Modeling microbial drug-resistance: from mathematics to pharmacoeconomics

Complicated intra-abdominal infection (IAI) requires increased health care expenditures and additional resources to compensate for an ineffective starting therapy. Aim. To select the economically optimal algorithm for using antimicrobial agents (AMA) that would minimize the evolving drug-resistance of microbial flora exemplified by E. coli. Methods . Based on the published data and our own clinical experience with antimicrobial drugs,  we calculated the cost of treatment of complicated IAI when either effective or ineffective  starting antibiotic therapy was applied. The developing drug-resistance of E. coli was simulated  by a mathematical model that incorporated real data on the antimicrobial drugs usage. The  model was also able to propose the optimal mode of AMA consumption, which is expected to minimize the microbial drugresistance. Results. According to the model, the current volume of AMA consumption (which includes more than 60% of fluoroquinolones, 3d generation cephalosporins and inhibitor-protected penicillin  derivatives) will increase the proportion of the Extended-spectrum betalactamase (ESBL)-positive strains of E. coli by 7% over the next 5 years. In contrast, the proposed alternative (optimized)  mode of AMA consumption (almost complete withdrawal of inhibitor-protected penicillins and  fluoroquinolones, against an increase in carbapenems by 30% and an increase in 3d generation  cephalosporins by 20%), will decrease the proportion of ESBL (+) E. coli strains by 7%. The cost  of care of complicated IAI under the current AMA regimen will grow due to the increase in the  proportion of ESBL (+) strains of E. coli. In contrast, the alternative (optimal) AMA therapy leading to the decrease in E. coli drug-resistance is expected to reduce the cost of care of complicated IAI to the level where the real and alternative (optimized) AMA consumption expenditures are comparable. Conclusion. The proposed mathematical model allows one to predict the changes in microbial  drug-resistance and choose the optimal algorithm of AMA consumption able to restrain the growth of drug-resistance.