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. 2025 Jul 2;20(7):e0325826.
doi: 10.1371/journal.pone.0325826. eCollection 2025.

Financing drug development via adaptive platform trials

Joonhyuk Cho  1 , Eugene Sorets  2 , Shomesh Chaudhuri  3 , Annette De Mattos  4 , Kristin Drake  4 , Merit E Cudkowicz  4 , Ricardo Ortiz  4 , Meredith Hasenoehrl  4 , Marianne Chase  4 , Brittney Harkey  4 , Sabrina Paganoni  4 , John Frishkopf  3 , Andrew W Lo  1   5   6   7
Affiliations

Financing drug development via adaptive platform trials

Joonhyuk Cho et al. PLoS One. .

Abstract

We propose a new approach to funding disease-specific drug development via a variation of the adaptive platform trial. This trial is designed to test a portfolio of drug candidates in parallel, with the cost of the trial partially covered by investors who receive payments from a royalty fund of the candidates in exchange for investment. Under realistic assumptions for cost, revenue, probability of success, drug sales, and royalty rates, investors may expect a return of 28%, but with a 22% probability of total loss. Such return distributions may be attractive to hedge funds, family offices, and philanthropic investors seeking both social impact and financial return. Return distributions palatable to mainstream investors may be achieved by funding multiple platform trials simultaneously and securitizing the aggregate cash flows.

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Conflict of interest statement

Andrew Lo reports personal investments in private biotech companies, biotech venture capital and mutual funds. He is a co-founder and partner of QLS Advisors, a healthcare analytics and investment company; a co-founder of BridgeBio Pharma, Gondola Bio, Quantile Health, Uncommon Cures; an advisor to Apricity Health, Aracari Bio, BrightEdge Impact Fund, Quantile Health, Think Therapeutics; and director of AbCellera, Atomwise, BridgeBio Pharma, n-Lorem, Uncommon Cures, Vesalius Therapeutics. Shomesh Chaudhuri and John Frishkopf are affiliated with QLS Advisors, a healthcare analytics and investment company. No other authors have competing interests. This does not alter our adherence to PLOS ONE policies on sharing data and materials

Figures

Fig 1
Fig 1. Scheduling of regimen enrollment for platform trials.
The figure shows the planned schedule for regimens in Batch #1 and Batch #2, each consisting of four regimens. The diagram highlights the one-month interval between regimens within a batch and the 15-month interval between the start of each batch. Our simulation includes a total of five batches.
Fig 2
Fig 2. Statistical distributions of the fund’s key performance indicators from 10,000 runs of Monte Carlo simulations.
The figure illustrates probability distributions for three key performance indicators: (a) the number of drug approvals, (b) the net present value (NPV) in millions of dollars, and (c) the internal rate of return (IRR). Each distribution represents the results from 10,000 simulation runs. (c) includes cases with “N/A” IRR values, corresponding to scenarios with no approvals.
Fig 3
Fig 3. Cumulative number of drug approvals and projected financial metrics over time from Monte Carlo simulations.
Panel (a) shows the cumulative number of drug approvals over time with percentile lines (5th, 25th, 50th, 75th, and 95th). Panels (b), (c), and (d) display annual projections for revenue, cash flow, and cash balance, respectively. Each panel demonstrates the variability in outcomes under different approval scenarios, highlighting the distribution of potential financial metrics.
See this image and copyright information in PMC

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