‘one-size-fits-all’ guide on how to achieve it.


The intended use guides the appropriate level of validation.  A higher degree of validation evidence is required from biomarkers that pose a greater risk and/or have greater patient consequences. Effective validation, therefore, requires that the intended use is clearly established. 

Defining the Intended Use

Even at the early stages of development, an attempt should be made to outline a product's intended use statement. The statement should address the points below while acknowledging known gaps. It should be updated based on evidence as the product development life cycle progresses, locking it down before validation.


  • Intended patient population
  • Test purpose/What will the test results inform
  • Type of patient specimen required for testing
  • Intended User of the test
  • Benefit to patients
  • Risks to patients
  • Contraindications
  • Is there an associated medicinal product or product group
  • Testing delivery model (local vs central)

Validation planning

Planning early for a robust validation will help you to move efficiently forward when the time is right.

Biomarker Validation Readiness

  • It measures an objective, quantifiable characteristic successfully
  • It correlates strongly with the clinical endpoint within the intended use population
  • It's performance is reproducible in independent test cohorts

Scope and Scale of Validation

  • Novel vs Established biomarker
  • Current ‘Gold standard’ or ‘State of the art’
  • Observational vs Interventional vs Commercial use
  • Geographical regions of use and Regulator jurisdiction
  • Patient Risk/benefit ratio associated with it's introduction to clinical use
  • Performance claims must be supported with sufficient evidence

Practical considerations

  • Selection of an analytical platform suitable for the intended use
  • Appropriateness of supply, quality and/or commercialisation agreements
  • Availability of  samples representative of the intended patient population
  • Ensuring diversity and inclusivity in the patient population
  • Sample collection, storage and stability
  • Inclusion of appropriate positive and negative controls
  • Study design to minimize bias or confounding variables
  • Ensuring statistical power when necessary
  • Data management planning

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The biomarker validation process

1. Analytical method development and Research Use Only validation

Once the performance is demonstrated as reproducible in relevant independent data sets, and the intended use has been defined, a test method is developed that initiates the process of transitioning a discovered biomarker into an in vitro diagnostic (IVD) product. The real-world implementation is a critical factor as the chosen method will impact the accessibility of the product to the market.

Although not essential, it can be beneficial to include a Research Use Only (RUO) phase in a product development program. There is no regulatory standard for this stage of validation. When planning for RUO validation studies, the level of validation is defined by the evidence required to give the confidence to move forward to analyze a set of patient samples retrospectively. RUO validation studies are typically much smaller in scope and scale than those required for an interventional (investigational use only) or commercial product.

Things to consider at this stage: The technologies available, the most appropriate technology, and the time and cost investment. The advantage of including an RUO validation is that it allows for decisions to be made on whether to keep moving forward or to pause and reflect while in a relatively low-cost phase. The further into the validation process, the more investment is required.

2. Retrospective clinical validation

Like the previous stage, retrospective clinical validation is not strictly essential but provides the opportunity to collect additional evidence about the performance of the biomarker within purpose-designed testing parameters and to identify potential areas of weakness in test delivery that can be ironed out ahead of the next stage. 


Things to consider at this stage: There are different ways to approach a retrospective clinical validation. The right approach will depend on the study purpose and other influences, such as the timing of this product development phase with relevant clinical trial(s). Where the option is available, collecting patient samples within a clinical trial for retrospective or observational analysis is a great way to assess its performance and test delivery performance. Alternatively, acquiring a representative clinical study sample cohort for analysis will provide valuable additional evidence on performance and could be a faster way to advance to the next stage.

3. Analytical Validation suitable for investigational use in a clinical trial

Where a biomarker is to be used for a medical purpose but is not yet approved for marketing, it is normally termed ‘investigational use only’ (in the US) or ‘device for performance evaluation’ (in the EU). This stage will typically involve one or more clinical studies using the biomarker to inform patient treatment decisions. As such, the risk to patients associated with the use of the biomarker must be carefully considered and used to drive further development and validation.  

The applicable regulations in the countries where it will be tested also influence manufacturer decisions at this stage. For example, testing of US patient samples must comply with CLIA  and with FDA IDE requirements, or for European (EU) studies, approval to proceed with a performance evaluation study will require manufacturers to satisfy Notified Body of conformity to the EU In-Vitro Diagnostics Regulation (IVDR).

Things to consider at this stage: 


  • Patient sample matrix stability and impact on shipping and storage conditions.
  • Performance limits obtained during this stage of validation should be used to monitor performance during the clinical study. 
  • If it is to be used for screening or enrollment decisions, the turnaround time to results should be as short as possible. Manufacturers should evaluate the actual turnaround time of their test, considering the existing analytical test method and availability of sample processing staff and optimize if possible.
  • Manufacturers may wish to use the resulting clinical trial data to support marketing approval. It is, therefore, prudent to minimize further development from this stage to de-risk downstream bridging studies. 

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4. Validation suitable for marketing approval

The level of evidence required to demonstrate the performance and safety of the biomarker device is related to the device safety classification relevant to the regulatory jurisdiction(s) of the countries in which the device is intended to be marketed. 

