The Promise and Perils of Royalty-Financed Platform Trials: Can Wall Street Finally Cure ALS?

A new financing model proposes to bridge biotech's "valley of death" by combining adaptive platform trials with royalty payments. The math looks compelling on paper—28% returns for investors, faster drug development for patients. But can it actually work?

The Innovation That Caught My Eye

When a PLOS ONE paper landed in July 2025 proposing a "Fund of Adaptive Royalties" (FAR) to finance ALS drug development, I had to dig deep.

The authors—including MIT's Andrew Lo and the team behind the HEALEY ALS Platform Trial—argue they've cracked the code on biotech financing. Their model combines two proven innovations: the operational efficiency of adaptive platform trials with the capital structure of royalty-based financing.

The pitch sounds simple: Investors fund 50% of clinical trial costs for drugs entering the HEALEY platform. In exchange, they receive 7% royalties on any approved drugs. The model projects a 28% median internal rate of return (IRR) with a median of 2 drug approvals from 20 candidates tested.

There's just one problem: the 22% probability of total loss masks risks that would make most institutional investors run for the exits.

What Makes This Actually Novel

Before we tear apart the financials, let's acknowledge what's genuinely innovative here. The FAR model achieves a true first by bridging two previously separate innovations into a coherent investment structure.

Traditional royalty financing through firms like Royalty Pharma focuses almost exclusively on late-stage or already-approved drugs. Royalty Pharma's 90% success rate for development-stage investments reflects this de-risked positioning—they're buying royalty streams after most development risk has vanished. The FAR model instead proposes funding the actual Phase 2/3 trials themselves, taking on development risk that royalty investors typically avoid.

Venture capital provides the closest comparison for risk profile, but VC firms receive equity ownership with unlimited upside potential rather than capped royalty streams. A successful biotech investment might return 10-50x for VC investors, while FAR's 7% royalty rate caps returns regardless of commercial success.

Andrew Lo's megafund proposals from 2012 onward share the portfolio diversification logic but operate at vastly different scale ($5-15 billion vs. $250 million) and rely on securitization to attract mainstream institutional capital. The megafund would pool 150+ drug development programs across multiple diseases, then create debt and equity tranches with different risk profiles—essentially mortgage-backed securities for drug development.

The key innovation FAR adds is leveraging the operational efficiency of adaptive platform trials rather than relying solely on statistical diversification across uncorrelated assets. Platform trials can deliver 76% reduction in decision time and 37% median cost savings compared to sequential trials.

This combination fills a genuine gap. The model positions itself between venture philanthropy (mission-driven) and venture capital (equity-focused), offering rare disease drug developers reduced capital requirements while providing investors diversified exposure to multiple candidates within proven trial infrastructure.

The Financial Case Falls Apart Under Scrutiny

Here's where my 15 years structuring royalty deals started setting off alarm bells. A rigorous analysis of FAR's financial parameters reveals the model would struggle to attract sophisticated institutional capital in its current form.

The Success Probability Mirage

The 15% combined Phase 2/3 success assumption represents the model's most significant vulnerability.

Cho et al. project 15% based on historical ALS trial data from Citeline databases: 54.5% Phase 2, 38.5% Phase 3, and 71.4% NDA/BLA approval. Multiply those together and you get 15%.

But here's the problem: ALS Phase 3 trials specifically have achieved only 7.1% success rates—2 successful trials out of 28—among the lowest success rates in all drug development. This disease presents exceptional challenges: patient heterogeneity, rapidly progressive nature, endpoint variability, and a long history of promising Phase 2 results failing in Phase 3.

The FDA has issued specific guidance acknowledging ALS trial complexities. Using 15% success probability when actual outcomes suggest 7-10% means the model potentially overestimates approval rates by 2x, fundamentally undermining return projections.

The 22% total loss probability similarly appears optimistic. Industry-wide data shows 80-87% of drugs entering Phase 1 never reach approval. For ALS specifically, the 92.9% historical Phase 3 failure rate suggests investors should expect 40-60% probability of total loss across a portfolio of Phase 2/3 candidates, not 22%.

The Royalty Rate Doesn't Compensate for Risk

The 7% royalty rate provides insufficient upside capture for the risk profile.

Pharmaceutical licensing deals for development-stage assets typically carry 10-15% royalties to compensate for clinical and commercial risk. At $168,000 annual drug pricing—a realistic and even conservative figure for effective ALS treatments given orphan drug pricing norms—peak sales might reach $300-500 million annually if a drug captures 50% of the treatable market.

