Financial Intermediation and the Funding of Biomedical Innovation: A Review
Lo, Andrew W., Richard T. Thakor (2023), Financial Intermediation and The Funding of Biomedical Innovation: A Review, Journal of Financial Intermediation 54.
We review the literature on financial intermediation in the process by which new medical therapeutics are financed, developed, and delivered. We discuss the contributing factors that lead to a key finding in the literature—underinvestment in biomedical R&D—and focus on the role that banks and other intermediaries can play in financing biomedical R&D and potentially closing this funding gap. We conclude with a discussion of the role of financial intermediation in the delivery of healthcare to patients.
Leveraging Patient Preference Information in Medical Device Clinical Trial Design
Rincon-Gonzalez, Lilianna, Wendy K. D. Selig, Brett Hauber, Shelby D. Reed, Michelle E. Tarver, Shomesh E. Chaudhuri, Andrew W. Lo, Dean Bruhn-Ding, and Barry Liden (2023), Leveraging Patient Preference Information in Medical Device Clinical Trial Design, Therapeutic Innovation & Regulatory Science 57, 152–159.
Use of robust, quantitative tools to measure patient perspectives within product development and regulatory review processes offers the opportunity for medical device researchers, regulators, and other stakeholders to evaluate what matters most to patients and support the development of products that can best meet patient needs. The medical device innovation consortium (MDIC) undertook a series of projects, including multiple case studies and expert consultations, to identify approaches for utilizing patient preference information (PPI) to inform clinical trial design in the US regulatory context. Based on these activities, this paper offers a cogent review of considerations and opportunities for researchers seeking to leverage PPI within their clinical trial development programs and highlights future directions to enhance this field. This paper also discusses various approaches for maximizing stakeholder engagement in the process of incorporating PPI into the study design, including identifying novel endpoints and statistical considerations, crosswalking between attributes and endpoints, and applying findings to the population under study. These strategies can help researchers ensure that clinical trials are designed to generate evidence that is useful to decision makers and captures what matters most to patients.
Levy, Moshe, and Andrew W. Lo (2022), Hamilton’s rule in economic decision-making, Proceedings of the National Academy of Sciences 119 (16).
Hamilton’s rule [W. D. Hamilton, Am. Nat. 97, 354–356 (1963); W. D. Hamilton, J. Theor. Biol. 7, 17–52 (1964)] quantifies the central evolutionary ideas of inclusive fitness and kin selection into a simple algebraic relationship. Evidence consistent with Hamilton’s rule is found in many animal species. A drawback of investigating Hamilton’s rule in these species is that one can estimate whether a given behavior is consistent with the rule, but a direct examination of the exact cutoff for altruistic behavior predicted by Hamilton is almost impossible. However, to the degree that economic resources confer survival benefits in modern society, Hamilton’s rule may be applicable to economic decision-making, in which case techniques from experimental economics offer a way to determine this cutoff. We employ these techniques to examine whether Hamilton’s rule holds in human decision-making, by measuring the dependence between an experimental subject’s maximal willingness to pay for a gift of $50 to be given to someone else and the genetic relatedness of the subject to the gift’s recipient. We find good agreement with the predictions of Hamilton’s rule. Moreover, regression analysis of the willingness to pay versus genetic relatedness, the number of years living in the same residence, age, and sex shows that almost all the variation is explained by genetic relatedness. Similar but weaker results are obtained from hypothetical questions regarding the maximal risk to her own life that the subject is willing to take in order to save the recipient’s life.
Halem, Zachery M., Andrew W. Lo, Egor Matveyev, and Quraishi, Sarah (2022), The Effects of Spending Rules and Asset Allocation on Non-Profit Endowments, Journal of Portfolio Management 49 (1), 81-106.
The long-run impact and implications of an endowment’s spending policy and asset allocation decisions are examined. Using a dynamic model, the authors explore how different endowment spending rules influence the dynamics of an endowment’s size and future spending. They find that different parameters within each spending rule have significant long-term impact on wealth accumulation and spending capacity. Using Merton's (1993) endowment model and compiled asset allocation data, they estimate the intertemporal preferences and risk aversion of several major endowments and find significant variation across endowments in their propensity to increase portfolio risk in response to increased spending needs.
