Reminder: FDA Approval Is Not Synonymous With Commercial Success

A conversation between Alvarez & Marsal Life Sciences Managing Director Rena Rosenberg, Ph.D., and Clinical Leader Executive Editor Abby Proch

At Clinical Leader, we often hear from clinical research professionals about all things trials — from how drug candidates progressed toward the clinic to how patient input influenced endpoints to how AI has helped identify patient populations and beyond.

What we don’t often discuss is commercialization. That’s mostly because it brings with it a slew of topical areas our readers aren’t tuned into. But perhaps they should be — and us, too — if just a little.

This Q&A with life sciences expert Rena Roseberg, Ph.D., is a sobering reminder that research and commercial efforts cannot operate within a vacuum. Here, Rosenberg pulls on her 30-plus year experience supporting pharma companies to share her best advice on developing a therapeutic product with solid understanding of its commercial viability.

Clinical Leader: Increasingly, approvable therapies struggle commercially. How do you define this growing gap between what gets approved and what actually succeeds in the market?

Rena Rosenberg: The gap is really about sufficiency versus relevance. Trials are designed to clear a regulatory bar, of course, but approval alone does not a commercial strategy make! To take a practical example, exclusion criteria in clinical trial protocols have become exceedingly specific and onerous; sponsors end up finding the exact right patient and putting them on a very structured plan to use the therapy. However, once a product is approved as safe and effective, several practical questions can still be unanswered: How difficult is it to find the appropriate patients? How entrenched are existing provider workflows?

For example, for hospital-initiated therapies, prescribing is highly protocolized in emergency departments, post-operative pathways, and discharge order sets. That means new drugs must effectively change entrenched workflows and institutional practices, not just spark individual prescriber enthusiasm. And just because the clinical guidelines say so, is that actually how treatment algorithms work in practice? If patients don’t adhere to a treatment — and the ROI isn’t there for payers — what’s the benefit of a payer covering it? Those gaps sit in the areas safety and efficacy don’t cover: payer value, physician behavior, patient identification, and hospital incentives.

The other area I’d briefly point out is that your development and regulatory pathway can be built for a standard of care that’s already moved on by the time you get to market. Sometimes that’s because timelines slip and plans take longer than expected to materialize, but it can just as easily be because biopharma works in years, not months. In the interim, medical practice shifts, and trialists don’t always update program designs to reflect changes in the care pathway, competitive dynamics, or emerging real-world evidence.

There can be a disconnect between how trials are designed and how their therapies are actually used in the real world. Where do you see clinical development most often failing to replicate real-world conditions, and what are the consequences of that?

I’ll call out a few. The first is diagnostic bottlenecks, especially for therapies where patients may be asymptomatic. Identifying patients becomes challenging because the vast majority remain undiagnosed, and uptake can depend on regular testing. If you’re also talking about a niche indication, the realistic addressable market can be smaller than the topline epidemiology suggests. You can see some of this playing out in type 1 diabetes treatments focused upstream on delaying progression.

Second is misalignment with established clinical guidelines. A good example involves a dual antiplatelet agent. Its U.S. launch was uniquely difficult because the drug was proved to be most effective when used alongside low-dose aspirin, yet the standard of care in the U.S. at the time was high-dose aspirin. One hypothesis is that the pivotal trial design underappreciated geographic heterogeneity. A closer look at real-world data would have highlighted the low-dose/high-dose disparity between the U.S. and ex-U.S. markets. The drug ultimately became part of the standard of care, but it had a notoriously sluggish U.S. start.

Third is shifting treatment paradigms. As care shifts toward earlier, more preemptive intervention, therapies positioned downstream in the disease course can see volumes plateau as newer options enter and move the treatment algorithm upstream. If you don’t anticipate that evolution, you can end up with a program — and a commercial story — built for a pathway that no longer reflects how patients are actually treated.

In highly competitive categories such as obesity and oncology, approval is no longer a differentiator. What separates therapies that gain traction from those that are technically successful but commercially underwhelming?

In areas such as obesity and oncology, what separates a blockbuster from a technically successful but commercially underwhelming drug lies in how well it navigates the complex ecosystem of market access, patient experience, and real-world clinical utility.

What both of these particular therapeutic areas have in common is that raw primary efficacy is no longer sufficient; quality of life, durability, and tolerability have become table stakes. Payers and physicians are increasingly demanding obesity therapies that offer secondary benefits (e.g., cardiovascular risk reduction, muscle mass preservation). In oncology, it’s combinability with the standard of care. Through initiatives such as the FDA’s Project Optimus, drug developers are increasingly balancing efficacy and safety to preserve the patient’s quality of life, whereas the paradigm of dosing used to be maximum tolerated dose.

The other clear phenomenon in obesity is manufacturing and market access. Given the extreme price sensitivity of the market, innovative commercial strategies, including DTC platforms and telehealth partnerships, have begun to capture patients and bypass traditional access barriers.

AI and analytics are accelerating protocol development, but internal governance and review cycles don’t always keep pace. How big a bottleneck is that, and what are companies doing about it?

This is a big topic, and the short answer is that the speed of AI has fundamentally outpaced the governance models the industry, and regulators, were built around. While AI can now draft protocols, optimize trial designs, and surface insights in days, those outputs still move through approval and compliance processes designed for a slower era, creating real bottlenecks. We’re seeing companies respond by embedding governance earlier and more continuously.

For example, large pharma companies are using automated prescreening tools to flag protocol gaps before human review and safety organizations are deploying AI to continuously monitor adverse event data rather than relying on episodic manual checks. Others have stood up dedicated AI governance councils to streamline decision-making without compromising rigor. Importantly, the FDA itself is evolving in parallel, signaling greater openness to adaptive designs, real-world data, and more dynamic review models, but alignment across industry and regulators is still a work in progress.

Looking ahead, what does a well-designed clinical program built for long-term commercial success look like in 2026?

While I’m not a clinical trialist, I’ve had the privilege of supporting many clinical development programs, and I do not want to speak in too much generality since most companies will know that a well‑designed clinical program looks less like a regulatory checklist and more like a commercial blueprint. However, the specifics vary meaningfully by therapeutic area.

In precision oncology, for example, success increasingly depends on clearly defining the right biomarker‑selected population up front, proving strong single‑agent activity, and demonstrating combinability with standard of care early. Programs that win are explicit about where they fit in treatment sequencing, design for overall survival, not just progression metrics, and generate tolerability and patient‑reported data that matter in real‑world practice.

About The Expert:

Rena Rosenberg is a managing director with Alvarez & Marsal Life Sciences in New York. Ms. Rosenberg’s expertise encompasses corporate strategy, clinical development, go-to-market, and growth and innovation. Her experience ranges from early-stage to Fortune 500 companies, including work in biopharmaceuticals, digital health, medtech, and generics manufacturers. She has developed brand strategies and marketing plans for 10+ multibillion-dollar products in retail and hospital environments.

Prior to joining A&M, Ms. Rosenberg was an operator and investor with hands-on leadership in early-stage ventures across healthcare and higher education. Previously, she was a partner at McKinsey & Company in the pharmaceuticals and medical devices practice and cofounded the firm’s digital healthcare practice.

Ms. Rosenberg earned a bachelor’s degree in economics from Columbia University, where she currently serves as an adjunct faculty member in economics, and a Ph.D. in economics from the University of Chicago. She is also an angel investor in early-stage medtech, biotech, and digital health ventures.