title: Population pharmacokinetic and pharmacodynamic modeling of norvancomycin.
abstract: In this paper, population pharmacokinetics (PPK) and pharmacodynamics of norvancomycin in patients were investigated. The studied dataset was derived from 146 patients with confirmed or suspected gram-positive bacterial infections, as well as 20 healthy volunteers. A PPK model was developed and validated by the nonlinear mixed effect model (NONMEM) software. The norvancomycin minimum inhibitory concentrations (MICs) for the isolates from patients were determined by the agar dilution method. The best model was a two-compartment pharmacokinetic model with exponential inter-individual error and an additive residual error statistic model. The findings of the present study indicated that the change in CLcr values had different effects on drug clearance (CL). In patients with renal dysfunction (CLcr< or =85 ml/min), CL (L/h)=2.54.(CLcr /50)1.20, while in patients with normal renal function (CLcr>85 ml/min), CL=6.0.(WT/60)0.52. An increased volume of peripheral distribution (V2) was observed when norvancomycin was co-administered with diuretics. Inter-individual variability in CL, V1, Q, and V2 was 35.92%, 11.40%, 0, and 79.75%, respectively. Residual variability was 3.05 mg/L. The logistic stepwise analyses revealed that only the ratio of AUC24 /MIC was a major factor which could significantly predict the clinical outcome and bacterial eradication in patients. As the AUC24/MIC ratio was >579.90 for staphylococcal infection and >637.67 for enterococcal infections, approximately 95% of patients would be predicted to achieve a cured clinical outcome. In conclusion, AUC24/MIC should be a major pharmacokinetics/pharmacodynamics (PK/PD) parameter to predict the clinical efficacy of norvancomycin. An optimized regimen of norvancomycin can be simulated and developed for different subgroups of patients who have special physiologic and pathologic conditions.
authors.affiliation: Institute of Antibiotics, Huashan Hospital, Fudan University, Shanghai 200040, China. zhangj61@fudan.edu.cn
authors.forename: J
authors.initials: J
authors.lastname: Zhang
authors.forename: Y
authors.initials: Y
authors.lastname: Zhang
authors.forename: Y
authors.initials: Y
authors.lastname: Shi
authors.forename: J
authors.initials: J
authors.lastname: Rui
authors.forename: J
authors.initials: J
authors.lastname: Yu
authors.forename: G
authors.initials: G
authors.lastname: Cao
authors.forename: J
authors.initials: J
authors.lastname: Wu
mesh_terms: D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D000900:Anti-Bacterial Agents / Q000097:blood / Q000493:pharmacokinetics* / Q000627:therapeutic use*; D019540:Area Under Curve; D001424:Bacterial Infections / Q000188:drug therapy; D003198:Computer Simulation; D005260:Female; D006801:Humans; D008297:Male; D008826:Microbial Sensitivity Tests; D008875:Middle Aged; D008954:Models, Biological; D014640:Vancomycin / Q000031:analogs & derivatives* / Q000097:blood / Q000493:pharmacokinetics / Q000627:therapeutic use
chemical_list: D000900:Anti-Bacterial Agents; D014640:Vancomycin; C511195:norvancomycin