The pharmaceutical industry has a structural problem it rarely states plainly: its entire financing model is built around drugs that do not work too well.

This is not a polemic. It is arithmetic. Venture capital, public equity markets, licensing deals, and royalty finance all depend on recurring revenue. A drug that patients take for years generates an annuity. An annuity can be valued, financed, and sold. The present value of a 20-year biologic prescription is calculable. You can build a company around it.

A drug that cures a disease in a single dose generates one payment. That payment has to recover R&D costs averaging $2 billion per approved asset,¹ cover manufacturing and launch, and yield a return that justifies the original capital. All at once. For a patient population that, in many of the disease areas where curative biology is now most advanced — rare pediatric conditions, autoimmune diseases, inherited metabolic disorders — may number in the hundreds per year.

This is why the most scientifically exciting programs in biopharma are often the least well-financed. The 2024 Deloitte analysis of drugs and biologics launched since 2022 puts average return on R&D investment at 2.5% — one-third of the figure recorded a decade prior.² That decline is concentrated precisely in the categories with the most clinical promise. The misalignment is not incidental. It is intrinsic to how the industry raises and deploys capital.

This piece reviews four financing models that have emerged to address this misalignment, assesses where each falls short, and examines a recently proposed structured finance instrument — cure-backed securities (CBS) — that attempts a synthesis.

The commercial case failure problem

Before reviewing solutions, it is worth being precise about the problem.

Commercial case failure in drug development is not the same as scientific failure. A molecule that clears autoimmune disease in a single infusion has not failed scientifically. It has failed commercially — meaning the projected revenue, discounted across the expected market size and competitive landscape, does not justify the investment required to complete development and launch.

This distinction matters because the remedies are different. Scientific failure requires better biology. Commercial case failure is, in principle, a financing design problem — one that finance has tools to address.

The commercial case problem is worst in three overlapping settings. In orphan diseases, patient populations are small by definition, which caps revenue regardless of pricing. In curative autoimmune therapies — CAR-T for lupus and systemic sclerosis being the most visible current examples — the biology is now showing durable remission in Phase 1 data, but the commercial architecture for one-time curative payment is absent. And in pediatric disease more broadly, pool sizes are small, development timelines are long, and regulatory requirements are appropriately demanding.

In each of these settings, standard venture financing logic does not apply. A Series B biotech investor expects a multiple on invested capital at exit — typically through acquisition or IPO, both of which price a company on projected peak sales. A curative therapy for 400 patients a year, priced at $1.5 million per patient, generates $600 million in peak annual sales. That is not a trivial revenue figure. But it is also not the kind of recurring, predictable stream that commands a large acquisition premium or supports a durable public equity story. The upfront capital required to run a Phase 3 trial in this setting — $300–600 million in many cases — often cannot be recovered under conventional assumptions.

The result is a selection effect: programs in these disease areas are deprioritized, delayed, or abandoned not because the science does not work, but because the financing does not.

Four models and their limits

1. Royalty monetization

Pharmaceutical royalty finance is the oldest structural workaround. Under the standard transaction, a drug developer sells a portion of the future revenue stream from a licensed asset — typically expressed as a percentage of net sales — to a specialized capital provider in exchange for upfront cash. The developer gets capital immediately. The royalty investor takes the revenue risk.

Royalty Pharma, the largest player in this market, has deployed this model at scale across academic institutions, biotechs, and large pharmaceutical companies. DRI Healthcare, over three decades, has acquired 77 royalties on 50 drugs, deploying over $3 billion.³ The Gibson Dunn Royalty Finance Report for 2020–2024 documents sustained expansion of the market, with transaction volume increasing across both traditional royalties (arising from pre-existing license agreements) and synthetic royalties (created specifically for the financing transaction).⁴

The limitation of royalty finance in the curative therapy context is structural. A royalty investor prices a transaction off projected revenue. A curative therapy treating 400 patients a year at $1.5 million per patient generates $600 million in gross annual revenue — but that revenue lasts only as long as the untreated patient pool. Once prevalent cases are treated, incidence-based demand may be 50 or 100 new patients per year. The royalty stream collapses. A royalty buyer modeling a conventional multiple on peak sales gets the arithmetic badly wrong.

Royalty finance also provides capital primarily after proof-of-concept data exist — it is predominantly a late-clinical or post-approval instrument. It does not solve the problem of how to fund Phase 2 and Phase 3 trials in a program with an unconvincing commercial projection.

2. The megafund and research-backed obligations

In 2012, Andrew Lo and colleagues at MIT proposed a more architecturally ambitious solution: a pharmaceutical megafund that would finance a large portfolio of early-stage drug programs and issue tradeable debt instruments — research-backed obligations (RBOs) — collateralized by the portfolio’s intellectual property.⁵

The financial logic behind the megafund is portfolio theory applied to drug development. Individual drug programs have very high failure rates — approximately 95% across all clinical phases for a given indication. But these failures are largely uncorrelated. A molecule targeting lupus fibrosis does not fail because a molecule targeting ALS motor neurons fails. The failure events are biologically, mechanistically, and commercially independent. A sufficiently large portfolio will therefore exhibit predictable aggregate outcomes even if individual program outcomes are highly uncertain. The law of large numbers, applied to drug discovery, converts catastrophic individual failure risk into actuarially manageable portfolio risk.

This is the same logic that underlies insurance and, more directly, asset-backed securities. The megafund would slice that portfolio risk into tranches. Senior RBOs — paid first from the portfolio’s cash flows, carrying low credit risk — would attract institutional fixed-income investors: pension funds, insurance companies, sovereign wealth funds. These investors cannot hold early-stage biotech equity due to volatility, regulatory capital requirements, or mandate restrictions. But they can hold investment-grade debt instruments. Equity tranches, paid last and carrying higher risk, would attract conventional venture capital.

Lo’s simulation work, using historical oncology trial data from 1990 to 2011, found that megafunds of $5–15 billion could yield average equity returns of 8.9–11.4% and bond returns of 5–8% — below venture capital hurdle rates, but within range for pension and insurance investors.⁵ A subsequent paper by Fagnan and colleagues applied the same framework specifically to orphan diseases, where lower development costs and faster FDA timelines improve the math: a $575 million orphan megafund with 10–20 programs could generate double-digit expected returns.⁶

The megafund was never operationalized at scale. The reasons are instructive. Credit rating agencies had no established methodology for scoring a portfolio of preclinical drug candidates — the RBO concept depends on investment-grade ratings to attract institutional debt investors, and those ratings require standardized models that did not exist. Investor education was substantial: the asset class was genuinely new, and institutions with fixed-income mandates were not equipped to diligence biomedical IP. And the concentration of program selection within a single fund manager raised governance questions that had no ready answer.

The megafund’s intellectual contribution — the substitution of portfolio diversification for commercial scale — remains the most rigorous framing of the financing problem. Every serious proposal since has, in some form, borrowed from it.

3. Outcomes-based agreements

Outcomes-based agreements (OBAs) emerged on the payer side as the practical response to a specific tension: a payer asked to pay $1.8 million upfront for a gene therapy whose long-term durability rests on follow-up data spanning three to five years faces an uncomfortable gamble. If the therapy fails at year four, the payer has already paid in full for an outcome that did not materialize.

OBAs restructure this relationship. Under a typical OBA, the manufacturer and payer agree that some portion of the payment is contingent on defined clinical outcomes at defined time points. Bluebird Bio’s commercial launch of Zynteglo (betibeglogene spartacept) for transfusion-dependent beta-thalassemia included a guarantee to refund up to 80% of the therapy’s $2.8 million price if patients did not achieve and maintain transfusion independence within two years.⁷ Novartis launched Zolgensma with an optional five-year instalment plan. Hemgenix, the CSL/UniQure hemophilia B gene therapy approved at $3.5 million — the highest launch price ever recorded for a drug — prompted federal-level discussions about outcomes-based reimbursement architecture.

The most significant recent development in this space is the CMS Cell and Gene Therapy Access Model, which went live in January 2025. Under this model, two gene therapy manufacturers — Bluebird Bio (for Lyfgenia) and Vertex Pharmaceuticals (for Casgevy) — negotiated outcomes-based arrangements with CMS covering Medicaid beneficiaries with sickle cell disease. Thirty-two states, the District of Columbia, and Puerto Rico — collectively representing 84% of Medicaid beneficiaries with the condition — agreed to participate.⁸

OBAs solve a real problem: they lower payer resistance to coverage, align manufacturer revenue with therapeutic durability, and create a financial incentive to collect real-world outcomes data. But they do not address the upstream financing problem. An OBA governs the payment relationship between manufacturer and payer after the drug is approved and launched. It does not fund Phase 2 or Phase 3 trials. A company that cannot convince a venture investor to fund the program in the first place cannot use an OBA as collateral.

There is also an administrative complication that has become more visible as OBAs proliferate: tracking patient outcomes across insurer transitions. American patients change insurers frequently — at job changes, at retirement, at birth of dependents, at state relocation. A five-year outcomes obligation negotiated between a manufacturer and a commercial insurer becomes difficult to enforce if the patient moves to a different insurer in year two. The infrastructure to track, adjudicate, and transfer these obligations does not currently exist in any systematic form.

4. Venture philanthropy

The venture philanthropy model re-routes capital into drug development through disease foundations operating as investment vehicles rather than grant-making organizations. The canonical example is the relationship between the Cystic Fibrosis Foundation (CFF) and Vertex Pharmaceuticals.

Beginning in the late 1990s, the CFF committed approximately $150 million to Vertex’s modulator research program — initially funding work that the venture market considered too early and too uncertain. In return, the Foundation received royalty rights on any products that resulted. The program eventually produced ivacaftor (Kalydeco), then lumacaftor/ivacaftor (Orkambi), and finally elexacaftor/tezacaftor/ivacaftor (Trikafta) — the last of which is eligible for approximately 90% of CF patients by genotype. When the CFF sold its royalty stake to Royalty Pharma in 2014, it received $3.3 billion. A subsequent transaction in 2020 returned an additional $575 million.⁹ That capital has been deployed back into the next generation of CF research.

The CFF model has been replicated, in modified form, in other disease areas. The National Bleeding Disorders Foundation established Pathway to Cures as an explicit venture philanthropy vehicle for hemophilia. NCATS and the Foundation for the NIH operate as federal scaffolding for similar public-private structures, connecting NIH basic research funding with industry development capital. The Bespoke Gene Therapy Consortium, co-led by NIH and FDA with private partners, committed approximately $76 million over five years to rare disease gene therapy programs that would not otherwise attract commercial development capital.

