A Biomarker Playbook For Building Faster, Smarter Clinical Trials

By Jason C. Bork, CEO and founder, Pintail Solutions

For years, life sciences leaders have accepted a frustrating reality: clinical trials are too slow, too expensive, and too operationally fragile. Recruitment drags. Screen failure rates remain high. Trial populations are often poorly matched to the biology under investigation. Sites work hard, but too much effort is spent on participants who ultimately do not qualify, do not progress as expected, or do not generate the signal needed to answer the scientific question with confidence. That model is no longer good enough, and biomarkers offer one of the clearest paths to changing it.

Used well, biomarkers do far more than support diagnosis or stratification. They can improve trial design, sharpen enrollment, reduce operational waste, and increase the probability that a study answers the question it was built to answer. Regulators have increasingly recognized this value. FDA materials explicitly describe biomarkers as tools that can support enrichment, patient selection, stratification, safety assessment, and other trial functions. EMA has also supported biomarker qualification and enrichment approaches in multiple development contexts.

I saw this firsthand while serving as the chief operating officer of the Global Alzheimer’s Platform Foundation (GAP). During that time, I helped lead Bio-Hermes, a first-of-its-kind study designed to evaluate a broad set of blood-based and digital biomarkers against amyloid PET and traditional cognitive assessments. The study enrolled more than 1,000 participants in 18 months, included meaningful representation from traditionally underrepresented communities, and generated a large, openly available data set that now sits within the Alzheimer’s Disease Data Initiative ecosystem for broader research use. Bio-Hermes examined 25 blood-based biomarkers and 15 digital tests, with results published in Alzheimer’s & Dementia.

The bigger lesson was not simply that biomarkers are scientifically promising; it was that they can fundamentally improve how we run trials.

The Real Problem Is Waste

Too many clinical programs still begin with a faulty protocol and then researchers try to operationalize their way out of its structural weaknesses. If the wrong patients enter the funnel, the burden falls downstream. Sites absorb the screening load, budgets swell, and timelines slip. Sponsor teams often respond with more vendors, more meetings, and more oversight, but the underlying issue remains: The trial is not identifying the right participant population early enough and efficiently enough.

Biomarkers address that problem upstream.

When biomarkers are used to enrich a study population, they help sponsors identify participants more likely to exhibit the pathology, progression pattern, or treatment response relevant to the intervention. That means fewer unnecessary procedures, lower screen failures, cleaner data sets, and a better chance of detecting a signal. The FDA has long described enrichment strategies as a way to make trials more informative and efficient, especially when trying to identify patients more likely to respond or to experience an endpoint during the trial period. To that end, my team, on behalf of a large pharmaceutical company, designed and implemented a single clinical trial prescreening program that acted as a stand-alone mechanism to enrich the trial population where screen fail rates can be >90%. Under that program, we improved the trial randomization rates by ~300%. On a large scale, efficient participant phenotyping can save tens of millions of dollars in actual trial costs, optimize site resources, and reduce unnecessary participant burden.

For C-suite leaders, the implication is straightforward: Biomarkers are an operating model decision.

Biomarkers Are A Portfolio Strategy, Not A Protocol Accessory

One of the most common mistakes is treating biomarkers as a late-stage add-on. A sponsor reaches a pressure point in development and asks whether a biomarker could be inserted to improve odds. Sometimes that works. Often, it creates complexity without enough strategic benefit.

The better approach is to decide early what role biomarkers should play across the portfolio. Are they being used to identify the right patients? To predict progression? To improve endpoint sensitivity? To reduce dependence on high-cost or low-access procedures? To support decentralization or community-based access? Those are different strategic choices, and they require different evidence plans, operating models, and partner capabilities.

In Alzheimer’s disease, for example, one of the largest barriers to trial efficiency has historically been reliance on expensive and capacity-limited tools, such as amyloid PET. Bio-Hermes was built around a practical question: Can less invasive, more scalable blood and digital biomarkers help identify the right participants and broaden access without sacrificing scientific rigor? That question matters well beyond neurology. The same logic applies in oncology, immunology, rare disease, and cardiometabolic development. If a sponsor can reduce friction in identifying biologically relevant patients, the entire trial system improves.

A Practical Biomarker Playbook For Executives

The companies that gain the most from biomarkers tend to do five things well.

1. Start with the decision, not the technology.

Do not begin by asking which biomarker is exciting. Begin by asking which trial decision is currently inefficient. Is recruitment too slow? Are screen failures too high? Are endpoints too noisy? Is the eligible population too hard to find? Are access limitations reducing diversity and representativeness? The biomarker strategy should solve a defined operational and scientific bottleneck.

This matters because not every biomarker adds value. Some create cost without improving decision quality. Executive teams should require a clear statement of intended use tied to trial economics and development risk.

2. Build enrichment into design, not just screening.

A biomarker that sits in the background is much less valuable than one that shapes the design itself. When used well, biomarkers can define inclusion criteria, stratify randomization, support subgroup analysis, or help determine who is likely to progress during the study window. Regulators evaluate biomarkers in the context of use, meaning the specific way the biomarker will be applied in development matters greatly. Sponsors that define that context clearly are better positioned to build robust evidence and engage regulators constructively.

This is where executive discipline is essential. A biomarker strategy cannot live only with translational science or clinical operations. It must align across development, regulatory, biostatistics, commercial thinking, and site execution.

While at Lilly, we took advantage of the well-known cardiometabolic lipid biomarkers (LDL, HDL, triglycerides) by optimizing the patient population and trial design to show a pharmacodynamic effect in Phase 1 studies. Instead of completing safety testing in the traditional “healthy normal” participants, we included “obese, but otherwise healthy” participants. We also incorporated a pre-dosing high fat meal into the study and demonstrated a very robust lipid biomarker effect. This effect increased our confidence in the molecule, enabled a more sophisticated Phase 2 design, and improved our modeling for optimized dose ranges. In this case, the biomarkers were not new, but the trial design was novel and generated a much more robust data set because of how we incorporated the biomarkers.   

