Life Sciences · Market Intelligence
Drug Development Is Not a Pipeline. It Is a Loop.
For sixty years, drug R&D got scientifically better and economically worse at the same time — a documented, quantified contradiction with a name. Part of the diagnosis points directly at treating development as a straight line instead of a connected loop.
The Real, Documented Problem
Jack Scannell and colleagues formalized this contradiction in a 2012 Nature Reviews Drug Discovery paper that coined the term "Eroom's Law" — Moore's Law spelled backward. Between 1950 and 2010, the number of new drugs approved by the FDA per billion inflation-adjusted dollars of R&D spending fell roughly 80-fold, halving on a consistent nine-year cycle, even as the scientific tools available — genomics, high-throughput screening, computational chemistry — became dramatically cheaper and more powerful over the same period.
The efficiency didn't collapse because the science got worse. It collapsed while the science was demonstrably getting better — which is exactly the pattern you'd expect if the problem were structural, not scientific.
The Decline, By the Numbers
Scannell's paper identifies four primary causes behind the decline, one of which is directly relevant here: what the authors call a basic-research-versus-brute-force bias — a persistent tendency in the industry to treat fundamental biological research and downstream drug screening as separate, sequential activities rather than a connected system feeding back into itself.
A Fair Complication
Eroom's Law isn't a permanent law of nature — a 2020 Nature Reviews Drug Discovery follow-up documented a real uptick in drug development productivity in the years after the original paper, suggesting the decades-long trend had at least partially broken. The improvement came with a caveat worth including rather than ignoring: much of the gain reflects diseases being segmented more precisely by genetics, so more drugs are being approved for narrower patient populations — a real productivity gain by one measure, and a more fragmented commercial picture by another.
What Vaxa Calls the Alternative
The conventional way drug development gets described — discovery, then preclinical, then Phase I through III trials, then approval, then commercial launch — is accurate about the order things happen in and misleading about where value and insight are actually created. Treating each stage as a discrete handoff to the next one is a structural version of exactly the basic-research-versus-brute-force bias Scannell's research names as a real contributor to the productivity decline: insight generated at one stage stays siloed there instead of feeding back into the stages before it.
Treating the same six stages as a connected system instead of a line is what Vaxa calls the Development Loop — the same six points in the cycle, but each one explicitly informing the others rather than only passing forward:
The loop closes at step six specifically because that's the point most conventional pipelines treat as an endpoint instead of a feedback input. What a program learns after launch — which patients actually responded, which comparative claims held up commercially — is exactly the kind of insight step one needs for the next program, and in a linear model it usually never gets there.
01
Patient & Market Selection
Which patient populations represent addressable markets with a viable reimbursement pathway.
02
Target Competitive Intelligence
Which targets competitors are pursuing, and which have failed for non-biological reasons.
03
Indication Sequencing
The fastest path to proof of concept that de-risks the broadest range of future indications.
04
Trial Design for Market Access
Designing trials to generate the comparative evidence payers require, not just regulatory approval.
05
Launch Channel Architecture
Channel and partnership decisions at launch that determine commercial trajectory.
06
Real-World Evidence Capture
Post-approval data capture that feeds directly back into Patient & Market Selection for the next program.
Who This Actually Touches
Every one of the six points above is a commercial, strategic, or market-access decision that happens to sit inside a life sciences program — the technology and market intelligence layer that runs alongside the science, not the science itself. Vaxa has worked directly with drug companies on exactly this layer, including the technology side of clinical trials, such as wearable data capture.
Two groups get real value from that lens: life sciences companies whose commercial, market access, or strategy functions need an outside read on these six decisions specifically — and technology companies evaluating whether their own capabilities have a genuine application inside a life sciences program, the way any capability might apply to an adjacent industry.
A Number Doesn't Tell You Which Loop You're In
Eroom's Law and its four diagnosed causes are public, peer-reviewed research — any team can read Scannell's paper directly. What the research doesn't do is tell a specific organization which of its own six stages are actually feeding back into each other today and which have quietly become one-way handoffs. Strategic Innovation — Vaxa's proprietary methodology, referenced across engagements including Nokia, Intel, DuPont, and P&G — is what maps a specific program's real information flow against the six-point loop and identifies exactly where the feedback has broken down.
The Actual Question
At which of the six points does your organization actually operate — and is real-world evidence from a completed program feeding back into the next one's patient selection, or does the loop stop being a loop the moment a drug reaches market?
Our Work in Life Sciences.
Related Material
Scannell, J.W., Blanckley, A., Boldon, H. & Warrington, B., "Diagnosing the Decline in Pharmaceutical R&D Efficiency," Nature Reviews Drug Discovery, 11, 191–200 (2012) — origin of Eroom's Law. ·
Nature Reviews Drug Discovery, 2020 follow-up on the partial reversal of the Eroom's Law trend.