Venture philanthropy has a clear limiting condition: it requires a disease community wealthy enough and organized enough to build an investment-capable foundation, with scientific infrastructure — patient registries, biobanks, natural history data — mature enough to attract initial industry partners. The CF Foundation took decades to build both. Many rare disease communities do not have this foundation. And even for diseases where it exists, the model depends on the foundation absorbing early-stage risk that venture will not — which is only viable when the foundation has sufficient capital to sustain a long development timeline.

The gap none of them closes

Each of these models addresses a different part of the financing problem. Royalty monetization provides capital, but only where adequate commercial scale exists. The megafund solves the diversification problem, but was not operationalized. OBAs align payer incentives but do not fund trials. Venture philanthropy works for a subset of well-organized disease communities.

The gap that remains — the one that explains why curative therapies for rare and autoimmune diseases are systematically underdeveloped — is this: how does a developer finance the clinical development of a therapy that will generate a single payment per patient, in a population measured in hundreds to low thousands per year, when no royalty stream is large enough to monetize, no disease foundation exists, and no OBA helps until after approval?

The conventional answer — price the drug high enough that one payment is sufficient — has produced a sequence of approvals at $1.8 million (Zolgensma), $2.8 million (Zynteglo), and $3.5 million (Hemgenix). Each launch has triggered political, payer, and public backlash that makes subsequent approvals harder. The answer is self-defeating.

Cure-backed securities: a structured finance proposal

A March 2026 paper in Gene Therapy from Lu, Cherla, Carter, and Mossialos at the London School of Economics proposes an instrument designed specifically to close this gap.¹⁰ Their argument begins with a reframing: the crisis in curative therapy financing is not primarily a pricing crisis. It is a timing crisis.

The manufacturer needs capital now — at development, at approval, at launch. The payer cannot absorb a $1.8–3.5 million charge in a single budget cycle. Both facts are simultaneously true. They are structurally incompatible unless a third party intermediates the time horizon.

Lu and colleagues propose cure-backed securities (CBS): instruments that separate when the payer pays from when the manufacturer receives payment, using the same securitization logic that underwrites 30-year fixed-rate mortgages.

The MBS analogy is precise, not decorative. A mortgage-backed security works because a homebuyer cannot pay $400,000 on day one, a bank does not want to wait 30 years for repayment, and a capital market exists that will buy the future payment stream at a discount. The homebuyer pays monthly over 30 years. The bank sells those payment rights to investors and recovers capital immediately. Investors receive a low-risk yield. The key innovation is the dissociation of who pays, when they pay, and who holds the interim risk.

CBS applies this structure to curative drugs. Under the proposed model, the payer would pay $130,000 per year for up to 30 years — but only while the patient is alive. This is a survival-contingent annuity. No survival, no payment. The manufacturer packages the future payment streams from a pool of treated patients, tranches the pool by actuarial risk, and sells senior and junior bond tranches to institutional investors. The manufacturer receives the net present value of most of that future stream upfront. Critically — and this is the structural departure from all prior models — the manufacturer retains the equity tranche.

The equity tranche is the residual claim: it pays out if the therapy performs better than actuarial expectations and absorbs losses if it performs worse. By retaining this tranche, the manufacturer has a 30-year financial stake in whether the cure holds. This is a genuine incentive alignment that no prior model achieves. OBAs create short-term outcome contingency — two to five years. CBS creates a 30-year financial bond between manufacturer and patient outcome. Patient registries cease to be regulatory overhead and become assets that protect the manufacturer’s equity position.

The authors modeled the structure using Zolgensma case data and ran 1,000 Monte Carlo simulations across base-case, sensitivity, and pessimistic survival assumptions. Senior bond tranches showed default probability below 0.1% under every scenario — investment-grade by any standard. Under pessimistic clinical assumptions, payers paid $1.27 million per patient versus $1.8 million under existing instalment structures or $1.8 million upfront. Manufacturers could front-load 50–83% of expected net present value at time of sale.¹⁰

CBS in context: what it borrows and what it adds

The CBS proposal does not emerge from nowhere. It is the convergence of the four preceding models.

From the megafund, it takes the tranching and pooling logic: diversification across a patient pool reduces the actuarial risk of any individual survival trajectory, just as diversification across drug programs reduces the portfolio failure risk in Lo’s framework. From OBAs, it takes the survival-contingent payment structure — the idea that pharmaceutical payment should track clinical outcome rather than precede it. From royalty finance, it takes the securitization mechanism: future payment rights converted into tradeable instruments sold to institutional capital. And it creates an incentive for real-world outcomes data collection that mirrors the CF Foundation’s registry infrastructure with Vertex.

What CBS adds — the equity tranche retained by the manufacturer — is the piece that prior models lacked. It converts the manufacturer from a party paid once at approval into a party with a continuing financial interest in therapeutic durability across the full patient life.

Limitations and open questions

The Lu et al. paper is careful about scope, and those limits warrant direct acknowledgment.

Orphan disease scope. The CBS model is designed for settings where generic or biosimilar entry over a 30-year horizon is unlikely. Orphan drugs with small patient populations often qualify. For large autoimmune markets — rheumatoid arthritis, psoriasis — where biosimilar entry is certain within 10 years of approval, the annuity structure breaks down. A payer obligated to pay $130,000 per year for 30 years on a therapy for which a $5,000 biosimilar is available in year 12 has been structurally disadvantaged. The CBS structure would need significant modification for non-orphan settings.

Price decoupling. CBS restructures payment timing. It does not constrain what the manufacturer charges. An instrument that spreads a $3.5 million drug over 30 years at $130,000 per year reduces annual budget shock but does not address the underlying pricing question. CBS works best when paired with price negotiation mechanisms — value-based pricing, ICER thresholds, reference pricing — rather than as a substitute for them.

Administrative infrastructure. Tracking patient survival across insurer transitions over 30 years requires infrastructure that does not exist. American patients change insurers at job change, at retirement, at Medicare transition. The payment obligation in a CBS must follow the patient — but no system currently tracks the obligation across payer transitions, adjudicates disputes, or enforces transfer. Building this infrastructure is a non-trivial policy and operational challenge.

Ratings methodology. The megafund failed partly because ratings agencies had no framework to score a portfolio of drug IP. CBS faces an analogous challenge: survival-contingent pharmaceutical annuities are a new asset class. Investment-grade ratings — which the CBS structure depends on to attract pension and insurance capital — require standardized actuarial models that do not yet exist for this instrument type. OBA precedents and the established MBS methodology provide more foundation than existed in 2012, but the gap is real.

The pipeline is coming regardless

CAR-T programs for systemic autoimmune disease — lupus, systemic sclerosis, myositis — are in Phase 1 and Phase 2 now. Several have reported complete drug-free remission in small cohorts. The early data are credible enough that the questions have shifted from “does it work?” to “how durable is it?” and “can it scale?” If any fraction of these programs reaches Phase 3 approval — and the probability is not trivial — the payment infrastructure will face precisely the problem that CBS is designed to address.

The 30-year fixed-rate mortgage did not emerge as a working instrument when the first long-term real estate loan was made. It required decades of financial engineering, regulatory scaffolding, federal mortgage agencies, and secondary market development before it operated reliably at scale. The analogy to curative therapy finance is apt. The intellectual architecture exists. The operational infrastructure does not.

What CBS does — and what the prior models collectively did — is demonstrate that the financing gap for commercially unviable but clinically essential therapies is not a market failure in the pejorative sense. It is a market design failure. The tools to fix it exist. The question is whether the regulatory, institutional, and financial engineering required to implement them can be assembled before the clinical pipeline outpaces the payment system.

References

1. DiMasi JA, Grabowski HG, Hansen RW. Innovation in the pharmaceutical industry: new estimates of R&D costs. Journal of Health Economics. 2016;47:20–33. doi:10.1016/j.jhealeco.2016.01.012

2. Deloitte. Measuring the return from pharmaceutical innovation 2024. Deloitte Centre for Health Solutions; 2024. Available at: https://www.deloitte.com/us/en/Industries/life-sciences-health-care/articles/measuring-return-from-pharmaceutical-innovation.html

3. DRI Healthcare. About DRI Healthcare. Available at: https://drihealthcare.com/about/

4. Gibson Dunn. Royalty report: royalty finance transactions in the life sciences 2020–2024. 2025. Available at: https://www.gibsondunn.com/royalty-report-royalty-finance-transactions-in-the-life-sciences-2020-2024/

5. Fernandez J-M, Stein RM, Lo AW. Commercializing biomedical research through securitization techniques. Nature Biotechnology. 2012;30(10):964–975. doi:10.1038/nbt.2374

6. Fagnan DE, Gromatzky AA, Stein RM, Fernandez J-M, Lo AW. Financing drug discovery for orphan diseases. Drug Discovery Today. 2014;19(5):533–538. doi:10.1016/j.drudis.2013.11.007

7. Bluebird Bio. Bluebird bio announces U.S. commercial infrastructure to enable patient access to ZYNTEGLO. Business Wire. August 17, 2022. Available at: https://investor.bluebirdbio.com/news-releases/news-release-details/bluebird-bio-announces-us-commercial-infrastructure-enable

8. Centers for Medicare & Medicaid Services. Cell and Gene Therapy Access Model — frequently asked questions. 2025. Available at: https://www.cms.gov/cgt-access-model-frequently-asked-questions

9. Cystic Fibrosis Foundation. Cystic Fibrosis Foundation receives $3.3 billion royalty pay out. Philanthropy News Digest. 2014. Available at: https://philanthropynewsdigest.org/news/cystic-fibrosis-foundation-receives-3.3-billion-royalty-pay-out

10. Lu JM, Cherla AJ, Carter AW, Mossialos EA. Securitization as a means to pay for cell and gene therapies for orphan diseases: a simulation study. Gene Therapy. 2026. doi:10.1038/s41434-026-00604-6

Author: Eswar Krishnan, MD Date: 2026-05-09

Eswar Krishnan is a physician and principal at Olmsted Capital LLC. He consults on clinical development strategy at Drug Development Associates.

#DrugDevelopment #RareDisease #BioPharmFinance #StructuredFinance

WSJ article [paywall]

Yes, Western pharma spent nearly $5.6 billion licensing Chinese drug candidates last year. Yes, China now represents 30% of the global experimental pipeline. But before we declare a dynasty change in biopharma, let’s ask a harder question:

Is this innovation — or sophisticated iteration?