Alhamdan, Abdullah, Zachery Halem, Irene Hernandez, Andrew W. Lo, Manish Singh, and Dennis Whyte (2022), Financing Fusion Energy, Journal of Investment Management 21 (1).
The case for investing in fusion energy has never been greater, given increasing global energy demand, high annual carbon dioxide output, and technological limitations for wind and solar power. Nevertheless, financing for fusion companies through traditional means has proven challenging. While fusion startups have an unparalleled upside, their high upfront costs, lengthy delay in payoff, and high risk of commercial failure have historically restricted funding interest to a niche set of investors. Drawing on insights from investor interviews and case studies of public–private partnerships, we propose a megafund structure in which a large number of projects are securitized into a single holding company funded through various debt and equity tranches, with first loss capital guarantees from governments and philanthropic partners. The megafund exploits many of the core properties of the fusion
industry: the diversity of approaches to engender fusion reactions, the ability to create revenue-generating divestitures in related fields, and the breadth of auxiliary technologies needed to support a functioning power plant. The model expands the pool of available capital by creating tranches with different risk–return tradeoffs and providing a diversified “fusion index” that can be viewed as a long hedge against fossil fuels. Simulations of a fusion megafund demonstrate positive returns on equity (ROE) and low default rates for the capital raised using debt.
Elkind, Daniel, Kathryn Kaminski, Andrew W. Lo, Kien-Wei Siah, and Chi Heem Wong (2022), When Do Investors Freak Out? Machine Learning Predictions of Panic Selling, Journal of Financial Data Science 4 (1), 11-39.
Using a novel dataset of 653,455 individual brokerage accounts belonging to 298,556 households, we document the frequency, timing, and duration of panic sales, which we define as a decline of 90% of a household account’s equity assets over the course of one month, of which 50% or more is due to trades. We find that a disproportionate number of households make panic sales when there are sharp market downturns, a phenomenon we call ‘freaking out.’ We show that panic selling and freak-outs are predictable and fundamentally different from other well-known behavioral patterns such as overtrading or the disposition effect.
An Artificial Intelligence-Based Industry Peer Grouping System
Bonne, George, Andrew W. Lo, Abilash Prabhakaran, Kien-Wei Siah, Manish Singh, Xinxin Wang, Peter Zangari, and Howard Zhang (2022), An Artificial Intelligence-Based Industry Peer Grouping System, The Journal of Financial Data Science 4 (2), 9-36.
In this article, the authors develop a data-driven peer grouping system using artificial intelligence (AI) tools to capture market perception and, in turn, group companies into clusters at various levels of granularity. In addition, they develop a continuous measure of similarity between companies; they use this measure to group companies into clusters and construct hedged portfolios. In the peer groupings, companies grouped in the same clusters had strong homogeneous risk and return profiles, whereas different clusters of companies had diverse, varying risk exposures. The authors extensively evaluated the clusters and found that companies grouped by their method had higher out-of-sample return correlation but lower stability and interpretability than companies grouped by a standard industry classification system. The authors also develop an interactive visualization system for identifying AI-based clusters and similar companies.
Use of Bayesian Decision Analysis to Minimize Harm in Patient-Centered Randomized Clinical Trials in Oncology
Montazerhodjat, Vahid, Shomesh E. Chaudhuri, Daniel J. Sargent, Andrew W. Lo (2017), Use of Bayesian Decision Analysis to Minimize Harm in Patient-Centered Randomized Clinical Trials in Oncology, JAMA Oncology 3 (9).
Importance Randomized clinical trials (RCTs) currently apply the same statistical threshold of alpha = 2.5% for controlling for false-positive results or type 1 error, regardless of the burden of disease or patient preferences. Is there an objective and systematic framework for designing RCTs that incorporates these considerations on a case-by-case basis?
Objective To apply Bayesian decision analysis (BDA) to cancer therapeutics to choose an alpha and sample size that minimize the potential harm to current and future patients under both null and alternative hypotheses.
Data Sources We used the National Cancer Institute (NCI) Surveillance, Epidemiology, and End Results (SEER) database and data from the 10 clinical trials of the Alliance for Clinical Trials in Oncology.
