Due to the “boundary effect” bias, PoS estimates in the most recent years are inflated. To address this issue, we compute a bias-adjustment factor using historical data and multiply the PoS in recent years by this factor.

We perform an event study analysis that quantifies the market reaction to clinical trial result announcements for 13,807 trials from 2000 to 2020, one of the largest event studies of clinical trials to date. We first determine the specific dates in the clinical trial process on which the greatest impact on the stock prices of their sponsor companies occur. We then analyze the relationship between the abnormal returns observed on these dates due to the clinical trial outcome and the properties of the trial, such as its phase, target accrual, design category, and disease and sponsor company type (biotechnology or pharmaceutical). We find that the classification of a company as “early biotechnology” or “big pharmaceutical” had the most impact on abnormal returns, followed by properties such as disease, outcome, the phase of the clinical trial, and target accrual. We also find that these properties and classifications by themselves were insufficient to explain the variation in excess returns observed due to clinical trial outcomes.

BACKGROUND: With multimorbidity becoming the norm rather than the exception, the management of multiple chronic diseases is a major challenge facing healthcare systems worldwide.

METHODS: Using a large, nationally representative database of electronic medical records from the United Kingdom spanning the years 2005–2016 and consisting over 4.5 million patients, we apply statistical methods and network analysis to identify comorbid pairs and triads of diseases and identify clusters of chronic conditions across different demographic groups. Unlike many previous studies, which generally adopt cross-sectional designs based on single snapshots of closed cohorts, we adopt a longitudinal approach to examine temporal changes in the patterns of multimorbidity. In addition, we perform survival analysis to examine the impact of multimorbidity on mortality.

RESULTS: The proportion of the population with multimorbidity has increased by approximately 2.5 percentage points over the last decade, with more than 17% having at least two chronic morbidities. We find that the prevalence and the severity of multimorbidity, as quantified by the number of co-occurring chronic conditions, increase progressively with age. Stratifying by socioeconomic status, we find that people living in more deprived areas are more likely to be multimorbid compared to those living in more affluent areas at all ages. The same trend holds consistently for all years in our data. In general, hypertension, diabetes, and respiratory-related diseases demonstrate high in-degree centrality and eigencentrality, while cardiac disorders show high out-degree centrality.

CONCLUSIONS: We use data-driven methods to characterize multimorbidity patterns in different demographic groups and their evolution over the past decade. In addition to a number of strongly associated comorbid pairs (e.g., cardiac-vascular and cardiac-metabolic disorders), we identify three principal clusters: a respiratory cluster, a cardiovascular cluster, and a mixed cardiovascular-renal-metabolic cluster. These are supported by established pathophysiological mechanisms and shared risk factors, and largely confirm and expand on the results of existing studies in the medical literature. Our findings contribute to a more quantitative understanding of the epidemiology of multimorbidity, an important pre-requisite for developing more effective medical care and policy for multimorbid patients.

Disease-focused foundations have used venture philanthropy (VP) for decades to develop interventions that have patient impact and generate revenue to support their mission. We articulate the distinguishing motives and features of VP funds and their distinct role in the life sciences innovation ecosystem. In particular, we focus on how entrepreneurs and VP funds can work together to help patients and generate economic value. We recommend that entrepreneurs seeking VP support understand a fund’s mission and objectives, and position themselves to fit the fund’s strategic and financial portfolio needs. Finally, we provide case studies of three specific initiatives — the JDRF T1D Fund, targeting type 1 (juvenile) diabetes; MPM Capital’s Oncology Impact Fund; and the American Heart Association’s Cardeation Capital — to showcase these efforts and benefits in practice.

Alzheimer’s disease (AD) is one of the biggest challenges to modern medicine. However, before February 2021, the last AD drug approval occurred in 2003, implying a 100% failure rate of AD therapeutic programs over the 17 years to that point; the lowest probability of success among all diseases. One of the key challenges is funding, which we explore in more depth in this chapter by first reviewing the current funding landscape for AD, and then considering the strengths and weaknesses of various commercialization strategies. Despite the discouraging track record of the biopharma industry in addressing AD, there is reason to be hopeful due to substantial scientific progress in developing a deeper understanding of the biology of the disease as well as increased federal funding for AD research. However, we also we need the private sector to translate these scientific breakthroughs into new medicines, which takes additional funding and new business models so as to reduce risk and improve returns for investors. If we can change the narrative of AD therapeutics to give investors new hope, the private sector can serve as a powerful partner to the biomedical community.