Look closely at what Chinese biotechs are actually doing. They are extraordinarily good at:

• Scanning Western patent filings and published research within days of release

• Identifying structural wrinkles and freedom-to-operate gaps

• Running efficient, low-cost chemistry to produce “me-better” variants

• Moving rapidly through clinical stages using China’s regulatory shortcuts and patient pools

That is genuinely impressive execution. But it is not the same as discovering a new mechanism, identifying a novel target, or taking a truly first-in-class molecule from concept to clinic. Reverse engineering at scale is a business achievement — not a scientific one.

The molecular glue story in the article makes this plain: Chinese scouts analyzed a Novartis paper, published a how-to guide within four days, and biotechs went to work improving on what Western companies had already invented. That’s speed and efficiency. It is not leadership.

The domestic paradox no one is discussing.

China is producing medicines it largely cannot afford to buy. The high-cost biologics, ADCs, and GLP-1 therapies flowing out of Chinese labs are priced for Western markets — not for the 1.4 billion people at home. A nation building an export-dependent biotech sector on drugs its own population cannot access is not a model of strength. It is a structural vulnerability.

The real risk isn’t losing to China. It’s becoming dependent on it.

US biotechs licensing Chinese “me-better” compounds get a short-term pipeline win — cheaper assets, faster timelines. But over time, this pattern could hollow out early-stage domestic R&D, create supply chain dependencies, and train the industry to reach for the easy incremental gain rather than do the harder, riskier work of genuine first-in-class innovation.

The US doesn’t need to fear Chinese biotech dominance. It needs to avoid outsourcing its scientific ambition to it.

China has world-class scientists, enormous capital, and real government commitment. When it chooses to invest in true de novo discovery — not just optimizing what others have found — it will be a formidable force. Until then, calling this an impending takeover of Western biopharma is flattering to China and alarmist to everyone else.

The WSJ piece was a good story. It was not the whole story.

#DrugDiscovery #Biotech #Pharma #ChinaBiotech #Innovation #LifeSciences #BiopharmStrategy #MediumTerm

Yet for every child who has received a therapy like this, many more have not. Companies that pioneered these one-time gene therapies have struggled commercially—some withdrawing products despite documented efficacy. The science is ready. The system for approving and delivering these therapies is not.

A paper published in Nature Medicine by Abou-el-Enein and colleagues at USC, UCSF, and UCLA describes a practical approach to fix this. Funded by the Advanced Research Projects Agency for Health (ARPA-H), the UNICORN system—Unifying Cell Therapy Outcome Prediction and Regulatory Navigation—is designed for exactly the situation where conventional drug development fails: when the trial has one patient, or three, or ten.

The core problem

Drug approval rests on three pillars: manufactured product quality, evidence of efficacy, and regulatory confidence. For large trials, these support each other naturally. Hundreds of patients generate statistical power. Manufacturing at scale smooths batch variability. Regulators see enough data to make confident decisions.

Rare and ultra-rare pediatric therapies break all three pillars at once. When a product is made in small batches—sometimes for a single child—donor variability and batch-to-batch differences directly affect quality, but there aren’t enough cases to characterize how. Clinical endpoints that work in large trials either don’t exist for rare diseases or don’t occur quickly enough to guide approval decisions. And regulators, charged with ensuring safety and efficacy, face an impossible ask: apply evidentiary thresholds built for populations of thousands to studies of one.

The result is a paradox. We can design a therapy for an individual child in months. We cannot reliably tell a regulator whether that therapy will work.

What UNICORN does, practically

The system has three connected parts.

Product signatures. A spectral flow cytometry panel developed at USC captures phenotypic, metabolic, and functional features of each cell therapy product. Instead of relying on a handful of traditional potency assays, this multi-parameter platform generates a high-dimensional product “signature”—a detailed fingerprint of each batch. The panel can be adapted to different products and run consistently across sites. This matters because it creates a common, comparable language for product quality across small-batch therapies that would otherwise be evaluated in isolation.

AI-driven outcome models. Machine learning models are trained on accumulated cases to identify correlations between product signatures and clinical outcomes. As more children are treated and more data are added, the models learn which product characteristics predict therapeutic benefit—and which predict risk. The point isn’t to replace human judgment. It’s to make the limited data that exists work harder. A model trained on 30 prior cases can say something meaningful about case 31. A regulator reviewing case 31 in isolation cannot.

Regulatory decision support. The third component translates model outputs into practical tools for regulatory conversations: lot-release criteria that define what a product must look like before it proceeds to clinical use, and evidence thresholds calibrated to what is realistically achievable when patient numbers are very small. The FDA has already signaled a willingness to adapt—its guidance on individualized antisense oligonucleotide therapies exemplifies this flexibility. UNICORN gives that flexibility an operational structure.

The system is designed to get stronger with use. Longitudinal sampling—repeated measurements before and after treatment, rather than single timepoints—multiplies the informational value of each case. Each new patient, product batch, and outcome dataset refines the models. What starts as a small evidence base grows into something that can meaningfully support the next approval decision.

Challenges and real limitations

The authors are direct about what this system cannot guarantee, and they deserve credit for it.

Biology won’t always conform to modeling assumptions. No single product signature is likely to fully predict outcomes across different diseases or treatment centers. Early datasets will be sparse and biased toward conditions already being treated—meaning less common ultra-rare diseases may be underrepresented until more cases accumulate. Unmeasured confounders and center-specific practices will influence both product signatures and patient outcomes in ways no model can fully account for. Some correlations that look predictive in retrospect may not hold going forward.

These are genuine constraints, not rhetorical caution. A system trained on small datasets is making probabilistic inferences under real uncertainty. The appropriate response is to build more rigorously and to be honest about what the current evidence can and cannot support.

Why this still represents real progress

The current alternative—applying population-trial standards to n-of-1 therapies—has already failed in practice. Products with documented clinical benefit have been pulled from the market. Children who could be treated are not. The status quo has its own costs, and they are measured in lives.

UNICORN’s value is not that it eliminates uncertainty. It is that it gives regulators, manufacturers, and clinicians a principled way to work with the uncertainty that exists, rather than demanding a level of certainty that can never arrive when the patient pool is a single child. A spectral flow cytometry signature contains more information than a traditional potency assay. A model trained on prior cases informs a release decision better than intuition alone. A decision-support tool that makes prior cases visible to the next regulator closes what is now a significant gap.

Each child treated under this system contributes to the evidence base for the next one. That is how rare disease knowledge accumulates when large randomized trials are not possible—incrementally, deliberately, with each case building on the last. The system is designed to learn. That is exactly what medicine needs when the study has one patient.

Source: Abou-el-Enein M et al. “A blueprint to accelerate rare pediatric gene therapy approvals.” Nature Medicine. 2025. doi:10.1038/s41591-025-04115-6. Funded by ARPA-H.

There’s a little theatre that plays out on Substack, LinkedIn and other social media. A tidy post appears—hint: correct use of oxford commas—and almost on cue someone comments: “This sounds like AI.” The tone implies a sting operation: the grammar is too clean, the paragraphs too disciplined, the culprit surely silicon.

Since when did coherent prose become suspicious behaviour?

Somewhere along the way, “authentic” got quietly redefined as “unfiltered.”

Typos became personality. Rambling and raging turned into sincerity.

We began treating rough edges as proof of life.

Is the only honest song the one performed out of tune?

It’s charming in its way, like old cassette hiss (remember the SONY Walkman?).

But must every post sound like a live rehearsal?

A rude question: Are we defending authenticity—or defending the old inefficiencies that made us feel special?For years, first drafts were expensive in time and ego. That effort conferred a little halo: “I suffered for these sentences; kindly clap.” Now, with a single nudge, a language model can take your thought from muddy to presentable. The halo slips, and we call the polish “fake.”

But authenticity was never about the presence or absence of mistakes. It was about the presence of self. A post feels human when there’s a recognisable mind behind it: a choice made, a risk taken, a detail only you could supply. Whether the comma arrives by your hand or with a bit of algorithmic help is frankly none of the reader’s concern. They want clarity; they care who’s speaking; they don’t mind who wrote the script.

Do you think President Obama wrote all the soaring speeches he gave?

“But AI posts feel robotic,” people say. Sometimes they do. Then again, many human posts feel robotic too—parades of well-meaning platitudes.

The real offence isn’t the polish; it’s emptiness.

When a post has no friction, no specificity, no argument, it is humbug. Whether typed by a monk with a quill or a model with an NVDA Hopper GPU.

This is hardly the first time presentation tools made us anxious about “realness.” We fretted at print, then telephones, then radio, then the web, and yet somehow the human voice kept smuggling itself through.

A small confession: we are sentimental about creative suffering. We love the myth of the writer battling the page like a wrestler in a dusty akhada. It flatters the rest of us when our own drafts look scruffy; at least we are “real.”

So what is the bottomline? AI is here to stay. DEAL WITH IT !.

If you’re still uneasy, try this framing: AI doesn’t diminish writing; it exposes it.

AI writing does all of us a great service by leveling the playing field. By removing the role of writing aesthetics, it allows concepts to be judged for their truth.

Food to be judged by smell, taste, and nutritional value- not by visual presentation

It brings the idea to the front and asks, politely but firmly, “Is there anything here?” If the answer is no, the gloss won’t rescue it. If the answer is yes, the gloss helps it land. That is uncomfortable for anyone who relies on visible effort as a substitute for visible thought. It is also, modestly put, progress.

If you need a rule of thumb, make it this: Soul belongs to the writer; shine can come from anywhere. If you bring only shine, the post glides across the feed and vanishes. If you bring only soul and refuse any shine, the post may trip over its own shoelaces before it reaches a reader. But if you bring a real idea and allow a little polish to help it arrive on time, full marks. The reader is served, the conversation moves, and the platform becomes slightly less of a fish market.

A final, mildly naughty question to leave hanging in the air: if you can have an authentic thought and a clear sentence, why insist they travel in separate compartments? The muse does not award extra credit for split ends. She cares that you brought something only you could bring.

So yes—boo the emptiness. Applaud the clarity. And if the clarity arrives with a touch of robot polish, please adjust. The human inside the words is still the one wearing the shirt.