A 7% royalty on $400 million yields $28 million annually, requiring 5-10 years post-approval to recover the investment. Compare this to venture capital equity stakes: 10-20% equity ownership in a biotech company that achieves acquisition or IPO could return $100-300 million on the same successful drug.

The capped upside fundamentally disadvantages FAR versus traditional VC structures.

Early-stage biotech VC funds target 30-40% gross IRR to generate 25% net IRR for limited partners after fees. The 28% median IRR therefore sits at the low end of requirements for early-stage risk, and below requirements when actual loss probabilities reach 50%.

Late-stage biotech VC shows lower returns (9.7% median IRR according to industry data) but with dramatically reduced risk—Phase 3 drugs have 58-70% approval probability, not 7-15%.

What Returns Actually Work

Royalty Pharma's track record illuminates why the firm focuses exclusively on late-stage or approved products. With 15-21% returns on invested equity, 90% success rates on development-stage investments, and rigorous selection criteria, Royalty Pharma demonstrates that royalty-based returns work when risk is sufficiently de-risked.

FAR attempts to apply royalty structures two stages earlier in development, where the risk-return tradeoff becomes much less favorable.

Platform Trials Deliver Real Benefits (With Caveats)

Real-world evidence from implemented adaptive platform trials reveals substantial operational benefits alongside significant implementation challenges. This validates FAR's operational assumptions while highlighting practical barriers to execution.

Time Savings Are Real

Time savings have materialized consistently across multiple platforms.

HEALEY ALS demonstrated 50% reduction in trial duration—37 months for the platform versus an estimated 6.7 years for sequential Phase 2 and Phase 3 trials. REMAP-CAP's rapid adaptation during COVID-19 enabled stopping decisions based on 403 participants for one arm while evaluating multiple interventions simultaneously.

STAMPEDE in prostate cancer enabled testing treatments that would have taken "many decades" sequentially. General estimates suggest adaptive trials save 56 days on average, with platform-specific gains reaching 76% decision time reduction.

These temporal efficiencies directly translate to reduced carrying costs and faster time-to-market for successful drugs.

Cost Savings Are Less Certain

Cost savings remain less well-documented with significant caveats.

HEALEY ALS claims 30% research cost reduction, and simulation studies project 37% median savings. However, actual comparative cost data is sparse, primarily derived from models rather than head-to-head empirical comparisons.

Platform build costs are substantial—$2.26 million for HEALEY's infrastructure—with per-regimen costs still reaching $21.2 million over 37 months. One rigorous cost-effectiveness study of bloodstream infection trials found 38% savings with adaptive platform design, but results proved "highly sensitive" to stopping rules and drug efficacy. When drugs had no true effect, cost advantages disappeared.

But Do More Drugs Actually Get Approved?

The more critical question is whether these operational efficiencies translate into higher drug approval rates. The evidence here is sobering.

HEALEY ALS has tested seven regimens through 2025, with five discontinued after failing primary endpoints and two advancing to Phase 3 (CNM-Au8, pridopidine)—a 29% advancement rate with zero approvals yet. Most drugs still fail, but the platform identifies failures efficiently.

I-SPY 2 in breast cancer has graduated multiple drugs to Phase 3, but "graduation" doesn't guarantee Phase 3 success—several failed in subsequent trials. The platform efficiently screens candidates but cannot overcome fundamental biological challenges.

REMAP-CAP and STAMPEDE represent the clearest success stories with practice-changing results that directly influenced treatment guidelines. REMAP-CAP identified corticosteroids (dexamethasone) as life-saving for severe COVID-19—estimates suggest 0.5-2 million lives saved globally—while rapidly stopping futile treatments like hydroxychloroquine and convalescent plasma.

These successes occurred in contexts with either urgent public health need (pandemic), strong existing infrastructure (UK NHS integration), or mature disease understanding (prostate cancer after decades of research).

Implementation Is Harder Than It Looks

Implementation challenges prove more substantial than initial expectations.

Operational reports from STAMPEDE and FOCUS4 acknowledged "initial underestimation of the work required" and of the inherent, largely unanticipated challenges, with reports of high workloads and significant stress for personnel.

Data management complexity substantially exceeds traditional trials, requiring continuous interim analyses with clean, high-quality real-time data. Startup time for platform trials exceeds traditional trials due to complex planning, multi-stakeholder coordination, and regulatory negotiations.

Statistical concerns about non-concurrent control groups persist despite adaptive methods. When interventions enter the platform at different times but share control groups enrolled earlier, temporal trends in patient populations create potential bias. Critics in NEJM note that "one cannot say with certainty that statistical modeling was successful in eliminating bias."

The Andrew Lo Factor: Brilliant Ideas, Limited Implementation

Andrew Lo brings exceptional credibility and a mixed implementation track record.

