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  1. Regulatory Science in Action

Proteomics-Based Biomarker Discovery: A Systematic Framework to Address Regulatory and Scientific Challenges Across Therapeutic Areas

CDER researchers have developed a structured and systematic framework for biomarker discovery using plasma proteomics

Background

Biomarkers are defined characteristics that can be objectively measured and evaluated as indicators of normal biological processes, pathogenic processes, or responses to therapeutic interventions.1 They serve as critical tools throughout drug development and regulatory evaluation. Traditional biomarker discovery methods often rely on hypothesis-driven approaches that target a single molecule. While effective for well-understood mechanisms, these methods are inherently limited in scope and may miss broader patterns of drug response or fail to capture the full spectrum of molecular changes that accompany disease progression or therapeutic intervention. These limitations are particularly evident in complex or rare diseases, where multiple biological pathways interact, and limited sample sizes constrain statistical power.

To evaluate the utility of omics technologies (scientific methods that study thousands of biological molecules simultaneously) for biomarker discovery in drug development and regulatory evaluation, CDER scientists have assessed aptamer-based plasma proteomics as a representative high-throughput omics approach (Figure 1).             

How can this work advance regulatory science?

This work addresses the challenge of integrating novel, data-rich biomarker strategies into drug development. The plasma proteomics framework developed by CDER researchers shows how proteomics methods and analytical frameworks can be designed, executed, and interpreted with scientific rigor, reproducibility, and regulatory relevance, and establishes a foundation for biomarker discovery and assessment that may be applied across a range of therapeutic areas.

                             

Figure 1

Figure 1. Aptamer-based Proteomics (SOMAscan Platform): Text details in caption.

Figure 1. Overview of the aptamer-based (SomaScan™) proteomic assay. Comparative analysis of signal intensities allows assessment of plasma protein level changes across clinical samples or study groups.2,3 The method uses single-stranded DNA aptamers (SOMAmerTM) that specifically bind target proteins.4 Bound aptamers are quantified using microarray-based detection, enabling simultaneous measurement of >7,000 proteins.5,6 

This large-scale method uses DNA sequences that bind specifically to proteins to measure thousands of proteins in plasma samples simultaneously.²,³ This method also allows researchers to capture a comprehensive analysis of how drugs affect multiple biological pathways simultaneously, revealing pharmacodynamic responses and mechanistic insights that go beyond traditional single-target approaches. This work reflects an emerging direction in regulatory science of applying large-scale omics analysis to understand biological responses to therapeutic products in a structured, data-driven way.

Scientific Challenges

To be useful for drug regulation, biomarkers need three key qualities: they must be accurate, clearly show how the drug affects the body, and produce reliable results when tested repeatedly7,8. Plasma proteomics offers a path forward for the discovery of comprehensive biomarkers that meet these criteria, but it brings its own considerations. High-dimensional protein data (datasets with thousands of measured proteins) require sophisticated analytical methods to extract meaningful biological insights. Variability between samples must be tightly controlled, and replication of findings across datasets is essential. Like other biomarker discovery methods, plasma proteomics faces challenges when sample numbers are extremely small, such as in single-patient scenarios in ultra-rare diseases, highlighting the importance of robust analytical frameworks for reliable biomarker identification.

A New Systematic Approach to Biomarker Discovery in Drug Development

To address these challenges, CDER researchers developed a structured and systematic framework for biomarker discovery using plasma proteomics. This framework was established through complementary studies. The first study demonstrated the Discovery phase,2 and the subsequent study built on this work by reproducing the findings in the Replication phase and further evaluating the biomarkers in the Characterization phase (Figure 2).3

Figure 2

Figure 2. Plasma Proteomics Framework for Biomarker Discovery. Details in caption.

Figure 2. A systematic framework for plasma proteomic biomarker discovery and validation developed and utilized by CDER researchers.2,3 Biomarkers play a critical role throughout drug development and regulatory evaluation, serving as essential tools for informing efficacy, monitoring safety, and supporting evidence-based decision-making. In the current work, plasma proteomics has shown that high-throughput protein profiling can effectively measure biological responses to therapeutic agents. The approach integrates large-scale aptamer proteomic profiling, statistical prioritization, and biological network analysis to identify candidate biomarkers mechanistically linked to drug action (Discovery phase). Quantitative verification and replication studies using the original or orthogonal methods assess reproducibility and specificity (Replication phase). Confirmed candidates are further evaluated using regulatory recommendations if available (Characterization phase). Together, these steps define a pathway for converting high-dimensional proteomic data into interpretable, mechanistically grounded evidence for drug development and regulatory evaluation across therapeutic areas.