Note: I have no conflicts of interest with Nektar

Autoimmune diseases, pathologically characterized by the immune system's aberrant recognition and assault upon autologous tissues, represent intricate and multifactorial disorders. These conditions frequently arise from a dysfunctional interplay or compromised efficacy of intrinsic immunoregulatory mechanisms, often conceptualized as "immune checkpoints" or "brakes," thereby permitting an unconstrained escalation of immune activation. Rezpegaldesleukin (Rezpeg), a meticulously designed and strategically engineered cytokine analog, introduces a novel therapeutic paradigm. Its overarching objective is the precise re-establishment of immunological homeostasis, fundamentally diverging from conventional, broadly immunosuppressive intervention strategies. Rezpeg is specifically calibrated to engage and potentiate these pivotal regulatory mechanisms.

Molecular Design: Targeting Immunoregulatory T-Cell Subsets

The foundational principle underpinn…

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The concern stems from mercury, a known neurotoxin. Thiomersal, used in some vaccines as a preservative, contains ethylmercury. This is a critical distinction from methylmercury. Methylmercury is the form found in contaminated fish and is known to cause severe neurological damage.

PK- Toxikinetics is the main reason why mercury in vaccines is considered alarming

• 🧪 Different Mercury Forms: Ethylmercury is fundamentally different from methylmercury in how the body handles it.

• ⚡ Rapid Elimination: Ethylmercury is quickly metabolized and eliminated from the body. It does not accumulate in tissues to the same extent as methylmercury.The half-life of ethylmercury is typically around 3-7 days. In contrast, methylmercury has a significantly longer half-life in humans, often ranging from 40 to 60 days.

• 🚽 Efficient Excretion: It's primarily excreted through feces.

• 📉 Short Half-Life: The half-life of ethylmercury in humans is short, typically 3-7 days. This rapid clearance minimizes potential toxicity.

Several human studies have found no adverse effects linked to thiomersal in vaccines:

• 🧪A 2004 study published in Pediatrics by Thompson et al. found no association between thimerosal-containing vaccines and neurodevelopmental disorders.

• ⚡The Institute of Medicine (IOM) in 2004 concluded that there is no evidence to support a causal relationship between thimerosal-containing vaccines and autism.

• 🚽Multiple studies from countries like Denmark, Sweden, and the UK, which either removed thiomersal from vaccines or never used it extensively, have also shown no change in autism rates.

The Bottomline

Despite the short half-life, a few days is still notable. If it takes five half-lives for a drug to be largely eliminated, then even a 3-day half-life means mercury can be present in the body for about 15 days, resulting in a significant "area under the curve" of exposure. This raises questions for some about potential impacts on rapidly growing tissues, which may induce mutations or epigenetic changes.

What's next?

Ultimately, there's no such thing as a "fully safe" level of mercury exposure.

It's always about weighing the risk-benefit ratio. For vaccines, the overwhelming public health benefits in preventing infectious diseases typically outweigh the theoretical risks associated with thiomersal. This is not a one-size-fits-all recommendation. It remains a crucial decision to be made collaboratively between a doctor and their patient, taking into account individual circumstances.

The Maverick Physician: A Personal Crusade

Dr. Robert Atkins wasn't some detached academic. He was a physician, a cardiologist, who, like so many of us, grappled with his own weight. This personal struggle became the crucible for his professional passion. He delved into existing research, notably influenced by Alfred W. Pennington's low-carbohydrate work from the World War II era. This wasn't a completely new idea. In 1959, he opened his private practice in New York City, specializing in both cardiology and complementary medicine. He was looking for answers.

His ideas truly took flight with the publication of Dr. Atkins' Diet Revolution in 1972. This book, and its 1992 reissue, Dr. Atkins' New Diet Revolution, became an absolute sensation. It sold an astonishing 12 million copies. Think about that for a moment: 12 million copies! It cemented its place as one of the bestselling diet books in history. It was even one of the top 50 best-selling books across all genres. By the early 2000s, an estimated 1 in 11 North American adults claimed to be following its principles. This wasn't just a diet; it was a cultural phenomenon.

Atkins' core premise was disarmingly simple, yet radical for its time: drastically cut carbohydrates, and your body would shift into a fat-burning mode, leading to weight loss. He even posited a "metabolic advantage." He claimed that "burning fat takes more calories so you expend more calories." This, he argued, offered "a high calorie way to stay thin forever." This meant loading up on meat, cheese, eggs, butter, and mayonnaise. It meant saying a firm no to bread, pasta, cereals, and most fruits and starchy vegetables. It was a complete reversal of the low-fat dogma of the era.

From Fad to Franchise: Bypassing the Establishment

So, how did a diet that flew in the face of conventional wisdom become so incredibly popular? Dr. Atkins was a master of direct appeal. When the mainstream medical community largely rejected his unconventional ideas, he simply bypassed them. He went straight to the public. This wasn't just a marketing tactic. It was a deliberate choice that resonated deeply with his followers. They saw him as a "courageous David standing up to the establishment Goliath." This perception, fueled by his bestselling books, allowed his message to bypass traditional gatekeepers. It reached millions.

He didn't stop at books. In 1989, Atkins founded Atkins Nutritionals, Inc. This wasn't just about selling books. It was about building an empire. The company marketed and sold Atkins-branded low-carb foods and supplements. Crucially, it also generated revenue for further research into his nutritional protocols. This commercial arm, coupled with extensive media coverage and late

r celebrity endorsements from figures like Kim Kardashian, Rob Lowe, and Wanda Sykes, propelled the Atkins brand into the stratosphere. It was a smart business move.

However, this direct-to-consumer approach came with a significant Achilles' heel: Atkins largely failed to publish rigorous clinical data on his patients in peer-reviewed journals. This absence of scientific validation became a significant point of contention. It led to accusations of making "unsupported statements about health." This lack of traditional scientific rigor, combined with his commercial success, only deepened the chasm between Atkins and the medical establishment. The stage was set for a monumental clash.

The Storm of Scorn: "Unbalanced, Unsound, Unsafe"

The medical establishment's reaction to the Atkins diet was swift, severe, and almost universally condemnatory. It was consistently "dismissed as a faddist or worse by most mainstream experts." It was labeled a "low-carbohydrate fad diet." The language was harsh

.

The criticisms were manifold and deeply felt:

• High Saturated Fat: This was a primary concern. The diet's exceptionally high saturated fat content (often 60-68% of calories, with around 26% from saturated fats) was seen as a direct threat to heart health. It was widely believed to increase the risk of heart disease. Critics worried about increased free radical production and oxidative stress.

• Nutritional Imbalance: Critics argued it was "unbalanced" and "seriously deficient" in essential nutrients, fiber, fruits, and whole grains. Warnings of long-term malnutrition were common. It seemed to defy all conventional wisdom about a balanced diet.

• Lack of Data: Atkins' failure to publish clinical data was a fundamental issue. This led to accusations that his theories relied on "poorly controlled, non-peer-reviewed studies, anecdotes and non-science rhetoric." For scientists, this was a cardinal sin.

• "Metabolic Advantage" Debunked: His central claim that burning fat required more calories was largely refuted. Review studies concluded dieters simply consumed fewer calories overall, not that their bodies burned fat more efficiently. This undermined a key selling point.

• Long-Term Safety Concerns: Dire warnings were issued about potential risks for osteoporosis, colon cancer, kidney damage, gout, and adverse changes to the gut microbiome. These were not minor concerns.

• Short-Term Symptoms: Common side effects like headache, dizziness, fatigue, weakness, constipation, and irritability were attributed to the initial phase of mild ketosis. These made the diet difficult to stick to.

Major medical journals and professional societies did not mince words. The highly respected Medical Letter on Drugs and Therapeutics concluded the Atkins Diet was "unbalanced, unsound and unsafe." Twenty-seven years later, they noted its safety had still "not been established." A Medical Times review called it "ridiculously unbalanced and unsound" and "hazardous."

The American Dietetic Association (ADA) famously labeled it "a nightmare of a diet." Their official spokesperson elaborated, criticizing "any eating regimen that encourages gorging on bacon, cream and butter while shunning apples, all vegetables, and whole grains." It was a stark contrast to their recommendations.

Former U.S. Surgeon General C. Everett Koop publicly stated it was "unhealthy and can be dangerous." This was a powerful voice against the diet.

The American Medical Association (AMA) formally condemned the diet. The Chair of their Council on Food and Nutrition testified that it "poses a serious threat to health." The AMA also expressed "deep concern about 'any diet that advocates an 'unlimited' intake of saturated fats and cholesterol-rich foods'." They even asserted that "the greatest danger of the Atkins Diet... lies in the heart." The AMA and the American Heart Association even initiated a million-dollar class action lawsuit against Atkins and his publisher to recover medical expenses incurred due to the diet's purported side effects. This was a serious legal challenge.

The American Heart Association (AHA) issued a "strong recommendation" against high-protein weight loss plans like Atkins. They cited a "lack of credible scientific evidence" for long-term weight loss and a "possibility of increased risk" for individuals with diabetes and heart disease. Sachiko St. Jeor, an AHA committee member, stated, "You might get short-term effects... But the health benefits are not demonstrated over the long term." The AHA warned of "compromised vitamin and mineral intake, as well as potential cardiac, renal [kidney], bone, and liver abnormalities overall." They explicitly declared low-carbohydrate diets to be "bad for your health."

The American Institute for Cancer Research (AICR) officially condemned diets high in "animal grease." They concluded that "a low carb diet can be a high-risk option when it comes to health." They issued a stark warning about ketosis: "Those are the short-term effects. The long-term effects are even more dire."

The American Kidney Fund (AKF) expressed significant concern. Paul W. Crawford, M.D., stated, "We have long suspected that high-protein weight loss diets could have a negative impact on the kidneys, and now we have research to support our suspicions." He worried about irreversible "scarring in the kidneys." He concluded, "This research shows that even in healthy athletes, kidney function was impacted and that ought to send a message to anyone who is on a high-protein weight loss diet."

In the UK, a collective of prominent medical organizations, including the British Government's Medical Research Council, the British Nutrition Foundation, and the British Dietetic Association, collectively condemned the Atkins Diet as "negligent," "nonsense and pseudo-science," and posing a "massive health risk."

The Cleveland Clinic Journal of Medicine published an article asserting that the Atkins Diet "can jeopardize health in a variety of ways." It was "deficient in nutrients that cannot be replaced by supplements and are excessive in nutrients that may increase the risk of mortality and chronic disease."

The Harvard School of Public Health also weighed in. Its Director of Nutrition advised physicians to distribute handouts warning about the diet's adverse effects. Walter Willett, then Chair of Harvard's nutrition department, explicitly stated, "I certainly don't recommend it," citing "heart disease and cancer" as his reasons. The Harvard Health Letter concisely declared, the Atkins Diet "is not a healthy way to eat."

