Measuring the Economic and Academic Impact of Philanthropy: The Breast Cancer Research Foundation
Vasileva, Detelina, Larry Norton, Marc Hurlbert, and Andrew W. Lo, 2019, Working Paper.
ABSTRACT Using survey data gathered from grantees of the non-profit Breast Cancer Research Foundation (BCRF), we investigated the commercial and non-commercial impact of their research funding. We found significant impact in both domains. Commercially, 19.5% of BCRF grantees filed patents, 35.9% had a project that has reached clinical development, and 12 companies have or will be spun off from existing projects, thus creating 127 new jobs. Non-commercially, 441 graduate students have been trained by 116 grantees, 767 post-doctoral fellows have been trained by 137 grantees, 66% of grantees have used funding for faculty salaries, 93% have achieved collaboration with other researchers, and 42.7% have enacted process improvements in research methodology. Econometric analysis identifies BCRF funding and associated process improvements as key factors in driving the likelihood to file patents. However, we also found that the involvement of more than one institution in a collaborative project had a negative impact on subsequent development. This may point to frictions introduced by multi-university interactions.
Funding Long Shots
Hull, John C., Andrew W. Lo, and Roger M. Stein, 2019, Working Paper.
ABSTRACT We define long shots as investment projects with four features: (1) low probabilities of success; (2) long gestation lags before any cash flows are realized; (3) large required up-front investments; and (4) very large payoffs (relative to initial investment) in the unlikely event of success. Funding long shots is becoming increasingly difficult—even for high-risk investment vehicles like hedge funds and venture funds—despite the fact that some of society’s biggest challenges such as cancer, Alzheimer’s disease, global warming, and fossil fuel depletion depend critically on the ability to undertake such investments. We investigate the possibility of improving financing for long shots by pooling them into a single portfolio that can be financed via securitized debt, and examine the conditions under which such funding mechanisms are likely to be effective.
Venture Philanthropy: A Case Study of the Cystic Fibrosis Foundation
Kim, Esther and Andrew W. Lo, 2019, Working Paper.
ABSTRACT Advances in biomedical research have created significant opportunities to bring to market a new generation of therapeutics. However, early-stage assets often face a dearth of funding, as they have a high risk of failure and significant development costs. Historically, this has been particularly true for assets intended to treat rare diseases, where market sizes are often too small to attract much attention and funding. Venture philanthropy (VP)—which, for the purpose of this paper, is defined as a model in which nonprofit, mission-driven organizations fund initiatives to advance their objectives and potentially achieve returns that can be reinvested toward their mission—offers an alternative to traditional funding sources like venture capital or the public markets. Here we highlight the Cystic Fibrosis (CF) Foundation, widely considered to be the leading VP organization in biotech, which facilitated the development of Kalydeco, the first disease-modifying therapy approved to treat cystic fibrosis. We evaluate the CF Foundation’s example, including its agreement structures and strategy, explore the challenges that other nonprofits may have in adopting this strategy, and draw lessons from the CF Foundation for other applications of VP financing.
Machine Learning with Statistical Imputation for Predicting Drug Approvals
Lo, Andrew W., Kien Wei Siah, and Chi Heem Wong, 2019, Harvard Data Science Review, https://doi.org/10.1162/99608f92.5c5f0525.
ABSTRACT 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.