ESKAPEing the labyrinth of antibacterial discovery

Journal name:
Nature Reviews Drug Discovery
Volume:
14,
Pages:
529–542
Year published:
DOI:
doi:10.1038/nrd4572
Published online
Corrected online
Corrected online

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.

At a glance

Figures

  1. Mean LogD values for internal AstraZeneca antibacterial project compounds and for exemplar hits from other disease areas.
    Figure 1: Mean LogD values for internal AstraZeneca antibacterial project compounds and for exemplar hits from other disease areas.

    The mean LogD values were calculated at pH 7.4 using AstraZeneca internal software that was parameterized on a continual basis using new data. Error bars indicate the 99% confidence interval for the mean of calculated LogD values for each category on the x axis. a | The mean LogD values for active compounds targeting 10 key pathogens are indicated by yellow diamonds, and the number of active compounds (n) with a minimum inhibitory concentration (MIC) ≤8 μg mL−1 is indicated in parentheses. The mean LogD values of inactive compounds (MIC >64 μg mL−1) are indicated by red hexagons. The mean of a random sample of 45,000 compounds from the AstraZeneca screening collection is shown for reference. b | The mean LogD values for hits from ten representative high-throughput screens are indicated by green circles, and the number of hits (n) included in the analysis is indicated in parentheses. A. baumannii, Acinetobacter baumannii; AccA, acetyl-CoA carboxyltransferase α-subunit; AccC, biotin carboxylase; AccD, acetyl-CoA carboxyltransferase β-subunit; CoaD, phosphopantetheine adenylyltransferase; E. coli, Escherichia coli; FabH, β-ketoacyl-(acyl carrier protein) synthase III; H. influenzae, Haemophilus influenzae; K. pneumoniae, Klebsiella pneumoniae; LigA, DNA ligase; MurC, uridine diphosphate (UDP)-N-acetylmuramate-L-alaninetransferase; P. aeruginosa, Pseudomonas aeruginosa; ParE, topoisomerase IV; PyrH, uridine monophosphate (UMP) kinase; S. aureus, Staphylococcus aureus; S. pneumoniae, Streptococcus pneumoniae; S. pyogenes, Streptococcus pyogenes; Tmk, deoxy-thymidine monophosphate (dTMP) kinase; TrmD, tRNA (guanine37-N1)-methyltransferase.

  2. The relationship over time between the biochemical potency against Pseudomonas aeruginosa MurC and the cLogD of newly synthesized programme compounds.
    Figure 2: The relationship over time between the biochemical potency against Pseudomonas aeruginosa MurC and the cLogD of newly synthesized programme compounds.

    As chemists were designing and synthesizing compounds over the course of the project (shown as sequential compound registrations on the x axis) the most potent examples trended towards having a high hydrophobicity (dark red squares). Efforts to reduce the hydrophobicity were generally met with reduced biochemical potency (green squares). The molecules are grouped by hydrophobicity, as measured by 'computed' LogD (cLogD) values, calculated using AstraZeneca's proprietary predictive model, AZlogD7.4, as indicated in the key.

  3. Gram-negative and Gram-positive cell walls.
    Figure 3: Gram-negative and Gram-positive cell walls.

    Gram-negative bacteria rely on both an inner and an outer membrane surrounding a thin peptidoglycan matrix and a periplasmic space (part a), whereas Gram-positive bacteria generally use a thicker peptidoglycan layer to protect a single cytoplasmic membrane (part b)27. Transport of antibiotics and other extracellular compounds across bacterial membranes occurs both actively and passively, depending on the nature of the transporter. There are numerous components associated with both types of cell walls that limit the ability of antibiotics to penetrate these structures, such as efflux pumps that expunge toxins, defensive enzymes, such as β‑lactamases, and complex carbohydrate networks that create a protective capsule coating. Integral and peripheral membrane proteins are shown in light and dark green, respectively. LPS, lipopolysaccharide; LTA, lipoteichoic acid; WTA, wall teichoic acid.

  4. Effect of porin point mutations on antibiotic transport.
    Figure 4: Effect of porin point mutations on antibiotic transport.

