Drug design, development and therapy · 2026
Population Pharmacokinetics and Dose Optimization of Vancomycin in Adult Cancer Patients.
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Gao Jinglin, Gao Xiaonan, Xu Yuxiang, Feng Zhangying, Wang Peiyuan, Gong Wenlin, Wang Mingxia
Аннотация
PURPOSE: This study aimed to develop a population pharmacokinetics (PPK) model of vancomycin (VAN) in adult cancer patients, to identify the factors affecting VAN pharmacokinetics, and to perform different dosing simulations in order to propose optimized individually dosing regimens.
PATIENTS AND METHODS: A total of 303 samples from 132 adult patients (age range, 18 to 85 years; 48 females and 84 males) diagnosed with cancer and receiving vancomycin were enrolled. A PPK model was developed using NONMEM 7.5. Goodness-of-fit (GOF) plots, visual predictive check (VPC), and bootstrap were used as internal evaluation. External evaluation of the final model was performed. The dosing regimen was optimized using Monte Carlo dose simulations. The ratio of the area under the concentration-time curve up to 24 h to minimum inhibitory concentration (AUC0-24h/MIC)≥400 was defined as pharmacokinetic-pharmacodynamic (PK/PD) target.
RESULTS: A two-compartment pharmacokinetic model with zero-order absorption and first-order elimination described the VAN PK data best. In the final model, CL (L/h)=9.12×e0.370× (AGE/57)-4.61× (BUN/5.35)-0.226; V1 (L)=11.6; V2 (L)=49.5; Q (L/h)=7.46. The internal and external validation of the model showed a good prediction performance. Monte Carlo simulations revealed that when MIC was 1 mg/L, the recommended doses to achieve a target of AUC0-24h/MIC ≥400 were ranging 3-5g/day. For patients over age of 79 years, a dose of 2 g/day is recommended.
CONCLUSION: Unlike previous population pharmacokinetic studies that relied on serum creatinine or creatinine clearance, this study incorporated blood urea nitrogen (BUN) and age as the key covariates for vancomycin clearance in adult cancer patients. Based on these covariates and MIC, a validated PPK model enabled individualized dosing to achieve efficacy targets.
PATIENTS AND METHODS: A total of 303 samples from 132 adult patients (age range, 18 to 85 years; 48 females and 84 males) diagnosed with cancer and receiving vancomycin were enrolled. A PPK model was developed using NONMEM 7.5. Goodness-of-fit (GOF) plots, visual predictive check (VPC), and bootstrap were used as internal evaluation. External evaluation of the final model was performed. The dosing regimen was optimized using Monte Carlo dose simulations. The ratio of the area under the concentration-time curve up to 24 h to minimum inhibitory concentration (AUC0-24h/MIC)≥400 was defined as pharmacokinetic-pharmacodynamic (PK/PD) target.
RESULTS: A two-compartment pharmacokinetic model with zero-order absorption and first-order elimination described the VAN PK data best. In the final model, CL (L/h)=9.12×e0.370× (AGE/57)-4.61× (BUN/5.35)-0.226; V1 (L)=11.6; V2 (L)=49.5; Q (L/h)=7.46. The internal and external validation of the model showed a good prediction performance. Monte Carlo simulations revealed that when MIC was 1 mg/L, the recommended doses to achieve a target of AUC0-24h/MIC ≥400 were ranging 3-5g/day. For patients over age of 79 years, a dose of 2 g/day is recommended.
CONCLUSION: Unlike previous population pharmacokinetic studies that relied on serum creatinine or creatinine clearance, this study incorporated blood urea nitrogen (BUN) and age as the key covariates for vancomycin clearance in adult cancer patients. Based on these covariates and MIC, a validated PPK model enabled individualized dosing to achieve efficacy targets.