LIMITATIONS OF SINGLE POINT PHARMACODYNAMIC ANALYSIS

To The Editors: The article by Trujillo et al. 1 describes a study that evaluated concentrations of cefprozil in the middle ear fluid and serum of infants and children relative to its MIC values for Streptococcus pneumoniae and Haemophilus influenzae. The article presents a pharmacodynamic analysis that utilized a mean drug-concentration time curve on which an MIC90 value obtained from the medical literature was superimposed. From the time during which the drug concentrations remained above its MICs for these pathogens, comments were made regarding the clinical utility of the agent. In this type of analytic approach neither the variability observed in a patient population receiving an oral medication nor the variability in MIC values existent in a population of microorganisms is accounted for. These “single point” pharmacodynamic estimates only provide information on what is possible, not on what is probable. The uncertainties in the pharmacokinetic and microbiologic data are too complex to be solved by such simple analytical methods. Basically there are too many possible combinations of drug-concentration time profiles and MIC values to calculate every possible result. It is important to remember that population pharmacokinetic and microbiologic data are stochastic in nature and analytically need to be treated as such. An ideal pharmacodynamic analysis should (1) take into account all possible drug exposures following standard dosing and (2) include all pathogen MIC values that are treated clinically. By doing this, more complete and accurate information will be obtained regarding the likelihood that an agent will effectively treat a patient infected with a particular organism. Pharmacodynamic analysis via the Monte Carlo method is one technique available to achieve this goal. The Monte Carlo method uses a probability density function to generate random values across pharmacokinetic and MIC distributions that conform to their probabilities. Each set of random values effectively simulates a “what if” scenario. As the method proceeds a large number of scenarios can be calculated and their probability of occurrence can plotted. The resultant probability distributions can be utilized to examine the entire range of possible outcomes and the probability of achieving each of them. The Food and Drug Administration advisory committee on antiinfective drug products found this methodology, as presented by Drusano, 2 to be a reasonable approach in October, 1998. The use of Monte Carlo analysis is somewhat new to pharmacodynamics, but has been presented several times at national and international meetings. 3, 4 A member of the Sinus and Allergy Health Partnership (a conjoint group of the American Academy of Otolaryngology Head and Neck Surgery, the American Academy of Otolaryngic Allergy and the American Rhinology Society in consultation with representatives of the Centers for Disease Control and Prevention, the Food and Drug Administration and specialists from the fields of infectious disease, pediatric infectious disease, microbiology and infectious disease clinical pharmacy) conducted a Monte Carlo analysis of several oral cephalosporins and S. pneumoniae as part of the process of developing treatment guidelines for acute bacterial rhinosinusitis. 2 Data utilized in this analysis included full drug-concentration time profiles from ∼70 subjects and 1022 1999 clinical isolates of S. pneumoniae (cefuroxime MIC50/90 0.06/4 μg/ml; range, 0.03 to 32 μg/ml; cefprozil MIC50/90 0.12/8 μg/ml; range, 0.03 to 32 μg/ml). Pharmacodynamic target hit rates (e.g. 40 to 60% time above MIC) were nearly identical for cefuroxime (75%) and cefprozil (72%). Had a single point pharmacodynamic analysis been done on these same data, one would have come to the same conclusion as Trujillo et al. did. Thus the two analytical methods may result in very different conclusions. In brief there is a need for a reassessment of breakpoints established for antibiotic susceptibility because the current values have not taken into consideration variability in microbiologic and pharmacokinetic data. Paul G. Ambrose Pharm. D. Richard Quintiliani M.D.

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