Key Takeaways
- Cash Runway Focus: Biotechs are prioritizing de-risked assets and precision recruitment to extend runways in a constrained funding environment.
- Shared-Service Models: Centralizing recruitment and testing across multiple programs is emerging as a key strategy to reduce trial costs and duplication.
- AI-Driven Efficiency: AI is being used to analyze EMR and genomic data for smarter patient matching and leaner trial protocols.
- Diversity as ROI: Inclusive recruitment is essential for regulatory viability, scientific accuracy, and broader market access.
- Early CDx Integration: Implementing companion diagnostics early in development accelerates market uptake and supports pricing strategies post-approval.
How constrained funding is shaping biotech development decisions
Venture capital funding for biotech fell significantly in the first half of 2025 compared to prior years. This has especially impacted early-stage biotechs. Amidst financial and political uncertainty, investors are cautious and risk-averse. While some late-stage companies with strong data have continued to secure large investments, many biotechs are navigating a capital-constrained environment
Q: The biotech funding environment is still challenging after 2024. Why do you think companies are so focused on extending their cash runways?
“It comes down to investor priorities. We haven’t returned to pre-2022 highs, and investors are putting money into later-stage, de-risked assets. That means early-stage companies are delaying expensive Phase 2b or Phase 3 trials or waiting longer to sign partnerships. In such a crowded space, standing out early is harder than ever. At Sano, we help our partners generate early validation data quickly with right-sized solutions for their lifecycle stage. Precision recruitment is a core part of this—not just reducing timelines, but improving the quality of the patient population that reaches eligibility, which directly affects data quality and the credibility of early-phase outcomes.”
In constrained funding environments, early-stage programs that generate credible, well-scoped evidence tend to be better positioned for partnership conversations and future capital allocation.
How shared-service models reduce trial costs and operational complexity
In a climate where every dollar and day counts, many biotechs are rethinking the traditional, siloed approach to clinical trial operations. Most trials struggle to meet their enrollment targets on time, and as protocol complexity increases, the operational burden on each study compounds. One model more companies are adopting is the shared-service approach, which centralizes key functions like recruitment and testing across multiple programs. This not only streamlines processes and reduces duplication, but also creates a more consistent and patient-friendly experience.
Q: Shared-service models are getting more attention. How are they helping biotechs cut trial costs?
“We’re seeing more companies pool recruitment and testing infrastructure across multiple studies or phases. This reduces duplication, consolidates vendor management, and lowers the operational risk associated with fragmented trial start-up. Our platform is designed to unify recruitment, testing, and long-term engagement. From the patient’s perspective, it’s a smoother, less fragmented journey with fewer handoffs and clearer communication — and that trust translates into better retention and stronger outcomes.”
Using AI to design leaner clinical trials
In a tight funding climate, trial efficiency is a priority. Yet most trial participants today are drawn from patients already known to the recruiting site, and the broader population rarely considers clinical research as part of their care journey. AI is giving biotechs new ways to close this gap by improving how patients are identified and recruited. Beyond recruitment, by processing large volumes of real-world data, AI models can help researchers simulate eligibility criteria, refine trial protocols, and can even generate synthetic control arms.
Q: How do you think AI is making a difference in trial design?
“The real value is in pre-screening. We can analyze electronic medical records (EMRs) and genomic data — handled in accordance with HIPAA and GDPR requirements — to flag patients most likely to respond to treatment. AI helps identify patterns and refine cohorts, which speeds up recruitment and improves trial quality. At Sano, we focus on using genetic and health data for smarter matching — not just casting a wider net. And we’re clear on one thing: AI supports empathy; it doesn’t replace it. Our team remains closely involved to guide patients through the process, address questions, and support informed decision-making at each stage.”
Building diversity into trials for stronger science and broader market access
Prioritizing diversity in trials enhances regulatory viability and clinical utility across populations. Yet, real-world representation still lags far behind. A review of 93 precision oncology trials (5,867 participants) highlights a significant lack of representation:
- Non-Hispanic White: 82.3%
- Black: 10.0%
- Asian: 4.1%
This overrepresentation of White participants fails to capture the genetic, cultural, and environmental diversity seen in real-world clinical settings. This lack of representation can skew efficacy and safety signals, limit the generalizability of trial results, and create downstream barriers to regulatory approval, market access, and patient trust.
Q: Diversity in recruitment has moved from a compliance consideration to a strategic one. Beyond regulatory requirements, what’s the ROI for biotechs?
“Without diverse cohorts, efficacy and safety signals reflect a narrow slice of the real-world patient population—limiting the generalizability of results and creating downstream barriers to access and trust. By engaging underrepresented groups early, you open doors to more markets and reduce the risk of post-approval setbacks. Our approach to diversity is embedded in how patient identification and qualification are designed—integrating inclusive prescreening pathways and tailored engagement that reduces barriers to participation from the start, rather than compensating for underrepresentation later in the funnel.”
To learn more about the importance of diversity and representation in precision trials, read our whitepaper here.
Using companion diagnostics to support post-approval market access
In today’s competitive launch environment, Regulatory approval establishes that a therapy works; it does not automatically establish that it reaches the right patients. Therapies need a clear path to the right patients, and companion diagnostics can improve patient identification at launch, support payer negotiations, and strengthen post-market evidence generation.
Q: How do companion diagnostics (CDx) fit into the strategy post-approval?
“CDx and stratification tools meaningfully improve market access by enabling more precise patient selection and generating real-world evidence that supports payer and regulatory decisions. When you integrate them early, you’re generating real-world evidence alongside clinical data, making regulatory submissions smoother and targeting the right patients from launch day. When genetic testing is embedded into recruitment and eligibility workflows from the outset, it creates a continuous data asset that supports not only trial execution but post-approval access strategies—because the evidence base is built in a coordinated, traceable way from patient identification through enrollment.”
In closing, Ben emphasized that centering trials around patients requires:
- Early Alignment: Considering patient experiences during drug development to ensure therapies meet actual needs.
- Barrier Reduction: Designing trials that minimize participation burdens and logistical hurdles.
- Human-Centricity: Treating participants as people rather than just data points to improve retention and data quality.
Equally important is what happens after enrollment. Maintaining engagement throughout the study, and between studies, is what turns early recruitment wins into durable outcomes. When participants feel informed, supported, and respected over time, retention improves and the value of each enrolled patient compounds across the program.
“Patient experience is not separate from operational performance. When participants are well-informed, appropriately supported, and not overburdened, the downstream effects are measurable: better retention, higher data quality, and faster enrollment. That connection is built into how Sano approaches every program.”
For more practical strategies on how to advance innovation on limited budgets, access our whitepaper here.