    Crystal structure of the porin OccD1 (Protein Data Bank identifier: 3SY7)6, 30, illustrating the effect of point mutations in the porin on translocation of meropenem36. The locations of the point mutations are indicated by white boxes, and the effects of the point mutations are colour coded: red indicates a reduction in permeability, yellow indicates no effect and green indicates an increase in permeability.

Accession codes

Referenced accessions

Protein Data Bank

Change history

Corrected online 14 July 2015
In reference 67 the journal name was incorrect. It has now been corrected online and in print.
Corrected online 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.

References

  1. World Health Organization. Antimicrobial Resistance: Global Report on Surveillance http://apps.who.int/iris/bitstream/10665/112642/1/9789241564748_eng.pdf?ua=1 (WHO 2014).
  2. Boucher, H. W. et al. 10 × '20 progress — development of new drugs active against Gram-negative Bacilli: an update from the Infectious Diseases Society of America. Clin. Infect. Dis. 56, 16851694 (2013).
  3. Rex, J. H., Goldberger, M., Eisenstein, B. I. & Harney, C. The evolution of the regulatory framework for antibacterial agents. Ann. NY Acad. Sci. 1323, 1121 (2014).
  4. Payne, D. J., Gwynn, M. N., Holmes, D. J. & Pompliano, D. L. Drugs for bad bugs: confronting the challenges of antibacterial discovery. Nat. Rev. Drug Discov. 6, 2940 (2007).
    This paper provides an overview of the HTS efforts directed at the identification of novel antibacterials at GSK, which comprised the study of more than 300 genes and 70 high-throughput screens over a period of 7 years. Furthermore, a thorough analysis of potential reasons for failure is presented along with perspectives on how to improve the likelihood of success through focusing on broader chemical diversity.
  5. O'Shea, R. & Moser, H. E. Physicochemical properties of antibacterial compounds: implications for drug discovery. J. Med. Chem. 51, 28712878 (2008).
  6. Eren, E. et al. Substrate specificity within a family of outer membrane carboxylate channels. PLoS Biol. 10, e1001242 (2012).
  7. de Jonge, B. L. et al. Discovery of inhibitors of 4′-phosphopantetheine adenylyltransferase (PPAT) to validate PPAT as a target for antibacterial therapy. Antimicrob. Agents Chemother. 57, 60056015 (2013).
  8. Copeland, R. A. Evaluation of Enzyme Inhibitors in Drug Discovery: A Guide for Medicinal Chemists and Pharmacologists. 2nd edn (John Wiley & Sons, 2013).
  9. Roemer, T., Davies, J., Giaever, G. & Nislow, C. Bugs, drugs and chemical genomics. Nat. Chem. Biol. 8, 4656 (2012).
  10. Buurman, E. T. et al. In vitro validation of acetyltransferase activity of GlmU as an antibacterial target in Haemophilus influenzae. J. Biol. Chem. 286, 4073440742 (2011).
  11. Mills, S. D. et al. Novel bacterial NAD+-dependent DNA ligase inhibitors with broad-spectrum activity and antibacterial efficacy in vivo. Antimicrob. Agents Chemother. 55, 10881096 (2011).
  12. Lajiness, M. S., Maggiora, G. M. & Shanmugasundaram, V. Assessment of the consistency of medicinal chemists in reviewing sets of compounds. J. Med. Chem. 47, 48914896 (2004).
  13. Sanguinetti, M. C. & Tristani-Firouzi, M. hERG potassium channels and cardiac arrhythmia. Nature 440, 463469 (2006).
  14. Seidler, J., McGovern, S. L., Doman, T. N. & Shoichet, B. K. Identification and prediction of promiscuous aggregating inhibitors among known drugs. J. Med. Chem. 46, 44774486 (2003).
