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.
Algorithmic Models of Investor Behavior2021
We propose a heuristic approach to modeling investor behavior by simulating combinations of simpler systematic investment strategies associated with well-known behavioral biases—in functional forms motivated by an extensive review of the behavioral finance literature—using parameters calibrated from historical data. We compute the investment performance of these heuristics individually and in pairwise combinations using both simulated and historical asset-class returns. The mean-reversion or momentum nature of a heuristic can often explain its effect on performance, depending on whether asset returns are consistent with such dynamics. These algorithms show that seemingly irrational investor behavior may, in fact, have been shaped by evolutionary forces and can be effective in certain environments and maladaptive in others.
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.
World of EdCraft: Challenges and Opportunities in Synchronous Online Teaching2021
Online teaching at higher educational institutions has become a much higher priority in the face of the COVID-19 pandemic, but most faculty and staff at these institutions are ill-prepared to adapt their teaching methods and content to this new medium. In this article, we describe our approach to dealing with the challenges and opportunities of synchronous online teaching by borrowing ideas and tools from the gaming community. The gamification of education is a well- known concept, but we found few applications in higher-education settings to rely on when we were forced to move online in March 2020. We hope to remedy this gap by providing colleagues with a step-by-step guide to setting up their own home studios, including a complete listing of the software and hardware that we use and how we use them in three distinct online teaching applications: a large (90-student) graduate healthcare finance course at MIT, an even larger (200- student) undergraduate statistics course at the University of Tennessee Knoxville, and a medium- sized (50-student) graduate operations management course at MIT.
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.
Robert C. Merton: The First Financial Engineer2020
This is an edited version of a talk given at the Robert C. Merton 75th Birthday Celebration Conference held at MIT on August 5 and 6, 2019. A video of the talk is available at https://bit.ly/2nvITM6.
This article is one of a pair of articles published in this volume about Robert C. Merton's contributions to the science of financial economics. The other article in this pair is “Robert C. Merton and the Science of Finance” by Zvi Bodie.
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.
SCRAM: A Platform for Securely Measuring Cyber Risk2020
We develop a new cryptographic platform called SCRAM (Secure Cyber Risk Aggregation and Measurement) that allows multiple entities to compute aggregate cyber-risk measures without requiring any entity to disclose its own sensitive data on cyberattacks, penetrations, and losses. Using the SCRAM platform, we present results from two computations in a pilot study with six large private-sector companies: (1) benchmarks of the adoption rates of 171 critical security measures and (2) links between monetary losses from 49 security incidents and the specific sub-control failures implicated in each incident. These results provide insight into problematic cyber-risk-control areas that need additional scrutiny and/or investment, but in a completely anonymized and privacy-preserving way.
Measuring Risk Preferences and Asset-Allocation Decisions: A Global Survey Analysis2020
We use a global survey of over 22,400 individual investors, 4,892 financial advisors, and 2,060 institutional investors between 2015 and 2017 to elicit their asset allocation behavior and risk preferences. We find substantially different behaviors among these three groups of market participants. Most institutional investors exhibit highly contrarian reactions to past returns in their equity allocations. Financial advisors are also mostly contrarian; a few of them demonstrate passive behavior. However, individual investors tend to extrapolate past performance. We use a clustering algorithm to partition individuals into five distinct types: passive investors, risk avoiders, extrapolators, contrarians, and optimistic investors. Across demographic categories, older investors tend to be more passive and risk averse.
Macroeconomic Models for Monetary Policy: A Critical Review from a Finance Perspective2020
We provide a critical review of macroeconomic models used for monetary policy at central banks from a finance perspective. We review the history of monetary policy modeling, survey the core monetary models used by major central banks, and construct an illustrative model for those readers who are unfamiliar with the literature. Within this framework, we highlight several important limitations of current models and methods, including the fact that local-linearization approximations omit important nonlinear dynamics, yielding biased impulse-response analysis and parameter estimates. We also propose new features for the next generation of macrofinancial policy models, including: a substantial role for a financial sector, the government balance sheet and unconventional monetary policies; heterogeneity, reallocation, and redistribution effects; the macroeconomic impact of large nonlinear risk-premium dynamics; time-varying uncertainty; financial sector and systemic risks; imperfect product market and markups; and further advances in solution, estimation, and evaluation methods for dynamic quantitative structural models.