Listen to the Article
Precision medicine, “an emerging approach for disease treatment and prevention that takes into account individual variability in genes, environment, and lifestyle for each person,” might be pharma’s next big bet, but at what cost, and are stakeholders prepared to pay?
It goes without saying that biopharma companies are investing heavily in personalized treatments that target smaller patient populations with greater accuracy. Be it gene therapies for rare diseases or biomarker-guided oncology treatments, the value proposition is clear: better outcomes, fewer side effects, and a stronger scientific foundation for care.
But as innovation advances, the economics also grow more complicated. Developing precision therapies costs more, takes longer, and yields narrower commercial markets because traditional models simply weren’t built to support high-cost, low-volume treatments.
This article explains the tension between precision and affordability, exploring what it really costs to pursue individualized medicine at scale, and what it will take to make the model work long-term.
The value proposition is clear, but so are the costs
In oncology alone, targeted therapies have reshaped treatment protocols. For one, the introduction of trastuzumab (Herceptin) for HER2-positive breast cancer patients increased five-year survival rates from 75% to nearly 90%, according to a 2022 National Cancer Institute review.
Yet tailoring drugs to increasingly smaller patient subgroups makes development more complex and expensive. It is estimated that the average cost of developing a precision oncology therapy exceeds $2 billion; this is 20% higher than with traditional treatments.
While high costs are nothing new in drug development, the challenge with precision medicine is scale. Because the more segmented a patient population becomes, the harder it is to recoup development costs across a smaller market, particularly in diseases with rare or ultra-rare genetic markers.
Biopharma’s current cost model isn’t built for N-of-1 therapies
The traditional pharma playbook relies on scale. Blockbusters succeed because they treat millions, and manufacturing spreads costs across massive volumes. Precision medicine flips that script, offering targeted, often one-time therapies for narrow patient subsets. That pushes unit prices up and strains the system.
Take Zolgensma, for example. Approved for spinal muscular atrophy in infants, the gene therapy is a one-dose treatment priced at over $2.1 million. It treats fewer than 1,000 children annually in the U.S., making it one of the most expensive drugs on the market. Despite its potential curative value, the pricing model has sparked debate about its sustainability and accessibility.
As more N-of-1 therapies come online, payers and health systems are hitting pause. Reimbursement resistance is growing, and even well-funded systems are questioning how many of these treatments they can sustainably support.
Smaller trials, bigger barriers
Precision medicine changes not only which drugs are made, but also how they’re tested. Smaller, biomarker-defined patient pools mean clinical trials have to be narrower and more specialized, particularly in early-phase studies.
This can accelerate recruitment and improve endpoint targeting. However, reduced sample sizes can undercut statistical power, making it harder to detect clinically meaningful outcomes and even harder to convince regulators that the results are successful.
In response, regulators like the United States Food and Drug Administration are pushing for more adaptive and basket trial models, where multiple subtypes or indications are tested under a single framework. These approaches are promising, but they require digital infrastructure, advanced modeling, and operational fluency—not yet the norm across all sponsors.
Diagnostics: The hidden cost driver
Precision medicine can’t function without precision diagnostics. Yet diagnostic development and infrastructure have lagged behind therapeutic innovation, creating a structural mismatch that’s hard to ignore.
Companion diagnostics (those developed in parallel with a therapy) require their own R&D cycles, clinical trials, and regulatory clearance. This dual-path development adds time, complexity, and cost to the precision pipeline.
The economics are telling. In oncology, diagnostic-related expenses can account for up to 40% of total treatment pathway costs, according to various real-world analyses. And access remains deeply uneven. In low—and middle-income regions, the absence of testing infrastructure effectively locks out entire patient populations from receiving targeted treatments.
Reimbursement remains a moving target
When a therapy is tailored to a patient’s biology, the value proposition shifts—but so does the pricing logic. That’s why outcome-based reimbursement models are gaining traction. In theory, they tie payment to actual patient response, aligning incentives across the board.
Novartis’ agreement for Kymriah is a leading example. The company offered a “no cure, no pay” deal: Hospitals would only be charged if patients showed measurable improvement within a certain timeframe. It’s a bold model, but still the exception, not the rule.
These contracts are often bespoke, negotiated case by case, and require longitudinal tracking that most health systems aren’t yet equipped to deliver. Until reimbursement frameworks catch up at scale, friction around pricing will continue to limit adoption.
Industry levers to make precision medicine sustainable
The precision medicine model may be expensive, but it’s not immutable. There are proven strategies the biopharma industry can leverage to improve affordability without compromising innovation.
Start with shared infrastructure. Real-world data platforms that connect biopharma, payers, and regulators can help standardize safety monitoring, support value-based pricing, and streamline post-market surveillance. The result? Better decisions, faster.
Smarter trials are also within reach. AI-driven protocol design, virtual patient recruitment, and synthetic control arms are already showing promise. McKinsey estimates that AI tools can shorten clinical trial timelines by 15–30%, driving down costs and accelerating time-to-market.
Integrated development is another play. By building early partnerships between diagnostic companies and drug developers, sponsors can de-risk co-development cycles and avoid last-minute mismatches that add delays and expense.
And finally, platform investments matter. Modalities like mRNA, lipid nanoparticles, and CRISPR delivery systems offer repeatable frameworks for multiple therapies. Instead of building one-off assets, biopharma can spread R&D costs across modular pipelines.
A future still worth chasing
This innovation has all the elements of a transformative healthcare model, but today’s financial reality is holding it back.
The industry’s pursuit of personalized therapies has introduced new challenges: smaller trial populations, higher development costs, complex diagnostics, and uncertain reimbursement. The very features that make precision medicine so clinically compelling strain its economic viability.
This isn’t a problem of science, but a systems one, and the solution lies in rethinking how trials are run, drugs are priced, diagnostics are integrated, and data is shared across the ecosystem.
Precision medicine will keep advancing. However, for sustainable scaling, biopharmaceutical companies will require innovative business models capable of supporting personalization.