  15. Clinical and Laboratory Standards Institute. CLSI document M07-A9: Methods for Dilution Antimicrobial Susceptibility Tests for Bacteria That Grow Aerobically; Approved Standard 9th edn (CLSI, 2012).
  16. Uria-Nickelsen, M. et al. Novel topoisomerase inhibitors: microbiological characterisation and in vivo efficacy of pyrimidines. Int. J. Antimicrob. Agents 41, 363371 (2013).
  17. Keating, T. A. et al. In vivo validation of thymidylate kinase (TMK) with a rationally designed, selective antibacterial compound. ACS Chem. Biol. 7, 18661872 (2012).
  18. Brown, D. G., May-Dracka, T. L., Gagnon, M. M. & Tommasi, R. Trends and exceptions of physical properties on antibacterial activity for Gram-positive and Gram-negative pathogens. J. Med. Chem. 57, 1014410161 (2014).
  19. Hann, M. M. Molecular obesity, potency and other addictions in drug discovery. Med. Chem. Commun. 2, 349355 (2011).
  20. Leeson, P. D. & Springthorpe, B. The influence of drug-like concepts on decision-making in medicinal chemistry. Nat. Rev. Drug Discov. 6, 881890 (2007).
  21. Wenlock, M. C., Austin, R. P., Barton, P., Davis, A. M. & Leeson, P. D. A comparison of physiochemical property profiles of development and marketed oral drugs. J. Med. Chem. 46, 12501256 (2003).
  22. Hernandez, V. et al. Discovery of a novel class of boron-based antibacterials with activity against Gram-negative bacteria. Antimicrob. Agents Chemother. 57, 13941403 (2013).
  23. Rees, D. C., Congreve, M., Murray, C. W. & Carr, R. Fragment-based lead discovery. Nat. Rev. Drug Discov. 3, 660672 (2004).
  24. Ling, L. L. et al. A new antibiotic kills pathogens without detectable resistance. Nature 517, 455459 (2015).
  25. Brown, D. G., Lister, T. & May-Dracka, T. L. New natural products as new leads for antibacterial drug discovery. Bioorg. Med. Chem. Lett. 24, 413418 (2014).
  26. Cabeen, M. T. & Jacobs-Wagner, C. Bacterial cell shape. Nat. Rev. Micro 3, 601610 (2005).
  27. Silhavy, T. J., Kahne, D. & Walker, S. The bacterial cell envelope. Cold Spring Harb. Perspect. Biol. 2, a000414 (2010).
  28. Denyer, S. P. & Maillard, J. Y. Cellular impermeability and uptake of biocides and antibiotics in Gram-negative bacteria. J. Appl. Microbiol. 92, 35S45S (2002).
  29. Hancock, R. E. & Brinkman, F. S. Function of pseudomonas porins in uptake and efflux. Annu. Rev. Microbiol. 56, 1738 (2002).
  30. Biswas, S., Mohammad, M. M., Patel, D. R., Movileanu, L. & van den Berg, B. Structural insight into OprD substrate specificity. Nature Struct. Mol. Biol. 14, 11081109 (2007).
    This paper provides an encompassing structural and biophysical rationale for the adoption of the OccD and OccK nomenclature standard for P. aeruginosa outer-membrane carboxylate channels (porins). Additionally, it presents and summarizes the main methods that are presently at the forefront of studying porin dynamics and elucidating substrate selectivity: single-channel electrophysiology and radioactive-tracer direct measurement (or competition). The authors also demonstrate the selective binding and transport of antibiotics by these channels.
  31. Ceccarelli, M., Vargiu, A. & Ruggerone, P. A kinetic Monte Carlo approach to investigate antibiotic translocation through bacterial porins. J. Phys. Condens. Matter 24, 104012 (2012).
  32. Eren, E. et al. Toward understanding the outer membrane uptake of small molecules by Pseudomonas aeruginosa. J. Biol. Chem. 288, 1204212053 (2013).
  33. Kattner, C., Zaucha, J., Jaenecke, F., Zachariae, U. & Tanabe, M. Identification of a cation transport pathway in Neisseria meningitidis PorB. Proteins 81, 830840 (2013).
