How Biotechs Can Reduce Development Risk With RWE

Drug development is, to put it mildly, a high-stakes gamble. The path from discovery to an approved medicine is littered with failures some estimates suggest over 90% of programmes don't make it. For small biotechs, the financial and operational pressures are even more acute. You don’t have endless shots on goal. Every research avenue explored, every clinical trial initiated, must count. While there’s no magic wand to eliminate the inherent uncertainties, real-world evidence offers a pragmatic way to navigate this treacherous landscape and make more informed, less risky decisions.

Think of RWE insights derived from electronic health records, claims data, patient registries, and other sources outside traditional trials as a toolbox that can both accelerate and sense check development decision making.

Clinical trials are essential, but they operate in controlled, somewhat artificial environments. RWE helps bridge the gap between the efficacy seen in these ideal settings and the effectiveness a therapeutic might achieve in the messy, complex reality of everyday clinical practice. For smaller players, making smarter, evidence-backed choices earlier in the development cycle isn't just good practice; it's crucial for survival.

So, how can you use RWE to de-risk your development programme?

Sharpen Your Aim: De-risking Early Discovery and Target Validation

Many biotechs still treat RWE as something for the later stages, perhaps for market access or post-marketing studies. That’s a missed opportunity. The seeds of later-stage failure are often sown right at the beginning, with poorly validated targets or a misunderstanding of the disease in a real-world context.

RWE can help here. Imagine using anonymised genomic data linked to patient records to understand how specific genetic markers correlate with disease progression or treatment response in actual patient populations. This can help you validate (or invalidate) a novel target by seeing its relevance in the real world, not just in a preclinical model. You can analyse RWE to identify patient subgroups with the highest unmet need for a specific mechanism of action, ensuring you’re not just developing a drug, but a solution for a well-defined and receptive patient group. This early intelligence helps you avoid investing precious resources in pursuing targets that might look promising in the lab but have limited applicability or impact in the complexities of human disease. It’s about reducing the risk of betting on the wrong horse from the very start.

Design Smarter Trials: Optimising Clinical Trial Design and Execution

This is a big one. Clinical trial failures are a primary cause of drug development attrition, burning through cash and time. RWE can significantly de-risk this critical phase.

• Stop Guessing with Feasibility and Site Selection: How often are recruitment timelines missed? RWE can move feasibility assessments from educated guesses to data-driven strategies. Instead of just looking at broad patient numbers, you can use RWE to pinpoint where your target patients are being managed, understand current care pathways, and identify sites likely to have the patients you need as opposed to going to the same centers as everyone else. This means selecting sites that don’t just say they have the patients, but can enrol them, reducing the risk of costly delays.
  
  
• Refine Your Protocol for the Real World: 
  Your trial protocol dictates who you enrol. Overly narrow inclusion/exclusion criteria can make recruitment a nightmare and limit the generalisability of your findings. Too broad, and you might dilute treatment effects or struggle to identify subgroups where your drug truly shines. RWE allows you to model the impact of different criteria based on real patient characteristics, ensuring your study population better reflects the patients who will ultimately use your medicine. This can reduce the risk of a trial failing simply because the protocol was misaligned with reality.
  
  
• Consider External Control Arms (ECAs) Where Appropriate: In specific situations, such as rare diseases were recruiting a concurrent placebo or standard-of-care arm is challenging, or for early phase signal-seeking studies, RWE can be used to develop an external control arm. Data from patients treated in real-world settings can provide a comparator group, potentially reducing the size or even the need for a traditional control arm. This can accelerate timelines, save costs, and lessen the burden on patients. While the regulatory landscape for ECAs in pivotal trials is still evolving and requires careful navigation, their utility in de-risking internal go/no-go decisions or supporting early discussions with regulators is increasingly recognised.

Prove the Problem: Enhancing Understanding of Disease Burden and Unmet Need

Investors, payers, and regulators need to be convinced that your therapeutic addresses a genuine and significant problem. Assumptions about unmet need aren’t enough; you need evidence.

RWE is invaluable for painting a detailed, data-backed picture of the current reality for patients. You can use it to deeply characterise the natural history of a disease – how it progresses, what complications arise, and how different patient subgroups are affected. Critically, you can analyse treatment patterns, understand the limitations of the current standard of care (efficacy, safety, tolerability, adherence), and quantify the actual clinical, economic, and quality-of-life burden on patients and healthcare systems. This ensures your development programme is targeting a well-defined problem of sufficient magnitude, reducing the risk of creating a solution for a need that isn't as pressing or valuable as initially thought.

Smooth the Path: Informing Regulatory Strategy and Value Proposition

Getting a drug through development is one mountain to climb; getting it approved and paid for is another. RWE can help de-risk these later-stage hurdles by informing your strategy from an earlier point.

Proactively using RWE can support your regulatory narrative. It can provide context for your clinical trial results, help justify endpoint selection, characterise the patient population for whom the drug is intended, and even support label expansions or fulfil post-marketing commitments. Regulators are increasingly receptive to well-conducted RWE studies.

Similarly, for payers, an early understanding of the real-world value your drug could deliver is crucial. RWE can help identify the current costs of managing a disease and how your therapeutic might offer efficiencies or better outcomes compared to existing treatments. It helps you identify and measure patient-relevant outcomes that resonate with those holding the purse strings. Building this value story early, backed by RWE, reduces the risk of facing insurmountable market access barriers post-approval.

Making RWE Work for Your Biotech

So, how does a small biotech, likely without a dedicated RWE department, put this into practice?

• Start Sooner, Not Later: Don’t wait until Phase III or pre-launch. Integrate RWE thinking into your development strategy from the earliest stages. Ask how real-world data could answer key questions or mitigate risks at each decision point.
• Be Focused: You can't analyse everything. Identify the 2-3 biggest development risks your programme faces and explore how RWE could specifically address those. Is it target validation? Trial recruitment? Demonstrating unmet need?
• Prioritise Quality: The insights from RWE are only as good as the data and the methods used. Ensure any data sources are fit-for-purpose, and your analytical approach is robust. Rubbish in, rubbish out absolutely applies here.
• Don’t Be Afraid to Collaborate: If you lack in-house expertise, consider partnering with organisations that specialise in RWE. The right partner can help you define your strategy, access appropriate data, and conduct credible analyses.

Reducing development risk isn't about finding a silver bullet; it's about systematically making more informed decisions. For small biotechs, where every penny and everyday counts, leveraging real-world evidence intelligently isn't just an option – it’s a critical component of a savvier, more resilient development strategy. It empowers you to navigate the development minefield with greater confidence and a higher chance of success.