Drug Discovery


of ~36 NCEs per year over a period of 30 years of R&D. At face value, this analysis does not make sense otherwise these companies would be out of business and long gone. Actually, it does make per- fect sense for those companies playing the danger- ous game of chasing blockbuster drugs knowing how much they will be worth in the end. A block- buster drug is defined as the one which achieves revenues of more than $1 billion at global level for its owner per year27; it is estimated that 125 drugs have met the target sale. The top 10 best selling drugs in the Unites States alone generated more than $70 billion in sales in 201128-29. Seven of these blockbusters (Table 1) have an origin linking them to products naturally produced by nature including the number one blockbuster of all time, Lipitor, generating more than $125 billion in sales for Pfizer29.


The search for blockbuster drugs is more lucra- tive and important than ever; Pfizer’s success with Lipitor makes the chase even more addictive and risky, though Pharma chiefs claim that recent restructuring and portfolio derisking activities are gaining traction towards rebuilding a strong and potentially profitable pharmaceutical industry29. I would argue that it is hardly the case, consider- ing several indicators such as prescription costs are under pressure, patents have sensitive land- scapes, generic sales has reached up to 50% of the market and only two in 10 drugs are profitable, when combined report that the drug discovery business is indeed under siege (Figure 1). Furthermore, the patent cliff of the best selling drugs is upon us and the recent approvals may result in unanticipated best seller replacements –


wishful thinking by the Pharma chiefs. In 2013, it is a reality that making any investments in R&D is even more challenging than gambling, leaving the dreaded question of where will the next blockbuster drug come from?


The Pharma and biotech industries have been exploring the chemical space for more than 30 years, through the use of sophisticated synthetic methodologies and CC and resulting multi-mil- lion compound libraries screened against a diverse range of targets and biologies. The out- come of these huge investments is rather disap- pointing with only 36% of the approved NCEs attributed to this gigantic endeavour. It is not that surprising, considering that manmade chemistries will always rely on simple coupling reactions with nitrogen enrichment in the final molecules. Figure 2 summarises the unreasonableness of chemical exploration by synthetic chemistries leading to success. The numbers clearly bias those molecules with natural source origins and contributing to 64% of the approved NCEs. Newman and Cragg alluded to various academic chemistry groups modifying active natural product skeletons as leads for novel drugs21, but failed to caution against the continuous use of combinatorial chemistry approaches with a new coat of diversi- ty paints. If these approaches were to be success- ful, then we would have observed their impact by now. This leaves us with only one choice and that is to return to Mother Nature as a golden source of novel agents and drugs. Billions of years of evolution can only make better chemical mole- cules than mankind will ever come close to mak- ing synthetically.


Continued from page 75


13 Shum, D et al. High-content assay to identify inhibitors of dengue virus infection. Assay Drug Dev Technol 8, 553-570 (2010). 14 Antczak, C et al.


Identification of benzofuran- 4,5-diones as novel and selective non-hydroxamic acid, non-peptidomimetic based inhibitors of human peptide deformylase. Bioorg Med Chem Lett 21, 4528-4532 (2011). 15 Somwar, R et al. Superoxide dismutase 1 is a probable target for a small molecule identified in a screen for inhibitors of the growth of lung adenocarcinoma cell lines. PNAS (USA) 108, 16375- 16380 (2011). 16 Feldman, T et al. Class of Allosteric Caspase Inhibitors Identified by High-Throughput Screening. Mol Cell 47, 585-95 (2012). 17 Lee, G et al. IKBKAP Expression Rescue in Neural Crest of Familial


 Chemical Space Estimate  Likely Candidates  Known Chemicals


 Commercial Chemicals  Filtered for Lead-Like*  Approved Drugs (NCEs)


10180 1018


1,000,000,000 68,000,000 1,014,926 1,073


 HTS or modification of an existing molecule  Synthetic with Natural Product pharmacophore


 Blockbuster Drugs to Date Figure 2: Chemical space exploration an unattainable task. * PAINS filters as described by Baell19 Drug Discovery World Spring 2013


387 55


 Natural Product with semi-synthetic modification 299  Natural Product mimic  Natural Product


268 64


124


Dysautonomia-iPSC Cells by Novel RT-PCR Based High Throughput Screening. Nature Biotech 30, 1244-1248 (2012). 18 Shum, D et al. An Image- Based Biosensor Assay Strategy to Screen for Modulators of the microRNA 21 Biogenesis Pathway. Comb Chem High Through Screen 15, 529-541 (2012). 19 Baell, JB. Broad Coverage of Commercially Available Lead-like Screening Space with Fewer than 350,000 Compounds. J Chem Inf Model 53, 39-55 (2013). 20 Zhang, JH et al. A simple statistical parameter for use in evaluation and validation of high throughput screening assays. J Biomol Screen 4, 67- 73 (1999). 21 Newman, DJ, Cragg, GM. Natural products as sources of new drugs over the 30 years from 1981 to 2010. J Nat Prod 75, 311-35 (2012). 22 CAS Database Counter accessed on 14 February 2013; http://www.cas.org/content/ counter.


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