  34. Shaw, D. E. et al. Atomic-level characterization of the structural dynamics of proteins. Science 330, 341346 (2010).
    This paper describes a landmark 1-millisecond molecular-dynamics simulation of bovine pancreatic trypsin inhibitor (BPTI) as well as simulations of FiP35 and villin, which demonstrate that not only are long timescale simulations stable — a point which was previously in doubt — but they are also capable of accurately reproducing the experimentally observed behaviour of these systems, including the kinetics of protein folding to the native state, in addition to the detail these methods provide at atomic resolution.
  35. Laio, A. Parrinello, M. Escaping free-energy minima. Proc. Natl Acad. Sci. USA. 99, 1256212566 (2002).
  36. Isabella, V. M. et al. Towards the rational design of carbapenem uptake in Pseudomonas aeruginosa. Chem.Biol. 22, 535547 (2015).
    Using a multidisciplinary approach, including genetics, molecular dynamic simulations and medicinal chemistry, these authors discovered a novel mechanism of carbapenem uptake in P. aeruginosa, which led to the generation of novel carbapenems with altered uptake properties.
  37. van Opijnen, T., Lazinski, D. W. & Camilli, A. Genome-wide fitness and genetic interactions determined by Tn-seq, a high-throughput massively parallel sequencing method for microorganisms. Curr. Protoc. Microbiol. 36, 1E.3.11E.3.24 (2015).
    This study describes Tn-seq, a newly developed genomic platform that deploys high-throughput next-generation sequencing on saturated transposon-insertion libraries of microorganisms to decipher complex fitness phenotypes on a global scale.
  38. Tamber, S. & Hancock, R. E. W. Involvement of two related porins, OprD and OpdP, in the uptake of arginine by Pseudomonas aeruginosa. FEMS Microbiol. Lett. 260, 2329 (2006).
  39. Möllmann, U., Heinisch, L., Bauernfeind, A., Köhler, T. & Ankel-Fuchs, D. Siderophores as drug delivery agents: application of the “Trojan Horse” strategy. Biometals 22, 615624 (2009).
  40. deCarvalho, C. C. & Fernandes, P. Siderophores as “Trojan Horses”: tackling multidrug resistance? Frontiers Microbiol. 5, 290 (2014).
  41. Rivault, F. et al. Synthesis of pyochelin–norfloxacin conjugates. Bioorg. Med. Chem. Lett. 17, 640644 (2007).
  42. Page, M. G. P. Siderophore conjugates. Ann. NY Acad. Sci. 1277, 115126 (2013).
  43. Page, M. G. P. et al. In vitro and in vivo properties of BAL30376, a β-lactam and dual β-lactamase inhibitor combination with enhanced activity against Gram-negative Bacilli that express multiple β-lactamases. Antimicrob. Agents Chemother. 55, 15101519 (2011).
  44. Tomaras, A. P. et al. Adaptation-based resistance to siderophore-conjugated antibacterial agents by Pseudomonas aeruginosa. Antimicrob. Agents Chemother. 57, 41974207 (2013).
  45. Ricci, V. & Piddock, L. Accumulation of garenoxacin by Bacteroides fragilis compared with that of five fluoroquinolones. J. Antimicrob. Chemother. 52, 605609 (2003).
  46. Williams, K. J. & Piddock, L. J. Accumulation of rifampicin by Escherichia coli and Staphylococcus aureus. J. Antimicrob. Chemother. 42, 597603 (1998).
  47. Bhat, J., Narayan, A., Venkatraman, J. & Chatterji, M. LC–MS based assay to measure intracellular compound levels in Mycobacterium smegmatis: linking compound levels to cellular potency. J. Microbiol. Methods 94, 152158 (2013).
