In the last century, most drugs approved by the U.S. Food and Drug Administration (FDA) were from natural sources or derived from compounds first isolated in nature, however in recent decades the proportion of approved drugs isolated from nature has plummeted to just below 50 percent (Li, J., Vederas, J., 2009). In that time the pharmaceutical industry moved away from screening natural sources, in favor of screening synthetic libraries against biological targets (Paul, S., et al. 2010). With the development of high throughput screening (HTS) methodologies, it was thought that natural extracts were incompatible with HTS as they are highly complex chemical mixtures composed of many structurally related molecules, at times present in only trace amounts, and therefore more HTS compatible samples should be pursued for hit identification (Broach, J., Thorner, J., 1996). Chemists, on the other hand, could produce massive libraries of defined chemical composition and quantity, whose components could be systematically decorated, and were perfectly suited for HTS. While combinatorial libraries were less structurally diverse, and complex then natural products (Feher, M., Schmidt, M., 2003), it was thought that HTS could overcome these deficiencies (Paul, S., et al. 2010). Unfortunately HTS is no substitute for chirality, and as biological receptors are stereospecific, combinatorial libraries were destined to fail with only 0.4 chiral centers per molecule, on average. In comparison FDA approved drugs have, on average, 2.3 chiral centers per molecule and naturally derived molecules have, on average, 6.2 chiral centers per molecule (Feher, M., Schmidt, M., 2003). Since the shift away from screening naturally derived drugs, approved new molecular entities (NMEs) have dropped dramatically (1996 to 2011), bottoming out at only 17 NMEs in 2002 (FDA, 2013). The failure of combinatorial chemistry is perfectly illustrated by the fact that only one NME has ever been brought to market through this initiative; Bayer’s sorafineb (Nexavar) (Newmann, D., Cragg, G., 2007).
In an attempt to recapture the chemical complexity of natural products the pharmaceutical industry has been exploring “diversity oriented syntheses” (Schreiber, S., 2000; Tan, D., 2005). While improved, the chemical complexity of these newer synthetic libraries is still nowhere near that of nature. As a result, there is renewed interest in bioprospecting, and the development of natural products as pharmaceuticals (Li, J., Vederas, J., 2009; De Luca, V., et al. 2012; Zhu, F., et al. 2011; Saslis-Lagoudakis, C., et al. 2012). Shareholders, who have grown accustomed to enormous growth rates and profits during the ‘golden age’ of pharma, would reactive negatively to a perceived shift back into the pharmaceutical ‘stone age’ (Malik, N., 2008), however modern bioprospecting is anything but primitive. It is no longer necessary for ethnobotanists to drudge through tropical rain forests to identify plants containing interesting, biologically active compounds. Saslis-Lagoudakis et al. has shown that traditionally used medicinal flora from South Africa, New Zealand, and Nepal phylogenetic cluster together in what they refer to as ‘hot nodes’ (Saslis-Lagoudakis, C., et al. 2012). Similar systematic, evolutionary analysis showed that the organisms which accumulate close to 1300 FDA approved and clinical trial drugs or their lead compounds, were clustered in only 144 organism families; a hit rate of approximately 9 drugs per family, where a random distribution of drug accumulation through the tree-of-life would predict only one drug per five families (Zhu, F., et al. 2012). Clearly, privileged structures accumulate in evolutionarily closely related species, and therefore large scale sequencing and phylogenetic analysis of taxa can be used to identify pharmaceutically useful organisms that have yet to be explored. While much more systematic then past ethnobotanical efforts, this chemical and phylogenetic approach still succumbs to the reality that many, potentially valuable, natural products accumulate to levels that preclude their testing against biological targets.
Combinatorial biosynthesis, first described the engineering of polyketide synthases to make over 200 new polyketides (Weissman, K., Leadlay, P., 2005), has since been used to engineer benzylisoquinoline and monoterpene indole alkaloid, as well as terpene biosynthesis in genetically modified yeast strains (Facchini, P., et al. 2012). This platform has the remarkable potential to generate high levels of naturally occurring compounds, with appropriate stereocenters, that are otherwise inaccessible to drug development pipelines due to their natural low abundance (Glenn, W., et al. 2012). For example; Conolidine, a mono-terpene indole alkaloid (MIA) from Tabernaemontana diverticata, accumulates to only 0.00014% yield in stem bark and was identified as potent non-opioid analgesic only after an efficient de novo chemical synthesis was developed (Tarselli, M., et al. 2011). Essentially, combinatorial biosynthesis has the potential to be to low abundance natural products and drug researchers, what PCR has been to low abundance transcripts and molecular biologists. What combinatorial biosynthetic initiatives need are properly annotated, repositories of functionally characterized enzymes, and their corresponding genes.
The natural chemical diversity in plants has yielded almost 22 % of all approved drugs (Zhu, F., et al. 2012), and the fact that only a fraction of all plants have been adequately explored for biological activity presents remarkable pharmaceutical opportunities (Saslis-Lagoudakis, C., et al. 2012). Mining, and effectively exploiting the biochemical, and pharmacological potential of medicinal plants requires deep transcriptome sequencing of phylogenetically distant species to ensure adequate representation, and annotation, of the taxonomically restricted biochemical pathways. Unfortunately, most large scale sequencing initiatives, to date, have focused on major crop species that generate few privileged structures (Facchini, P., et al. 2012). However, since the development of next-generation DNA sequencing technologies the cost of sequencing genomes and transcriptomes has been dramatically reduced (Sboner, A., et al. 2011). This cost reduction has spurred massive investment towards deep sequencing of medicinal plant transcriptomes in Canada (PhytoMetaSyn, http://www.phytometasyn.ca/), and the United States (Medicinal Plant Genomics Resource, http://medicinalplantgenomics.msu.edu/ ; MedPlants, http://medplants.ncgr.org/) all of whom have made their transcriptome assemblies publicly available for BLAST analysis. These resources have generated conditions where interested researchers can perform the same phylogenetic and chemical analysis described earlier (Zhu, F., et al. 2011; Saslis-Lagoudakis, C., et al. 2012), but for the purposes of identifying ‘hot nodes’ involved in unique biochemistries, with the intention of cloning the corresponding genes, and functionally characterizing the recombinant enzymes to supply combinatorial biosynthetic pipelines.
I expect in the near future we will begin to see genetically modified microbial platform strains where molecular biologists will be playing biosynthetic Lego; popping in and out genes that code for enzymes which carry out unique pharmaceuticly desirable modifications to ferment optomized lead compounds.
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