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Can Financial Economics Cure Cancer?2021
Funding for early-stage biomedical innovation has become more difficult to secure at the same time that medical breakthroughs seem to be occurring at ever increasing rates. One explanation for this counterintuitive trend is that increasing scientific knowledge can actually lead to greater economic risk for investors in the life sciences. While the Human Genome Project, high-throughput screening, genetic biomarkers, immunotherapies, and gene therapies have made a tremendously positive impact on biomedical research and, consequently, patient lives, they have also increased the cost and complexity of the drug development process, causing many investors to shift their assets to more attractive investment opportunities. This suggests that new business models and financing strategies can be used to reduce the risk and increase the attractiveness of biomedical innovation so as to bring new and better therapies to patients faster.
Patterns of Multimorbidity2021
With multimorbidity becoming the norm rather than the exception, the management of multiple chronic diseases is a major challenge facing healthcare systems worldwide. Using a large, nationally representative database of electronic medical records from the United Kingdom spanning the years 2005 to 2016 and consisting over 4.5 million patients, we apply statistical methods and network analysis to identify comorbid pairs and triads of diseases and identify clusters of chronic conditions across different demographic groups. Unlike many previous studies, which generally adopt cross-sectional designs, we examine temporal changes in the patterns of multimorbidity. In addition, we perform survival analysis to examine the impact of multimorbidity on mortality.
Accelerating glioblastoma therapeutics via venture philanthropy2021
Development of curative treatments for glioblastoma (GBM) has been stagnant in recent decades largely because of significant financial risks. A portfolio-based strategy for the parallel discovery of breakthrough therapies can effectively reduce the financial risks of potentially transformative clinical trials for GBM. Using estimates from domain experts at the National Brain Tumor Society (NBTS), we analyze the performance of a portfolio of 20 assets being developed for GBM, diversified across different development phases and therapeutic mechanisms. We find that the portfolio generates a 14.9% expected annualized rate of return. By incorporating the adaptive trial platform GBM AGILE in our simulations, we show that at least one drug candidate in the portfolio will receive US Food and Drug Administration (FDA) approval with a probability of 79.0% in the next decade.
Life sciences intellectual property licensing at the Massachusetts Institute of Technology2021
Academic institutions play a central role in the biotech industry through technology licensing and the creation of startups, but few data are available on their performance and the magnitude of their impact. Here we present a systematic study of technology licensing by one such institution, the Massachusetts Institute of Technology (MIT). Using data on the 76 therapeutics-focused life sciences companies formed through MIT’s Technology Licensing Office from 1983 to 2017, we construct several measures of impact, including MIT patents cited in the Orange Book, capital raised, outcomes from mergers and acquisitions, patents granted to MIT intellectual property licensees, drug candidates discovered and US drug approvals—a key benchmark of innovation in the biopharmaceutical industry. As of December 2017, Orange Book listings for four approved small-molecule drugs cite MIT patents, but another 31 FDA-approved drugs (excluding candidates acquired after phase 3) had some involvement of MIT licensees. Fifty-five percent of the latter were either a new molecular entity or a new biological entity, and 55% were granted priority review, an indication that they address an unmet medical need. The methodology described here may be a useful framework for other academic institutions to track outcomes of intellectual property in the therapeutics domain.
Incorporating Patient Preferences via Bayesian Decision Analysis2021
The regulatory process for market authorization of medical diagnostic and therapeutic products is fraught with ethical dilemmas that regulators outside the medical industry do not face. The consequences of approving an ineffective therapy with potentially dangerous side effects (a “Type I error” or false positive) must be weighed against not approving a safe and effective therapy (a “Type II error” or false negative) that could help ease the burden of disease for many patients. Regulators must strike the proper balance by considering multiple factors, including scientific merit; clinical evidence from randomized, control trials; the burden of disease; the current standard of care and alternatives; and patient preferences. How these factors are—and should be—weighed is not always clear, which only encourages criticism by whichever stakeholder group disagrees with the decision.
