Impact of economic, regulatory, and patent policies on innovation in cancer chemoprevention.

Chemoprevention agents are an emerging new scientific area that holds out the promise of delaying or avoiding a number of common cancers. These new agents face significant scientific, regulatory, and economic barriers, however, which have limited investment in their research and development (R&D). These barriers include above-average clinical trial scales, lengthy time frames between discovery and Food and Drug Administration approval, liability risks (because they are given to healthy individuals), and a growing funding gap for early-stage candidates. The longer time frames and risks associated with chemoprevention also cause exclusivity time on core patents to be limited or subject to significant uncertainties. We conclude that chemoprevention uniquely challenges the structure of incentives embodied in the economic, regulatory, and patent policies for the biopharmaceutical industry. Many of these policy issues are illustrated by the recently Food and Drug Administration-approved preventive agents Gardasil and raloxifene. Our recommendations to increase R&D investment in chemoprevention agents include (a) increased data exclusivity times on new biological and chemical drugs to compensate for longer gestation periods and increasing R&D costs; chemoprevention is at the far end of the distribution in this regard; (b) policies such as early-stage research grants and clinical development tax credits targeted specifically to chemoprevention agents (these are policies that have been very successful in increasing R&D investment for orphan drugs); and (c) a no-fault liability insurance program like that currently in place for children's vaccines.

Developing drugs for developing countries.

Infectious and parasitic diseases create enormous health burdens, but because most of the people suffering from these diseases are poor, little is invested in developing treatments. We propose that developers of treatments for neglected diseases receive a "priority review voucher." The voucher could save an average of one year of U.S. Food and Drug Administration (FDA) review and be sold by the developer to the manufacturer of a blockbuster drug. In a well-functioning market, the voucher would speed access to highly valued treatments. Thus, the voucher could benefit consumers in both developing and developed countries at relatively low cost to the taxpayer.

VizMaps: A Bayesian Topic Modeling Based PubMed Search Interface

A common challenge that users of academic databases face is making sense of their query outputs for knowledge discovery. This is exacerbated by the size and growth of modern databases. PubMed, a central index of biomedical literature, contains over 25 million citations, and can output search results containing hundreds of thousands of citations. Under these conditions, efficient knowledge discovery requires a different data structure than a chronological list of articles. It requires a method of conveying what the important ideas are, where they are located, and how they are connected; a method of allowing users to see the underlying topical structure of their search. This paper presents VizMaps, a PubMed search interface that addresses some of these problems. Given search terms, our main backend pipeline extracts relevant words from the title and abstract, and clusters them into discovered topics using Bayesian topic models, in particular the Latent Dirichlet Allocation (LDA). It then outputs a visual, navigable map of the query results.