Bridging the Gap: Translating Preclinical Biomarkers to Clinical Success

Despite remarkable strides in biomarker discovery, a troubling chasm persists between preclinical promise and clinical utility. This blog explores the scientific and strategic approaches necessary to overcome translational hurdles and improve the predictive validity of preclinical biomarkers—ultimately accelerating their path to regulatory approval and patient benefit.

The translational gap is a major roadblock in drug development, often due to preclinical models that fail to reflect human biology accurately.

• Integrating human-relevant models and multi-omics profiling can increase clinical predictability.
• Longitudinal and functional validation strategies are essential to de-risk biomarker progression.
• Strategic partnerships and advanced analytics (e.g., AI-driven correlations) are key enablers in this space.

Crown Bioscience provides robust, translationally aligned biomarker platforms to support this journey.

1. Why So Many Biomarkers Fail to Cross the Preclinical-Clinical Divide

• Over-reliance on traditional animal models with poor human correlation.
• Lack of robust validation frameworks and inadequate reproducibility across cohorts.
• Disease heterogeneity in human populations vs. uniformity in preclinical testing.

2. Closing the Gap with Human-Relevant Models and Multi-Omics Technologies

• Use of PDX, organoids, and 3D co-culture systems that better mimic patient physiology.
• Integration of multi-omics (genomics, transcriptomics, proteomics) to identify context-specific, clinically actionable biomarkers.
• Case studies where improved model systems led to better clinical translation.

3. The Power of Longitudinal and Functional Validation Strategies

• Importance of capturing temporal biomarker dynamics (e.g., longitudinal plasma sampling).
• Using functional assays to confirm biological relevance and therapeutic impact.
• Strategies for bridging animal and human biomarker data (e.g., cross-species transcriptomic analysis).

4. Data-Driven Decision Making and the Role of Strategic Partners

• Leveraging AI/ML to predict clinical outcomes based on preclinical biomarker data.
• Importance of big data integration and collaborative platforms for biomarker qualification.
• How companies like Crown Bioscience help accelerate this process with validated preclinical tools and expert insight.

Bridging the Gap: Translating Preclinical Biomarkers to Clinical Success

Despite remarkable advances in biomarker discovery, a gap persists between the preclinical promise and clinical utility, creating a significant roadblock in drug development. This article summarizes the reasons for this and explores the scientific and strategic approaches necessary to overcome translational hurdles, improve the predictive power of preclinical biomarkers, and accelerate their path to regulatory approval and patient benefit.

Why So Many Biomarkers Fail to Cross the Preclinical-Clinical Divide

Each biomarker’s journey from discovery to clinical use is long and arduous, and less than 1% of published cancer biomarkers actually enter clinical practice. This results in delayed treatments for patients, as well as wasted investments and reduced confidence in this otherwise promising new avenue in oncology research.

There are many reasons behind this statistic:

• Traditional animal models available to research teams investigating potential biomarkers, including platforms like syngeneic mouse models, do not match directly with all aspects of human clinical disease, so treatment responses in these models can be a poor predictor of clinical outcomes.
  
  
• Unlike the well-established phases of drug discovery, the process of biomarker validation lacks a proper methodology and is characterized by a proliferation of a myriad of exploratory studies using dissimilar strategies, most of which fail to identify any promising targets and are seldom validated. Without agreed-upon protocols to control variables or sample sizes, results can vary between tests and laboratories, or fail to translate to wider patient populations. Additionally, a lack of guidelines means different research teams may use varying evidence benchmarks for validation, making it difficult to accurately assess the reliability of any new biomarkers that have been identified.
  
  
• Preclinical studies rely on controlled conditions to ensure any results generated are clear and reproducible. However, cancers in human populations are highly heterogeneous and constantly evolving, varying not just from patient to patient but within individual tumors. Genetic diversity and varying treatment histories, comorbidities, progressive disease stages, and the highly variable nature of tumor microenvironments (TME) introduce a wide range of real-world variables that cannot be fully replicated in a preclinical setting. Therefore, biomarkers that appear robust in controlled conditions may demonstrate poor performance in patient populations.

However, utilizing biomarkers in oncology is already revolutionizing treatments and helping to usher in a new paradigm in cancer management. Therefore, finding strategies to overcome these preclinical blockers is essential.

Closing the Gap with Human-Relevant Models and Multi-Omics Technologies

Unlike conventional preclinical models, advanced platforms like organoids, patient-derived xenografts (PDX), and 3D co-culture systems can better simulate the host-tumor ecosystem and forecast real-life responses, which is essential if biomarkers are to translate from preclinical to clinical settings.

Organoids are 3D structures with cells that establish or recapitulate the identity of the organ or tissue being modeled. Within organoids, particularly patient-derived organoids, the expression of characteristic biomarkers is more likely to be retained than in two-dimensional culture models, so they have been used to effectively predict therapeutic responses and guide the selection of personalized treatments. These models have also been used in the identification of prognostic and diagnostic biomarkers, and in the modelling of biomarker-informed patient selection.

