Rare-disease drug development is both scientifically and commercially challenging. This case study highlights Agilis Biotherapeutics (Agilis), a small private biotechnology company that has developed the most clinically advanced adeno-associated virus (AAV) gene therapy for the brain. In an international collaboration led by Agilis with National Taiwan University (NTU) Hospital and the Therapeutics for Rare and Neglected Diseases (TRND) program of the National Center for Advancing Translational Sciences (NCATS) at the National Institutes of Health, Agilis’ gene therapy for aromatic L-amino acid decarboxylase deficiency (AADC), AGIL-AADC, was granted biologics license application (BLA)-ready status by the FDA in 2018 only 18 months after being licensed from NTU by Agilis. Here, we highlight the factors that have enabled this remarkable pace of successful drug development for an ultra-rare disease.

The value added by an active investor is traditionally measured using alpha, tracking error, and the information ratio. However, these measures do not characterize the dynamic component of investor activity, nor do they consider the time horizons over which weights are changed. In this paper, we propose a technique to measure the value of active investment that captures both the static and dynamic contributions of an investment process. This dynamic alpha is based on the decomposition of a portfolio’s expected return into its frequency components using spectral analysis. The result is a static component that measures the portion of a portfolio’s expected return resulting from passive investments and security selection and a dynamic component that captures the manager’s timing ability across a range of time horizons. Our framework can be universally applied to any portfolio and is a useful method for comparing the forecast power of different investment processes. Several analytical and empirical examples are provided to illustrate the practical relevance of this decomposition.

IMPORTANCE: Few patient populations are as helpless and in need of advocacy as children with cancer. Pharmaceutical companies have historically faced significant financial disincentives to pursue pediatric oncology therapeutics, including low incidence, high costs of conducting pediatric trials, and a lack of funding for early-stage research.

OBSERVATIONS: Review of published studies of pediatric oncology research and the cost of drug development, as well as clinical trials of pediatric oncology therapeutics at ClinicalTrials.gov, identified 77 potential drug development projects to be included in a hypothetical portfolio. The returns of this portfolio were simulated so as to compute the financial returns and risk. Simulated business strategies include combining projects at different clinical phases of development, obtaining partial funding from philanthropic grants, and obtaining government guarantees to reduce risk. The purely private-sector portfolio exhibited expected returns ranging from −24.2% to 10.2%, depending on the model variables assumed. This finding suggests significant financial disincentives for pursuing pediatric oncology therapeutics and implies that financial support from the public and philanthropic sectors is essential. Phase diversification increases the likelihood of a successful drug and yielded expected returns of −5.3% to 50.1%. Standard philanthropic grants had a marginal association with expected returns, and government guarantees had a greater association by reducing downside exposure. An assessment of a proposed venture philanthropy fund demonstrated stronger performance than the purely private-sector–funded portfolio or those with traditional amounts of philanthropic support.

CLINICAL RELEVANCE: A combination of financial and business strategies has the potential to maximize expected return while eliminating some downside risk—in certain cases enabling expected returns as high as 50.1%—that can overcome current financial disincentives and accelerate the development of pediatric oncology therapeutics.

We apply Bayesian decision analysis (BDA) to incorporate patient preferences in the regulatory approval process for new therapies. By assigning weights to type I and type II errors based on patient preferences, the significance level (a) and power (1 b) of a randomized clinical trial (RCT) for a new therapy can be optimized to maximize the value to current and future patients and, consequently, to public health. We find that for weight-loss devices, potentially effective low-risk treatments have optimal as larger than the traditional one-sided significance level of 5%, whereas potentially less effective and riskier treatments have optimalas below 5%. Moreover,the optimal RCT design, including trial size, varies with the risk aversion and time-to-access preferences and the medical need of the target population.

Previous estimates of drug development success rates rely on relatively small samples from databases curated by the pharmaceutical industry and are subject to potential selection biases. Using a sample of 406,038 entries of clinical trial data for over 21,143 compounds from January 1, 2000 to October 31, 2015, we estimate aggregate clinical trial success rates and durations. We also compute disaggregated estimates across several trial features including disease type, clinical phase, industry or academic sponsor, biomarker presence, lead indication status, and time. In several cases, our results differ significantly in detail from widely cited statistics. For example, oncology has a 3.4% success rate in our sample vs. 5.1% in prior studies. However, after declining to 1.7% in 2012, this rate has improved to 2.5% and 8.3% in 2014 and 2015, respectively. In addition, trials that use biomarkers in patient-selection have higher overall success probabilities than trials without biomarkers.

