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An econometric model, which examines the sources that correlate the hedge funds returns and other alternative investments, is proposed.

The article indicates that execution times of sucurities purchase or sale relate strongly to execution time; size limits do not.

Ordered probit analysis can be used to measure conditional distribution of price variations in stock trading. The statistical model employs discrete random variables and assumes that such price changes occur in discrete increments on an irregular time basis. Data from 1988 transactions of US stocks are used to measure the ordered probit model and analyze factors including trade price impact, price reversals and price discreteness.

Temporal granularity is developed as a concept and used in a model to examine the behavior of derivatives.

Background In this case study, we profile the I-SPY 2 TRIAL ( I nvestigation of S erial studies to P redict Y our T herapeutic R esponse with I maging A nd molecular ana L ysis 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. Methods We conduct several interviews with the I-SPY leadership as well as a literature review of relevant publications to assess the I-SPY 2 initiative. Results To date, six drugs have graduated from I-SPY 2, identified as excellent candidates for phase 3 trials in their corresponding tumor subtype, and several others have been or are still being evaluated. These trials are also more efficient, typically involving fewer subjects and reaching conclusions more quickly, and candidates have more than twice the predicted likelihood of success in a smaller phase 3 setting compared to traditional trials. Conclusions 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. [ABSTRACT FROM AUTHOR]

Highlights • Rare-disease therapies are scientifically and commercially challenging to develop. • Agilis, NTU, NCATS, FDA collaborate on a gene therapy for ultra-rare brain disease. • AGIL-AADC in-licensed by Agilis from NTU and yields life-changing results. • NCATS assists Agilis in submission of Phase 2 data package to the FDA. • Agilis receives BLA-ready status 18 months after in-licensing. 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 enabled this remarkable pace of successful drug development for an ultra-rare disease. [ABSTRACT FROM AUTHOR]

Translating academic medical research into new therapies is an important challenge for the biopharmaceutical industry and investment communities, which have historically favored later-stage assets with lower risk and clearer commercial value. The Stanford SPARK program is an innovative model for addressing this challenge. The program was created in 2006 to educate students and faculty about bringing academic research from bench to bedside. Every year, the program provides mentorship and funding for approximately a dozen SPARK ‘scholars,’ with a focus on impacting patient lives, regardless of economic factors. By reviewing the detailed structure, function and operation of SPARK we hope to provide a template for other universities and institutions interested in de-risking and facilitating the translation of biomedical research. [ABSTRACT FROM AUTHOR]

We extend the megafund concept for funding drug discovery to enable dynamic leverage in which the portfolio of candidate therapeutic assets is predominantly financed initially by equity, and debt is introduced gradually as assets mature and begin generating cash flows. Leverage is adjusted so as to maintain an approximately constant level of default risk throughout the life of the fund. Numerical simulations show that applying dynamic leverage to a small portfolio of orphan drug candidates can boost the return on equity almost twofold compared with securitization with a static capital structure. Dynamic leverage can also add significant value to comparable all-equity-financed portfolios, enhancing the return on equity without jeopardizing debt performance or increasing risk to equity investors. [ABSTRACT FROM AUTHOR]

• Drug discovery for glioblastoma (GBM) is stagnant largely due to financial risks. • Portfolio-based drug discovery strategy significantly reduces the financial risks. • Risk–return profile of a GBM megafund is attractive to a wide group of investors. • Success of a GBM megafund depends on biomedical expertise and diversification. • GBM AGILE, a global clinical trial platform, expedites drug development process. 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. [ABSTRACT FROM AUTHOR]

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 ( α ) and power (1 − β ) 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 α s larger than the traditional one-sided significance level of 5%, whereas potentially less effective and riskier treatments have optimal α s 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. [ABSTRACT FROM AUTHOR]

Highlights: [•] Drug discovery is more likely to succeed with multiple shots on goal. [•] Higher success rates imply much lower risk to investors. [•] Lower risk will draw more funding for portfolios of multiple projects. [•] Orphan drug candidates are particularly attractive targets for such financing. [•] Simulated returns of hypothetical orphan drug portfolios are 10.2–52.3%. [Copyright &y& Elsevier]