A Monte Carlo simulation for bioprospecting the endemic New Zealand terrestrial flora for antibiotic drug leads

Natural product libraries are important tools for drug discovery. However, until now, there has not been a system to allow projections of the potential number of hits from creating these libraries. The objective of this study was to develop a stochastic model system that predicts the number of hits from creating a natural product library. A Monte Carlo simulation was developed with data from the peer-reviewed literature. Using types of endemic New Zealand terrestrial flora as examples, the number of antibacterial hits expected from creating natural product libraries were calculated. The model predicts the following bounds for the 90% range of validated antibiotic leads for the categories of the terrestrial endemic flora of New Zealand with a right skewed distribution: [grasses: 1.43-6.50; liverworts: 2.75-12.5; fungi: 45.2-207; mosses: 0.98-4.48; vascular plants: 21.4-97.8]. Furthermore, per full-time equivalent (FTE) person employed on the project, a mean of 1.31 hits (90% range 0.48-2.42) is expected. This model system allows the number of expected hits to be calculated when developing a natural product library for a therapeutic target. There is an opportunity to create a natural product library from New Zealand endemic terrestrial flora. This model is scalable to other geographic areas as well as to other therapeutic targets and screening systems.