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Statistical Metamodeling for Revealing Synergistic Antimicrobial Interactions

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  • Chia Hsiang Chen
  • Vincent Gau
  • Donna D Zhang
  • Joseph C Liao
  • Fei-Yue Wang
  • Pak Kin Wong

Abstract

Many bacterial pathogens are becoming drug resistant faster than we can develop new antimicrobials. To address this threat in public health, a metamodel antimicrobial cocktail optimization (MACO) scheme is demonstrated for rapid screening of potent antibiotic cocktails using uropathogenic clinical isolates as model systems. With the MACO scheme, only 18 parallel trials were required to determine a potent antimicrobial cocktail out of hundreds of possible combinations. In particular, trimethoprim and gentamicin were identified to work synergistically for inhibiting the bacterial growth. Sensitivity analysis indicated gentamicin functions as a synergist for trimethoprim, and reduces its minimum inhibitory concentration for 40-fold. Validation study also confirmed that the trimethoprim-gentamicin synergistic cocktail effectively inhibited the growths of multiple strains of uropathogenic clinical isolates. With its effectiveness and simplicity, the MACO scheme possesses the potential to serve as a generic platform for identifying synergistic antimicrobial cocktails toward management of bacterial infection in the future.

Suggested Citation

  • Chia Hsiang Chen & Vincent Gau & Donna D Zhang & Joseph C Liao & Fei-Yue Wang & Pak Kin Wong, 2010. "Statistical Metamodeling for Revealing Synergistic Antimicrobial Interactions," PLOS ONE, Public Library of Science, vol. 5(11), pages 1-7, November.
    • Handle: RePEc:plo:pone00:0015472
    DOI: 10.1371/journal.pone.0015472
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