Therapeutic development for rare diseases is difficult for pharmaceutical companies due to significant scientific challenges, extensive costs, and low financial returns. It is increasingly common for caregivers and patient advocacy groups to partner with biomedical professionals to finance and develop treatments for rare diseases. This case study illustrates the story of Terry Pirovolakis, a father who partnered with biomedical professionals to develop the novel gene therapy, Melpida, within 36 months of the diagnosis of his infant son. We identify the factors that led to the success of Melpida and analyze the business model of Elpida Therapeutics, a social purpose corporation founded by Pirovolakis to reproduce the success of Melpida for other rare diseases. We conclude with four lessons from Melpida to inform caregivers like Pirovolakis on developing novel gene therapies to save their loved ones.

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.

We apply survival analysis as well as machine learning models to predict the duration of clinical trials using the largest dataset so far constructed in this domain. Neural network-based DeepSurv yields the most accurate predictions and we identify key factors that are most predictive of trial duration. This methodology may help clinical researchers optimize trial designs for expedited testing, and can also reduce the financial risk of drug development, which in turn will lower the cost of funding and increase the amount of capital allocated to this sector.

We develop an evolutionary model for individual discriminatory behavior that emerges naturally in a mixed population as an adaptive strategy. Our findings show that, when individuals have finite memory and face uncertain environments, they may rely on prior biases and observable group traits to make decisions, leading to discriminatory practices. We also demonstrate that a finite memory is a consequence of natural selection because it leads to higher fitness in dynamic environments with mutations. This adaptability allows individuals with finite memory to better respond to environmental variability, offering a potential evolutionary advantage. Our study suggests that memory constraints and environmental changes are critical factors in sustaining biased behavior, suggesting insights into the persistence of discrimination in real-world settings and possible mitigation strategies across fields, including education, policymaking, and artificial intelligence.

Biomedicine is experiencing an inflection point in which the origins of many human diseases have been decoded, leading to new treatments and, in some cases, complete cures. Many domain experts acknowledge that the gating factor to innovation is not knowledge, but rather a lack of financial resources to translate theory into practice, the so-called “valley of death” between scientific discovery and the clinical testing that must be done with human subjects before regulators will approve a new drug or medical device. This process of translational medicine is largely an exercise in risk management—organized as a carefully planned sequence of experiments, each one involving a progressively larger number of subjects that may or may not be allowed to continue, depending on the results of the prior experiment. It is, therefore, a natural setting in which to apply modern portfolio theory. The authors describe one such application involving a biotechnology company focused on genetic diseases and the lessons learned from that experience.

CONTEXT: Cell and gene therapies (CGTs) offer treatment to rare and oftentimes deadly diseases. Because of their high price and uncertain clinical outcomes, US insurers commonly restrain patient access to CGTs, and these barriers may create or perpetuate existing disparities. A reconsideration of existing insurance policies to improve access and reduce disparities is currently underway. One method insurers use to support access and protect them from large, unexpected claims is the purchase of reinsurance. In exchange for an upfront per-member-per-month (PMPM) premium, the reinsurer pays the claim and rebates the insurer at the end of the contract period if there are funds leftover. However, existing reinsurance plans may not cover CGTs or charge exorbitant fees for coverage.

METHODS: We simulate the incremental annual per-person reinsurer costs to cover CGTs existing or expected between 2023 and 2035 for the US population and by payer type based on previously published estimates of expected US spending on CGTs, assumed US population of 330 persons, and current CGT reinsurance fees. We illustrate our methods by estimating the incremental annual per-person costs overall payers and to state Medicaid plans of sickle cell disease–targeted CGTs.

FINDINGS: We estimate annual incremental spending on CGTs 2023–2035 to amount to $20.4 billion, or $15.69 per person. Total annual estimated spending is expected to concentrate among commercial plans. Sickle cell–targeted CGTs add a maximum of $0.78 PMPM in costs to all payers and will concentrate within state Medicaid programs. Reinsurance fees add to expected costs.

CONCLUSIONS: Annual per-person costs to provide access to CGTs are expected to concentrate in commercial and state Medicaid plans. Policies that improve CGT coverage and affordability are needed.

We propose a new performance attribution framework that decomposes a constrained portfolio’s holdings, expected returns, variance, expected utility, and realized returns into components attributable to (1) the unconstrained mean-variance optimal portfolio; (2) individual static constraints; and (3) information, if any, arising from those constraints. A key contribution of our framework is the recognition that constraints may contain information that is correlated with returns, in which case imposing such constraints can affect performance. We extend our framework to accommodate estimation risk in portfolio construction using Bayesian portfolio analysis, which allows one to select constraints that improve—or are least detrimental to—future performance. We provide simulations and empirical examples involving constraints on environmental, social, and governance portfolios. Under certain scenarios, constraints may improve portfolio performance relative to a passive benchmark that does not account for the information contained in these constraints.

Within the contemporary context of environmental, social, and governance (ESG) investing principles, the authors explore the risk–reward characteristics of portfolios in the United States, Europe, and Japan constructed using the foundational tenets of Markowitz’s modern portfolio theory with data from six major ESG rating agencies. They document statistically significant excess returns in ESG portfolios from 2014 to 2020 in the United States and Japan. They propose several statistical and voting-based methods to aggregate individual ESG ratings, the latter based on the theory of social choice. They find that aggregating individual ESG ratings improves portfolio performance. In addition, the authors find that a portfolio based on Treynor–Black weights further improves the performance of ESG portfolios. Overall, these results suggest that significant signals in ESG rating scores can enhance portfolio construction despite their noisy nature.

We identify some of the most pressing issues facing the adoption of large language models (LLMs) in practical settings, and propose a research agenda to reach the next technological inflection point in generative AI. We focus on three challenges facing most LLM applications: domain-specific expertise and the ability to tailor that expertise to a user’s unique situation, trustworthiness and adherence to the user’s moral and ethical standards, and conformity to regulatory guidelines and oversight. These challenges apply to virtually all industries and endeavors in which LLMs can be applied, such as medicine, law, accounting, education, psychotherapy, marketing, and corporate strategy. For concreteness, we focus on the narrow context of financial advice, which serves as an ideal test bed both for determining the possible shortcomings of current LLMs and for exploring ways to overcome them. Our goal is not to provide solutions to these challenges—which will likely take years to develop—but to propose a framework and road map for solving them as part of a larger research agenda for improving generative AI in any application.

The National Center for Advancing Translational Sciences' virtual 2021 conference on gene-targeted therapies (GTTs) encouraged multidisciplinary dialogue on a wide range of GTT topic areas. Each of three parallel working groups included social scientists and clinical scientists, and the three major sessions included a presentation on economic issues related to their focus area. These experts also coordinated their efforts across the three groups. The economics-related presentations covered three areas with some overlap: (1) value assessment, uncertainty, and dynamic efficiency; (2) affordability, pricing, and financing; and (3) evidence generation, coverage, and access. This article provides a synopsis of three presentations, some of their key recommendations, and an update on related developments in the past year. The key high-level findings are that GTTs present unique data and policy challenges, and that existing regulatory, health technology assessment, as well as payment and financing systems will need to adapt. But these adjustments can build on our existing foundation of regulatory and incentive systems for innovation, and much can be done to accelerate progress in GTTs. Given the substantial unmet medical need that exists for these oft-neglected patients suffering from rare diseases, it would be a tragedy to not leverage these exciting scientific advances in GTTs.