Natural products vs. combinatorials:: A case study

Historically pharmaceuticals have largely been derived from natural product sources. With the development of combinatorial approaches to chemical synthesis in the last decade, drug discovery programs have adapted to large-scale screening programs of combinatorial chemical libraries of up to several hundred thousands of wells. Natural product screening programs are perceived by some in the pharmaceutical industry as antiquated, inefficient or even unproductive, despite a steady flow of natural product derived New Chemical Entities (NCE's) into the market. Natural products hold great potential for novel drug discovery. Newer, automated and high-throughput technologies, which make high-throughput combinatorial chemical synthesis possible can be adapted to improve the efficiency of natural product discovery programs. Libraries of synthetic combinatorial chemicals complement libraries of natural product metabolites, but do not duplicate or replace them. Examples of side-by-side screening of synthetic combinatorial chemicals and microbial fermentation extracts indicate that both sets of chemical diversity can provide unique leads. The quantity of leads generated by each method is irrelevant, if the quality of the lead is not considered. Therefore, scientific research should be focussed not on which type of library is better, but rather how to take advantage of both resources in a cost-effective and timely manner. To position natural products to meet the current drug discovery paradigm of high to ultra-high throughput random screening, certain well-known technologies should be used. Biological characterization as well as separation chemistries for semi-purification or full purification can be employed prior to screening to reduce the number of compounds in the screening mixture and to create links from the physical entities to databases containing chemical and biological characteristics. Lead candidate compounds from both synthetic and natural product sources can both be used to commence computational approaches to analogue compound generation for optimal lead drug development.