This unified, aggressive condemnation wasn't just scientific disagreement. It was a public health battle. The medical establishment, in its role as guardian of public well-being, felt compelled to protect people from what it perceived as dangerous and unproven claims.

The Science Evolves: Not an Apology, But a Re-evaluation

So, after all that, did these major medical organizations issue a grand apology? It did better. It embraced Atkins approach.

The scientific understanding of low-carbohydrate diets has undergone a significant transformation since those early, fiery condemnations. This shift has been driven by new, more robust research, particularly in the post-2000s era.

Consider the Duke Randomized Controlled Trial, presented in 2002. It showed surprising results. Patients on a high-fat, low-carbohydrate diet not only lost weight but also saw a reduction in their lipid levels. This challenged many prevailing skeptical views. Then, in 2003, two studies in the prestigious New England Journal of Medicine suggested that individuals following low-carbohydrate, high-fat diets could indeed achieve greater weight loss than those on conventional low-fat diets.

A 2007 Stanford University study further contributed. It found that women on the Atkins diet showed greater reductions in BMI, triglycerides, and blood pressure. They also had a more significant increase in beneficial HDL cholesterol. Christopher Gardner, the lead researcher, acknowledged the prior skepticism but highlighted the study's real-world design. Even the Journal of the American Medical Association (JAMA) published studies indicating that the Atkins diet modestly reduced body weight and several cardiac risk factors over a year.

Crucially, the scientific understanding of saturated fat's impact has also become a subject of ongoing debate. While some studies still suggest that replacing saturated fat with polyunsaturated fats can reduce cardiovascular events, other reviews indicate no direct association between lowering saturated fat intake and a reduced risk of developing or dying from cardiovascular disease. This is a big shift.

This evolving perspective is reflected in the American Heart Association's (AHA) adjusted stance. The AHA no longer endorses a specific diet for weight loss. Instead, it recommends individualized plans. Rachel Johnson, former chair of the AHA's Nutrition Committee, articulated this change: "We're not saying anymore that a low-fat diet is the answer. We recommend moderate fat with a focus on healthy fats and your choices around carbohydrates need to be focused." The AHA now emphasizes limiting added sugars and refined carbohydrates. They acknowledge that "The science has evolved."

Most recently, a forum involving leading nutrition and health researchers achieved "unanimous scientific agreement" across more than 15 areas concerning lower carbohydrate eating patterns. This is a huge milestone. They even established a standardized definition for a low-carbohydrate diet: 50-129 grams of carbohydrates per day (approximately 10-26% of daily calories). This is a substantial departure from the traditional dietary reference intake of 56-65%.

Experts now generally agree that "Low-carb diets are safe for the general public." However, initial medical supervision may be advisable for individuals with complex medical conditions or those on certain medications. These diets are consistently shown to "significantly improve insulin resistance and other risk markers for cardiovascular disease." Well-planned low-carb diets are recognized as capable of providing adequate nutrition and supporting high-quality eating patterns. Jeff Volek, a lead author in this area, notes there is now a "critical mass of scientific evidence" supporting the benefits of low-carb diets "beyond disease management."

The Enduring Legacy: A Complex Truth

Despite the profound controversies and the eventual bankruptcy of Atkins Nutritionals in 2005, the Atkins diet holds a significant place in dietary history. It undeniably introduced the low-carbohydrate trend to a mass audience. Its influence even shifted consumer behavior. Over 80% of shoppers check nutrition labels. 40% specifically look for low carbohydrate content. Today, the fundamental low-carb approach continues to be utilized for weight management, blood sugar control, and fostering healthy eating habits.

The Atkins story is a compelling case study. It illuminates the intricate dynamics between popular health movements, commercial enterprises, and the rigorous scrutiny of the scientific community. It vividly demonstrates how a dietary approach can achieve immense popular and commercial success even while facing strong scientific opposition. Crucially, it also shows how scientific understanding can evolve over time, leading to a re-evaluation of concepts initially dismissed.

Perhaps the most compelling lesson from the Atkins experience lies in its paradoxical legacy. A diet initially and widely scorned as a dangerous "fad" and "pseudo-science" ultimately paved the way for a broader scientific re-evaluation. Subsequent rigorous research, some even supported by the Atkins Foundation itself, significantly contributed to a consensus supporting well-formulated low-carbohydrate diets as safe and effective for various health outcomes.

This profound paradox indicates that even highly controversial, unproven popular health ideas can sometimes contain a foundational concept that, through subsequent scientific inquiry and refinement, can be validated and integrated into evidence-based practice. Dr. Atkins, in his unwavering conviction and willingness to challenge the status quo, was, in a sense, ahead of his time. While his initial methods lacked the rigorous scientific backing that would later emerge, his core insight into the role of carbohydrates in metabolism proved prescient.

His story is a powerful reminder that scientific truth is not a static entity. It is a continuous process of inquiry, re-evaluation, and adaptation based on accumulating evidence. It highlights the critical importance of ongoing, rigorous research. It also underscores the willingness of the scientific community to adapt its recommendations based on robust data, even if it means revisiting previously held strong positions.

However, it also serves as a cautionary tale about the medical establishment's arrogance. Their wholesale dismissal of Atkins, rather than an open-minded investigation, arguably contributed to the very distrust in medical institutions we see today.

When the public perceives that established authorities are unwilling to consider new ideas, even if unconventional, they are acting like the clergy in the Middle Ages.

A more nuanced, less condemnatory approach from the outset might have fostered a healthier dialogue. It might have accelerated the scientific understanding of low-carb diets. It might have built more bridges, rather than walls, between the public and the experts.

If African Americans are supposed to be deeply suspicious and distrustful NIH funded clinical research due to the dark legacy of racism, why do they not have any hesitancy in enrollment in US military ? After all, patriotism, like volunteeering for clincial research, is based on the core value/attitude of altruism.

The Military Success Story:

• Look at the U.S. Armed Forces. African Americans are actually overrepresented there. Their 14% share of the U.S. population jumps to 18% of the active-duty military force. In the Army, it's even higher: 21%.

• This wasn't accidental. It's a story of intentional policy, like President Truman's Executive Order 9981 in 1948. It's about deep community presence. Recruiters are in schools. They're at job fairs. They offer clear career paths and real benefits. It works.

The Clinical Research Conundrum:

• Now, shift your gaze to clinical trials where African Americans are critically underrepresented. Despite being 14% of the population, a 2020 FDA report reveals they make up only 8% of all research participants.

• This gap widens in specific areas. In oncology trials (2010-2021), Black patients were just 8.5% of participants. Prostate cancer hits Black men disproportionately hard. Yet, in trials leading to FDA approval, their participation-to-prevalence ratio was a meager 0.18.

• The issue extends beyond oncology. Consider Systemic Lupus Erythematosus (SLE) which disproportionately affects African American women, both in frequency and severity. Yet, enrollment in immunology and inflammation trials for conditions like SLE frequently fails to reflect this demographic reality, hindering our ability to truly understand and treat these complex diseases across all affected populations.

What Can We Learn?

This isn't just about statistics. It highlights fundamental flaws in our approach. It's about how we build trust. It's about systemic barriers.

• Are the clear pathways and tangible benefits of military service—stable employment, healthcare, education—the missing pieces in research?

• Is the clinical research enterprise truly failing to acknowledge its past? Are we not addressing current inequities, letting perceived risks outweigh potential personal benefits for a community with valid grievances?

• Are we simply not showing up where it counts? Are we missing the genuine understanding and resources needed to foster authentic participation?

We defend our nation together. Now, we must advance medical science together. This isn't just a goal; it's a profound necessity for true health equity.

This long-term goal — precision prevention — can end cancer as we know it by preventing suffering and death for those at risk and by helping those not at risk avoid unnecessary tests and treatments.”

— National Cancer Institute“

The Cancer Prevention Trial Impasse

The gold-standard randomized controlled trial (RCT), the model for therapeutic drug approval, is fundamentally misaligned with the realities of prevention research. Prevention trials are defined by their massive scale and complexity.

For example, the Breast Cancer Prevention Trial (NSABP P-1) enrolled over 13,000 women, and its successor, the STAR trial, enrolled nearly 20,000. The Prostate Cancer Prevention Trial (PCPT) involved over 8,600 men for a period of seven years. These trials are massive and long because the cancer incidence rate in a healthy population is very low. To achieve the statistical power needed for definitive proof of benefit, investigators must enroll huge numbers of participants for many years.

This statistical requirement leads to practical failures. The GOG-0199 ovarian cancer trial required 63 cancer events but accrued only 12, making its objective impossible. Accrual is often slow, especially in targeted populations (e.g., the NRG-CC008 trial for BRCA1 carriers), and long follow-up periods lead to high dropout rates, compromising data integrity.

The economic consequence is a bias against innovation. The high cost and risk of a massive, long-term trial are only justifiable for a large pharmaceutical company with a new, patent-protected molecule. This framework makes it economically unviable to test the preventative potential of vitamins, nutritional supplements, or repurposed generic drugs. The system creates an "infeasibility trap" where promising, low-cost interventions are considered untestable because the evidentiary bar is economically insurmountable.

From "Definitive Proof" to "Reasonable Benefit"

To move forward, we must redefine the evidentiary standard. The current system demands "substantial evidence of effectiveness," the standard for drug approval, which necessitates large-scale randomized controlled trials (RCTs). This creates a paradox: we apply the most stringent evidentiary standards to the lowest-risk interventions (e.g., dietary changes) but accept less rigorous evidence, such as single-arm trials, for the highest-risk scenarios (e.g., treating refractory cancer via Accelerated Approval).

The solution is modeled on the FDA's Accelerated Approval pathway, which allows access to therapies based on endpoints that are "reasonably likely to predict clinical benefit." This shows the agency's capacity for flexibility. We propose applying this principle to cancer prevention. The goal must shift from generating "definitive proof" to demonstrating a "reasonable likelihood of benefit."

This is not about lowering standards, but matching the standard to the context. In prevention, the benefit-risk calculus is different. The potential benefit of even a modest risk reduction is significant, while the intervention's risk, especially for a nutritional supplement, is often very low. Demanding the same certainty as for a cytotoxic chemotherapy agent is not appropriate. A reasonable certainty of a modest benefit is an acceptable trade-off for a very low-risk intervention.

