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. 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 V 1 , V 2 , CL and Q 2 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.

Immunosuppressive therapy in paediatric transplant recipients is changing as a consequence of the increasing number of available immunosuppressive agents. Generic and other new formulations are now emerging onto the market, clinical experience is growing, and it is expected that clinicians should tailor immunosuppressive protocols to individual patients by optimising dosages and drugs according to the maturation and clinical status of the child. Most information about the clinical pharmacokinetics of immunosuppressive drugs in paediatrics is centred on cyclosporin, tacrolimus and mycophenolate mofetil in renal and liver transplant recipients; data regarding other immunosuppressants and transplant types are limited. Although the clinical pharmacokinetics of these drugs in paediatric transplant recipients are still under investigation, it is evident that the pharmacokinetic parameters observed in adults may not be applicable to children, especially in younger age groups. In general, patients younger than 5 years old show higher clearance rates irrespective of the organ transplanted or drug used. Another important factor that frequently affects clearance in this patient population is the post-transplant time. In accordance with these findings, and in contrast with the usual under-dosage in children, the need for higher dosages in younger recipients and during the early post-transplant period seems evident. To achieve the best compromise between prevention of rejection and toxicity, dosage individualisation is required and this can be achieved through therapeutic drug monitoring (TDM). This approach is particularly useful to ensure the cost-effective management of paediatric transplant recipients in whom the pharmacokinetic behaviour, target concentrations for clinical use and optimal dosage strategies of a particular drug may not yet be well defined. Although TDM may be a tool for improving immunosuppressive therapy, there is little information concerning its positive contribution to clinical events, including outcomes, for paediatric patients. Substantial information to support the use of TDM exists for cyclosporin and, to a lesser extent, for tacrolimus, but a diversity of options affects their implementation in the clinical setting. The role of TDM in therapy with mycophenolate mofetil and sirolimus has yet to be defined regarding both methods and clinical indications. Pharmacodynamic monitoring appears more suited to other immunosuppressants such as azathioprine, corticosteroids and monoclonal or polyclonal antibodies. If coupled with pharmacokinetic measurements, such monitoring would allow earlier and more precise optimisation of therapy. Very few population pharmacokinetic studies have been carried out in paediatric transplant patients. This type of study is needed so that techniques such as Bayesian forecasting can be applied to optimise immunosuppressive therapy in paediatric transplant patients.

The aim of the present study was to estimate valproic acid (VPA) clearance values for adult patients with epilepsy, using serum concentrations gathered during their routine clinical care. Retrospective steady state serum concentrations data (n=534) collected from 208 adult patients receiving VPA were studied. Data were analysed according to a one‐compartment model using the NONMEM program. The influence of VPA daily dose (Dose), gender, age, total body weight (TBW), and comedication with carbamazepine (CBZ), phenytoin (PHT) and phenobarbital (PB) were investigated. The results of the population pharmacokinetics analysis were validated in a group of 30 epileptic patients. The final regression model for VPA clearance (Cl) was: The inter‐individual variability in VPA clearance, described by a proportional error model, had a variation coefficient (CV) of 23.4% and the residual variability, described using an additive model, was 11.4 mg/L. These results show that VPA clearance increased linearly with TBW, but increases nonlinearly with increasing VPA daily dose. Concomitant administration of CBZ, PHT and PB led to a significant increase in VPA clearance. The model predictions in the validation group were found to have satisfactory precision and bias. In conclusion, inter‐individual variability in VPA clearance can be partly explained by TBW, daily dose and bitherapy with CBZ, DPH or PB. Inclusion of these factors allows this variability to be reduced by 37.23% which may be very useful for clinicians when establishing the initial VPA dosage regimen. However, the magnitude of inter‐individual plus residual variabilities, remaining in the final model, render these dosage predictions imprecise and justify the need for VPA serum level monitoring in order to individualize dosage regimens more accurately. Copyright © 1999 John Wiley & Sons, Ltd.

The proposed model for tacrolimus CL can be applied for a priori dosage calculations, although the results should be used with caution because of the unexplained variability in the CL. We therefore recommended close monitoring of tacrolimus whole blood concentrations, especially within the first months of treatment. The best use of the model would be its application in dosage adjustment based on therapeutic drug monitoring and the Bayesian approach.

AIMSThe aims of the study were: (i) to characterize the pharmacokinetics (PK) of doxorubicin (DOX) and doxorubicinol (DOXol) in patients diagnosed with non-Hodgkin's lymphoma (NHL) using a population approach; (ii) to evaluate the influence of various covariates on the PK of DOX; and (iii) to evaluate the role of DOX and DOXol exposure in haematological toxicity. METHODSPopulation PK modelling (using NONMEM) was performed using DOX and DOXol plasma concentration-time data from 45 NHL patients treated with R-CHOP (rituximab, cyclophosphamide, doxorubicin, vincristine and prednisone). The influence of drug exposure on haematological toxicity was analysed using the Mann-Whitney-Wilcoxon test. RESULTSA five-compartment model, three for DOX and two for DOXol, with first-order distribution and elimination for both entities best described the data. Population estimates for parent drug (CL) and metabolite (CL m ) clearance were 62 l h À1 and 27 l h À1 , respectively. The fraction metabolized to DOXol (F m ) was estimated at 0.22. While bilirubin and aspartate aminotransferase showed an influence on the CL and CL m , the objective function value decrease was not statistically significant. A trend towards an association between the total area under the concentration-time curve (AUC total ), the area under the concentration-time curve for DOX (AUC) plus the area under the concentration-time curve for DOXol (AUC m ), and the neutropenia grade (P = 0.068) and the neutrophil counts (P = 0.089) was observed, according to an exponential relationship. CONCLUSIONSThe PK of DOX and DOXol were well characterized by the model developed, which could be used as a helpful tool to optimize the dosage of this drug. The results suggest that the main active metabolite of DOX, DOXol, is involved in the haematological toxicity of the parent drug.