Insanity has been defined as "doing the same thing over and over again and expecting different results” (Albert Einstein). This aphorism is certainly applicable to the most common approach employed in drug discovery, screening large collections of chemicals against a particular biological target, and especially to the approach universally employed during the golden era of antibiotic discovery: screening large collections of freshly isolated soil
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microorganisms for their ability to inhibit growth of a relatively few target pathogens and keep expecting an infinite resource of novel chemical compounds. That is why it is imperative to change one or more of the screening conditions in order to increase the probability of finding new molecular structures (Donadio et al., 2010).
In the early days of pharmacology, therapeutic drugs were obtained from medicinal plants exploiting thousands of years of traditional medicine. This was the case for aspirin, which was derived from the bark of the willow tree. As analytical chemistry, biology and pharmacology progressed, the discoveries went from the occasional serendipity (i.e. the discovery of penicillin) to a rational approach (cephalosporin was discovered as a solution for penicillin resistant infections) (Clever, 1999).
At the beginning, drug discovery was essentially based on screening natural products, isolating the active ingredients and testing them for their ability to treat a given disease. Synthetic chemistry was mostly an aid in optimizing existing scaffolds against resistance mechanisms (e.g., analogues of penicillin that would be resistant to the action of known β- lactamases) or in conferring more drug-like properties (e.g., transforming nalidixic acid into the fluoroquinolone class of antibiotics). The continuous progress in spectroscopy (NMR and MS) and separation techniques (HPLC) enabled determining the chemicals structures using increasingly lower amounts of natural products, making this approach the pillar of drug discovery by the pharmaceutical industry.
In the early 1980s, the increases in computing power enabled a shift away from random searches to a “rational” computational approach to drug discovery, namely, computer-aided drug design. This shift was also possible thanks to significant advances in structural biology (and a growing number of solved 3D structures of biologically relevant proteins), an improved understanding of protein-protein interactions, and the identification of protein targets important for different pathologies. While there are a few successes of the “rational drug design” approach (e.g., dorzolamide reached the market as a antiglaucoma agent in 1995 (Greer et al., 1994)), this discovery approach performed surprisingly poorly and was supplanted by a return to the fundamentally empirical method of screening, albeit with important differences.
By mid-1990’s a new strategy had taken over prompted, again, by new technological advances: combinatorial chemistry, laboratory automation and genomic sequencing. Combinatorial chemistry presented the opportunity of rapidly synthesising tens of thousands of compounds with a reduced effort, providing quick access to sensible libraries for screening.
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Automation enabled the robotic handling of small amounts of liquids, enabling assays to be performed in small volumes in – originally – 96-well microtiter plates, which allowed increased throughput with a decreased use in reagents and chemicals. Finally, the ability to rapidly sequence whole bacterial genomes offered the possibility of rapidly accessing any target from any pathogen. At the same time, advances in the genetic manipulation of most bacterial (and fungal) pathogens enabled establishing the essentiality of any potential target, which had to be conserved in the desired spectrum of pathogens and absent, or significantly divergent, in humans. Such targets could then be expressed with a proper tag in a convenient host, single- step purified by affinity chromatography and turned into powerful and miniaturized enzymatic assays. The scene was then set for rapidly screening every essential target in a given pathogen against synthetic and combinatorial libraries in a relatively short time. As mentioned above, this approach also delivered below expectations (and the investment made!).
Science is a dynamic landscape that changes as knowledge is introduced and adapts to new discoveries. A great example of this feature is the case of the Lipinski’s rule of five. In the late 1990s Christopher A. Lipinski created a set of rules of thumb to help choose molecules that could have higher potential in terms of drug-likeness and oral availability (Lipinski et al., 2001). These, shortly, consisted of:
o No more than five hydrogen bond donors
o No more than 10 hydrogen bond acceptors
o A molecular weight under 500
o A partition coefficient log P (a measure of lipophilicity) of less than 5
Although these are still valuable guidelines, we are aware that these rules do not apply to every drug in the market. In fact, antibiotics like erythromycin do not fit completely to these rules and Lipinski himself had warned about this caveat. This represents another example on the power of million years of evolution for natural products, which have thus reached a degree of complexity that cannot be restricted to a set of rules devised for synthetic drugs.
Therefore, a lack of truly effective screening alternatives, the availability of a large number of unexplored bacteria and the unusual chemical and biological of natural products make the latter source an attractive target for drug discovery.
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