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Universal Right to Try with Evidence: Potential Impact of Adoption in All 50 States

Keywords

war-on-disease, 1-percent-treaty, medical-research, public-health, peace-dividend, decentralized-trials, dfda, dih, victory-bonds, health-economics, cost-benefit-analysis, clinical-trials, drug-development, regulatory-reform, military-spending, peace-economics, decentralized-governance, wishocracy, blockchain-governance, impact-investing

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Summary

The proposed state framework would create licensed centers where patients can pay for treatment-related care while eligible participants enroll in standardized, pooled pragmatic trials. Patient and payer revenue can cover treatment delivery, trial-site services, and permitted study costs. Treatments abandoned because nobody can profitably finance conventional Phase 2 and Phase 3 trials become testable.

If all 50 states adopt, the central model estimates:

Result Conditional central estimate
Treatment-discovery multiplier 5.48x
Average first treatment arrives 181 years earlier
Premature deaths prevented

9.19 billion
Healthy years restored 483 billion DALYs
Disability-equivalent suffering prevented 1.65 quadrillion hours
Campaign plus shared registry cost

$65 million
Philanthropic cost per healthy year restored

$0.000134

These are conditional lifetime schedule-shift totals across future generations. They are not claims that billions of people alive today are saved, or that passage guarantees the modeled effect.

The headline is allowed to be enormous because its conditional parameter uncertainty is visible. The Monte Carlo varies the launch cost, treatment-discovery multiplier, avoidable disease burden, and upstream inputs already in the model. It does not include the chance of political adoption or model-form uncertainty about using the rare-disease queue as a proxy for the global therapeutic frontier.

Monte Carlo Distribution: Lives Saved from Universal Right to Try with Evidence (10,000 simulations)

Monte Carlo Distribution: Lives Saved from Universal Right to Try with Evidence (10,000 simulations)

Simulation Results Summary: Lives Saved from Universal Right to Try with Evidence

Statistic Value
Baseline (deterministic) 9.19 billion
Mean (expected value) 9.4 billion
Median (50th percentile) 8.82 billion
Standard Deviation 4.13 billion
90% Range (5th-95th percentile) [3.71 billion, 17.2 billion]

The histogram shows the distribution of Lives Saved from Universal Right to Try with Evidence across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.

The legislation

Montana has already created licensed experimental treatment centers. Its law covers treatments that have passed Phase 1 but are not approved for general FDA use, permits centers to charge patients, and requires part of center profits to support access136.

Montana’s rules require centers to track treatments and patient outcomes, report serious adverse events, and publish annual aggregate safety and treatment outcomes. The same rules explicitly state that the centers do not administer clinical trials137. Outcome tracking is useful. It is not automatically causal evidence.

The model bill therefore adds four requirements:

  1. Enroll. Register a prospective protocol, obtain informed consent, report serious safety events, and place each patient in the applicable federal trial or treatment-access pathway.
  2. Compare. Prespecify outcomes and use simple randomization or another adequate control when feasible. Without an adequate comparison, label the result a signal, not a validated treatment effect.
  3. Pool. Send standardized, de-identified baseline and outcome data to a shared registry.
  4. Publish. Continuously publish results for each treatment-condition pair, including negative and harmful results.

The model does not require a new state appropriation. Philanthropy pays for the shared registry’s first ten years, and participating centers fund continued operation through license or enrollment assessments. This keeps the lifetime schedule shift from depending on a data system that disappears when the launch grant ends.

Montana can be the first node

Montana has already done the politically difficult part: it created the licensed-center and independent-review framework. The next move is to make its protocols interoperable, make every eligible treatment generate standardized data and comparable controlled evidence when feasible, and give other states a model they can copy.

Infinita publicly describes a coordinated pathway that includes experimental treatment review board (ETRB) review, licensed clinical administration, monitoring, evidence generation, and early commercialization for post-Phase-1 therapies138. That matters because the system is not only a philanthropic expense. Review, compliance, treatment delivery, manufacturing, registry infrastructure, and successful treatment assets can all produce revenue. Revenue can keep evidence generation alive after the original donors go home to fund a building with their name on it.

The flywheel is a map of possible cash flows, not a promise of investor returns. Actual returns depend on treatment demand, lawful charging, reimbursement, liability, protocol quality, and whether the evidence changes clinical or regulatory decisions.

Same evidence logic, lower collection cost

This does not give a state registry the power to approve drugs or lower the federal evidence standard. It changes where and how effectiveness evidence is produced.

The proposed state framework and federal Right to Try are not the same legal pathway. Federal Right to Try is limited to eligible patients who are unable to participate in a clinical trial involving the drug139. A patient enrolled in an interventional pragmatic trial must instead be treated under an active investigational new drug application or another applicable FDA-authorized pathway. Patients who cannot join the trial may still contribute separately labeled observational outcomes through federal Right to Try or expanded access if eligible. The state bill supplies centers, consent, comparison, and shared data rules. It does not waive federal trial authorization or Right to Try eligibility.

