Screening microbial secondary metabolites is an established method to identify novel biologically active molecules. Preparation of biological screening samples from microbial fermentation extracts requires growth conditions that promote synthesis of secondary metabolites and extraction procedures that capture the secondary metabolites produced. High-performance liquid chromatography (HPLC) analysis of fermentation extracts can be used to estimate the number of secondary metabolites produced by microorganisms under various growth conditions but is slow. In this study we report on a rapid (approximately 1 min per assay) surrogate measure of secondary metabolite production based on a metabolite productivity index computed from the electrospray mass spectra of samples injected directly into a spectrometer. This surrogate measure of productivity was shown to correlate with an HPLC measure of productivity with a coefficient of 0.78 for a test set of extracts from 43 actinomycetes. This rapid measure of secondary metabolite productivity may be used to identify improved cultivation and extraction conditions by analyzing and ranking large sets of extracts. The same methods may also be used to survey large collections of extracts to identify subsets of highly productive organisms for biological screening or additional study.Microbial extracts have been and continue to be a productive source of new biologically active molecules for drug discovery (2, 13). It is estimated that more than 30% of worldwide human pharmaceutical sales have compounds from natural sources as their origin (12). With advances in genomics and high-throughput screening (HTS) technology, many new therapeutic targets are accessible for identifying pharmaceutical agents. In addition to the historical practice of screening microbial fermentation extracts for antibiotic activity, extracts can now routinely be screened with a variety of new functional, receptor binding, enzyme inhibition, and protein-protein interaction assays. HTS formats used at many large pharmaceutical research organizations are, however, generally incompatible with complex fermentation extracts. The reasons for the incompatibility include nonspecific interference with assay systems, cost in dollars and time to identify and dereplicate active components from a complex mixture, and adverse physical properties for automated liquid-handling equipment. Therefore, additional investment in selection and preparation of fermentation extracts is one strategy to align natural product drug discovery with today's automated HTS assay systems. A typical approach to improve the compatibility of fermentation extracts with HTS was recently reported by Schmid et al., who described a multistage automated solid-phase extraction (SPE) system (12).To make the best use of finite resources in natural product discovery organizations, it is important to identify collections of organisms that produce secondary metabolites, culture conditions that generally support secondary metabolite synthesis, and sample prepar...

This paper describes a method for quantitatively differentiating crude natural extracts using high-performance liquid chromatography-electrospray mass spectrometry (HPLC-ESI-MS). The method involves performing an HPLC-MS analysis using standard reversed-phase C18 gradient separation on the crude extract. The HPLC system used in this study was a dual-column system designed to optimize throughput. Using image analysis techniques, the data are reduced to a list containing the m/z value and retention time of each ion. The ion lists are then compared in a pairwise fashion to compute a sample similarity index between two samples. The similarity index is based on the number of ions common to both and is scaled from 0 to 1. Extract controls were analyzed throughout a run of 88 unknown fungal extracts. The controls provided information about column and spectrometer stability and overall sensitivity. Pairwise comparison of all control samples indicates that the similarity index is high (0.8) for replicate samples. Comparison between the unknown extract samples produces a distribution of similarities ranging from replicates (0.8) to very dissimilar (0.1). This information can be used to judge the chemical diversity of natural extract samples, which is one approach to determining the quality of libraries being used for drug discovery via high-throughput screening.