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Keywords:

  • β-Lactam antibiotics—protein binding and metabolism, rats, pharmacokinetics, physiologically based model;
  • Pharmacokinetics—β-lactam antibiotics, protein binding and metabolism in rats, physiologically based model;
  • Metabolism—β-lactam antibiotics in rats, pharmacokinetics, physiologically based model

Abstract

The disposition characteristics of β-lactam antibiotics in rats were investigated, and a physiologically based pharmacokinetic model capable of predicting the tissue distribution and elimination kinetics of these drugs was developed. Protein-binding parameters in rat serum were determined by equilibrium dialysis. Linear binding was found for penicillin G, methicillin, dicloxacillin, and ampicillin; however, nonlinear binding was observed for penicillin V and cefazolin. After intravenous bolus dosing, cefazolin was recovered almost completely in urine and bile, while for the penicillins, penicilloic acid was found to be the major metabolite. Biliary excretion of cefazolin followed Michaelis–Menten kinetics, and no significant inhibition of urinary secretion was observed after probenecid administration. The renal clearance of unbound drug was 0.82 ml/min with a reabsorption ratio (R) of 0.22. Tubular secretion was inhibited for the penicillins by probenecid plasma concentrations of 50 μg/ml, resulting in an R-value of 0.32. Erythrocyte uptake, serum protein binding, and tissue-to-plasma partition coefficient (Kp) were measured. Theoretical Kp values were calculated and found to be in good agreement with the Kp values for three of the antibiotics. Plasma and tissue concentrations (lung, heart, muscle, skin, gut, bone, liver, and kidney) were measured as a function of time at various doses for inulin and cefazolin in rats after an intravenous bolus dose, and were found to be in reasonable agreement with concentrations predicted by the model. These correlations demonstrate that the proposed model can accurately describe the plasma and tissue contributions of inulin and cefazolin in the rat and suggest that this model could have utility in predicting drug distribution in humans.