Population Pharmacokinetic/Pharmacodynamic Modelling of Daptomycin for Schedule Optimization in Patients with Renal Impairment
Abstract
:1. Introduction
2. Materials and Methods
3. Results
3.1. Patient Population and Study Design
3.2. Population Pharmacokinetic Model
3.2.1. Base Population PK Model
3.2.2. Final Population PK Model
3.3. Population Pharmacokinetic/Pharmacodynamic Simulations and Optimal Dosage Selection
4. Discussion
5. Conclusions
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Characteristics | 46 Patients Median (IQR)/n (%) | |
---|---|---|
Demographics | Sex (Male) (n, %) | 43 (93%) |
Age (years) | 68 (59–81) | |
Body weight (kg) | 75 (65–85) | |
Height (m) | 1.7 (1.6–1.7) | |
BMI (Kg/m2) | 25.9 (23.4–31.1) | |
Treatment | Dose (mg) | 675 (500–765) |
Dose per kilogram (mg/kg) | 9.1 (7.5–10.0) | |
Treatment duration (days) | 11 (7–15) | |
Clinical data | Serum albumin (g/dL) | 2.9 (2.4–3.4) |
Serum protein (g/dL) | 6.0 (5.0–6.4) | |
Serum cretinine (g/dL) | 0.9 (0.6–1.3) | |
Creatinine clearance (mL/min/1.73 m2) | 93 (50–136) | |
Renal function | ||
>90 mL/min/1.73 m2 | 16 (34.9%) | |
60–89 mL/min/1.73 m2 | 13 (28.2%) | |
30–59 mL/min/1.73 m2 | 14 (30.4%) | |
15–29 mL/min/1.73 m2 | 3 (6.5%) | |
<15 mL/min/1.73 m2 | 0 (0%) | |
Pathogenic micro-organism (36/46) | S. aureus | 17 (47.2%) |
S. epidermidis | 12 (33.3%) | |
S. hominis | 2 (5.6%) | |
E. fecalis | 2 (5.6%) | |
S. sacrophyticus | 2 (5.6%) | |
S. lugdunensis | 1 (2.7%) | |
MIC micro-organism | 0.5 (0.25, 0.5) |
Population PK Model Estimates | Bootstrap Results | |||||
---|---|---|---|---|---|---|
Value | RSE (%) | Shrinkage (%) | Median | RSE * (%) | 95%CI | |
Fixed-Effect | ||||||
CL (L/h) | 6.98 | 14 | 7.01 | 15 | [6.63–7.44] | |
V1 (L) | 0.95 | 9 | 0.97 | 10 | [0.92–1.09] | |
Q (L/h) | 1.96 | 21 | 1.93 | 19 | [1.43–2.48] | |
V2 (L/h) | 21 | 19 | 20.5 | 21 | [19.3–22.1] | |
Bmax (mg/L) | 160 | 26 | 157 | 24 | [129–183] | |
KD (mg/L) | 3.56 | 15 | 3.61 | 12 | [3.17–3.93] | |
CrCl on CL | 0.19 | 12 | 0.19 | 13 | [0.18–0.22] | |
Inter-individual variability | ||||||
CL (%) | 32 | 11 | 12 | 33 | 10 | [21–42] |
V2 (%) | 47 | 23 | 17 | 46 | 24 | [52–94] |
Residual unexplained variability | ||||||
Additive on Log-scale (%) | 22 | 8 | 5 | 21 | 8 | [18–24] |
Moderate Renal Impairment (CLCR = 30 mL/min/1.73 m2) | Mild Renal Impairment (CLCR = 60 mL/min/1.73 m2) | Normal Renal Function (CLCR = 90 mL/min/1.73 m2) | Body Weight | |
---|---|---|---|---|
MIC ≤ 0.5 mg/L | 10 mg/kg q24h | 11 mg/kg q24h | 12 mg/kg q24h | 50 kg |
9 mg/kg q24h | 10 mg/kg q24h | 10 mg/kg q24h | 60 kg | |
8 mg/kg q24h | 9 mg/kg q24h | 9 mg/kg q24h | 70 kg | |
7 mg/kg q24h | 7 mg/kg q24h | 8 mg/kg q24h | 80 kg | |
6 mg/kg q24h | 7 mg/kg q24h | 7 mg/kg q24h | 90 kg | |
5 mg/kg q24h | 6 mg/kg q24h | 6 mg/kg q24h | 100 kg | |
MIC ≤ 1 mg/L | 17 * mg/kg q48h | 17 * mg/kg q48h | 17 * mg/kg q48h | 50 kg |
16 mg/kg q48h | 17 * mg/kg q48h | 17 * mg/kg q48h | 60 kg | |
14 mg/kg q48h | 16 mg/kg q48h | 17 mg/kg q48h | 70 kg | |
12 mg/kg q48h | 14 mg/kg q48h | 15 mg/kg q48h | 80 kg | |
11 mg/kg q48h | 12 mg/kg q48h | 13 mg/kg q48h | 90 kg | |
10 mg/kg q48h | 11 mg/kg q48h | 12 mg/kg q48h | 100 kg |
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García-Martínez, T.; Bellés-Medall, M.D.; García-Cremades, M.; Ferrando-Piqueres, R.; Mangas-Sanjuán, V.; Merino-Sanjuan, M. Population Pharmacokinetic/Pharmacodynamic Modelling of Daptomycin for Schedule Optimization in Patients with Renal Impairment. Pharmaceutics 2022, 14, 2226. https://doi.org/10.3390/pharmaceutics14102226
García-Martínez T, Bellés-Medall MD, García-Cremades M, Ferrando-Piqueres R, Mangas-Sanjuán V, Merino-Sanjuan M. Population Pharmacokinetic/Pharmacodynamic Modelling of Daptomycin for Schedule Optimization in Patients with Renal Impairment. Pharmaceutics. 2022; 14(10):2226. https://doi.org/10.3390/pharmaceutics14102226
Chicago/Turabian StyleGarcía-Martínez, Teresa, María Dolores Bellés-Medall, Maria García-Cremades, Raúl Ferrando-Piqueres, Victor Mangas-Sanjuán, and Matilde Merino-Sanjuan. 2022. "Population Pharmacokinetic/Pharmacodynamic Modelling of Daptomycin for Schedule Optimization in Patients with Renal Impairment" Pharmaceutics 14, no. 10: 2226. https://doi.org/10.3390/pharmaceutics14102226
APA StyleGarcía-Martínez, T., Bellés-Medall, M. D., García-Cremades, M., Ferrando-Piqueres, R., Mangas-Sanjuán, V., & Merino-Sanjuan, M. (2022). Population Pharmacokinetic/Pharmacodynamic Modelling of Daptomycin for Schedule Optimization in Patients with Renal Impairment. Pharmaceutics, 14(10), 2226. https://doi.org/10.3390/pharmaceutics14102226