The Methodological Path Forward: Bayesian Statistics and Single-Arm Trials

A "reasonable benefit" standard enables innovative and efficient trial methodologies. First, Bayesian statistical methods are well-suited for this framework. Instead of a simple p-value, Bayesian analysis provides a direct probability of benefit. These methods can incorporate prior evidence to reduce sample sizes and facilitate adaptive trial designs that adjust as data accumulates, making trials faster and more efficient. The FDA's Center for Clinical Trial Innovation (C3TI) is already promoting these approaches.

Second, we must use single-arm trials (SATs). While the FDA has concerns about SATs for oncology treatments, the context of prevention is different. For many prevention questions, the natural history of the disease is well-understood, making comparisons to historical or external control arms reliable. The economic infeasibility of RCTs for non-patentable agents is analogous to rare diseases, where the FDA already accepts SATs. By combining a SAT with safeguards like a well-matched external control arm and a Bayesian analytical framework, we can generate interpretable results efficiently.

A Call to Action for a New Era of Prevention

This modernized approach would create a viable regulatory pathway for testing nutritional hypotheses and repurposing drugs. Sponsors could conduct rigorous yet feasible trials to support specific cancer risk-reduction claims, replacing the weak "qualified health claims" currently used.

With new leadership, the FDA has an opportunity to act. We call on the FDA to:

1. Issue a Draft Guidance for Industry on clinical trial designs for cancer prevention, formally recognizing "reasonable benefit" as an appropriate evidentiary standard for low-risk interventions and outlining the role of Bayesian methods and single-arm trials.

2. Convene a Public Workshop with industry, academia, and patient advocates to collaboratively define the parameters of this new framework.

3. Launch a Pilot Program, similar to the Complex Innovative Designs (CID) program, to help sponsors design and execute the first generation of these innovative prevention trials.

By modernizing its approach to regulatory science, the FDA can remove barriers to progress and enable the development of new, evidence-based tools for cancer prevention.

Enserro, D. M., Gunn, H. J., Elsaid, M. I., Duan, F., & Pugh, S. L. (2025). Challenges to and considerations of designing cancer prevention trials. Journal of the National Cancer Institute Monographs, 2025(68), 49–55. https://doi.org/10.1093/jncimonographs/lgae044

As a physician who has spent decades at the bedside and an equal amount of time navigating the intricate world of drug development, I’ve seen laudable intentions curdle into counterproductive realities. One of the most pervasive and, frankly, exasperating trends is our collective obsession with quantifying everything in healthcare. From the physician's office to the pharmacy, we're awash in metrics, scores, and algorithms that promise quality, efficiency, and satisfaction. But are they delivering? Or are we, in our fervent pursuit of the measurable, inadvertently sacrificing the meaningful, not just in patient care, but in how we innovate and value new medicines? It often feels like we're meticulously polishing the brass on the Titanic.

1. The RVU Illusion: When Complexity Becomes a Calculation

Let's start with the Relative Value Unit (RVU). Conceived, perhaps innocuously, as a way to standardize physician work, it has, in many environments, morphed into a relentless driver of behavior that often feels divorced from genuine patient complexity or true quality of care.

• The Simplification Trap: How do you truly assign a "value" to the nuanced cognitive work of diagnosing a rare autoimmune condition versus a straightforward procedural intervention? RVUs attempt this, but the system can incentivize volume and throughput over the deep, time-consuming analysis that complex patients desperately need. It’s a model that, as observed in discussions about pay-for-performance (P4P) systems, risks conflicts between easily measurable outputs and actual patient needs.

• The Documentation Burden: Physicians now spend an inordinate amount of time ensuring their documentation justifies RVU assignments, sometimes at the expense of direct patient interaction or reflective practice. This isn’t just a time sink; it subtly reorients the focus from the patient's narrative to the requirements of the billing code.

• Ignoring the Unseen: The empathetic conversation that uncovers a critical social determinant of health, the meticulous review of outside records that prevents medical errors, and the collaborative call to another specialist – these vital activities often don't fit neatly into an RVU-driven framework, yet they are the bedrock of excellent care.

2. Goodhart's Ghost: The Target That Twists the Truth

The core of my "almost rant" lies in a principle articulated by the economist Charles Goodhart, who famously warned: "When a measure becomes a target, it ceases to be a good measure." This isn't just an academic concept; it's a daily, lived reality in our clinics and hospitals.

• Patient Satisfaction – A Flawed Mirror?: Take patient satisfaction scores. While no one argues against the importance of a positive patient experience, including good communication and respect, using these scores as high-stakes performance metrics can be problematic. As Michael J. Diaz, Jasmine T. Tran, and Jane M. Grant-Kels discuss in their insightful article on the ethics of pay-for-performance in dermatology, an emphasis on such scores might incentivize agreeable yet clinically suboptimal decisions. Are we subtly encouraging physicians to avoid necessary but uncomfortable conversations or treatments if they fear a negative review? Are we rewarding short-term gratification over long-term health benefits?

• "Teaching to the Test": When adherence to specific process metrics becomes paramount, the focus can shift from providing holistic care to merely checking boxes. If a clinic is heavily incentivized to, say, ensure that all diabetic patients have an annual eye exam, that’s generally a good thing. However, if it leads to hounding patients without addressing the reasons they can't get the exam (cost, transportation, or lack of understanding), the metric is met. Still, the patient's comprehensive health may not be truly improved. This echoes concerns that treatment adherence metrics can be misleading if patient nonadherence stems from systemic barriers rather than physician effort, a point also raised by Diaz, Tran, and Grant-Kels.

3. The Tyranny of Relentless Measurement

This relentless drive to quantify can lead to what Jerry Muller so aptly termed the "tyranny of metrics." The argument, also highlighted by Diaz, Tran, and Grant-Kels in their work, is that an excessive focus on measurable outcomes can overshadow and even undermine the quality of less tangible, but often more crucial, aspects of professional practice.

• Eroding Professional Autonomy and Judgment: When physicians are forced to practice "cookbook medicine" to meet narrowly defined metrics, it can stifle clinical judgment, innovation, and the ability to tailor care to the unique needs of each individual. The art of medicine, that nuanced blend of science, experience, and intuition, gets squeezed out.

• Burnout and Moral Distress: The constant pressure to meet institutional benchmarks, sometimes at odds with what a clinician feels is best for the patient, is a significant contributor to burnout and moral distress. Diaz, Tran, and Grant-Kels note that this tension can threaten traditional virtues like beneficence. It's demoralizing to feel that the system prioritizes scores over substance.

4. The Ethical Quagmire: When "Fair" Metrics Aren't Fair at All

Beyond inefficiency, these systems can create profound ethical issues and exacerbate health inequities.

• Penalizing Care for the Vulnerable: Pay-for-performance systems that don't adequately adjust for socioeconomic status or complex psychosocial factors can unfairly penalize clinicians and institutions serving the most vulnerable populations. As Diaz, Tran, and Grant-Kels discuss, this raises serious justice concerns. If metrics don't account for the systemic barriers faced by disadvantaged patients, we risk further entrenching disparities.

• Ignoring Systemic Design Flaws: It often seems easier to measure and hold individuals accountable than to address the upstream systemic issues (access, affordability, health literacy) that profoundly impact outcomes. This approach violates the spirit of fairness advocated by thinkers like John Rawls, who urged us to design systems from behind a "veil of ignorance" to ensure they are just for all, especially the least advantaged.

5. The Metric Shadow Over Drug Development is equally problematic

This metric-driven mindset doesn't stop at the clinic door; it casts a long shadow over how we develop and evaluate new medicines. As someone who has straddled both worlds, I find the parallels are striking and concerning:

• Endpoint Fixation vs. Holistic Benefit: In clinical trials, there's immense pressure to select primary endpoints that are easily quantifiable, statistically robust, and acceptable to regulators. While essential, this can sometimes lead to a focus on surrogate markers or specific parameters that may not fully capture the overall benefit to a patient's life. A drug might impressively move a biomarker, but if it doesn't translate into patients feeling tangibly better, functioning more fully, or living longer, higher-quality lives, what have we truly gained?

• The Challenge of "Real-World Value": When new drugs enter the market, their value is often assessed through health technology assessments and payer evaluations that are themselves heavily reliant on quantifiable data. If a drug's benefit lies in complex areas like reducing caregiver burden, improving functional independence in nuanced ways, or addressing underserved aspects of a disease, these benefits can be hard to "score" and thus may be undervalued.

• Innovation Under Pressure: A healthcare system and a development paradigm that are overly focused on predictable, easily measurable, short-term outcomes may inadvertently stifle truly disruptive innovation. Breakthrough therapies often address disease in entirely new ways, and their benefits might not fit neatly into pre-existing metric boxes. We risk becoming too risk-averse, favoring incremental improvements that are easy to measure over bold leaps that are initially harder to quantify.

• Reliability of Real-World Evidence (RWE): If the clinical data being generated in everyday practice is itself skewed by RVU-chasing or metric-optimization, how reliable is that data when aggregated for RWE studies to assess a drug's long-term effectiveness and safety? The biases in the clinic can become amplified in our RWE.

6. Towards a More Meaningful Measurement: Alternatives and Compromises

So, what's the alternative? We can't abandon accountability or the desire to improve. But we can be much smarter and more holistic.

• Focus on Patient-Centered Processes and Outcomes: Diaz, Tran, and Grant-Kels suggest that better alternatives include metrics tied to shared decision-making, adherence to evidence-based guidelines (where appropriate and nuanced), and efforts to ensure continuity of follow-up care. In drug development, this means co-creating endpoints with patients and focusing on outcomes that matter most to them.

• Embrace Qualitative Data: Not everything meaningful can be measured by a number. Structured qualitative feedback from patients and clinicians, peer review, and case-based learning can provide richer insights than a dashboard of green and red arrows.

• Risk-Adjust for Complexity and Social Determinants: If we must use metrics, they must be sophisticated enough to account for patient complexity and the profound impact of social determinants of health. This is crucial for both fair clinical assessment and for understanding a drug's true potential across diverse populations.

• Foster Transparency and Collaboration: As Diaz, Tran, and Grant-Kels also advocate, transparent communication about how performance and value are assessed, along with collaboration among all stakeholders (patients, clinicians, researchers, payers, industry, and administrators), is vital to developing ethical and meaningful systems.

• Value Cognitive Work and Long-Term Impact: In both clinical care and drug R&D, we need systems that recognize and reward the deep intellectual work of diagnosis, complex decision-making, and innovation, even if these don't yield immediate, easily scorable outputs.