Named one of Time's 100 Most Influential People in 2012, the MIT professor has championed alternative biotech financing since losing his mother and four other family members to cancer within four years. His 2012 Nature Biotechnology megafund proposal—calling for $5-15 billion funds to finance 150+ biomedical programs through securitized debt tranches plus equity—generated substantial enthusiasm.

Yet the pure megafund structure has never been implemented as originally proposed. Critics immediately noted that "more money won't fix the scientific and regulatory slowdowns that contribute to decreased productivity of each research dollar." Lo himself acknowledged in 2020 research that "the performance of the mega-fund becomes less attractive when correlation between projects is introduced."

BridgeBio Shows What Actually Works

BridgeBio Pharma represents the model's most significant real-world validation.

Founded in 2015 by Lo's former student Neil Kumar with Lo as co-founder and board member, BridgeBio adapted megafund concepts to rare genetic diseases. The company built a parent-subsidiary structure with each subsidiary focusing on a single genetic disorder, achieving diversification through biologically uncorrelated diseases while centralizing operations.

The approach succeeded spectacularly: after raising $50+ million initially, BridgeBio completed a $135 million Series C with Viking Global and KKR, IPO'd in 2019, reached ~$9 billion market cap by 2021, and secured $750 million in non-dilutive debt financing.

The lesson from BridgeBio: Lo's theoretical insights about portfolio diversification and financial engineering are sound, but implementation requires adapting to existing capital market structures rather than creating entirely new financial instruments. BridgeBio didn't need research-backed obligations or debt tranches; it succeeded by convincing sophisticated investors that uncorrelated rare diseases create an attractive portfolio within a standard corporate structure.

Who Might Actually Fund This?

The model's parameters would likely attract specific investor types but not broad institutional capital.

Most Realistic: Patient Advocacy and Family Offices

Family offices with long-term horizons, illiquidity tolerance, and impact motivations represent the most realistic audience. These investors can absorb 10-25 year investment horizons, tolerate total illiquidity during trial phases, and value social impact alongside financial returns.

Patient advocacy groups pursuing venture philanthropy with commercial discipline could find the structure appealing. The ALS Association has already partnered with ALS Investment Fund II targeting $100 million, explicitly "applying Venture Capital model to attract additional investment to ALS." These organizations understand and employ innovative financing structures.

Less Likely: Mainstream Institutional Capital

Top-tier venture capital firms like a16z, Sequoia, or major healthcare-focused hedge funds would likely find the risk-adjusted returns insufficient compared to alternative biotech investments offering equity upside.

To become competitive for mainstream institutional capital, the model would require structural enhancements:

• Increasing royalty rates to 10-15%
• Adding equity warrants capturing 5-10% ownership for M&A scenarios
• Implementing milestone payments and downside protections
• Raising target IRR to 35-40% through improved terms
• Assembling a larger portfolio of 8-10 assets across different rare diseases

Alternatively, creating a megafund structure testing multiple platform trials simultaneously across disease areas, then securitizing aggregate cash flows into senior and junior tranches, could provide "favorable risk characteristics for mainstream investors." But this adds another layer of complexity and scale requirements.

Critical Limitations the Paper Doesn't Address

The Correlation Problem

The model assumes 20% platform-based systemic correlation—all regimens tested on the same platform share some common risk factors. If HEALEY's infrastructure encounters regulatory issues, operational problems, or systematic endpoint measurement challenges, all 20 regimens suffer simultaneously.

This correlation is higher than the 10% disease-based correlation assumed for external competitors. Lo's own research showed that "performance of the mega-fund becomes less attractive when correlation between projects is introduced." The FAR model's concentration within a single disease area and single platform creates correlation risk that reduces effective diversification.

Governance Complexity Gets Hand-Waved Away

Who controls platform decisions when multiple stakeholders have competing interests?

If one drug shows promising biomarker signals but requires additional enrollment that delays testing other sponsors' drugs, how are trade-offs resolved? The authors note the fund might "gravitate toward commercially attractive indications" and "could sideline high-burden but less commercially viable conditions," proposing tiered royalty rates, affordable-pricing clauses, and voluntary licensing for low- and middle-income countries as solutions.

But these mechanisms aren't modeled or implemented. Without detailed governance structures defining decision rights, conflict resolution procedures, and safeguards against mission drift, institutional investors would hesitate to commit capital.

The Securitization Step Is Missing

The authors acknowledge that creating a megafund structure testing multiple platform trials simultaneously across disease areas, then securitizing aggregate cash flows into senior and junior tranches, could provide "favorable risk characteristics for mainstream investors."

