Natural products profoundly impact many research areas, including medicine, organic chemistry, and cell biology. However, discovery of new natural products suffers from a lack of high throughput analytical techniques capable of identifying structural novelty in the face of a high degree of chemical redundancy. Methods to select bacterial strains for drug discovery have historically been based on phenotypic qualities or genetic differences and have not been based on laboratory production of secondary metabolites. Therefore, untargeted LC/MS-based secondary metabolomics was evaluated to rapidly and efficiently analyze marine-derived bacterial natural products using LC/MS-principal component analysis (PCA). A major goal of this work was to demonstrate that LC/MS-PCA was effective for strain prioritization in a drug discovery program. As proof of concept, we evaluated LC/MS-PCA for strain selection to support drug discovery, for the discovery of unique natural products, and for rapid assessment of regulation of natural product production.
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