Irrespective of regulator jurisdiction, robust analytical validation will be required at this stage. Guidance is available at the following sources and can also be sought through direct engagement with regulatory authorities (e.g. via the US FDA q submission process). For manufacturers intending to market their device commercially, early engagement with regulators is recommended.


This stage also incorporates clinical validation which assesses the sensitivity and specificity of the biomarker to identify, measure or predict the clinical outcome that it is intended to reflect. Sensitivity refers to the rate of true positive findings, and specificity is associated with the rate of true negative findings. IVD clinical validation studies can be observational or interventional. Under what circumstances can you proceed without an interventional study vs when must you do an interventional study to generate sufficient evidence to support marketing approval?


Clinical studies (term used by US FDA) or interventional clinical performance evaluation studies (EU term) are necessary in the case of novel biomarkers for which the manufacturer must demonstrate safety and effectiveness. This type of study is typically performed to generate evidence to support a device PMA submission. 


Where a predicate device to which equivalence can be demonstrated, such studies are not essential. In the case of a device 510(k) evidence from a retrospective evaluation is normally sufficient.


Things to consider at this stage: The commercial test delivery model (central lab test vs distributed kit) will impact the analytical validation requirements. The level of rigor required at this stage will expand the scale and scope of validation. For example, to include process validation,  evidence of sample, intermediate and reagent stability and guard banding studies.

5. Post-market surveillance

Post-market surveillance is the systematic collection and analysis of use and performance data for medical devices. All medical devices (including in vitro diagnostics in the field) must be monitored by the manufacturer for the full device life span. The data is collected directly from users via proactive (feedback, questionnaires and literature searches) and reactive channels (complaint handling). 

Analysis of the data enables manufacturers to identify any change to the expected occurrence of device issues, early identification of unforeseen hazards and device vulnerabilities, use errors or off-label use of the device. An output of Post Market Surveillance activities will be an affirmation of the devices benefit-risk profile and/or identification of any device changes which need to be put in place to maintain that device benefit-risk profile.

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Looking to the future

A modern approach to validation should improve on the systematic evaluation of the evidence and on promoting, from each translational step to the next, the biomarkers that have the best evidence and can be the eventual winners.

Strategising across the biomarker ecosystem

This requires a strategy to coordinate the wider research agenda and a fostering of international multiteam collaborations rather than fragmented efforts of small, opportunistic studies. 


This strategic approach makes more efficient use of the limited resources, for example, in reference sets, giving a better chance of successfully validating truly useful biomarkers. It also allows for the safe sharing and classification of failures which ultimately helps to avoid the same issues in future. The Early Detection Research Network has developed valuable specimen reference sets that can be used by multiple labs for biomarker validation and is an excellent example of collaboration.

Expanding horizons to encompass new methods and approaches

Despite the best efforts of academics, industry, and trial sponsors, there is a tendency to continue to use the same methods over time in regulated studies because of a shared level of comfort with the use of well-worn measures. By continuing to evolve in our current thinking about biomarkers, endpoints, and other tools, we will accelerate our understanding of biological science and improve the efficiency and pace at which effective treatments and technologies are developed for the prevention, diagnosis, and treatment of disease.


The evolution of our current thinking applies not only to the technical side of research but also to the human element. That comes in the form of much greater awareness and proactivity towards inclusion and diversity in clinical research and development. 


The coronavirus pandemic highlighted the impact of healthcare inequalities, which is one of the reasons the topic is now getting the attention it deserves. The result of inequality is the redundancy of our efforts in precision medicine. Is performance truly understood if the validation did not span an appropriately diverse population? How successful is testing if the people who need it do not have access to it? How successful is a therapy if the people who would benefit cannot access it? 


A recent study highlighted that 80% of cancer patients received care in community hospitals that lacked the facilities or expertise to implement precision medicine. Did you know it wasn’t until 1993 that it became law (in the US) to include women in clinical research? However shocking that may seem now, let's hope we look back in another 20 years and feel the same about our current situation.  More work is needed to address gaps in public education regarding testing to implement standardized care approaches and continue advocating for access to biomarker testing for all. 


Inclusion and diversity in clinical research has recently been addressed by the FDA, which issued  guidance on this in November 2020. They offered recommendations for making clinical trials more demographically and non-demographically inclusive. The recommendations included factors such as sex, race, ethnicity, age, location of residency, patients with organ dysfunction, comorbid conditions, disabilities, those at the extremes of the weight range, and populations with diseases or conditions with low prevalence. 


Yes, incorporating diverse populations into clinical research is complex. Still, it is well known that distinct populations respond differently to treatments, and it is critical to address the underrepresentation in clinical trials and to reflect the population who need the product. 


Nowadays, new high-throughput technologies have exploded the potential of biomarker research, and increased computer processing power has fuelled the development of methodological strategies based on artificial intelligence (AI). 


Knowledge extraction from big data is generating a new paradigm for AI-guided discovery and therapy. This is particularly important when assessing complex and composite biomarkers because the effort required to understand a single biomarker becomes many times more complex when the interrelationships of multiple biomarkers are considered. Fortunately, changes in computing and measurements are making such an approach increasingly feasible. 


Biomarker discovery extends beyond simple disease associations to the exploration of more complex and holistic representations of disease, targeting incidence, progression, or stratification. These developments indicate a period of explosive growth and rapid change in the field of biomarkers


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