FDA’s statutory standard remains substantial evidence from adequate and well-controlled investigations. Its June 2026 draft guidance explains that one rigorous investigation plus confirmatory evidence can sometimes satisfy that standard, while approval still requires sufficient safety evidence and a favorable benefit-risk assessment140. FDA also supports streamlined randomized trials integrated into routine clinical practice141. Project Pragmatica applies the same design logic to approved oncology products through simpler eligibility, routine-practice enrollment, and reduced data-collection burden while maintaining safety and data integrity142. It is an example of pragmatic design, not precedent for bypassing authorization of an unapproved drug.

A pragmatic randomized trial is still a randomized controlled trial. It keeps a comparison group, prespecified outcomes, safety reporting, and reliable analysis. It can reduce dedicated research visits and duplicate data collection when ordinary care can do the job. The existing cost model estimates $41,000 (95% CI: $20,000-$120,000) per participant for a traditional Phase 3 trial versus $929 (95% CI: $97-$3,000) for an embedded pragmatic trial, a 44.1x (95% CI: 12.8x-210x) reduction. That is a per-participant comparison, not a claim that every approval becomes 44.1x (95% CI: 12.8x-210x) cheaper. Complex or high-risk studies will still need specialized sites, procedures, and monitoring.

Federal charging rules remain in force. An IND sponsor needs FDA authorization to charge for the investigational drug and generally may recover only direct drug costs. FDA separately states that trial sites may recover pharmacy, nursing, equipment, and study-related procedure costs without charging authorization under that rule143. The model therefore assumes patient or payer revenue covers treatment delivery, site services, and permitted study costs, while center assessments sustain the common registry. It does not assume that states can finance research through unrestricted profits on the investigational drug. Whether lawful revenue is sufficient to sustain the modeled system is part of the uncertain discovery multiplier.

Medicare already uses the analogous payment principle in Coverage with Evidence Development: specified services may be covered only within an approved study while additional evidence is collected, then CMS can reconsider coverage using the resulting evidence144. This proposal is access with evidence development, financed primarily by patients and participating centers rather than Medicare. The Continuous Evidence Generation Protocol145 provides one compatible technical design for standardized outcome data, observational signal detection, and pragmatic-trial confirmation. The legislation need not mandate that protocol.

The original Right to Try model spread from its first state in 2014 to 41 states by 2018146. That establishes a distribution channel. It does not prove that this broader access-plus-data bill will spread at the same speed.

Why previously unprofitable treatments matter

Post-Phase-1 compounds, repurposing candidates, combinations, doses, and disease subtypes can each produce multiple treatment hypotheses. The relevant candidate pool is therefore much larger than a count of compounds. No precise inventory exists.

Candidate abundance is not multiplied by global disease burden. Ten successful treatments for one disease cannot avert that disease’s burden ten times. Additional candidates matter because they raise the rate at which the first effective treatment is found.

The model compresses the entire causal mechanism into one uncertain input: 5.48x. That input represents all of the following together:

  • patient-funded treatment and evidence generation;
  • treatments revived because conventional trials were not economically viable;
  • enough candidate-condition pairs to keep enrollment capacity occupied;
  • protocols capable of producing credible efficacy evidence;
  • the share of completed evaluations that find an effective first treatment.

This avoids pretending that patient volume, candidate supply, evaluation size, and success rates are independent benefit multipliers. The automatic Monte Carlo samples the resulting discovery multiplier directly. The 5.48x central value is a calibration assumption, not a measured causal effect.

Why the model covers global diseases and aging

The goal is to move forward treatments for the entire eventually treatable global burden, including common diseases and aging-related degeneration. The model therefore applies the treatment-schedule shift to global avoidable deaths and DALYs, not only to rare-disease burden.

The status quo treatment queue is built from untreated rare diseases because that is the clearest available count of diseases still awaiting a first effective treatment. Here it serves as a proxy for the pace of exploring the wider therapeutic frontier. That is a strong modeling assumption. It is intentional, not an accidental multiplication of rare-disease benefits by global burden.

The schedule-shift calculation

At the status quo discovery rate, the average therapeutic target waits 222 years (95% CI: 128 years-420 years) for its first effective treatment. A 5.48x discovery rate moves the average first treatment forward by 181 years.