Parkinson’s Patients’ Tolerance for Risk and Willingness to Wait for Potential Benefits of Novel Neurostimulation Devices: A Patient-Centered Threshold Technique Study2021
Background. Parkinson's disease (PD) is neurodegenerative, causing motor, cognitive, psychological, somatic, and autonomic symptoms. Understanding PD patients' preferences for novel neurostimulation devices may help ensure that devices are delivered in a timely manner with the appropriate level of evidence. Our objective was to elicit preferences and willingness-to-wait for novel neurostimulation devices among PD patients to inform a model of optimal trial design. Methods. We developed and administered a survey to PD patients to quantify the maximum levels of risks that patients would accept to achieve potential benefits of a neurostimulation device. Threshold technique was used to quantify patients' risk thresholds for new or worsening depression or anxiety, brain bleed, or death in exchange for improvements in "on-time," motor symptoms, pain, cognition, and pill burden. The survey elicited patients' willingness to wait to receive treatment benefit. Patients were recruited through Fox Insight, an online PD observational study. Results. A total of 2740 patients were included and a majority were White (94.6%) and had a 4-year college degree (69.8%). Risk thresholds increased as benefits increased. Threshold for depression or anxiety was substantially higher than threshold for brain bleed or death. Patient age, ambulation, and prior neurostimulation experience influenced risk tolerance. Patients were willing to wait an average of 4 to 13 years for devices that provide different levels of benefit. Conclusions. PD patients are willing to accept substantial risks to improve symptoms. Preferences are heterogeneous and depend on treatment benefit and patient characteristics. The results of this study may be useful in informing review of device applications and other regulatory decisions and will be input into a model of optimal trial design for neurostimulation devices.
Financing Correlated Drug Development Projects2021
Current business models have struggled to support early-stage drug development. In this paper, we study an alternative financing model, the megafund structure, to fund drug discovery. We extend the framework proposed in previous studies to account for correlation between phase transitions in drug development projects, thus making the model a more realistic representation of biopharma research and development. In addition, we update the parameters used in our simulation with more recent estimates of the probability of success (PoS). We find that the performance of the megafund becomes less attractive when correlation between projects is introduced. However, the risk of default and the expected returns of the vanilla megafund remain promising even under moderate levels of correlation. In addition, we find that a leveraged megafund outperforms an equity-only structure over a wide range of assumptions about correlation and PoS.
A Cost/Benefit Analysis of Clinical Trial Designs for COVID-19 Vaccine Candidates2020
We compare and contrast the expected duration and number of infections and deaths averted among several designs for clinical trials of COVID-19 vaccine candidates, including traditional and adaptive randomized clinical trials and human challenge trials. Using epidemiological models calibrated to the current pandemic, we simulate the time course of each clinical trial design for 756 unique combinations of parameters, allowing us to determine which trial design is most effective for a given scenario. A human challenge trial provides maximal net benefits—averting an additional 1.1M infections and 8,000 deaths in the U.S. compared to the next best clinical trial design—if its set-up time is short or the pandemic spreads slowly. In most of the other cases, an adaptive trial provides greater net benefits.
Financially Adaptive Clinical Trials via Option Pricing Analysis2020
The regulatory approval process for new therapies involves costly clinical trials that can span multiple years. When valuing a candidate therapy from a financial perspective, industry sponsors may terminate a program early if clinical evidence suggests market prospects are not as favorable as originally forecasted. Intuition suggests that clinical trials that can be modified as new data are observed, i.e., adaptive trials, are more valuable than trials without this flexibility. To quantify this value, we propose modeling the accrual of information in a clinical trial as a sequence of real options, allowing us to systematically design early-stopping decision boundaries that maximize the economic value to the sponsor. In an empirical analysis of selected disease areas, we find that when a therapy is ineffective, our adaptive financing method can decrease the expected cost incurred by the sponsor in terms of total expenditures, number of patients, and trial length by up to 46%. Moreover, by amortizing the large fixed costs associated with a clinical trial over time, financing these projects becomes less risky, resulting in lower costs of capital and larger valuations when the therapy is effective.
© 2020 Elsevier B.V. All rights reserved.
Estimating the Financial Impact of Gene Therapy2020
We assess the potential financial impact of future gene therapies by identifying the 109 late-stage gene therapy clinical trials currently underway, estimating the prevalence and incidence of their corresponding diseases, developing novel mathematical models of the increase in quality-adjusted life years for each approved gene therapy, and simulating the launch prices and the expected spending of these therapies over a 15-year time horizon. The results of our simulation suggest that an expected total of 1.09 million patients will be treated by gene therapy from January 2020 to December 2034. The expected peak annual spending on these therapies is $25.3 billion, and the total spending from January 2020 to December 2034 is $306 billion. We decompose their annual estimated spending by treated age group as a proxy for U.S. insurance type, and consider the tradeoffs of various methods of payment for these therapies to ensure patient access to their expected benefits.
