While strategic use of biomarkers in clinical trials has traditionally been associated with oncology, the potential for growth across other therapeutic areas is becoming increasingly apparent. From cardiology researchers using NT-proBNP thresholds to enrich populations in acute coronary syndrome trials, to Alzheimer’s studies that require amyloid PET positivity for patient eligibility, biomarkers are distinguishing true safety and efficacy signals from noisy, inconclusive data and enhancing the efficiency and likelihood of success in clinical trials. As this continues to evolve, biomarker strategies are restructuring trial designs, informing go/no go decisions, and even supporting accelerated regulatory approvals.

In nephrology, traditional ‘hard’ endpoints such as progression to end-stage renal disease (ESRD) or death can take decades to occur, making clinical trials long and extremely expensive. Biomarkers could accelerate these studies and help teams make critical decisions faster and with more information. Yet the full possibilities have barely been tapped. So how far has the field come today, and what might the future hold for biomarkers in nephrology?

The evolution of renal biomarkers

The early stages of nephrology’s biomarker journey were mostly defined by serum creatinine and proteinuria data used to characterize disease severity and monitor patients’ progression. In the late 1990s, doubling of serum creatinine, or dSCr, was established as a surrogate endpoint for kidney failure. However, with this occurring very late in disease, renal trials continued to be long and costly.

In the 1990s and 2000s, large diabetic nephropathy studies such as the RENAAL trial showed that large early reductions in albuminuria shortly after RAAS blockade strongly predicted lower ESRD risk. With this, biomarkers such as albuminuria and proteinuria evolved into prognostic tools. Researchers began enrolling patients with macroalbuminuria under the expectation they would progress faster and generate more renal events, thus improving trial efficiency through enriched populations.

Biomarker analysis also entered into dose selection, allowing investigators to compare doses based on albuminuria lowering, for example. At the same time, biomarkers began informing go/no go decisions on an increasing basis, enabling sponsors to answer important questions – such as whether the drug is hitting the correct pathway – earlier on in development.

A major turning point came in the early 2010s, when the FDA began to accept estimated glomerular filtration rate (eGFR) signals as surrogate endpoints, such as a > 40% decline in eGFR. This enabled earlier readouts, smaller studies, and accelerated development timelines. Since then, eGFR endpoints have continued to progress. In some applications, a 30% decline is now considered for approval. Meanwhile, researchers commonly analyze the rate of kidney function decline using eGFR slopes, which may now be accepted as a valid primary endpoint by the FDA and EMA.

Compared to waiting for a distant, fixed event such as dSCr or ESRD, eGFR endpoints have made research in earlier-stage kidney disease increasingly possible. These milestones have been especially important in rare diseases, where biotechs are likely to experience particularly challenging enrollment and prohibitively expensive pediatric-to-adult follow-up periods. Under the accelerated approval framework, orphan drugs such as Tarpeyo and Filspari – the first disease-specific therapies approved for IgA nephropathy and focal segmental glomerulosclerosis (FSGS) respectively – became available based on proteinuria and eGFR-based evidence.

The latest possibilities

Mechanistic biomarkers

Today, biomarker possibilities continue to expand. In recent years, investigators have been exploring an alternative subset of markers that could provide valuable insight into unique aspects of kidney tubule health. New biomarkers such as Kidney Injury Marker-1 and neutrophil gelatinase-associated lipocalin (NGAL) enable researchers to quantify the severity of tubule cell injury, which is now increasingly recognized as a central aspect of CKD progression. Importantly, these new biomarkers may detect injury much quicker than a decline in eGFR while also helping to distinguish hemodynamic effects from true structural injury.

Although new mechanistic biomarkers such as KIM-1 and NGAL are still exploratory from a regulatory standpoint, they can already help biotechs enhance the value proposition of their assets through better understanding of a drug’s mechanism of action. As the asset moves through development, such biomarkers could prove instrumental in enhancing clinical confidence, giving commercial leadership teams and investors the assurance they need to expand populations, add new labels, and explore differentiated clinical positioning possibilities.

Biomarker panels and the future role of AI

Research has demonstrated that kidney disease is highly heterogenous, with multiple different pathways altering the function and structure of the kidneys, from inflammation and fibrosis to tubular injury, glomerular damage, and immune activation. A single biomarker is unable to capture this complexity alone, especially since those in wide use today (i.e. eGFR and albuminuria) are generic markers of glomerular filtration and damage.

Biomarker panels combine signals from multiple pathways, such as eGFR and proteinuria for filtration function, KIM-1 for tubular injury, TNFR1/2 for inflammation, and suPAR for immune activation. This improves accuracy and mitigates the limitations of individual biomarkers. In addition, where single, exploratory biomarkers may lack adequate validation, combining them within a strategy can strengthen mechanistic plausibility, providing important support for regulatory discussions.

As the use of AI grows in clinical trials, there is increasing potential for machine learning and AI-based predictive modelling techniques to enter the picture, with new algorithms analyzing data across multiple biomarkers and patient information sources to predict a patient’s risk score for progressive kidney function decline.

Towards precision medicine in nephrology

With greater understanding of the many different underlying biological pathways that drive kidney disease, there is potential to shift from prognostic biomarkers towards predictive ones. By identifying pathway-specific responders through use of these predictive biomarkers, sponsors have the potential to advance drugs that would otherwise appear ineffective in heterogenous populations.

In one potential example, elevated chronic inflammation biomarkers such as TNFR-1 or 2 could be used to identify a subgroup of patients who may respond well to anti-inflammatory drugs. It’s a method that is already seeing significant success in oncology but that has been relatively untapped in nephrology, with the potential to improve patient outcomes through tailored treatments.

Maximizing the opportunities

As the possibilities of biomarkers in nephrology continue to strengthen, selecting the right signals and successfully integrating them within protocols and endpoints becomes critical, especially given the potential to accelerate trials and differentiate assets from competition.

To realize these possibilities, nephrology sponsors require strategic input and guidance from a high-touch CRO partner with deep expertise in the therapy area and strong understanding of kidney disease nuances. In Caidya, they can find the perfect collaborative partner with which to harness the latest biomarker trends in nephrology research, accelerating studies, enhancing patient selection, and guiding clinical strategies to bring innovative nephrology treatments to patients faster.