The global DALY benefit is annual avoidable disease burden multiplied by that schedule shift. Premature deaths use the same schedule shift and global avoidable mortality. Disability-equivalent hours convert the years-lived-with-disability share of DALYs into hours. The full cost-per-DALY chain is:

\[ \begin{gathered} Cost_{RTT,DALY} \\ = \frac{C_{RTT}}{DALYs_{RTT}} \\ = \frac{\$65M}{483B} \\ = \$0.000134 \end{gathered} \]
where:
\[ \begin{gathered} DALYs_{RTT} \\ = DALYs_{global,ann} \times Pct_{avoid,DALY} \times T_{accel,RTT} \\ = 2.88B \times 92.6\% \times 181 \\ = 483B \end{gathered} \]
where:
\[ \begin{gathered} T_{accel,RTT} \\ = T_{first,SQ} \times \left(1 - \frac{1}{k_{RTT}}\right) \\ = 222 \times \left(1 - \frac{1}{5.48}\right) \\ = 181 \end{gathered} \]
where:
\[ \begin{gathered} T_{first,SQ} \\ = T_{queue,SQ} \times 0.5 \\ = 443 \times 0.5 \\ = 222 \end{gathered} \]
where:
\[ \begin{gathered} T_{queue,SQ} \\ = \frac{N_{untreated}}{Treatments_{new,ann}} \\ = \frac{6{,}650}{15} \\ = 443 \end{gathered} \]
where:
\[ \begin{gathered} N_{untreated} \\ = N_{rare} \times 0.95 \\ = 7{,}000 \times 0.95 \\ = 6{,}650 \end{gathered} \]

What the money buys

Philanthropy: an asymmetric bet

Conditional cost per DALY is campaign plus registry-launch cost divided by the health benefit if the 50-state system operates at the modeled discovery rate. It excludes patient or payer spending on treatment delivery, trial-site services, and permitted study costs because those payments buy care and evidence generation rather than consuming the philanthropic budget.

At the central estimate, the framework’s modeled philanthropic cost per life saved is $0.00707, which is 636.2k times lower than the midpoint of GiveWell’s cited modeled range across top charities7. This is not because legislation cures people by itself. It is because a relatively small campaign and shared registry are modeled to unlock treatment, sponsor, payer, clinic, and investor spending that philanthropists do not have to supply.

The cost-per-DALY Monte Carlo below shows uncertainty in the conditional estimate itself.

Monte Carlo Distribution: Universal Right to Try with Evidence Philanthropic Cost per DALY (10,000 simulations)

Monte Carlo Distribution: Universal Right to Try with Evidence Philanthropic Cost per DALY (10,000 simulations)

Simulation Results Summary: Universal Right to Try with Evidence Philanthropic Cost per DALY

Statistic Value
Baseline (deterministic) $0.000134
Mean (expected value) $0.000171
Median (50th percentile) $0.000119
Standard Deviation $0.000192
90% Range (5th-95th percentile) [$4.01e-05, $0.000448]

The histogram shows the distribution of Universal Right to Try with Evidence Philanthropic Cost per DALY across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.

For a grant decision, multiply conditional lives saved by the donor’s own probability that the grant produces full adoption, mature implementation, and the modeled discovery effect. Equivalently, divide conditional cost per life saved by that probability. The probability judgment stays outside the model, so it does not require another scenario parameter.

Impact investors and operators: make evidence pay for itself

Philanthropy pays for the public-good layer that no single company can capture: legislation, common standards, and the shared registry launch. Commercial capital can finance the layers that can earn revenue:

  • ETRB review and compliance services;
  • licensed treatment and pragmatic-trial sites;
  • manufacturing and distribution;
  • treatment sponsors and holders of shelved post-Phase-1 assets;
  • interoperable registry, analytics, and monitoring infrastructure;
  • payer contracts that condition coverage on evidence production.

That separation is the point. Donors do not need to finance every future treatment. Investors do not need to pretend that legislation itself is a security. Each pays for the part whose returns it can actually capture.

What the numbers mean

The 9.19 billion deaths figure exceeds the current world population because it sums premature deaths prevented across approximately 181 years of future generations. It is a schedule-shift estimate, not a count of currently identifiable people.

The model also excludes the uncertain net direct health effect on participants, including earlier benefits from effective treatments and harms from ineffective or unsafe ones. That is a separate unmodeled channel, not part of the discovery schedule shift.

The 1.65 quadrillion hours figure means disability-weighted equivalent hours. A disability weight of 0.25 sustained for four hours equals one full-disability-equivalent hour. It does not mean the person consciously experienced maximum pain for one hour.

The global result assumes discoveries become public knowledge and eventually benefit patients outside the United States. The access right itself remains state law, and state legislation does not override federal FDA authority.

Policy implications

The most useful next step is a transparent Montana demonstration of interoperable enrollment, pooled outcomes, documented fees, and safety monitoring. If it produces useful evidence while remaining commercially sustainable, other states can adopt the same model through compatible legislation.

Any implementing bill should require Enroll, Compare, Pool, and Publish so the states produce comparable evidence instead of 50 disconnected experiments.