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Vancomycin Pharmacokinetics Throughout Life: Results from a Pooled Population Analysis and Evaluation of Current Dosing Recommendations

(2019) CLINICAL PHARMACOKINETICS. 58(6). p.767-780
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Abstract
Background and Objectives Uncertainty exists regarding the optimal dosing regimen for vancomycin in different patient populations, leading to a plethora of subgroup-specific pharmacokinetic models and derived dosing regimens. We aimed to investigate whether a single model for vancomycin could be developed based on a broad dataset covering the extremes of patient characteristics. Furthermore, as a benchmark for current dosing recommendations, we evaluated and optimised the expected vancomycin exposure throughout life and for specific patient subgroups.MethodsA pooled population-pharmacokinetic model was built in NONMEM based on data from 14 different studies in different patient populations. Steady-state exposure was simulated and compared across patient subgroups for two US Food and Drug Administration/European Medicines Agency-approved drug labels and optimised doses were derived.ResultsThe final model uses postmenstrual age, weight and serum creatinine as covariates. A 35-year-old, 70-kg patient with a serum creatinine level of 0.83mgdL(-1) (73.4 mu molL(-1)) has a V-1, V-2, CL and Q(2) of 42.9 L, 41.7 L, 4.10 Lh(-1) and 3.22Lh(-1). Clearance matures with age, reaching 50% of the maximal value (5.31 Lh(-1)70kg(-1)) at 46.4weeks postmenstrual age then declines with age to 50% at 61.6years. Current dosing guidelines failed to achieve satisfactory steady-state exposure across patient subgroups. After optimisation, increased doses for the Food and Drug Administration label achieve consistent target attainment with minimal (20%) risk of under- and over-dosing across patient subgroups.Conclusions p id=Par4 A population model was developed that is useful for further development of age and kidney function-stratified dosing regimens of vancomycin and for individualisation of treatment through therapeutic drug monitoring and Bayesian forecasting.
Keywords
INFECTIOUS-DISEASES SOCIETY, CONTINUOUS-INFUSION, MODEL, GUIDELINES, ADULT, CLEARANCE, CHILDREN, AMERICA

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Chicago
Colin, Pieter J., Karel Allegaert, Alison H. Thomson, Daan J. Touw, Michael Dolton, Matthijs de Hoog, Jason A. Roberts, et al. 2019. “Vancomycin Pharmacokinetics Throughout Life: Results from a Pooled Population Analysis and Evaluation of Current Dosing Recommendations.” Clinical Pharmacokinetics 58 (6): 767–780.
APA
Colin, P. J., Allegaert, K., Thomson, A. H., Touw, D. J., Dolton, M., de Hoog, M., Roberts, J. A., et al. (2019). Vancomycin Pharmacokinetics Throughout Life: Results from a Pooled Population Analysis and Evaluation of Current Dosing Recommendations. CLINICAL PHARMACOKINETICS, 58(6), 767–780.
Vancouver
1.
Colin PJ, Allegaert K, Thomson AH, Touw DJ, Dolton M, de Hoog M, et al. Vancomycin Pharmacokinetics Throughout Life: Results from a Pooled Population Analysis and Evaluation of Current Dosing Recommendations. CLINICAL PHARMACOKINETICS. Northcote: Adis Int Ltd; 2019;58(6):767–80.
MLA
Colin, Pieter J. et al. “Vancomycin Pharmacokinetics Throughout Life: Results from a Pooled Population Analysis and Evaluation of Current Dosing Recommendations.” CLINICAL PHARMACOKINETICS 58.6 (2019): 767–780. Print.
@article{8621635,
  abstract     = {Background and Objectives
Uncertainty exists regarding the optimal dosing regimen for vancomycin in different patient populations, leading to a plethora of subgroup-specific pharmacokinetic models and derived dosing regimens. We aimed to investigate whether a single model for vancomycin could be developed based on a broad dataset covering the extremes of patient characteristics. Furthermore, as a benchmark for current dosing recommendations, we evaluated and optimised the expected vancomycin exposure throughout life and for specific patient subgroups.MethodsA pooled population-pharmacokinetic model was built in NONMEM based on data from 14 different studies in different patient populations. Steady-state exposure was simulated and compared across patient subgroups for two US Food and Drug Administration/European Medicines Agency-approved drug labels and optimised doses were derived.ResultsThe final model uses postmenstrual age, weight and serum creatinine as covariates. A 35-year-old, 70-kg patient with a serum creatinine level of 0.83mgdL(-1) (73.4 mu molL(-1)) has a V-1, V-2, CL and Q(2) of 42.9 L, 41.7 L, 4.10 Lh(-1) and 3.22Lh(-1). Clearance matures with age, reaching 50% of the maximal value (5.31 Lh(-1)70kg(-1)) at 46.4weeks postmenstrual age then declines with age to 50% at 61.6years. Current dosing guidelines failed to achieve satisfactory steady-state exposure across patient subgroups. After optimisation, increased doses for the Food and Drug Administration label achieve consistent target attainment with minimal (20%) risk of under- and over-dosing across patient subgroups.Conclusions p id=Par4 A population model was developed that is useful for further development of age and kidney function-stratified dosing regimens of vancomycin and for individualisation of treatment through therapeutic drug monitoring and Bayesian forecasting.},
  author       = {Colin, Pieter J. and Allegaert, Karel and Thomson, Alison H. and Touw, Daan J. and Dolton, Michael and de Hoog, Matthijs and Roberts, Jason A. and Adane, Eyob D. and Yamamoto, Masato and Santos-Buelga, Dolores and Martin-Suarez, Ana and Simon, Nicolas and Taccone, Fabio S. and Lo, Yoke-Lin and Barcia, Emilia and Struys, Michel and Eleveld, Douglas J.},
  issn         = {0312-5963},
  journal      = {CLINICAL PHARMACOKINETICS},
  keywords     = {INFECTIOUS-DISEASES SOCIETY,CONTINUOUS-INFUSION,MODEL,GUIDELINES,ADULT,CLEARANCE,CHILDREN,AMERICA},
  language     = {eng},
  number       = {6},
  pages        = {767--780},
  publisher    = {Adis Int Ltd},
  title        = {Vancomycin Pharmacokinetics Throughout Life: Results from a Pooled Population Analysis and Evaluation of Current Dosing Recommendations},
  url          = {http://dx.doi.org/10.1007/s40262-018-0727-5},
  volume       = {58},
  year         = {2019},
}

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