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
Abstract
:1. Introduction
2. Methods
2.1. Literature Review and Model Selection
2.2. Monte Carlo Simulation of PK Profile in Patients with Normal Renal Function
2.3. Estimation of AUC Using Four Serum Concentrations (Method 1)
2.4. Estimation of AUC Using Three Serum Concentrations (Method 2)
2.5. Estimation of AUC Using Two Serum Concentrations (Method 3)
- Step 1: Estimation of elimination rate constant (k)
- Step 2: Estimation of concentration at the end of infusion (Cmax)
- Step 3: Calculation of volume of distribution (Vd)
- Step 4: Calculation of clearance (CL)
- Step 5: Calculation of AUC0–∞
2.6. Method Evaluation
2.7. Extrapolation to Patients with Various Degrees of Renal Impairment and Method Evaluation
2.8. Sensitivity Analysis
2.9. Development of the Dose Calculator Tool
3. Results
3.1. Literature Review and Model Selection
3.2. Monte Carlo Simulation and Method Evaluation
3.3. Method Evaluation in Patients with Various Degrees of Renal Impairment
3.4. Sensitivity Analysis
3.5. Excel Calculator
4. Discussion
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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CLcr (mL/min) | Time Reaching SS (day) | Dosage (mg) | Dosing Interval (h) | Infusion Rate (mg/h) | Sampling Time (h) | |
---|---|---|---|---|---|---|
Method 1 | Method 2 | |||||
100 | 15 | 1000 | 12 | 1000 | 1.5, 2.5, 9.5, 11.5 | 1, 9.5, 11.5 |
60 | 30 | 750 | 12 | 750 | 1.5, 2.5, 9.5, 11.5 | 1, 9.5, 11.5 |
50 | 30 | 500 | 12 | 500 | 1.5, 2.5, 9.5, 11.5 | 1, 9.5, 11.5 |
35 | 30 | 750 | 24 | 750 | 1.5, 2.5, 12, 23 | 1, 12, 23 |
25 | 30 | 500 | 24 | 500 | 1.5, 2.5, 12, 23 | 1, 12, 23 |
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Liu, Q.; Huang, H.; Xu, B.; Li, D.; Liu, M.; Shaik, I.H.; Wu, X. 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. Pharmaceutics 2022, 14, 1004. https://doi.org/10.3390/pharmaceutics14051004
Liu Q, Huang H, Xu B, Li D, Liu M, Shaik IH, Wu X. 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. Pharmaceutics. 2022; 14(5):1004. https://doi.org/10.3390/pharmaceutics14051004
Chicago/Turabian StyleLiu, Qingxia, Huiping Huang, Baohua Xu, Dandan Li, Maobai Liu, Imam H. Shaik, and Xuemei Wu. 2022. "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" Pharmaceutics 14, no. 5: 1004. https://doi.org/10.3390/pharmaceutics14051004
APA StyleLiu, Q., Huang, H., Xu, B., Li, D., Liu, M., Shaik, I. H., & Wu, X. (2022). 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. Pharmaceutics, 14(5), 1004. https://doi.org/10.3390/pharmaceutics14051004