Prospective use of optimal sampling theory: Steady‐state ciprofloxacin pharmacokinetics in critically ill trauma patients

We examined the use of optimal sampling theory to determine a sparse sampling design to estimate pharmacokinetic parameters of ciprofloxacin in patients who had sustained trauma. Two serum sampling strategies, consisting of six sampling times each, were derived on the basis of the patient's renal function (patients with creatinine clearance ≥ 6 L/hr/1.73 m2 and patients with creatinine clearances < 6 L/hr/1.73 m2). Two additional serum samples were obtained for other aspects to the study. A timed urine collection was also obtained. Pharmacokinetic parameter estimates were determined by comodeling the serum and urine data with a three‐compartment open model (parameterized as microconstants) with a bayesian algorithm and by noncompartmental analysis. Bayesian‐derived parameter estimates were total body clearance of drug from plasma, 29.8 L/hr/1.73 m2; renal clearance, 17.0 L/hr/1.73 m2; and nonrenal clearance, 12.7 L/hr/1.73 m2 and were not significantly different from noncompartmentally derived parameters (p = 0.80, p = 0.65 and p = 0.333, respectively). The study demonstrates the use of optimal sampling theory to determine an informative yet relatively sparse sampling strategy for a drug with a complex pharmacokinetic model.