Limited-Sampling Strategies for Anti-Infective Agents
It has been proposed that therapeutic drug monitoring is warranted when a drug exhibits a narrow therapeutic range, therapy is of sufficient duration, pharmacokinetic parameters have been correlated with clinical outcome, the pharmacodynamic response is not readily assessable, and/or the drug assay results provide more information than clinical judgement alone. For a drug that is suitable for therapeutic drug monitoring, measurement of the area under the concentration- time curve (AUC) is considered a good representation of overall exposure to the drug. For selected anti-infective agents, pharmacokinetic parameters such as AUC/MIC (drug exposure relative to the bacterial minimum inhibitory concentration) and peak/MIC (peak concentration relative to the bacterial minimum inhibitory concentration) have been correlated with outcomes in animal, in vitro, and a small number of human studies. In their review of pharmacokinetic and pharmacodynamic considerations for selecting agents for outpatient parenteral antimicrobial therapy, Slavik and Jewesson discussed several anti-infectives for which AUC/MIC may correlate with clinical efficacy. Such agents include, but are not limited to, fluoroquinolones (ciprofloxacin, levofloxacin, gatifloxacin) and quinupristin-dalfopristin.
The use of AUC is limited, however, by the large number of blood samples required for its accurate determination. As many as 10 or more samples may be needed to characterize AUC in the research setting, but in clinical practice such frequent blood sampling is impractical, time-consuming, costly, and, for infants, potentially unethical. It also may not be possible to obtain blood samples frequently from elderly or critically ill patients with poor venous access. Cialis Jelly
One proposed method of reducing the cost and inconvenience of frequent sampling is the use of limited-sampling strategies. A limited-sampling strategy is a method of characterizing pharmacokinetic parameters, particularly the AUC, using relatively few blood samples, usually 3 or fewer. The methods used to develop and validate limited-sampling strategies have been reviewed elsewhere. Briefly, these approaches are usually developed using either multiple regression analysis with a stepwise approach or population estimates with the Bayesian approach. Multiple regression analysis determines the relation between the dependent variable (usually AUC) and various independent variables (i.g., timed concentrations from serially collected blood samples). The resulting limited-sampling strategy is described as follows:
AUC = b + mlC (t1) + m2C (t2) + m3C (t3)+ . . . m.p (t) where C(t) is the drug concentration at time t, b represents the j-intercept, and mi represents the slope of the equation at time t. The Bayesian method blends population estimates with data from individual patients; as such, both population and individual data are required. If population estimates are unavailable, the index data set can be used to determine them. Then, one or more timed concentrations from the validation data set are entered as individual data to predict the AUC. Equations with a high coefficient of determination (r2) are typical candidates for a limited-sampling strategy, which is then subjected to testing with a validation data set. Acceptable methods for validation include data splitting (ideally by randomly assigning patients to index and validation groups), cross-validation (multiple data splitting), jackknife resampling, and bootstrap resampling. Bias and the precision of limited- sampling strategies are often presented as mean prediction error and mean squared prediction error, according to the methods of Scheiner and Beal. A commonly accepted range of bias and precision values is 15% to 20%. Another important requirement of a clinically useful limited-sampling strategy would be a maximum of 3 conveniently timed (e.g., obtained within 4 h after administration of a dose) concentrations.
canadian cialis online
The objectives of this review were to critically evaluate published limited-sampling strategies for anti-infective agents, to discuss the clinical implications of these strategies as they apply to anti-infectives, and to propose improvements in methodology for future studies of limited-sampling strategies.