Open AccessMathematical modeling of antimicrobial susceptibility data of Staphylococcus haemolyticus for 11 antimicrobial agents, including three experimental glycopeptides and an experimental lipoglycopeptide
Author(s) -
Paul Hunter,
Robert C. George,
J W Griffiths
Publication year - 1990
Publication title -
antimicrobial agents and chemotherapy
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.07
H-Index - 259
eISSN - 1070-6283
pISSN - 0066-4804
DOI - 10.1128/aac.34.9.1769
Subject(s) - teicoplanin , antimicrobial , microbiology and biotechnology , gentamicin , glycopeptide , biology , antibacterial agent , vancomycin , antibiotics , antibiotic resistance , staphylococcus haemolyticus , staphylococcus aureus , bacteria , genetics
Antimicrobial MIC data were obtained for 96 strains of Staphylococcus haemolyticus and the following 11 antimicrobial agents: methicillin, gentamicin, rifampin, fusidic acid, ciprofloxacin, vancomycin, teicoplanin; three experimental glycopeptides, MDL 62,873, MDL 62,208, and MDL 62,224; and an experimental lipoglycopeptide, ramoplanin. Resistance to methicillin and gentamicin was present in over 50% of the strains, although resistance to the other agents was present in less than 10%. It is shown how application of mathematical modeling techniques can add to the understanding of such MIC data. MICs of methicillin and gentamicin were highly correlated, suggesting that evolutionary pressures for development of resistance to these agents were similar. The structural relationships among the glycopeptides were accurately reflected in their spatial relationships within the model. MICs of ramoplanin were negatively correlated with MICs of some other antimicrobial agents, particularly gentamicin, suggesting that this agent is more active against gentamicin-resistant strains. Methicillin-resistant strains were more tightly clustered than were methicillin-susceptible strains, suggesting that methicillin-resistant strains were more closely related to each other than were methicillin-susceptible strains. Mathematical modeling techniques enable more detailed analysis of MIC data.