Biomedical innovation suffers from a “funding gap” between the needs of drug development firms and the availability of funds. The requirement of large investments for drug development projects and the high pipeline risk associated with FDA approval causes this funding gap in part. In this paper, we propose a new financial instrument—the “FDA hedge”—that pays off upon FDA approval failure. We develop a theory to show that the FDA hedge can help eliminate the funding gap. Using novel project-level data, we establish empirically that FDA hedge risk is idiosyncratic, and show how better sharing this risk can spur welfare-enhancing R&D.

The interaction between product market competition, R&D investment, and the financing choices of R&D-intensive firms on the development of innovative products is only partially understood. We hypothesize that as competition increases, R&D-intensive firms will: i) increase R&D investment relative to existing assets in place; ii) carry more cash; and iii) maintain less net debt. Using the Hatch–Waxman Act as an exogenous shock to competition, we provide causal evidence supporting these hypotheses through a differences-in-differences analysis that exploits differences between the biopharma industry and other industries, and heterogeneity within the biopharma industry. We also explore how these changes affect innovative output.

Recent outbreaks of infectious pathogens such as Zika, Ebola, and COVID-19 have under-scored the need for the dependable availability of vaccines against emerging infectious diseases (EIDs). Prior to the COVID-19 pandemic, the cost and risk of R&D programs and uniquely unpredictable demand for EID vaccines discouraged many potential vaccine developers, and government and nonprofit agencies have struggled to provide timely or sufficient incentives for their development and sustained supply. However, the economic climate has changed significantly post-pandemic. To explore this contrast, we analyze the pre-pandemic economic returns of a portfolio of EID vaccine assets, and find that, under realistic financing assumptions, the expected returns are significantly negative, implying that the private sector is unlikely to address this need without public-sector intervention. However, in a post-pandemic policy landscape, the financing deficit for this portfolio can be closed, and we analyze several potential solutions, including enhanced public–private partnerships and subscription models in which governments would pay annual fees to obtain access to a portfolio of stockpiled vaccines in the event of an outbreak.

Brains are the indispensable drivers of human progress, but brain health issues can wreak havoc on society. Consider the devastation of disorders like depression, anxiety, and Alzheimer disease—which cost the economy trillions each year. There are currently $40.5 trillion allocated to Environment, Sustainability, and Governance (ESG) investing around the world. If only a portion of these funds were diverted into brain health, they could produce major improvements for our society.

We describe a novel collaboration between academia and industry, an in-house data science and artificial intelligence challenge held by Novartis to develop machine-learning models for predicting drug-development outcomes, building upon research at MIT using data from Informa as the starting point. With over 50 crossfunctional teams from 25 Novartis offices around the world participating in the challenge, the domain expertise of these Novartis researchers was leveraged to create predictive models with greater sophistication. Ultimately, two winning teams developed models that outperformed the baseline MIT model—areas under the curve of 0.88 and 0.84 versus 0.78, respectively—through state-of-the-art machine-learning algorithms and the use of newly incorporated features and data. In addition to validating the variables shown to be associated with drug approval in the earlier MIT study, the challenge also provided new insights into the drivers of drug-development success and failure.

BACKGROUND: Parkinson’s disease (PD) is neurodegenerative, causing motor, cognitive, psychological, somatic, and autonomic symptoms. Understanding PD patients’ preferences for novel neurostimulation devices may help ensure that devices are delivered in a timely manner with the appropriate level of evidence. Our objective was to elicit preferences and willingness-to-wait for novel neurostimulation devices among PD patients to inform a model of optimal trial design.

