Adaptive optimal therapy design: Integration of pharmacogenetics, pharmacokinetics and pharmacodynamics for the suppression of the emergence of antibiotic resistance (Streptococcus pneumoniae, Pseudomonas aeruginosa)

The emergence of drug-resistant infectious pathogens continues to escalate, in spite of increased knowledge of resistance mechanisms. In the past, infections due to resistant organisms were encountered primarily in the hospital's intensive care units, the site where antibiotic use is abundant and anatomic and immune barrier breaches occur. Nowadays, infections due to antibiotic-resistant organisms are commonplace. Reports of outbreaks of bacterial strains that are intrinsically drug-resistant, and the emergence of resistant mutants following antibiotic exposure has led to renewed interest in the discovery of new classes of drugs using molecular biology and combinatorial chemistry technologies. New classes of synthetic antimicrobials will likely be discovered to which no pre-existing resistance mechanisms exist. However, until these drugs become available it is clear that prudent use of currently available antibiotics, through the identification of the principles of drug dosing that prevent or delay the emergence of resistance, is our only recourse to prolong the efficacy of current agents. We hypothesized that: (a) the successful microbiological eradication, and (b) minimization of the selection of quinolone-resistant mutants could be achieved by the optimization of the treatment regimen. We developed a pharmacodynamic model that enables the evaluation of relationships between antimicrobial drug-exposure to therapeutic outcome. Our model relates antimicrobial serum concentration-vs.-time parameters to the probability of efficiently eradicating the infecting pathogen or the risk of emergence of drug-resistant mutants. Results show that emergence of quinolone-resistance in S. pneumoniae and P. aeruginosa can be predicted. More importantly, quinolone dose/dosing regimens that suppress emergence of resistance in these pathogens could be identified. Data from animal pharmacodynamic studies and clinical trial pharmacokinetics results were used to demonstrate how this model could be used to support clinic decision-making. The experimental and analytical methods developed provide insight on how rational dose and dosing regimen design should be determined in order to prolong the usefulness of currently available drugs. The proposed model can be applied to any agent for selection of the dose-scheduling regimen and the duration of therapy that maximizes therapeutic response, minimizes emergence of resistance, and prevents toxicity.