Thanks to a combination of scientific advances and economic incentives, the development of therapeutics to treat rare or orphan diseases has grown dramatically in recent years. With the advent of Food and Drug Administration–approved gene therapies and the promise of gene editing, many experts believe we are at an inflection point in dealing with these afflictions. In this article, the authors propose to document this inflection point by measuring the risk and reward of investing in the orphan drug industry. They construct a stock market index of 39 publicly traded companies that specialize in developing drugs for orphan diseases and compare the financial performance of this index, which they call ORF, to the broader biopharmaceutical industry and the overall stock market from 2000 to 2015. Although the authors report that ORF underperformed other biopharma companies and the overall stock market in the early 2000s, its performance has improved over time: from 2010 to 2015, ORF returned 608%, far exceeding the 317%, 320%, and 305% returns of the S&P, NASDAQ, and NYSE ARCA Biotech indexes, respectively, and the 83% of the S&P 500. ORF does have higher volatility than the other indexes but still outperforms even on a risk-adjusted basis, with a Sharpe ratio of 1.24 versus Sharpe ratios of 1.17, 1.14, and 1.05, respectively, for the other three biotech indexes and 0.71 for the S&P 500. However, ORF has a market beta of 1.16, which suggests significant correlation to the aggregate stock market and less diversification benefits than traditional pharmaceutical investments.

We consider a portfolio-based approach to financing ovarian cancer therapeutics in which multiple candidates are funded within a single structure. Twenty-five potential early-stage drug development projects were identified for inclusion in a hypothetical portfolio through interviews with gynecological oncologists and leading experts, a review of ovarian cancer-related trials registered in the ClinicalTrials.gov database, and an extensive literature review. The annualized returns of this portfolio were simulated under a purely private sector structure both with and without partial funding from philanthropic grants, and a public–private partnership that included government guarantees. We find that public–private structures of this type can increase expected returns and reduce tail risk, allowing greater amounts of private sector capital to fund early-stage research and development.

We apply machine-learning techniques to predict drug approvals using drug-development and clinical-trial data from 2003 to 2015 involving several thousand drug-indication pairs with over 140 features across 15 disease groups. To deal with missing data, we use imputation methods that allow us to fully exploit the entire dataset, the largest of its kind. We show that our approach outperforms complete-case analysis, which typically yields biased inferences. We achieve predictive measures of 0.78, and 0.81 AUC (“area under the receiver operating characteristic curve,” the estimated probability that a classifier will rank a positive outcome higher than a negative outcome) for predicting transitions from phase 2 to approval and phase 3 to approval, respectively. Using five-year rolling windows, we document an increasing trend in the predictive power of these models, a consequence of improving data quality and quantity. The most important features for predicting success are trial outcomes, trial status, trial accrual rates, duration, prior approval for another indication, and sponsor track records. We provide estimates of the probability of success for all drugs in the current pipeline.

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.

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.

Implicit in the drug-approval process is a trade-off between Type I and Type II error. We propose using Bayesian decision analysis (BDA) to minimize the expected cost of drug approval, where relative costs are calibrated using U.S. Burden of Disease Study 2010 data. The results for conventional fixed-sample randomized clinical-trial designs suggest that for terminal illnesses with no existing therapies such as pancreatic cancer, the standard threshold of 2.5% is too conservative; the BDA-optimal threshold is 27.9%. However, for relatively less deadly conditions such as prostate cancer, 2.5% may be too risk-tolerant or aggressive; the BDA-optimal threshold is 1.2%. We compute BDA-optimal sizes for 25 of the most lethal diseases and show how a BDA-informed approval process can incorporate all stakeholders’ views in a systematic, transparent, internally consistent, and repeatable manner.

We develop a theory of optimal financing for R&D-intensive firms. With only market financing, the firm relies exclusively on equity financing and carries excess cash, but underinvests in R&D. We use mechanism design to examine how intermediated financing can attentuate this underinvestment. The mechanism combines equity with put options such that investors insure firms against R&D failure and firms insure investors against high R&D payoffs not being realized.

Opinion article by Andrew W. Lo and Kenneth Kosik on the potential role of California in the space of Alzheimer's Disease research.

In the past decade, financial industry excesses have been cited as the source of many ills afflicting economies and political systems in the West. But, if used responsibly, finance could help provide the cure for some of humanity’s most pressing problems – from cancer to fossil fuel depletion and climate change.

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. 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. 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.

The high cost of capital for firms conducting medical research and development (R&D) has been partly attributed to the government risk facing investors in medical innovation. This risk slows down medical innovation because investors must be compensated for it. We propose new and simple financial instruments, Food and Drug Administration (FDA) hedges, to allow medical R&D investors to better share the pipeline risk associated with FDA approval with broader capital markets. Using historical FDA approval data, we discuss the pricing of FDA hedges and mechanisms under which they can be traded and estimate issuer returns from offering them. Using various unique data sources, we find that FDA approval risk has a low correlation across drug classes as well as with other assets and the overall market. We argue that this zero-beta property of scientific FDA risk could be a main source of gains from trade between issuers of FDA hedges looking for diversified investments and developers looking to offload the FDA approval risk. We offer proof of concept of the feasibility of trading this type of pipeline risk by examining related securities issued around mergers and acquisitions activity in the drug industry. Overall, our argument is that, by allowing better risk sharing between those investing in medical innovation and capital markets more generally, FDA hedges could ultimately spur medical innovation and improve the health of patients.

Recent cases of aggressive pricing behavior in the biopharmaceutical industry have raised serious concerns among payers and policymakers about industry ethics. However, these cases should not be confused with price increases motivated by challenging business conditions that ultimately lead to greater investment in R&D and improved patient access to therapeutics. We study the example of Questcor Pharmaceuticals, which was forced to choose between increasing the price of an effective drug in 2007 and ceasing production and shutting down. We consider Questcor’s journey from inception to its acquisition in 2014, analyze the factors leading up to the price hike of its main revenue generator, Acthar Gel, and discuss its resulting impact on patients after 2007. A counterfactual financial simulation of the company’s prospects in the case where prices were not increased shows that Questcor would have become insolvent between 2008 and 2010.

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.

In this case study, we profile the I-SPY 2 TRIAL (Investigation of Serial studies to Predict Your Therapeutic Response with Imaging And molecular anaLysis 2), a unique breast cancer clinical trial led by researchers at 20 leading cancer centers across the US, and examine its potential to serve as a model of drug development for other disease areas. This multicenter collaboration launched in 2010 to reengineer the drug development process to be more efficient and patient-centered. We observe that I-SPY 2 possesses several novel features that could be used as a template for more efficient and cost effective drug development, namely its adaptive trial design; precompetitive network of stakeholders; and flexible infrastructure to accommodate innovation.