METHODS: Published population pharmacokinetic models and the Australasian Neonatal Medicines Formulary were used to simulate antimicrobial concentration-time profiles in a virtual neonate population. Laboratory quality assurance data were used to quantify analytical variation in antimicrobial measurement methods used in clinical practice. Guideline-informed dosing recommendations based on drug concentrations were applied to compare the impact of analytical variation and nonanalytical factors on antimicrobial dosing.
RESULTS: Analytical variation caused differences in subsequent guideline-informed dosing recommendations in 9.3-12.1% (amikacin), 16.2-19.0% (tobramycin), 12.2-45.8% (gentamicin), and 9.6-19.5% (vancomycin) of neonates. For vancomycin, inaccuracies in drug administration time (45.6%), use of non-trough concentrations (44.7%), within-subject biological variation (38.2%), and dosing errors (27.5%) were predicted to result in more dosing discrepancies than analytical variation (12.5%). Using current analytical performance specifications, tolerated dosing discrepancies would be up to 14.8% (aminoglycosides) and 23.7% (vancomycin).
CONCLUSIONS: Although analytical variation can influence neonatal antimicrobial dosing recommendations, nonanalytical factors are more influential. These result in substantial variation in subsequent dosing of antimicrobials, risking inadvertent under- or overexposure. Harmonization of measurement methods and improved patient management systems may reduce the impact of analytical and nonanalytical factors on neonatal antimicrobial dosing.
METHODS: A 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.
RESULTS: The final model uses postmenstrual age, weight and serum creatinine as covariates. A 35-year-old, 70-kg patient with a serum creatinine level of 0.83 mg dL-1 (73.4 µmol L-1) has a V1, V2, CL and Q2 of 42.9 L, 41.7 L, 4.10 L h-1 and 3.22 L h-1. Clearance matures with age, reaching 50% of the maximal value (5.31 L h-1 70 kg-1) at 46.4 weeks postmenstrual age then declines with age to 50% at 61.6 years. 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: 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.
METHODS: Hospitalised adult patients on EID gentamicin were selected. We considered a DFP of between 2 and 8 h as appropriate. Data from two blood samples (2 and 6 h postdose) from each patient were used to estimate the duration of DFP (i.e. DFP method 1). DFP was also calculated for the same patient using an empirically estimated elimination rate constant (Ke ) and the same 6 h postdose concentration value (DFP method 2). Correlation between the two methods was made. An alternative graphical method to estimate DFP was attempted.
KEY FINDINGS: Correlation between Ke and age was favourable (r = -0.453; P = 0.001). Ke derived from this empirical relationship was used to estimate DFP method 2. DFP method 1 correlated well with DFP method 2 (r = 0.742; P
METHODS: Warfarin relies on regular monitoring of International Normalized Ratio which is a standardized test to measure prothrombin time and appropriate dose adjustment. Pharmacometabonomics is a novel scientific field which deals with identification and quantification of the metabolites present in the metabolome using spectroscopic techniques such as Nuclear Magnetic Resonance (NMR). Pharmacometabonomics helps to indicate perturbation in the levels of metabolites in the cells and tissues due to drug or ingestion of any substance. NMR is one of the most widely-used spectroscopic techniques in metabolomics because of its reproducibility and speed.
RESULTS: There are many factors that influence the metabolism of warfarin, making changes in drug dosage common, and clinical factors like drug-drug interactions, dietary interactions and age explain for the most part the variability in warfarin dosing. Some studies have showed that pharmacogenetic testing for warfarin dosing does not improve health outcomes, and around 26% of the variation in warfarin dose requirements remains unexplained yet.
CONCLUSION: Many recent pharmacometabonomics studies have been conducted to identify novel biomarkers of drug therapies such as paracetamol, aspirin and simvastatin. Thus, a technique such as NMR based pharmacometabonomics to find novel biomarkers in plasma and urine might be useful to predict warfarin outcome.