METHODS: We developed and administered a survey to PD patients to quantify the maximum levels of risks that patients would accept to achieve potential benefits of a neurostimulation device. Threshold technique was used to quantify patients’ risk thresholds for new or worsening depression or anxiety, brain bleed, or death in exchange for improvements in “on-time,” motor symptoms, pain, cognition, and pill burden. The survey elicited patients’ willingness to wait to receive treatment benefit. Patients were recruited through Fox Insight, an online PD observational study.

RESULTS: A total of 2740 patients were included and a majority were White (94.6%) and had a 4-year college degree (69.8%). Risk thresholds increased as benefits increased. Threshold for depression or anxiety was substantially higher than threshold for brain bleed or death. Patient age, ambulation, and prior neurostimulation experience influenced risk tolerance. Patients were willing to wait an average of 4 to 13 years for devices that provide different levels of benefit.

CONCLUSIONS: PD patients are willing to accept substantial risks to improve symptoms. Preferences are heterogeneous and depend on treatment benefit and patient characteristics. The results of this study may be useful in informing review of device applications and other regulatory decisions and will be input into a model of optimal trial design for neurostimulation devices.

Funding for early-stage biomedical innovation has become more difficult to secure at the same time that medical breakthroughs seem to be occurring at ever increasing rates. One explanation for this counterintuitive trend is that increasing scientific knowledge can actually lead to greater economic risk for investors in the life sciences. While the Human Genome Project, high-throughput screening, genetic biomarkers, immunotherapies, and gene therapies have made a tremendously positive impact on biomedical research and, consequently, patient lives, they have also increased the cost and complexity of the drug development process, causing many investors to shift their assets to more attractive investment opportunities. This suggests that new business models and financing strategies can be used to reduce the risk and increase the attractiveness of biomedical innovation so as to bring new and better therapies to patients faster.

Development of curative treatments for glioblastoma (GBM) has been stagnant in recent decades largely because of significant financial risks. A portfolio-based strategy for the parallel discovery of breakthrough therapies can effectively reduce the financial risks of potentially transformative clinical trials for GBM. Using estimates from domain experts at the National Brain Tumor Society (NBTS), we analyze the performance of a portfolio of 20 assets being developed for GBM, diversified across different development phases and therapeutic mechanisms. We find that the portfolio generates a 14.9% expected annualized rate of return. By incorporating the adaptive trial platform GBM AGILE in our simulations, we show that at least one drug candidate in the portfolio will receive US Food and Drug Administration (FDA) approval with a probability of 79.0% in the next decade.

The regulatory process for market authorization of medical diagnostic and therapeutic products is fraught with ethical dilemmas that regulators outside the medical industry do not face. The consequences of approving an ineffective therapy with potentially dangerous side effects (a “Type I error” or false positive) must be weighed against not approving a safe and effective therapy (a “Type II error” or false negative) that could help ease the burden of disease for many patients. Regulators must strike the proper balance by considering multiple factors, including scientific merit; clinical evidence from randomized, control trials; the burden of disease; the current standard of care and alternatives; and patient preferences. How these factors are—and should be—weighed is not always clear, which only encourages criticism by whichever stakeholder group disagrees with the decision.

Academic institutions play a central role in the biotech industry through technology licensing and the creation of startups, but few data are available on their performance and the magnitude of their impact. Here we present a systematic study of technology licensing by one such institution, the Massachusetts Institute of Technology (MIT). Using data on the 76 therapeutics-focused life sciences companies formed through MIT’s Technology Licensing Office from 1983 to 2017, we construct several measures of impact, including MIT patents cited in the Orange Book, capital raised, outcomes from mergers and acquisitions, patents granted to MIT intellectual property licensees, drug candidates discovered and US drug approvals—a key benchmark of innovation in the biopharmaceutical industry. As of December 2017, Orange Book listings for four approved small-molecule drugs cite MIT patents, but another 31 FDA-approved drugs (excluding candidates acquired after phase 3) had some involvement of MIT licensees. Fifty-five percent of the latter were either a new molecular entity or a new biological entity, and 55% were granted priority review, an indication that they address an unmet medical need. The methodology described here may be a useful framework for other academic institutions to track outcomes of intellectual property in the therapeutics domain.