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.

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.

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.

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.

Current brain research, innovation, regulatory, and funding systems are artificially siloed, creating boundaries in our understanding of the brain based on constructs such as aging, mental health, and/or neurology, when these systems are all inextricably integral.

Grand strategy provides a broad framework that helps to guide all elements of a major, long-term project. There are converging global trends resulting from the COVID pandemic compelling a Brain Capital Grand Strategy: widespread appreciation of the rise in brain health issues (e.g., increase prevalence of mental illness and high rates of persons with age-related cognitive impairment contracting COVID), increased automation, job loss and underemployment, radical restructuring of health systems, rapid consumer adoption and acceptance of digital and remote solutions, and recognition of the need for economic reimagination. If we respond constructively to this crisis, the COVID pandemic could catalyze institutional change and a better social contract.

Current business models have struggled to support early-stage drug development. In this paper, we study an alternative financing model, the megafund structure, to fund drug discovery. We extend the framework proposed in previous studies to account for correlation between phase transitions in drug development projects, thus making the model a more realistic representation of biopharma research and development. In addition, we update the parameters used in our simulation with more recent estimates of the probability of success (PoS). We find that the performance of the megafund becomes less attractive when correlation between projects is introduced. However, the risk of default and the expected returns of the vanilla megafund remain promising even under moderate levels of correlation. In addition, we find that a leveraged megafund outperforms an equity-only structure over a wide range of assumptions about correlation and PoS.

We compare and contrast the expected duration and number of infections and deaths averted among several designs for clinical trials of COVID-19 vaccine candidates, including traditional and adaptive randomized clinical trials and human challenge trials. Using epidemiological models calibrated to the current pandemic, we simulate the time course of each clinical trial design for 756 unique combinations of parameters, allowing us to determine which trial design is most effective for a given scenario. A human challenge trial provides maximal net benefits—averting an additional 1.1M infections and 8,000 deaths in the U.S. compared to the next best clinical trial design—if its set-up time is short or the pandemic spreads slowly. In most of the other cases, an adaptive trial provides greater net benefits.

The recent destructive outbreak of the novel coronavirus, SARS-CoV-2, that emerged from Wuhan, China, and rapidly spread to Europe and North America, demonstrates beyond doubt that emerging infectious diseases (EIDs) are a clear and present danger to the world and its economy. Uncontrolled outbreaks of EIDs can devastate populations around the globe, both in terms of lives lost and economic value destroyed. Emerging and re-emerging strains of infectious disease have become more diverse over time, and outbreaks have become more frequent. In 2006, Larry Brilliant stated that 90 percent of the epidemiologists in his confidence agreed that there would be a large pandemic—in which 1 billion people would sicken, 165 million would die, and the global economy would lose $1-3 trillion—within two generations. In 2020, this remarkable statement is playing out with each passing day.

In the midst of epidemics such as COVID-19, therapeutic candidates are unlikely to be able to complete the usual multi-year clinical trial and regulatory approval process within the course of an outbreak. We apply a Bayesian adaptive patient-centered model—which minimizes the expected harm of false positives and false negatives—to optimize the clinical trial development path during such outbreaks. When the epidemic is more infectious and fatal, the Bayesian-optimal sample size in the clinical trial is lower and the optimal statistical significance level is higher. For COVID-19 (assuming a static Ro = 2 and initial infection percentage of 0.1%), the optimal significance level is 7.1% for a clinical trial of a non-vaccine anti-infective therapeutic clinical trial and 13.6% for that of a vaccine. For a dynamic Ro ranging from 2 to 4, the corresponding values are 14.4% and 26.4%, respectively. Our results illustrate the importance of adapting the clinical trial design and the regulatory approval process to the specific parameters and stage of the epidemic.

The healthcare industry in the United States (U.S.) is a complex ecosystem with many different stakeholders. Unlike the universal single-payer healthcare systems of many European countries,the accessibility of prescription drugs in the U.S. is largely determined by contract negotiations between health plans and drug manufacturers about formulary placement. These negotiations can sometimes result in higher out-of-pocket costs for the patient, since the current structure of the U.S. healthcare system creates a perverse incentive for many health plans to elicit higher rebates from drug manufacturers in exchange for formulary placement of brand-name drugs, thereby increasing patients’ out-of-pocket costs.

A key driver in biopharmaceutical investment decisions is the probability of success of a drug development program. We estimate the probabilities of success (PoS) of clinical trials for vaccines and other anti-infective therapeutics using 43,414 unique triplets of clinical trial, drug, and disease between January 1, 2000, and January 7, 2020, yielding 2,544 vaccine programs and 6,829 non-vaccine programs targeting infectious diseases. The overall estimated PoS for an industry-sponsored vaccine program is 39.6%, and 16.3% for an industry-sponsored anti-infective therapeutic. Among industry-sponsored vaccines programs, only 12 out of 27 disease categories have seen at least one approval, with the most successful being against monkeypox (100%), rotavirus (78.7%), and Japanese encephalitis (67.6%). The three infectious diseases with the highest PoS for industry-sponsored nonvaccine therapeutics are smallpox (100%), CMV (31.8%), and onychomycosis (29.8%). Nonindustry- sponsored vaccine and non-vaccine development programs have lower overall PoSs: 6.8% and 8.2%, respectively. Viruses involved in recent outbreaks—MERS, SARS, Ebola, Zika—have had a combined total of only 45 non-vaccine development programs initiated over the past two decades, and no approved therapy to date (Note: our data was obtained just before the COVID-19 outbreak and do not contain information about the programs targeting this disease.) These estimates offer guidance both to biopharma investors as well as to policymakers seeking to identify areas most likely to be undeserved by private-sector engagement and in need of public-sector support.

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.

Researchers, clinicians, policymakers and patients are increasingly interested in questions about therapeutic interventions that are difficult or costly to answer with traditional, free-standing, parallel-group randomized controlled trials (RCTs). Examples include scenarios in which there is a desire to compare multiple interventions, to generate separate effect estimates across subgroups of patients with distinct but related conditions or clinical features, or to minimize downtime between trials. In response, researchers have proposed new RCT designs such as adaptive platform trials (APTs), which are able to study multiple interventions in a disease or condition in a perpetual manner, with interventions entering and leaving the platform on the basis of a predefined decision algorithm. APTs offer innovations that could reshape clinical trials, and several APTs are now funded in various disease areas. With the aim of facilitating the use of APTs, here we review common features and issues that arise with such trials, and offer recommendations to promote best practices in their design, conduct, oversight and reporting.