Linear regression equations to predict β-lactam, macrolide, lincosamide and fluoroquinolone minimum inhibitory concentrations from molecular antimicrobial resistance determinants in Streptococcus pneumoniae

Antimicrobial resistance in Streptococcus pneumoniae represents a threat to public health and monitoring the dissemination of resistant strains is essential to guiding health policy. Multiple-variable linear regression modeling was used to determine the contributions of molecular antimicrobial resistance determinants to antimicrobial minimum inhibitory concentration (MIC) for penicillin, ceftriaxone, erythromycin, clarithromycin, clindamycin, levofloxacin, and trimethoprim/sulfamethoxazole. Training data sets consisting of Canadian S. pneumoniae isolated from 1995 to 2019 were used to generate multiple-variable linear regression equations for each antimicrobial. The regression equations were then applied to validation data sets of Canadian (n=439) and USA (n=607 and n=747) isolates. The MIC for β-lactam antimicrobials were fully explained by amino acid substitutions in motif regions of the penicillin binding proteins PBP1a, PPB2b, and PBP2x. Accuracy of predicted MICs within one doubling dilution to phenotypically determined MICs for penicillin was 97.4%, ceftriaxone 98.2%; erythromycin 94.8%; clarithromycin 96.6%; clindamycin 98.2%; levofloxacin 100%; and trimethoprim/sulfamethoxazole 98.8%; with an overall sensitivity of 95.8% and specificity of 98.0%. Accuracy of predicted MICs to the phenotypically determined MICs was similar to phenotype-only MIC comparison studies. The ability to acquire detailed antimicrobial resistance information directly from molecular determinants will facilitate the transition from routine phenotypic testing to whole genome sequencing analysis and can fill the surveillance gap in an era of increased reliance on nucleic acid assay diagnostics to better monitor the dynamics of S. pneumoniae.