Pharmacodynamics of levofloxacin: a new paradigm for early clinical trials.

CONTEXT One purpose of early clinical trials is to establish the appropriate dose of an antibiotic for phase 3 trials. Development of a relationship between the ratio of drug exposure to organism minimum inhibitory concentration (MIC) and therapeutic response early in the development process would allow an optimal choice of dose to maximize response. OBJECTIVE To prospectively quantitate the relationship between plasma levels of levofloxacin and successful clinical and/or microbiological outcomes and occurrence of adverse events in infected patients. DESIGN Multicenter open-label trial. SETTING Twenty-two enrolling university-affiliated medical centers. PATIENTS A total of 313 patients with clinical signs and symptoms of bacterial infections of the respiratory tract, skin, or urinary tract. MAIN OUTCOME MEASURES Clinical response and microbiological eradication of pathogenic organisms. RESULTS Of 313 patients, 272 had plasma concentration-time data obtained. Of these, 134 patients had a pathogen recovered from the primary infection site and had an MIC of the pathogen to levofloxacin determined. These patients constituted the primary analysis group for clinical outcome. Groups of 116 and 272 patients, respectively, were analyzed for microbiological outcome and incidence of adverse events. In a logistic regression analysis, the clinical outcome was predicted by the ratio of peak plasma concentration to MIC (Peak/MIC) and site of infection (P<.001). Microbiological eradication was predicted by the Peak/MIC ratio (P<.001). Both clinical and microbiological outcomes were most likely to be favorable if the Peak/MIC ratio was at least 12.2. CONCLUSIONS Levofloxacin generated clinical and microbiological response rates of 95% and 96%, respectively. These response rates included fluoroquinolone "problem pathogens," such as Streptococcus pneumoniae and Staphylococcus aureus. Exposure to levofloxacin was significantly associated with successful clinical and microbiological outcomes. The principles used in these analyses can be applied to other classes of drugs to develop similar relationships between exposure and outcome. This pharmacokinetic modeling could be used to determine optimal treatment dose in clinical trials in a shorter time frame with fewer patients. This modeling also should be evaluated for its potential to improve outcomes (maximizing therapeutic response, preventing emergence of resistance, and minimizing adverse events) of patients treated with this drug.