3. Design for scalability early.

A biomarker may be analytically elegant and still fail operationally. If the assay is hard to deploy, turnaround times are too long, site training is burdensome, or confirmatory testing pathways are unclear, the sponsor simply relocates inefficiency rather than eliminating it. Clinical trial efficiency improves when the biomarker pathway is accessible, repeatable, and integrated into real-world workflows.

One of the most important lessons from biomarker-driven studies is that speed is not just about assay performance but about total system design: site readiness, participant experience, logistics, data integration, and clarity of next-step decisions. A minimally invasive test that can be deployed broadly has strategic value that goes beyond pure scientific sensitivity.

In a similar patient enrichment application, while at Covance Central Labs (now LabCorp), we partnered with a study sponsor to incorporate lab-on-a-chip technology as part of their inclusion criteria. The sponsor ran an infectious disease trial, and the key lab assay was both their clinical study endpoint and part of their inclusion criteria.  It was also very expensive, and they were predicting screen fail rates greater than 80% primarily due to this assay.  By using a less-validated lab-on-a-chip technology, the sponsor significantly optimized trial costs by screen failing the wrong participants early and also minimized participant burden. The study enrollees that were not going to randomize into the trial found out much sooner (within minutes) and did not endure significant procedures or assessments that wouldn’t change their trial randomization outcome.  Of note, study sponsors need to take careful thought on how they are enhancing their study population and how that will translate into clinical practice. 

4. Use biomarkers to improve representation, not narrow it unintentionally.

There is a temptation to think of enrichment only as narrowing. In reality, the right biomarker strategy can broaden opportunities by making access easier.

Bio-Hermes demonstrated that community-based, diverse participation can be embedded into study design rather than treated as an afterthought. GAP reports that 24 percent of participants came from traditionally underrepresented communities, materially exceeding what has typically been seen in many Alzheimer’s studies. That matters scientifically and operationally. More representative data improves confidence in biomarker performance across populations and helps reduce downstream surprises in development and care delivery.

Similar to an earlier point, minimizing participant study burden, especially within diverse populations, can help build trust and confidence.  Participants spending 4, 6, or sometimes more than 8 hours in the initial trial visit(s) only to find out they will not randomize into the study can become frustrated, even feel demeaned for seeking help or wanting to make a difference. 

For industry leaders, this is an environmental, social, governance (ESG) point but also a quality point. A biomarker that performs unevenly across populations can create hidden development risks. Representation should be designed into validation efforts from the beginning.

5. Treat data generation as an enterprise asset.

Too many biomarker initiatives stop at immediate trial utility. That is shortsighted. A well-run biomarker program generates a reusable asset: a structured data set that can inform future protocol design, assay strategy, site selection, and evidence generation. The public release of the Bio-Hermes data set illustrates the broader value of making high-quality biomarker data available for scientific progress. The Alzheimer’s Disease Data Initiative describes its platform as a no-cost environment for data sharing, analytics, and collaboration to accelerate Alzheimer’s and related dementia research.

The strategic question for sponsors is this: Are you treating biomarker data as a one-study artifact or as part of a compounding capability?

The organizations that will outperform over the next decade are the ones that use biomarkers not only to rescue individual studies but to build smarter development systems. Many pharma companies have been collecting Big Data for some time; however, before the advent of AI tools, they really didn’t have the capability to utilize that data to its full extent. That paradigm is changing today, and companies are identifying new targets and disease mechanisms, honing personalized medicine beyond oncology and specific gene traits, and broadly optimizing clinical trial design, execution, and outcomes.

What The C-Suite Should Do Now

For smaller biotech companies, the opportunity is focus. A biomarker-led design can help stretch capital by improving target population selection and reducing avoidable operational waste. The key is discipline. Choose the biomarker strategy that most directly reduces uncertainty in your program and align it tightly to your development hypothesis.

For midsize companies, the opportunity is standardization. Many have enough portfolio breadth to benefit from a repeatable framework for biomarker assessment, regulatory planning, assay operations, and data integration. This is where biomarker strategy becomes a capability, not a one-off tactic.

For large pharma, the opportunity is transformation. Biomarkers can connect translational science, clinical development, data strategy, and global operations in ways that materially improve cycle times and portfolio quality. But scale can also make organizations slow to act. The biggest gains come when enterprise leaders make biomarker-enabled trial design a strategic priority rather than leaving it fragmented across functions.

The Future Belongs To Trials That Are Biologically Smarter And Operationally Simpler

The industry does not need more complexity disguised as innovation. It needs trials that identify the right participants sooner, reduce burden on sites and patients, make better use of capital, and generate evidence that is both rigorous and relevant. Biomarkers can help deliver that future, but only if they are approached with strategic clarity and operational seriousness. The lesson is not that every trial needs a biomarker. It is that every development leader should be asking where biomarkers can most effectively remove friction, improve signal, and create leverage across the system.

About The Author:

Jason C. Bork is a life science executive with more than 30 years of experience across large pharma, CRO, and start-up organizations. He is an entrepreneur known for seeing the essence, distilling the complex into actionable steps, and developing those around him to new levels of fulfillment. From business strategy, organizational change, and operational excellence, Jason enables organizations to solve critical challenges, deliver high-impact projects, and move into the future with clarity and confidence. He has authored multiple scientific papers and book chapters and is a distinguished public speaker. Jason founded Pintail Solutions, a life science consulting organization, in 2015.