Out of Patients EP437: First in (Wo)Man: Jessica J. Federer

The question that should make any rational operator pause is simple. If better data across more people produces better products and larger markets, why did modern drug development proceed for decades without studying half the population?

I brought Jessica Federer on the show because she has lived inside that contradiction. She ran digital for Bayer, a 120,000 person global pharmaceutical company, and she now sits on a Yale Institutional Review Board. She understands how decisions get made when capital, regulation, and timelines collide. She also understands how those decisions compound into blind spots that look obvious only after the fact.

Her story anchors this analysis in something real. Women were excluded from U.S. clinical trials until 1993. That decision did not emerge from malice. It emerged from a system that defined risk narrowly after the thalidomide crisis and then optimized around that definition for decades.  The system chose cleaner data sets and faster timelines. It accepted incomplete representation as a tradeoff. That tradeoff became embedded in protocols, supply chains, and statistical models.

The consequences now show up in outcomes that the system struggles to reconcile. Women experience adverse drug reactions at roughly 2x the rate of men across many therapies.  Preclinical work often relies on male cells and male animals, not because biology demands it, but because demand signals shaped procurement and cost. Laboratories report that female mice cost more because they are ordered less frequently.  When inputs skew, outputs follow.

The system behaves rationally within its incentives. Drug development operates under pressure from patent clocks, investor expectations, and regulatory timelines. Each additional variable increases complexity and risk. If regulators do not enforce sex based stratification and if reimbursement does not reward it, sponsors will not absorb that cost consistently. The result appears as bias. The mechanism is optimization.

Jessica explains what that looks like from inside. First in man studies still define early human testing.  Products reach market with incomplete understanding of how they perform across populations. Real world data then surfaces differences that trials did not capture, which leads to additional studies, label changes, and dosage adjustments.  Patients absorb risk first. Payers absorb cost next. Sponsors absorb reputational damage later.

Capital allocation reinforces the pattern. Dedicated women’s health funds remain small relative to the scale of global pharmaceutical enterprises. A $40,000,000 fund exists alongside organizations where that amount approximates an internal budget line.  Investors respond to perceived risk. When data gaps obscure outcomes, capital hesitates. That hesitation slows the evidence generation that would reduce risk. The loop sustains itself.

Policy has begun to respond, but late. In December 2025, the Food and Drug Administration issued guidance on designing studies to detect sex based differences.  That step signals recognition, not resolution. Guidance without aligned incentives will not shift behavior at scale.

The system also misprices participation. Clinical trial participants contribute time, biological samples, and exposure to uncertainty. In the United States, stipends above roughly $3,000 can be taxed as income.  That policy treats participation as labor rather than public contribution. It reduces access and slows enrollment. It increases cost for sponsors and delays evidence generation for everyone.

None of this requires a moral indictment to understand. The system produces these outcomes because incentives reward them. Change requires shifting those incentives upstream. Regulators can require stratification with enforcement that affects approval timelines. Payers can reward therapies that demonstrate efficacy across populations. Investors can fund platforms that generate representative data earlier in development. Policymakers can remove taxation on participation and treat it as a civic contribution.

Those changes align patient protection with economic efficiency. Better designed trials reduce post market corrections. Broader efficacy expands market size. Faster enrollment reduces cost. The system gains from the same adjustments that protect patients.

Jessica Federer operates at that intersection. She understands the internal logic that produced the current state. She also sees where the leverage sits to change it. Her work now focuses on bringing capital, science, and governance into alignment around women’s health as a category that reflects both unmet need and economic opportunity.

If you want to hear how these incentives play out inside the organizations that build and fund medicine, listen to my conversation with Jessica Federer. She explains the mechanics without abstraction and without theater.