A Brain Capital Grand Strategy: Toward Economic Reimagination2020
Current brain research, innovation, regulatory, and funding systems are artificially siloed, creating boundaries in our understanding of the brain based on constructs such as aging, mental health, and/or neurology, when these systems are all inextricably integral.
Grand strategy provides a broad framework that helps to guide all elements of a major, long-term project. There are converging global trends resulting from the COVID pandemic compelling a Brain Capital Grand Strategy: widespread appreciation of the rise in brain health issues (e.g., increase prevalence of mental illness and high rates of persons with age-related cognitive impairment contracting COVID), increased automation, job loss and underemployment, radical restructuring of health systems, rapid consumer adoption and acceptance of digital and remote solutions, and recognition of the need for economic reimagination. If we respond constructively to this crisis, the COVID pandemic could catalyze institutional change and a better social contract.
The Challenging Economics of Vaccine Development in the Age of COVID-19, and What Can Be Done About It2020
Financing Vaccines for Global Health Security2020
Recent outbreaks of infectious pathogens such as Zika, Ebola, and COVID‐19 have underscored the need for the dependable availability of vaccines against emerging infectious diseases (EIDs). The cost and risk of R&D programs and uniquely unpredictable demand for EID vaccines have discouraged vaccine developers, and government and nonprofit agencies have been unable to provide timely or sufficient incentives for their development and sustained supply. We analyze the economic returns of a portfolio of EID vaccine assets, and find that under realistic financing assumptions, the expected returns are significantly negative, implying that the private sector is unlikely to address this need without public‐sector intervention. We have sized the financing deficit for this portfolio and propose several potential solutions, including price increases, enhanced public‐private partnerships, and subscription models through which individuals would pay annual fees to obtain access to a portfolio of vaccines in the event of an outbreak.
Bayesian Adaptive Clinical Trials for Anti‐Infective Therapeutics During Epidemic Outbreaks2020
In the midst of epidemics such as COVID-19, therapeutic candidates are unlikely to be able to complete the usual multi-year clinical trial and regulatory approval process within the course of an outbreak. We apply a Bayesian adaptive patient-centered model—which minimizes the expected harm of false positives and false negatives—to optimize the clinical trial development path during such outbreaks. When the epidemic is more infectious and fatal, the Bayesian-optimal sample size in the clinical trial is lower and the optimal statistical significance level is higher. For COVID-19 (assuming a static Ro = 2 and initial infection percentage of 0.1%), the optimal significance level is 7.1% for a clinical trial of a non-vaccine anti-infective therapeutic clinical trial and 13.6% for that of a vaccine. For a dynamic Ro ranging from 2 to 4, the corresponding values are 14.4% and 26.4%, respectively. Our results illustrate the importance of adapting the clinical trial design and the regulatory approval process to the specific parameters and stage of the epidemic.
Estimating Probabilities of Success of Vaccine and Other Anti-Infective Therapeutic Development Programs2020
A key driver in biopharmaceutical investment decisions is the probability of success of a drug development program. We estimate the probabilities of success (PoS) of clinical trials for vaccines and other anti-infective therapeutics using 43,414 unique triplets of clinical trial, drug, and disease between January 1, 2000, and January 7, 2020, yielding 2,544 vaccine programs and 6,829 non-vaccine programs targeting infectious diseases. The overall estimated PoS for an industry-sponsored vaccine program is 39.6%, and 16.3% for an industry-sponsored anti-infective therapeutic. Among industry-sponsored vaccines programs, only 12 out of 27 disease categories have seen at least one approval, with the most successful being against monkeypox (100%), rotavirus (78.7%), and Japanese encephalitis (67.6%). The three infectious diseases with the highest PoS for industry-sponsored nonvaccine therapeutics are smallpox (100%), CMV (31.8%), and onychomycosis (29.8%). Nonindustry- sponsored vaccine and non-vaccine development programs have lower overall PoSs: 6.8% and 8.2%, respectively. Viruses involved in recent outbreaks—MERS, SARS, Ebola, Zika—have had a combined total of only 45 non-vaccine development programs initiated over the past two decades, and no approved therapy to date (Note: our data was obtained just before the COVID-19 outbreak and do not contain information about the programs targeting this disease.) These estimates offer guidance both to biopharma investors as well as to policymakers seeking to identify areas most likely to be undeserved by private-sector engagement and in need of public-sector support.