This is a pre-conference essay prepared for 'What Post-Crisis Changes Does the Economics Discipline Need?', a conference organized by Diane Coyle and Enlightenment Economics, the Bank of England, and the U.K. Government Economic Service on 7 February 2012. In this essay, I trace the origins of 'theory envy' to Paul Samuelson and the mathematization of economics over the past half century, and consider its implications for how economics should be taught. Although this research program has produced many genuine breakthroughs in economics, any virtue can become a vice when taken to an extreme, and the recent financial crisis has given us an opportunity to reinvent our field. One innovation is to teach economics not from an axiomatic and technique-oriented perspective, but by posing challenges that can only be addressed through economic logic. Instead of starting microeconomics with the consumer’s problem of maximizing utility subject to a budget constraint, begin by challenging students to predict the impact of a gasoline tax on the price of gasoline, or asking them to explain why diamonds are so much more expensive than water, despite the fact that the latter is critical for survival unlike the former. Instead of starting macroeconomics with national income accounts, begin with the question of how to measure and manage the wealth of nations, or why inflation can be so disruptive to economic growth. Without the proper institutional, political, and historical context in which to interpret economic models, constrained optimization methods and fixed-point existence proofs have much less meaning and are more likely to give rise to theory envy. However, when students understand the “why” of their course of study, even the most complex mathematical tools can be mastered and are almost always applied more meaningfully.

Implicit in most asset-allocation policies is the statistical assumption of “stationarity,” which means that the means, variances, and covariances of asset returns are assumed to be constant over time. This assumption is a reasonable approximation during normal market conditions but fails dramatically during periods of market turmoil and dislocation. In such periods, market volatility is highly dynamic, correlations can jump to 100% in a matter of days, and risk premia can become negative for months at a time. FTSE and AlphaSimplex Group have developed a family of rule-driven (passive), transparent, and high-capacity indices whose volatilities are rescaled as often as daily with the goal of maintaining more stable risk levels. By stabilizing the risk of each asset class over time, the FTSE StableRisk Indices have the potential to capture the long-term risk premia of asset classes and simple strategies with less severe maximum drawdowns than those of traditional indices, which have no risk controls.

As financial markets grow in size and complexity, risk management protocols must also evolve to address more challenging demands. One of the most difficult of these challenges is managing event risk, the risk posed by unanticipated news that causes major market moves over short time intervals. Often cited but rarely managed, event risk has been relegated to the domain of qualitative judgment and discretion because of its heterogeneity and velocity. In this chapter, we describe one initiative aimed at solving this problem. The Thomson Reuters NewsScope Event Indices Project is an integrated framework for incorporating real-time news from the Thomson Reuters NewsScope subscription service into systematic investment and risk management protocols. The framework consists of a set of real-time event indices—each one taking on numerical values between 0 and 100—designed to capture the occurrence of unusual events of a particular kind. Each index is constructed by applying disciplined pattern recognition algorithms to real-time news feeds, and validated using econometric methods applied to historical data.

Uncertainty surrounding the risk and reward of investments in biopharmaceutical companies poses a challenge to those interested in funding such enterprises. Using data on publicly traded stocks, we track the performance of 1,066 biopharmaceutical companies from 1930 to 2015—the most comprehensive financial analysis of this sector to date. Our systematic exploration of methods for distinguishing biotech and pharmaceutical companies yields a dynamic, more accurate classification method. We find that the performance of the biotech sector is highly sensitive to the presence of a few outlier companies, and confirm that nearly all biotech companies are loss-making enterprises, exhibiting high stock volatility. In contrast, since 2000, pharmaceutical companies have become increasingly profitable, with risk-adjusted returns consistently outperforming the market. The performance of all biopharmaceutical companies is subject not only to factors arising from their drug pipelines (idiosyncratic risk), but also from general economic conditions (systematic risk). The risk associated with returns has profound implications both for patterns of investment and for funding innovation in biomedical R&D.