Two Innovative Approaches to Optimize Vancomycin Dosing Using Estimated AUC after First Dose: Validation Using Data Generated from Population PK Model Coupled with Monte-Carlo Simulation and Comparison with the First-Order PK Equation Approach

• Published in: Pharmaceutics Vol. 14; no. 5; p. 1004
• Main Authors: Liu, Qingxia, Huang, Huiping, Xu, Baohua, Li, Dandan, Liu, Maobai, Shaik, Imam H., Wu, Xuemei
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 07.05.2022; MDPI
• Subjects: Antibiotics; dose optimization; Monte Carlo simulation; Patients; Pharmacokinetics; pharmacokinetics/pharmacodynamics; Population; population pharmacokinetics; Software; Staphylococcus infections; vancomycin; vancomycin; dose optimization; Monte Carlo simulation; pharmacokinetics/pharmacodynamics; population pharmacokinetics
• ISSN: 1999-4923; 1999-4923
• DOI: 10.3390/pharmaceutics14051004

The revised consensus guidelines for optimizing vancomycin doses suggest that maintaining the area under the concentration-time curve to minimal inhibitory concentration ratio (AUC/MIC) of 400–600 mg·h/L is the target pharmacokinetic/pharmacodynamic (PK/PD) index for efficacy. AUC-guided dosing approach uses a first-order pharmacokinetics (PK) equation to estimate AUC using two samples obtained at steady state and one-compartment model, which can cause inaccurate AUC estimation and fail to achieve the effective PK/PD target early in therapy (days 1 and 2). To achieve an efficacy target from the third or fourth dose, two innovative approaches (Method 1 and Method 2) to estimate vancomycin AUC at steady state (AUCSS) using two-compartment model and three or four levels after the first dose are proposed. The feasibility of the proposed methods was evaluated and compared with another published dosing algorithm (Method 3), which uses two samples and a one-compartment approach. Monte Carlo simulation was performed using a well-established population PK model, and concentration-time profiles for virtual patients with various degrees of renal function were generated, with 1000 subjects per group. AUC extrapolated to infinity (AUC0–∞) after the first dose was estimated using the three methods, whereas reference AUC (AUCref) was calculated using the linear-trapezoidal method at steady state after repeated doses. The ratio of AUC0–∞: AUCref and % bias were selected as the indicators to evaluate the accuracy of three methods. Sensitivity analysis was performed to examine the influence of change in each sampling time on the estimated AUC0–∞ using the two proposed approaches. For simulated patients with various creatinine clearance, the mean of AUC0–∞: AUCref obtained from Method 1, Method 2 and Method 3 ranged between 0.98 to 1, 0.96 to 0.99, and 0.44 to 0.69, respectively. The mean bias observed with the three methods was −0.10% to −2.09%, −1.30% to −3.59% and −30.75% to −55.53%, respectively. The largest mean bias observed by changing sampling time while using Method 1 and Method 2 were −4.30% and −10.50%, respectively. Three user-friendly and easy-to-use excel calculators were built based on the two proposed methods. The results showed that our approaches ensured sufficient accuracy and achieved target PK/PD index early and were superior to the published methodologies. Our methodology has the potential to be used for vancomycin dose optimization and can be easily implemented in clinical practice.