This represents a crucial evolution from the current proposal—but adds another layer of complexity and scale requirements. The senior tranches might offer mid-teens returns with reduced loss probability (similar to Royalty Pharma's late-stage focus), while junior tranches take higher risk for the 28%+ IRR.

Without this securitization, the model remains accessible only to "sophisticated investors such as hedge funds, sovereign wealth funds, impact investors"—a limited capital pool.

The Harsh Reality of Biotech Funding in 2025

The current biotech funding environment creates both opportunities and headwinds.

Biotech's share of U.S. venture capital fell to its lowest point in 20+ years (8% in 2025 vs. historical 15%+), with first financings declining from $2.6 billion in Q1 to $900 million in Q2 2025—the lowest in five quarters. The biotech IPO market remains largely paused since February 2025.

This capital scarcity creates demand for alternative financing structures and makes the FAR proposal timely. However, the same environment means risk-averse investors favor "surer bets," focusing on late-stage assets and proven companies rather than experimental financing structures for Phase 2/3 trials.

AI companies absorb vast capital that might otherwise flow to biotech, with nearly $90 billion into North American AI deals in H1 2025.

My Bottom Line Assessment

The Fund of Adaptive Royalties represents genuine innovation—the first documented proposal combining adaptive platform trial operational efficiency with royalty-based private capital for early-stage development. This intellectual contribution advances the field by demonstrating that operational innovations in trial design could enable alternative capital structures.

But will it actually get implemented? I'm skeptical.

The financial parameters fall short of attracting sophisticated institutional capital. The 28% median IRR appears competitive superficially but masks critical vulnerabilities: success probabilities twice as optimistic as ALS realities suggest, loss probabilities half as high as industry experience indicates, and royalty rates 30-40% below typical development-stage licensing deals.

The capped upside from 7% royalties cannot compensate for 50%+ probability of total loss when actual ALS Phase 3 success rates prove 7% rather than the assumed 15%.

Platform trial benefits—while real—consist primarily of time savings and enrollment efficiencies rather than transformative cost reductions or improved success rates. Drugs still fail at high rates; the platform just identifies failures faster.

What Would Make This Work

To achieve broader appeal, structural enhancements would be necessary:

1. Increase royalty rates to 10-15% to provide adequate upside
2. Add equity warrants capturing 5-10% ownership for M&A scenarios
3. Implement milestone-based deployment to reduce upfront risk
4. Create a portfolio across 8-10 rare disease platforms rather than concentrating in ALS
5. Securitize cash flows into senior and junior tranches to reach risk-averse institutional investors

These modifications would transform FAR from its current form into something closer to the megafund structure Lo originally proposed—proving once again that reaching mainstream capital markets requires conventional financial instruments rather than novel structures.

The Real Path Forward

BridgeBio Pharma demonstrates the path: Lo's insights about portfolio diversification and financial engineering prove sound, but successful implementation requires adapting to existing capital market structures.

BridgeBio achieved Lo's diversification objectives through conventional VC/private equity financing within a parent-subsidiary corporate structure, reaching $9 billion market cap without creating research-backed obligations or securitized tranches. The lesson is that intellectual innovation in financial engineering can improve biotech investment—but the vehicle must use familiar legal and financial structures that investors already understand and trust.

The Cho et al. paper's most significant contribution may ultimately be advancing the conversation about innovative financing during a challenging period for biotech capital rather than providing a ready-to-implement solution. By quantifying expected returns from combining platform trial efficiency with royalty financing, the paper demonstrates feasibility in principle while revealing practical barriers.

Future implementations will likely incorporate elements of FAR—portfolio approaches focused on rare diseases, leveraging platform trial infrastructure, employing royalty structures for alignment—while modifying parameters and governance to address the limitations I've identified here.

The proposal deserves serious consideration from patient advocacy groups, disease-focused foundations, and impact investors who might pilot the model at modest scale to generate real-world evidence. If several small FAR-style funds demonstrate successful deployment and reasonable returns over the next 5-7 years, the approach could evolve into a meaningful alternative capital source for rare disease drug development.

Until then, it remains what Andrew Lo's work has often produced: an intellectually compelling proposal awaiting the difficult transition from theory to practice.

Disclaimer: I am not a financial adviser, and this analysis does not constitute investment advice. The views expressed here are my own based on publicly available information and my professional experience in pharmaceutical business development. Clinical trial outcomes, drug development success rates, and investment returns are inherently uncertain. Anyone considering investment in biotech financing structures should consult with qualified financial and legal advisors. Information about specific companies, trials, and financial instruments may change. This analysis is provided for informational and educational purposes only.