Conclusion: Beyond the Scoreboard

Our relentless pursuit of easily quantifiable metrics, while often well-intentioned, risks leading us down a path of diminishing returns, fostering a culture of "hitting the target but missing the point."

It's time to step back and ask whether our scoreboards truly reflect the game we should be playing – a game centered on genuine patient well-being, clinician integrity, and transformative medical innovation. To echo a sentiment powerfully expressed by Diaz, Tran, and Grant-Kels after their article, "Metrics may shape medicine, but they should never define it." That wisdom needs to permeate every facet of healthcare, from the exam room to the research lab.

#HealthcareMetrics, #ValueBasedCare, #DrugDevelopment, #PatientCenteredCare, #MedicalEthics, and #HealthPolicy.

I wanted to share a quick note: while I typically skip citations in my blogs, I’m making an exception this time! Why, you ask? It's because many of my sources come from websites that might change their content over time.

The journey of a new drug from the laboratory bench to the patient's bedside is notoriously long and expensive. However, the U.S. Food and Drug Administration (FDA) is now championing a transformative shift, positioning artificial intelligence (AI) not merely as a technology it regulates in industry, but as a powerful tool to be wielded within its walls. This dual embrace of AI, particularly since early 2025, signals a new chapter in public health innovation, aiming to make drug development and review smarter, faster, and ultimately more effective.

The agency's strategy is twofold: guiding the pharmaceutical industry on the responsible use of AI and simultaneously revolutionizing its internal processes through AI adoption. This approach suggests a future where regulatory frameworks are shaped by hands-on experience with the technologies being overseen.

The Regulatory Compass: FDA's January 2025 AI Guidance for Industry

A pivotal moment in this journey arrived on January 7, 2025, with the release of the FDA's draft guidance, "Considerations for the Use of Artificial Intelligence to Support Regulatory Decision Making for Drug and Biological Products".² This document offers the industry a much-anticipated roadmap for integrating AI-generated data into submissions concerning drug safety, effectiveness, and quality.

FDA defines AI broadly as "a machine-based system that can, for a given set of human-defined objectives, make predictions, recommendations, or decisions influencing real or virtual environments.".²

Notably, the guidance focuses on AI applications that directly impact these regulatory endpoints, rather than earlier-stage drug discovery or operational efficiencies that do not have a direct bearing on patient safety or study reliability.³

At the heart of this guidance is the risk-based credibility assessment framework. This structured, seven-step process tailors the level of scrutiny and evidentiary requirements to the potential risk an AI model poses within its specific application, or Context of Use (COU).³ The COU is critical: the FDA evaluates AI models not in isolation but based on how their outputs influence regulatory decisions.³ For instance, an AI model that is the sole determinant for whether a clinical trial participant receives more rigorous safety monitoring would be deemed high risk. In contrast, an AI system used to direct vial fill volumes during manufacturing, but which includes human batch checks, might be considered medium risk due to lower model influence despite potentially high consequences of error.³

This framework signifies a proactive regulatory stance. Developed through extensive consultation, including an expert workshop in December 2022 and feedback from over 800 comments on a 2023 discussion paper ², the guidance aims to shape responsible AI development in the pharmaceutical sector. By setting clear, albeit evolving, expectations, the FDA seeks to foster innovation while mitigating risks, potentially reducing regulatory uncertainty for sponsors.

Figure 1: FDA's 7-Step AI Model Credibility Assessment Framework

Source: ⁴

An Agency Transformed: FDA's Internal AI Revolution

The FDA is transforming with AI. On May 8, 2025, Commissioner Martin A. Makary announced the completion of the agency's first AI-assisted scientific review pilot and plans to implement generative AI tools by June 30, 2025.6.

"I was blown away by the success of our first AI-assisted scientific review pilot," stated Commissioner Makary. "We need to value our scientists’ time and reduce the amount of non-productive busywork that has historically consumed much of the review process. The agency-wide deployment of these capabilities holds tremendous promise in accelerating the review time for new therapies".⁷ He further emphasized the urgency: "It is time to take action. The opportunity to reduce tasks that once took days to just minutes is too important to delay".⁷

Commissioner Makary

Leading this rollout are Jeremy Walsh, the FDA's Chief AI Officer, and Sridhar Mantha, former head of CDER's Office of Business Informatics.6 Adding a significant dimension to this evolving landscape is the May 2025 appointment of Dr. Vinay Prasad as the new Director of the Center for Biologics Evaluation and Research (CBER).¹⁰ Dr. Prasad, a hematologist-oncologist and professor of epidemiology, is known for his rigorous approach to medical evidence and has been described as a "vocal critic of the FDA" while also being a "rigorous and professional cancer research methodology expert".¹¹ He has previously voiced concerns about aspects of the FDA's accelerated approval pathways.¹⁵

CBER oversees the regulation of biologics, including many innovative therapies like gene and cell therapies, where AI is expected to play an increasingly crucial role. Dr. Prasad's known emphasis on high evidentiary standards could shape how AI-driven data is evaluated for these complex products. While CBER will undoubtedly leverage AI tools, his leadership may ensure that an exacting demand for robust validation of AI-derived evidence balances the push for efficiency.

The Path to 2025: A History of FDA's Growing AI Engagement

The FDA's current AI initiatives are not an overnight phenomenon, but rather the culmination of years of groundwork and accumulated experience, especially within the Center for Drug Evaluation and Research (CDER). This center has witnessed a "significant increase in the number of drug application submissions using AI components over the past few years".² Crucially, CDER gained experience with over 500 submissions incorporating AI components between 2016 and 2023, a rich dataset of real-world applications that directly informed the January 2025 guidance.²

The AI Tightrope: Balancing Benefits and Burdens for Drug Developers

The FDA's AI initiative presents a complex calculus of opportunities and challenges for pharmaceutical sponsors.

Potential Benefits:

The most direct advantage could be accelerated review timelines if the FDA's internal AI efficiencies translate into faster decision-making.6 The January 2025 guidance aims to provide clearer regulatory pathways, reducing ambiguity for sponsors incorporating AI.2 AI itself offers powerful tools to enhance innovation, enabling sponsors to analyze vast datasets for novel drug candidates, optimize clinical trial designs, and advance personalized medicine.3 Furthermore, AI can help extract improved data insights from complex information, potentially leading to more robust and persuasive regulatory submissions.18

Potential Harms and Challenges:

Significant concerns revolve around the transparency and explainability of AI models. The "black box" nature of some algorithms can make it difficult for sponsors to understand or contest AI-generated findings, whether from their own models or potentially from the FDA's internal review tools.6 Data security and confidentiality are paramount, especially if the FDA's internal generative AI tools are trained on broad datasets that might include proprietary information from multiple sponsors.6 Sponsors must remain vigilant in protecting their confidential commercial information and trade secrets.6

Algorithmic bias is another critical challenge. AI models trained on non-representative datasets can perpetuate or even amplify biases, potentially affecting drug safety and efficacy assessments across diverse demographic groups.⁴ Sponsors bear the primary responsibility for identifying and mitigating such biases in their AI systems. The costs of validation and compliance with the FDA's credibility assessment framework—including ensuring data quality, rigorous model validation, and comprehensive documentation—will require substantial investment from sponsors.³ Finally, the dynamic nature of AI necessitates robust lifecycle management. Models can experience "drift" as data inputs change over time, necessitating continuous monitoring, revalidation, and potential retraining to ensure they remain suitable for their intended purpose, thereby adding to long-term operational burdens.⁴

The FDA's AI initiative thus presents a classic risk-reward scenario. The allure of accelerated innovation and streamlined processes is substantial, but it is accompanied by new layers of complexity, responsibility, and cost for drug developers.

Best Practices for AI-Powered Submissions

For pharmaceutical sponsors navigating this new AI-driven regulatory terrain, several best practices emerge from the FDA's communications and guidance:

1. Early and Ongoing Engagement with FDA: This is repeatedly emphasized as crucial. Proactive discussions with the agency about AI model risk, the proposed credibility assessment plan, and potential challenges can de-risk the submission process and align expectations.³ The guidance "strongly encourages sponsors... to engage early with FDA".²³

2. Master the Credibility Assessment Framework: A thorough understanding and meticulous application of the FDA's 7-step framework is essential.⁴ This framework is the agency's primary tool for evaluating AI in submissions.

3. Prioritize Data Quality, Governance, and Representativeness: The foundation of any credible AI model is high-quality data. Sponsors must ensure that datasets used for training, tuning, and validation are relevant, reliable, and representative of the intended patient population to avoid introducing or perpetuating bias.⁴

4. Transparency in Model Development and Documentation: Comprehensive documentation is key. This includes detailing the AI model's architecture, the development process, data sources and preprocessing steps, performance metrics, and known limitations.³

5. Robust Validation and Lifecycle Performance Monitoring: Rigorous validation is necessary to demonstrate the AI model's safety and effectiveness, including performance across relevant subgroups. A plan for ongoing lifecycle management, including monitoring for performance degradation or data drift, is also expected.⁴

6. Address Cybersecurity: AI systems can introduce unique cybersecurity vulnerabilities. Sponsors must implement robust measures to protect data integrity and system security.²¹

Success in this evolving landscape demands a paradigm shift for sponsors towards greater transparency, more rigorous data stewardship, and a continuous, lifecycle-oriented approach to AI model governance. The FDA is effectively promoting "Good Machine Learning Practice" (GMLP) as a standard in drug development, analogous to existing standards like Good Clinical Practice (GCP) and Good Manufacturing Practice (GMP).²⁸

Insights for pharmaceutical innovators

Artificial intelligence holds undeniable potential to revolutionize drug development and the field of regulatory science. The FDA's initiatives in 2025 mark a decisive and bold stride towards harnessing this power, moving the agency into a new era of technological sophistication. This endeavor is more than a mere upgrade of tools; it represents a fundamental rethinking of how therapeutic innovations are evaluated and delivered to the public.

This journey is exciting and ever-changing! The FDA's frameworks and internal practices will surely evolve as technology advances and we gain more experience. It’s essential for everyone—agencies, industry, academic researchers, and patient advocacy groups—to collaborate in navigating the complexities that lie ahead. The FDA's proactive and flexible approach to AI regulation can inspire other regulatory bodies worldwide as they tackle the integration of artificial intelligence into vital sectors, all with the goal of improving public health in a rapidly advancing scientific landscape.