Study Selection The NCI SEER database was used because it is the most comprehensive cancer database in the United States. The Alliance trial data was used owing to the quality and breadth of data, and because of the expertise in these trials of one of us (D.J.S.).
Data Extraction and Synthesis The NCI SEER and Alliance data have already been thoroughly vetted. Computations were replicated independently by 2 coauthors and reviewed by all coauthors.
Main Outcomes and Measures Our prior hypothesis was that an alpha of 2.5% would not minimize the overall expected harm to current and future patients for the most deadly cancers, and that a less conservative alpha may be necessary. Our primary study outcomes involve measuring the potential harm to patients under both null and alternative hypotheses using NCI and Alliance data, and then computing BDA-optimal type 1 error rates and sample sizes for oncology RCTs.
Results We computed BDA-optimal parameters for the 23 most common cancer sites using NCI data, and for the 10 Alliance clinical trials. For RCTs involving therapies for cancers with short survival times, no existing treatments, and low prevalence, the BDA-optimal type 1 error rates were much higher than the traditional 2.5%. For cancers with longer survival times, existing treatments, and high prevalence, the corresponding BDA-optimal error rates were much lower, in some cases even lower than 2.5%.
Conclusions and Relevance Bayesian decision analysis is a systematic, objective, transparent, and repeatable process for deciding the outcomes of RCTs that explicitly incorporates burden of disease and patient preferences.
Financing pharmaceuticals and medical devices for pain treatment and opioid use disorder
Siah, Kien-Wei, Dermot P. Maher, and Andrew W. Lo (2022), Financing pharmaceuticals and medical devices for pain treatment and opioid use disorder, Emerging Trends in Drugs, Addictions, and Health 2, 1-8.
The opioid epidemic in the U.S. has resulted in significant costs in human lives as well as to the health care system, employers, and insurers. While there is great motivation and urgency to address the opioid crisis, there are currently few non-opioid pain management medications in the development pipeline. The growing regulatory pressures and stigma surrounding opioids have discouraged investments and research in the pain industry. Using estimates from the literature, our simulations show that a portfolio of pharmaceuticals and medical devices for pain treatment and opioid use disorder, diversified and optimized across different development pathways, yields single digit annualized returns. This suggests that active collaboration between the public and private sectors is needed to incentivize investments in pain research.
Identifying and Mitigating Potential Biases in Predicting Drug Approvals
Xu, Qingyang, Elaheh Ahmadi, Alexander Amini, Daniela Rus, and Andrew W. Lo, Identifying and Mitigating Potential Biases in Predicting Drug Approvals, Drug Safety 45, 521–533.
Machine learning models are increasingly applied to predict the drug development outcomes based on intermediary clinical trial results. A key challenge to this task is to address various forms of bias in the historical drug approval data.
We aimed to identify and mitigate the bias in drug approval predictions and quantify the impacts of debiasing in terms of financial value and drug safety.
We instantiated the Debiasing Variational Autoencoder, the state-of-the-art model for automated debiasing. We trained and evaluated the model on the Citeline dataset provided by Informa Pharma Intelligence to predict the final drug development outcome from phase II trial results.
The debiased Debiasing Variational Autoencoder model achieved better performance (measured by the F1�1 score 0.48) in predicting the drug development outcomes than its un-debiased baseline (F1�1 score 0.25). It had a much higher true-positive rate than baseline (60% vs 15%), while its true-negative rate was slightly lower (88% vs 99%). The Debiasing Variational Autoencoder distinguished between drugs developed by large pharmaceutical firms and those by small biotech companies. The model prediction is strongly influenced by multiple factors such as prior approval of the drug for another indication, whether the trial meets the positive/negative endpoints, and the year when the trial is completed. We estimate that the debiased model generates financial value for the drug developer in six major therapeutic areas, with a range of US$763–1,365 million.
Our analysis shows that debiasing improves the financial efficiency of late-stage drug development. From the pharmacovigilance perspective, the debiased model is more likely to identify drugs that are both safe and effective. Meanwhile, it may predict a higher probability of success for drugs with potential adverse effects (because of its lower true-negative rate), thus it must be used with caution to predict the development outcomes of drug candidates currently in the pipeline.