CHEMICAL PROSPECTING IN A COSTA RICAN CONSERVATION AREA

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This application describes an International Cooperative Biodiversity Group (ICBG) directed toward drug discovery based on natural products obtained via the Instituto Nacional de Biodiversidad (INBio) from the Guanacaste Conservation Area in Costa Rica. One specific aim is to introduce tropical insects and related arthropods into the process of drug search and development. While insects are well known to utilize a wide variety of secondary metabolites as defensive agents, venoms, and pheromones, they have received much less attention than plants, microbes, or marine organisms as potential sources of useful pharmaceutical agents. The INBio Associate Program will coordinate the collection of biological materials, will create a group of in-country scientists and paraprofessionals to conduct the collecting (as well as to pursue ecological and systematic studies), will prepare extracts from biological materials, and will carry out an anti-malarial screening program. INBio's activities will also contribute to the national biodiversity inventory, and establish a system of biodiversity information management and distribution. The Cornell Associate Program will coordinate the ICBG's activities. It will also pursue the chemical characterization and synthesis of selected natural products which appear especially interesting on the basis of ecological leads, and will train Costa Rican researchers in chemical characterization, bioassay development, and basic field and laboratory disciplines related to "chemical prospecting". The third Associate Program, to be carried out by the Pharmaceutical Research and Development Division of Bristol-Myers Squibb, will receive the bulk of the extracts prepared at INBio, and will carry out screening over a broad range of biological activities, including a search for anticancer, anti-infective, cardiovascular, CNS, and dermatological activities. Bristol-Myers Squibb has a long history of drug developing, and has a thoroughly modern system of screens and predictive models.