Works cited

1. Remarks by FDA Commissioner Robert M. Califf to the Coalition for Health AI (CHAI) - 03/05/2024, accessed May 29, 2025, https://www.fda.gov/news-events/speeches-fda-officials/remarks-fda-commissioner-robert-m-califf-coalition-health-ai-chai-03052024

2. Artificial Intelligence for Drug Development - FDA, accessed May 29, 2025, https://www.fda.gov/about-fda/center-drug-evaluation-and-research-cder/artificial-intelligence-drug-development

3. The Role of AI in Regulatory Decision-Making for Drugs & Biologics: the FDA's Latest Guidance | WCG, accessed May 29, 2025, https://www.wcgclinical.com/insights/the-role-of-ai-in-regulatory-decision-making-for-drugs-biologics-the-fdas-latest-guidance/

4. Exploring the FDA's draft guidance on AI in regulatory decision-making - Boyd Consultants, accessed May 29, 2025, https://boydconsultants.com/exploring-the-fdas-draft-guidance-on-ai-in-regulatory-decision-making/

5. FDA AI Guidance - A New Era for Biotech, Diagnostics and Regulatory Compliance, accessed May 29, 2025, https://www.duanemorris.com/alerts/fda_ai_guidance_new_era_biotech_diagnostics_regulatory_compliance_0225.html

6. FDA advances AI-powered review of medical product applications - Hogan Lovells, accessed May 29, 2025, https://www.hoganlovells.com/en/publications/fda-advances-aipowered-review-of-medical-product-applications

7. FDA Announces Completion of First AI-Assisted Scientific Review ..., accessed May 29, 2025, https://www.fda.gov/news-events/press-announcements/fda-announces-completion-first-ai-assisted-scientific-review-pilot-and-aggressive-agency-wide-ai

8. FDA commissioner talks of shortening drug approval process, benefits of AI | AHA News, accessed May 29, 2025, https://www.aha.org/news/headline/2025-05-06-fda-commissioner-talks-shortening-drug-approval-process-benefits-ai

9. FDA AI deployment: Innovation vs oversight in drug regulation - AI News, accessed May 29, 2025, https://www.artificialintelligence-news.com/news/fda-ai-deployment-innovation-vs-oversight-drug-regulation/

10. FDA CBER Director Dr. Vinay Prasad to Keynote NORD's Inaugural Rare Disease Scientific Symposium, Convening Top Researchers, Industry Leaders, and Federal Regulators, accessed May 29, 2025, https://rarediseases.org/fda-cber-dr-vinay-prasad-keynote-nords-rare-disease-scientific-symposium/

11. This Week at FDA: Makary names Vinay Prasad to lead CBER, more high-level hirings and departures | RAPS, accessed May 29, 2025, https://www.raps.org/news-and-articles/news-articles/2025/5/this-week-at-fda-makary-names-vinay-prasad-to-lead

12. Vinayak Prasad - FDA, accessed May 29, 2025, https://www.fda.gov/about-fda/fda-organization/vinayak-prasad

13. FDA to Restrict Future COVID-19 Vaccine Recommendations to Older Adults, High-Risk Groups - Pharmacy Times, accessed May 29, 2025, https://www.pharmacytimes.com/view/fda-to-restrict-future-covid-19-vaccine-recommendations-to-older-adults-high-risk-groups

14. FDA sets COVID vaccine formula as RFK Jr. narrows guidance for shots | BioPharma Dive, accessed May 29, 2025, https://www.biopharmadive.com/news/fda-covid-vaccines-guidance-narrow-strain-recommendation/748975/

15. Makary's 'Conditional Approval' Pathway for Rare Diseases Poses More Questions Than Answers - BioSpace, accessed May 29, 2025, https://www.biospace.com/fda/makarys-conditional-approval-pathway-for-rare-diseases-poses-more-questions-than-answers

16. FDA leaders seek industry input on 'listening tour' - BioPharma Dive, accessed May 29, 2025, https://www.biopharmadive.com/news/makary-fda-listening-tour-pharma-industry-ceos/749140/

17. FDA Announces Completion of AI-Assisted Scientific Review Pilot and Deployment of Agency-Wide AI-Assisted Review - King & Spalding, accessed May 29, 2025, https://www.kslaw.com/news-and-insights/fda-announces-completion-of-ai-assisted-scientific-review-pilot-and-deployment-of-agency-wide-ai-assisted-review

18. Unearthing the FDA's Treasure Trove: Using AI to Revolutionize Drug Development, accessed May 29, 2025, https://globalforum.diaglobal.org/issue/april-2025/unearthing-the-fdas-treasure-trove-using-ai-to-revolutionize-drug-development/

19. Harnessing the AI/ML in Drug and Biological Products Discovery and Development: The Regulatory Perspective - MDPI, accessed May 29, 2025, https://www.mdpi.com/1424-8247/18/1/47

20. NHC Submits Comments on FDA Draft Guidance on AI to Support Regulatory Decision-Making for Drugs/Biologics - National Health Council, accessed May 29, 2025, https://nationalhealthcouncil.org/letters-comments/nhc-submits-comments-on-fda-draft-guidance-on-ai-to-support-regulatory-decision-making-for-drugs-biologics/

21. FDA Issues Draft Guidance for AI-Enabled Devices: Key Takeaways - Dentons, accessed May 29, 2025, https://www.dentons.com/en/insights/alerts/2025/february/11/fda-issues-draft-guidance-for-ai-enabled-devices

22. AI Health Law & Policy: FDA's rapidly evolving regulatory paradigms - Hogan Lovells, accessed May 29, 2025, https://www.hoganlovells.com/en/publications/ai-health-law-policy-fda%E2%80%99s-rapidly-evolving-regulatory-paradigms

23. AI in drug development: FDA draft guidance addresses product lifecycle, risk | RAPS, accessed May 29, 2025, https://www.raps.org/news-and-articles/news-articles/2025/1/ai-in-drug-development-fda-draft-guidance-addresse

24. Key takeaways from FDA's draft guidance on use of AI in drug and biological life cycle, accessed May 29, 2025, https://www.dlapiper.com/insights/publications/2025/01/fda-releases-draft-guidance-on-use-of-ai

25. The Biden Administration's Swan Song on Digital Health: Two FDA Guidances on Artificial Intelligence and FDA's Defense of its Clinical Decision Support Guidance | Insights | Ropes & Gray LLP, accessed May 29, 2025, https://www.ropesgray.com/en/insights/alerts/2025/01/the-biden-administrations-swan-song-on-digital-health

26. FDA Issues Draft Guidances on AI in Medical Devices, Drug Development - Fenwick, accessed May 29, 2025, https://www.fenwick.com/insights/publications/fda-issues-draft-guidances-on-ai-in-medical-devices-drug-development-what-manufacturers-and-sponsors-need-to-know

27. FDA unveils long-awaited guidance on AI use to support drug and biologic development, accessed May 29, 2025, https://www.hoganlovells.com/en/publications/fda-unveils-longawaited-guidance-on-ai-use-to-support-drug-and-biologic-development

28. The Evolving FDA Regulatory Landscape of Artificial Intelligence - Gardner Law, accessed May 29, 2025, https://gardner.law/news/theevolving-fda-regulatory-landscape-of-artificial-intelligence

29. Navigating FDA Compliance for AI-Powered Healthcare Tools and EHRs, accessed May 29, 2025, https://hypersense-software.com/blog/2025/04/04/navigating-fda-compliance-ai-healthcare-ehrs/

#FDA #AIinHealthcare #RegulatoryGuidance #DrugDevelopment #FDAGuidance

#FutureofMedicine #RegulatoryAffairs #AIgovernance #sensiblemedicine

The Promise: IoT/DHTs offer richer data (vitals, activity, adherence), fuel-efficient DCTs, and enhance patient engagement.

The Global Regulatory Lens - Concrete Examples & Focus Areas:

• U.S. FDA & PDCO (Pediatric Committee):

  • Core Focus: Continues to emphasize "Fit-for-Purpose" validation (ensuring a device reliably measures the right thing for the specific trial) and robust Data Security/Privacy, as outlined in their guidance.

  • Pediatrics (PDCO Considerations): While specific PDCO guidance on DHTs is evolving, the principles are clear: Devices must be child-friendly in design and usability. There's heightened scrutiny on validation in pediatric populations due to physiological differences and activity patterns. The 2024 ICH E11A guideline (adopted by FDA) on pediatric extrapolation highlights the need for robust data, where DHTs could play a role, but must be rigorously justified.

  • Example: The increasing acceptance of Continuous Glucose Monitors (CGMs) in diabetes trials (including pediatric arms) showcases the FDA's willingness to embrace validated DHTs that provide clear clinical value and meet data integrity standards.

• European Medicines Agency (EMA):

  • Big Data & HTA Focus: Through its HMA-EMA Big Data Steering Group (active in 2024), the EMA is actively exploring mHealth data for regulatory decisions and working on new Health Technology Assessment (HTA) rules (effective Jan 2025) to streamline access, creating a pathway where robust DHT data can contribute.

  • Example (Landmark): The EMA's acceptance of Sarepta's SV95C (Stride Velocity 95th Centile) – a wearable-derived measure – as a primary endpoint for a Duchenne Muscular Dystrophy submission is a significant milestone, demonstrating clear precedent for DHT-based primary outcomes in Europe.

• China's NMPA (National Medical Products Administration):

  • Embracing Modern Trials: Recent NMPA activity (2024-2025) shows a clear push towards modernization. They've issued guidelines on DCTs (especially for rare diseases, May 2024), updated clinical trial inspection requirements (March 2025), and are building frameworks for Real-World Evidence (RWE) and data protection.

  • Example: NMPA's 2024 guidance explicitly addresses Decentralized Clinical Trials for Rare Disease Drugs, including provisions for remote monitoring and electronic consent, directly opening the door for IoT/DHT use in these critical areas and signaling broader acceptance, provided data integrity and GCP standards are met.

The Path Forward:

While each agency has its nuances, the global direction is clear: DHTs are becoming integral. The challenges – validation, security, interoperability, and harmonization across regions – remain. However, the momentum, driven by successful examples and clear regulatory engagement, is undeniable. AI will further amplify the value of this data.

Navigating this global landscape requires a deep understanding and early engagement. What are your biggest questions about deploying IoT/DHTs in multi-regional trials?

#ClinicalTrials #DigitalHealth #IoT #GlobalRegulation #FDA #EMA #NMPA #MedTech