Current business models have struggled to support early-stage drug development. In this paper, we study an alternative financing model, the mega-fund structure, to fund drug discovery. We extend the framework proposed in previous studies to account for correlation between phase transitions in...

Implicit in the drug-approval process is a trade-off between Type I and Type II error. We explore the application of 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...

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...

Implicit in the drug-approval process is a host of decisions---target patient population, control group, primary endpoint, sample size, follow-up period, etc.---all of which determine the trade-off between Type I and Type II error. We explore the application of Bayesian decision analysis (BDA)...

Implicit in the drug-approval process is a trade-off between Type I and Type II error. We explore the application of 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...

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 FDA-approved gene therapies and the promise of gene editing, many experts believe we are at an...