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ESKAPEing the labyrinth of antibacterial discovery

An Erratum to this article was published on 14 August 2015

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Abstract

Antimicrobial drug resistance is a growing threat to global public health. Multidrug resistance among the 'ESKAPE' organisms — encompassing Enterococcus faecium, Staphylococcus aureus, Klebsiella pneumoniae, Acinetobacter baumannii, Pseudomonas aeruginosa and Enterobacter spp. — is of particular concern because they are responsible for many serious infections in hospitals. Although some promising agents are in the pipeline, there is an urgent need for new antibiotic scaffolds. However, antibacterial researchers have struggled to identify new small molecules with meaningful cellular activity, especially those effective against multidrug-resistant Gram-negative pathogens. This difficulty ultimately stems from an incomplete understanding of efflux systems and compound permeation through bacterial membranes. This Opinion article describes findings from target-based and phenotypic screening efforts carried out at AstraZeneca over the past decade, discusses some of the subsequent chemistry challenges and concludes with a description of new approaches comprising a combination of computational modelling and advanced biological tools which may pave the way towards the discovery of new antibacterial agents.

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Figure 1: Mean LogD values for internal AstraZeneca antibacterial project compounds and for exemplar hits from other disease areas.
Figure 2: The relationship over time between the biochemical potency against Pseudomonas aeruginosa MurC and the cLogD of newly synthesized programme compounds.
Figure 3: Gram-negative and Gram-positive cell walls.
Figure 4: Effect of porin point mutations on antibiotic transport.

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Change history

  • 14 July 2015

    In reference 67 the journal name was incorrect. It has now been corrected online and in print.

  • 14 August 2015

    In the legend of Figure 3, Gram-negative and Gram-positive bacteria were incorrectly labelled. This has been corrected in the online version of the article.

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Acknowledgements

This review represents many years of hard work by former members of the AstraZeneca Infection Innovative Medicine and Early Development department, whom the authors would like to gratefully acknowledge for their intelligence, creativity and dedication to the advancement of this important field of research.

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Correspondence to Ruben Tommasi.

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All authors are current or former employees of AstraZeneca and, as such, may hold stock in the company.

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DATABASES

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Glossary

cLogD

The 'computed' LogD, using a predictive model. The values in this paper were calculated using AstraZeneca's proprietary model, AZlogD7.4.

Hill slope

A measure of binding or linkage cooperativity that can reflect a deviation from simple 1:1 stoichiometry (for which the Hill slope is equal to 1). The term is named in honour of A. V. Hill, the recipient of the 1922 Nobel Prize in Physiology or Medicine.

IC50

Inhibitor concentration at 50% effect. This is a generalized measure of inhibitory potency for a dose response, and dependent upon the conditions of the specific assay. Ideally, the relationship between this result and the intrinsic binding inhibition constant (Ki) may be understood from mechanism of inhibition studies.

LogD

The distribution coefficient, measured as the relative partitioning of all ionizable forms of a small molecule between a hydrophobic (octanol) and aqueous phase buffered to a particular pH, usually 7.4. This term describes the relative hydrophobicity of a chemical compound and is different to the related partition coefficient, logP, which describes the partitioning of only the neutral (non-ionized) form of the compound between phases.

Minimum inhibitory concentration

This measurement reflects the lowest concentration of compound that visibly inhibits the growth of an organism after overnight incubation.

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Tommasi, R., Brown, D., Walkup, G. et al. ESKAPEing the labyrinth of antibacterial discovery. Nat Rev Drug Discov 14, 529–542 (2015). https://doi.org/10.1038/nrd4572

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