Chronic kidney disease (CKD) affects more than 850 million people worldwide, yet treatment options remain limited, especially for rare and inherited forms of the disease. While advances in genetic testing have deepened our understanding of the underlying causes of CKD, translating these insights into new therapies has proven challenging.

One of the most persistent obstacles for clinical trials is patient recruitment. This is especially true for rare subtypes of CKD, where identifying and enrolling eligible participants is often slow and difficult. As a result, trials can stretch on for years, delay timelines, and sink promising therapies before they reach patients.

Now, a new generation of genetic platforms is flipping that script. By pairing genetic insights with clinicogenomic data, researchers are not only diagnosing patients earlier, but also finding and enrolling them into trials faster. Drug Discovery News spoke with Ronen Schneider, a nephrologist and Medical Director of Renal Genetics at Natera, to explore how tools like RenasightIQ™ are accelerating trial recruitment, improving diagnostic yield, and unlocking new possibilities for precision drug development in kidney disease.

How does genetic screening distinguish between monogenic and complex kidney disease, and what impact does this have on therapeutic development?

Genetic testing using broad renal gene panels is primarily designed to identify monogenic causes of kidney disease. Monogenic disorders result from pathogenic variants in a single gene and are typically highly penetrant, meaning that the presence of the mutation is strongly associated with the development of disease. These variants often act as the primary drivers of the disease phenotype, making them attractive and actionable targets for drug development.

Complex kidney diseases arise from the interplay between partially penetrant genetic risk variants and environmental factors. While some of the most prevalent and penetrant genetic variants associated with complex disease are included in renal gene panels, non-coding or low-penetrance polymorphic variants are typically excluded, as their individual contribution to disease risk is modest and context-dependent.

How can integrating clinicogenomic data from real-world populations help de-risk early-stage drug development?

More than 80 percent of clinical trials fail to meet their enrollment timelines, and approximately 30 percent are terminated entirely due to insufficient participant recruitment. This challenge is even more pronounced in rare diseases such as monogenic CKD, where each genetic subtype may affect only a small number of individuals dispersed globally. In fact, around 55 percent of rare disease clinical trials fail primarily due to inadequate patient enrollment.

More than 80 percent of clinical trials fail to meet their enrollment timelines, and approximately 30 percent are terminated entirely due to insufficient participant recruitment.
- Ronen Schneider

A critical bottleneck remains the gap between identifying eligible patients and successfully enrolling them in trials. Integrating clinicogenomic data can help bridge this gap by enabling the rapid and precise identification of eligible participants, particularly in rare diseases. Achieving recruitment targets early not only reduces the risk of trial failure but also conserves valuable time and financial resources during the early stages of drug development. Clinicogenomic data can also assist researchers with understanding rare disease populations better with real-world data based on the rare disease.

Have you seen genetic data directly shorten timelines for investigational new drug (IND)-enabling studies or proof-of-concept trials?

Yes. ENYO Pharma, a European biotech firm developing therapies for kidney disease encountered recruitment challenges in their Phase 2 trial of Vonefexor for Alport syndrome, a rare genetic condition affecting approximately 1 in 5,000 people.

To accelerate recruitment, ENYO partnered with Natera, using RenasightIQ’s Clinical Trial Support services. Using a genetic and clinically enriched dataset of over 150,000 patients, ENYO rapidly identified eligible individuals. Since patients and providers had already consented to outreach, we were able to engage directly. As a result, ENYO met 100 percent of its enrollment target within just six months, with a 31 percent screen failure rate.

Are there any specific rare kidney diseases where the genetic landscape is still poorly understood, and how is your platform helping to address that?

Yes, several rare kidney diseases still have poorly understood genetics, due to challenges such as technical limitations in sequencing certain genomic regions, small patient populations, phenotypic variability, and underrepresentation in genomic databases. Examples include autosomal dominant tubulointerstitial kidney disease (ADTKD) due to MUC1, atypical hemolytic uremic syndrome (aHUS), and various ciliopathies.

Key challenges include variants missed by standard panels, unclear interactions between rare variants and environmental triggers, incomplete penetrance, and overlapping clinical features.

RenasightIQ addresses these gaps by integrating clinical and genomic data from a large patient population. This allows us to uncover genotype-phenotype correlations, evaluate the penetrance of risk variants, and investigate environmental modifiers. We also explore advanced sequencing technologies to target hard-to-sequence regions — such as those implicated in MUC1-related ADTKD — significantly improving diagnostic accuracy where conventional methods fall short.

Do you think accelerated approval pathways for rare diseases are effectively incorporating genetic evidence today?

Accelerated approval pathways are increasingly recognizing the value of genetic evidence, but there's still significant room for progress, especially in monogenic CKD. As the body of evidence linking specific genetic variants to CKD continues to grow, and with professional societies increasingly endorsing genetic testing in nephrology, we anticipate greater integration of genetic data into regulatory decision-making. This trend will likely support more efficient drug development and accelerated approvals for rare kidney diseases.

What’s a key bottleneck you've observed when customers try to integrate genetic insights into their R&D workflows?

One of the primary bottlenecks is patient access and outreach. While monogenic kidney diseases may account for up to 20 percent of the chronic kidney disease (CKD) population — making them more common than previously recognized — individual genetic variants are still rare. This rarity makes it difficult to identify, engage, and enroll the right patients for research and clinical trials. Without access to well-characterized, genetically defined patient cohorts, it becomes challenging to translate genetic insights into actionable R&D strategies.

How do you see innovations in data architecture or analytics transforming the role of genetics databases in drug development?

Clinicogenomic data is incredibly rich, capturing countless potential gene-gene and gene-disease interactions. Innovations in data architecture and analytics, particularly the integration of AI, are transforming how we harness this complexity. By combining large-scale genetic and clinical datasets, we can now apply advanced computational methods to uncover novel disease mechanisms, stratify patients more precisely, and identify therapeutic targets with greater accuracy. This shift enables a move from gene discovery to pathway-based drug design, accelerating the development of targeted therapies, especially for complex and heterogeneous diseases.