  48. Davis, T. D., Gerry, C. J. & Tan, D. S. General platform for systematic quantitative evaluation of small-molecule permeability in bacteria. ACS Chem. Biol. 9, 25352544 (2014).
  49. Rubakhin, S. S., Lanni, E. J. & Sweedler, J. V. Progress toward single cell metabolomics. Curr. Opin. Biotechnol. 24, 95104 (2013).
  50. IMI. Translocation: molecular basis of the bacterial cell wall permeability. Innovative Medicines Initiative http://www.imi.europa.eu/content/translocation (2008).
  51. May, M. Drug development: time for teamwork. Nature 509, S4S5 (2014).
  52. Valentino, M. D. et al. Genes contributing to Staphylococcus aureus fitness in abscess- and infection-related ecologies. mBio 5, e01729-14 (2014).
  53. Subashchandrabose, S., Smith, S. N., Spurbeck, R. R., Kole, M. M. & Mobley, H. L. T. Genome-wide detection of fitness genes in uropathogenic Escherichia coli during systemic infection. PLoS Pathog. 9, e1003788 (2013).
  54. Palace, S. G., Proulx, M. K., Lu, S., Baker, R. E. & Goguen, J. D. Genome-wide mutant fitness profiling identifies nutritional requirements for optimal growth of Yersinia pestis in deep tissue. mBio 5, e01385-14 (2014).
  55. Wang, N., Ozer, E. A., Mandel, M. J. & Hauser, A. R. Genome-wide identification of Acinetobacter baumannii genes necessary for persistence in the lung. mBio 5, e01163-14 (2014).
  56. Skurnik, D. et al. Enhanced in vivo fitness of carbapenem-resistant oprD mutants of Pseudomonas aeruginosa revealed through high-throughput sequencing. Proc. Natl Acad. Sci. USA 110, 2074720752 (2013).
  57. Turner, K. H., Everett, J., Trivedi, U., Rumbaugh, K. P. & Whiteley, M. Requirements for Pseudomonas aeruginosa acute burn and chronic surgical wound infection. PLoS Genet. 10, e1004518 (2014).
  58. Moule, M. G. et al. Genome-wide saturation mutagenesis of Burkholderia pseudomallei K96243 predicts essential genes and novel targets for antimicrobial development. mBio 5, e00926-13 (2014).
  59. Gallagher, L. A., Shendure, J. & Manoil, C. Genome-scale identification of resistance functions in Pseudomonas aeruginosa using Tn-seq. mBio 2, e00315-10 (2011).
  60. Cabot, G. et al. Pseudomonas aeruginosa ceftolozane-tazobactam resistance development requires multiple mutations leading to overexpression and structural modification of AmpC. Antimicrob. Agents Chemother. 58, 30913099 (2014).
  61. Jones, C. J. et al. ChIP-Seq and RNA-Seq reveal an AmrZ-mediated mechanism for cyclic di-GMP synthesis and biofilm development by Pseudomonas aeruginosa. PLoS Pathog. 10, e1003984 (2014).
  62. Hua, X., Chen, Q., Li, X. & Yu, Y. Global transcriptional response of Acinetobacter baumannii to a subinhibitory concentration of tigecycline. Int. J. Antimicrob. Agents 44, 337344 (2014).
  63. Tan, S. Y.-Y. et al. Comparative systems biology analysis and mode of action of the isothiocyanate compound iberin on Pseudomonas aeruginosa. Antimicrob. Agents Chemother. 58, 66486659 (2014).
  64. Armengaud, J. Microbiology and proteomics, getting the best of both worlds! Environ. Microbiol. 15, 1223 (2013).
  65. Chavali, A. K., D'Auria, K. M., Hewlett, E. L., Pearson, R. D. & Papin, J. A. A metabolic network approach for the identification and prioritization of antimicrobial drug targets. Trends Microbiol. 20, 113123 (2012).
  66. Lebeis, S. L. & Kalman, D. Aligning antimicrobial drug discovery with complex and redundant host-pathogen interactions. Cell Host Microbe 5, 114122 (2009).
  67. Nayar, A.S. et al. Novel antibacterial targets and compounds revealed by a high-throughput cell wall reporter assay. J. Bacteriol. 197, 17261734 (2015)

