Comparison of antimicrobial in vitro activities against Streptococcus pneumoniae independent of MIC susceptibility breakpoints using MIC frequency distribution curves, scattergrams and linear regression analyses

• Published in: Journal of antimicrobial chemotherapy Vol. 48; no. 5; pp. 609 - 615
• Main Authors: Fass, Robert J., Barnishan, Jean
• Format: Journal Article
• Language: English
• Published: Oxford Oxford University Press 01.11.2001; Oxford Publishing Limited (England)
• Subjects: 4-Quinolones; Adult; Anti-Bacterial Agents - pharmacology; Anti-Infective Agents - pharmacology; Antibacterial agents; Antibiotics. Antiinfectious agents. Antiparasitic agents; Biological and medical sciences; Drug Resistance, Bacterial - physiology; Humans; Lactams; Linear Models; Medical sciences; Microbial Sensitivity Tests - standards; Microbial Sensitivity Tests - statistics & numerical data; Normal Distribution; Pharmacology. Drug treatments; Streptococcus pneumoniae - drug effects; Drug susceptibility test; β-Lactams; In vitro; Biological activity; Streptococcus pneumoniae; Streptococcaceae; Antibiotic; Fluoroquinolone derivatives; Minimum inhibitory concentration; Bacteria; Micrococcales; Antibacterial agent; Comparative study; Quinolone derivatives
• ISSN: 0305-7453; 1460-2091; 1460-2091
• DOI: 10.1093/jac/48.5.609

Comparing in vitro activities of antimicrobial agents against Streptococcus pneumoniae using per cent susceptible to recommended MIC breakpoints is not optimal. In this study, MICs of penicillin G, ampicillin/sulbactam, ceftriaxone, cefuroxime, erythromycin, tetracycline, trimethoprim/sulfamethoxazole, ciprofloxacin, levofloxacin, trovafloxacin and moxifloxacin were determined for 646 strains. Drug activities were compared using MIC frequency distribution curves, scattergrams and linear regression analyses of MICs (log2). MIC frequency distributions did not always correspond to recommended breakpoints for distinguishing susceptible, intermediate and resistant strains. Penicillin G, ampicillin/sulbactam and ceftriaxone had similar activities and were each c. 1–1.5 dilution steps more active than cefuroxime. For all β-lactam drug pairs, there was a high correlation of MICs with regression line slopes (a '1) and coefficients of determination (R2 = 0.90–0.97). Although β-lactam-resistant strains were more likely to be resistant to erythromycin, tetracycline and/or trimethoprim/sulfamethoxazole than were β-lactam-susceptible strains, MIC correlations were relatively poor (R2 = 0.14–0.46), as they were when the non-β-lactam drugs were compared with each other (R2 = 0.10–0.25). Trovafloxacin and moxifloxacin were each c. 2.5 dilution steps more active than ciprofloxacin and levofloxacin. There was no correlation of quinolone MICs with MICs of any other drug class (R2 0.02). Among the quinolones, however, there was a high correlation of MICs with a '1 and R2 = 0.81–0.92. With the quinolone drug pairs, lines of best fit were second-order polynomial equations, consistent with a dissociation of low level resistance mechanisms. In summary, β-lactam and quinolone MICs were predictable within drug classes and testing multiple derivatives within each class is probably not necessary. Although there was some relationship between β-lactam, erythromycin, tetracycline and trimethoprim/sulfamethoxazole MICs, predictability of MICs between drug classes was poor. There was no relationship between quinolone MICs and MICs of any of the other drugs tested.