PDX models (derived from immortalized cell lines, which are grown in vitro and implanted into immunodeficient mice) effectively recapitulate the characteristics of cancer, as well as tumor progression and evolution in human patients, producing “the most convincing” preclinical results. PDX models have proved to be a more accurate platform for biomarker validation than conventional cell line-based models, and have played a key role in the investigation of HER2 and BRAF biomarkers as well as predictive, metabolic, and imaging biomarkers. Additionally, a number of studies demonstrated that KRAS mutant PDX models do not respond to cetuximab and it has been argued that if these preclinical studies had been completed before or in parallel with the development of cetuximab, the discovery, validation, and approval of KRAS mutation as a marker of resistance would have been expedited.

Three-dimensional co-culture systems incorporate multiple cell types (including immune, stromal, and endothelial cells) to provide comprehensive models of the human tissue microenvironment. These systems have become essential for replicating in vivo environments and more physiologically accurate cellular interactions and microenvironments. Notably, they have been used to identify chromatin biomarkers that could be used to identify treatment-resistant cancer cell populations.

These advanced models become even more valuable when integrated with multi-omic strategies. Rather than focusing on single targets, multi-omic approaches make use of multiple technologies (including genomics, transcriptomics, and proteomics) to identify context-specific, clinically actionable biomarkers that may be missed if developers rely on a single approach. The depth of information that can be obtained via these multi-omic approaches enables the identification of potential biomarkers for early detection, prognosis, and treatment response, ultimately contributing to more effective clinical decision-making. For example, recent studies have demonstrated that multi-omic approaches have helped identify circulating diagnostic biomarkers in gastric cancer and discover prognostic biomarkers across multiple cancers.

The Power of Longitudinal and Functional Validation Strategies

While biomarker measurements taken at a single time-point offer a valuable snapshot of disease status, they cannot capture the ways in which biomarkers change due to cancer progression or treatment. Repeatedly measuring biomarkers over time provides a more dynamic view, revealing subtle changes that may indicate cancer development or recurrence even before symptoms appear. By revealing real-time changes in biomarker distribution or behaviour, patterns and trends can be identified. This offers a more complete and robust picture than single, static measurements can offer, further aiding translation to a clinical setting.

Traditional biomarker analysis relies on the presence or quantity of specific biomarkers. However, this approach may not confirm whether these biomarkers play a direct, biologically relevant role in disease processes or responses to treatment. Functional assays complement traditional approaches to reveal more about a biomarker’s activity and function. This shift from correlative to functional evidence strengthens the case for real-world utility – and many functional tests are already displaying significant predictive capacities.

Although a useful and necessary part of preclinical development, conventional animal models frequently fail to predict human clinical trial outcomes. This presents another barrier to successfully translating preclinical biomarkers to clinical settings. One of the causes for this is the inherent biological differences between animals and humans, including genetic, immune system, metabolic, and physiological variations, which affect biomarker expression and behavior.

What may seem to be a promising new biomarker in a preclinical setting can fail to translate to success in patients. Sophisticated strategies can overcome many of these challenges, as methods such as cross-species transcriptomic analysis integrate data from multiple species and models to provide a more comprehensive picture of biomarker behavior. For example, serial transcriptome profiling with cross-species integration has been successfully used to identify and prioritize novel therapeutic targets in neuroblastoma.

Data-Driven Decision Making and the Role of Strategic Partners

AI, including deep learning and machine-learning models, is revolutionizing biomarker discovery to enhance precision cancer screening and prognosis. These technologies are changing the way biomarkers are discovered by identifying patterns in large datasets that could not be found using traditional, manual means. The full potential of these new technologies is yet to be fully understood, but they are already being integrated into biomarker approaches. Specifically, in one study, AI-driven genomic profiling led to improved responses to targeted therapies and immune checkpoint inhibitors, which resulted in better response rates and survival outcomes for patients with various types of cancer.

Maximizing the potential of these new AI technologies relies on access to big, top-quality datasets that include comprehensive data and characterization from multiple sources. This can only be achieved when all stakeholders work together to give research teams access to larger sample sizes and more diverse patient populations. Clinical practice can only use AI-derived biomarkers with confidence if there is collaboration between AI researchers, oncologists, and regulatory agencies.

Therefore, enabling big data to be shared between institutions and organizations, and integrated with multiple studies, is essential for the successful translation of preclinical biomarkers. With this in mind, strategic partnerships between research teams and companies like Crown Bioscience can play a crucial role in accelerating biomarker translation. Working with these organizations allows developers to access validated preclinical tools, standardized protocols, and expert insights needed for successful biomarker development programs.