Download references

Author information

Affiliations

  1. Entasis Therapeutics, 35 Gatehouse Drive, Waltham, Massachusetts 02451, USA.

    • Ruben Tommasi &
    • Alita A. Miller
  2. AstraZeneca Pharmaceuticals, 35 Gatehouse Drive, Waltham, Massachusetts 02451, USA.

    • Dean G. Brown
  3. Agios Pharmaceuticals, 88 Sidney Street, Cambridge, Massachusetts 02139, USA.

    • Grant K. Walkup
  4. Novartis Institute for BioMedical Research, 186 Massachusetts Avenue, Cambridge, Massachusetts, USA.

    • John I. Manchester

Competing interests statement

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

Corresponding author

Correspondence to:

Author details

  • Ruben Tommasi

    Ruben Tommasi obtained his doctorate degree in organic chemistry from the State University of New York–Albany, USA, in 1992, with Frank M. Hauser. He spent a year at the University of Colorado Boulder, USA, with Gary Molander and was subsequently a post-doctoral fellow at Upjohn, Kalamazoo, Michigan, USA, where his work on the dihydropyrone class of HIV protease inhibitors ultimately lead to the discovery of Tipranavir. In 1994, he joined Ciba-Geigy (now Novartis) as a medicinal chemist where he worked in several therapeutic areas including arthritis, bone metabolism and infection. During his 17-year tenure at Novartis, he had the opportunity to build a new chemistry team dedicated to hit-to-lead optimization and chemogenetics as well as lead the infectious disease chemistry team. He successfully led his teams to advance two candidates to Phase II clinical studies (Hepatitis C virus protease inhibitor, BZF961, and EF-Tu inhibitor for Clostridium difficile, LFF571). In 2011, he joined AstraZeneca to lead the infection chemistry team, where he led teams that advanced a clinical candidate (ETX0914 for the treatment of gonorrhoea) as well as a new early portfolio of programmes. In May of 2015, Ruben became the Chief Scientific Officer at Entasis Therapeutics, AstraZeneca's new antibacterial spin-out company. One of his main interests is to develop a better understanding of the factors that affect drug permeation into Gram-negative organisms. He is the co-author of 33 papers and co-inventor of 12 patents. Ruben Tommasi homepage.

  • Dean G. Brown

    Dean G. Brown is currently the Director of Infection Chemistry at AstraZeneca Pharmaceuticals. He obtained a B.Sc. in chemistry at Abilene Christian University, Texas, USA, and a Ph.D. at the University of Minnesota, Minneapolis, USA, in organic chemistry. While at AstraZeneca, he has been responsible for building many new scientific programmes in both neuroscience and infection, several which have resulted in successful transition to clinical trials. His scientific interests are in lead generation, library design and medicinal chemistry. He is listed as an author or co-author on more than 40 publications and patent applications in medicinal chemistry and drug design, including granted patents on clinical candidates.

  • Grant K. Walkup

    Grant K. Walkup has over a decade of experience conducting and leading anti-infective drug discovery projects from the lead discovery stages through to selection of candidates for development. During this time, he has developed or overseen ~80 screens covering the gamut of target-, phenotypic-, virtual-, fragment- and pharmacophore-based approaches. His research interests include bioorganic chemistry, chemical biology and elucidating the structural and kinetic factors that underlie the selective engagement of biological targets by small molecules. He has more than 20 peer reviewed publications and several patents, and he is a frequent reviewer for several widely-read journals. He received a B.Sc. in chemistry from The University of California, San Diego (UCSD), USA. He completed a doctorate in organic chemistry (with honours) from the California Institute of Technology, Pasadena, USA, studying with Barbara Imperiali designing selective fluorescent chemosensors. He continued post-doctoral studies as a US National Institutes of Health and Howard Hughes Medical Institute Fellow with Roger Tsien at UCSD. He presently holds the positions of Associate Director of Enzymology at Agios Pharmaceuticals in Cambridge, Massachusetts, USA, and Adjunct Professor at Stony Brook University, New York, USA.

  • John I. Manchester

    John I. Manchester led the computer-aided drug design group in infection at AstraZeneca Pharmaceuticals, and for many years sought relationships between the physical properties of small molecules and their antibacterial activity, until the revolution in graphics processing unit (GPU)-enabled computing made it possible to study the interactions between small molecules and bacterial membrane components at atomic resolution. His background is in modelling drug metabolism using high-performance computing and machine learning. He is author of more than 25 publications and an inventor on several patents. He is presently a senior scientific informatics lead for biology research platforms at Novartis Institutes for BioMedical Research in Cambridge, Massachusetts, USA.

  • Alita A. Miller

    Alita A. Miller holds a B.A. in chemistry from Kalamazoo College, Michigan, USA, and a Ph.D. in biochemistry and molecular biology from the University of Chicago, Illinois, USA. Her postdoctoral training focused on the molecular drivers of bacterial pathogenesis in the laboratory of Victor DiRita at the University of Michigan, USA. The main focus of her career has been on antibacterial preclinical discovery with positions held at Pharmacia, Pfizer and AstraZeneca. She is currently Director and Head of Biology at Entasis Therapeutics. Among her research interests are novel approaches to antibacterial discovery, including new ways of characterizing antibiotic penetration across bacterial membranes. She serves on several US National Institutes of Health review panels for proposals related to the field. She has over 30 publications and recently co-edited a textbook on emerging trends in antibacterial discovery.

Additional data