This approach used existing omics knowledge from other scientific fields, including study design principles, best practices, and known challenges9 such as technical variation and statistical adjustments needed when testing many variables at once. This ensured proper use of the technology and generated reliable, meaningful results. The framework was designed from the start to include three phases: Discovery, Replication, and Characterization, based on available FDA recommendations for this specific biomarker type.10 The researchers also addressed several key factors throughout the process. These included controlling differences that occur during sample collection and processing, ensuring consistent results when experiments are repeated, choosing an omics platform with proven accuracy and reliability, normalizing data properly, and using statistical tools to interpret complex datasets. All these considerations were built into the proteomics workflow. This framework is broadly applicable across therapeutic areas and to different types of biomarkers, including pharmacodynamic, toxicity, and surrogate markers. The process starts with large-scale protein analysis to identify candidate biomarkers connected to how the drug works. Candidates that meet predetermined significance criteria are then tested in the Replication phase to confirm consistency. Finally, in the Characterization phase, confirmed candidates are evaluated using established criteria such as dose-response relationships, baseline return, and variability assessment, when regulatory recommendations exist for the specific biomarker type. By combining Discovery, Replication, and Characterization in a single framework, this approach creates a standardized and reproducible pathway for proteomic-based biomarker assessment. With rigorous design, this methodology may be particularly valuable in rare diseases and emerging therapeutic areas, where patient numbers are small and traditional measures of treatment success may be difficult to assess or take a long time to develop, by enabling systematic identification and confirmation of mechanistically relevant biomarkers. By defining best practices for analytical quality, tracking changes over time, and data integration, this framework provides an approach for biomarker discovery that aligns with FDA principles of transparency, reproducibility, and data integrity.

How Can This Work Advance Drug Development and Evaluation?

Applying this systematic plasma proteomic biomarker framework can accelerate drug development by providing early, mechanistic insights into therapeutic effects and enabling more informed decision-making in clinical trials through the selection of robust, biologically relevant biomarkers. Such biomarkers can guide dose selection, inform patient selection, serve as endpoints to measure treatment response, support monitoring of safety or toxicity, and allow smaller, more efficient studies, especially in rare or complex diseases. While plasma proteomics offers broad insight into drug response, it also introduces technical and analytical complexities. Careful control of preanalytical variation, assay sensitivity, data variability, and statistical and computational analysis is essential to ensure reliable, reproducible, and interpretable biomarker data suitable for regulatory use. The challenge lies not only in managing the technology itself but also in defining each biomarker's context of use to build confidence in its interpretation. Moreover, insights gained from this work²,³ offer a regulatory science roadmap for integrating innovative analytical strategies into broader drug evaluation. By connecting molecular-scale discoveries, including individual protein biomarkers, combinations of multiple proteins, and pathway-level responses (Figure 2), to clinical and regulatory decision-making, this approach helps ensure that emerging omics technologies are applied responsibly and effectively. This framework serves as a model for how large-scale, high-dimensional analytical methods (approaches for analyzing datasets with thousands of measured variables) such as proteomics can be applied in a scientifically rigorous and reproducible way that builds regulatory confidence, enhances data transparency, and supports modern, evidence-based evaluation of therapeutic products.

References

  1. FDA-NIH Biomarker Working Group. BEST (Biomarkers, EndPoints, and other Tools) Resource [Internet]. Silver Spring (MD): Food and Drug Administration (US); 2016 [updated 2021 Nov 29; cited 2026 Sep 10]. 
  2. Hyland PL, Chekka LMS, Samarth DP, Rosenzweig BA, Decker E, Mohamed EG, Guo Y, Matta MK, Sun Q, Wheeler W, et al. Evaluating the utility of proteomics for the identification of circulating pharmacodynamic biomarkers of IFNβ-1a biologics. Clin Pharmacol Ther. 2023 Jan;113(1):98-107. doi: 10.1002/cpt.2778.
  3. Chekka LMS, Samarth DP, Guo Y, Mohamed EG, Decker E, Matta MK, Sun Q, Wheeler W, Sanabria C, Wommack J, et al. Characterization of proteomic pharmacodynamic biomarkers of IFNβ-1a biologics to inform potential utility in biosimilar development. Clin Pharmacol Ther. 2025 Oct;118(4):935-945. doi: 10.1002/cpt.3754.
  4. Gold L, Ayers D, Bertino J, Bock C, Bock A, Brody EN, et al. Aptamer-based multiplexed proteomic technology for biomarker discovery. PLoS One. 2010 Dec 7;5(12):e15004. doi: 10.1371/journal.pone.0015004.
  5. SomaLogic Inc. SOMAscan Assay v4.1 [White Paper]. Boulder (CO): SomaLogic Inc.; 2022 Jan [cited 2026 Sep 10].
  6. Kraemer S, Schneider DJ, Paterson C, Perry D, Westacott MJ, Hagar Y, Katilius E, Lynch S, Russell TM, Johnson T, et al. Crossing the halfway point: aptamer-based, highly multiplexed assay for the assessment of the proteome. J Proteome Res. 2024 Nov 1;23(11):4771-4788. doi: 10.1021/acs.jproteome.4c00411.
  7. Haskins K. What does biomarker qualification do (and not do)? [Internet]. Silver Spring (MD): U.S. Food and Drug Administration, Center for Drug Evaluation and Research; 2017 [cited 2026 Sep 10].
  8. Food and Drug Administration (US). Biomarker qualification: evidentiary framework; draft guidance for industry and FDA staff [Internet]. Silver Spring (MD): Food and Drug Administration (US); 2018 [cited 2026 Sep 10]. 
  9. Misra BB, Langefeld C, Olivier M, Cox LA. Integrated omics: tools, advances and future approaches. J Mol Endocrinol. 2019 Jan;62(1):R21-R45. doi: 10.1530/JME-18-0055. Epub 2018 Oct 1.
  10. Strauss DG, Wang YM, Florian J, Zineh I. Pharmacodynamic biomarkers and evidentiary considerations for biosimilar development and approval. Clin Pharmacol Ther. 2023;113(1):55-61. doi: 10.1002/cpt.2788.
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