Population Pharmacokinetics of Valproic Acid in Pediatric and Adult Caucasian Patients.

(1) Background: The aim of this study was to explore the valproic acid (VPA) pharmacokinetic characteristics in a large population of pediatric and adult Caucasian patients and to establish a robust population pharmacokinetic (PopPK) model. (2) Methods: A total of 2527 serum VPA samples collected from 1204 patients included in a therapeutic drug monitoring program were retrospectively analyzed. Patients were randomly assigned to either a model development group or an external evaluation group. PopPK analysis was performed on 1751 samples from 776 patients with NONMEM using a nonlinear mixed-effect modelling approach. The influence of demographic, anthropometric, treatment and comedication variables on the apparent clearance (CL/F) of VPA was studied. The bootstrap method was used to evaluate the final model internally. External evaluation was carried out using 776 VPA serum samples from 368 patients. (3) Results: A one-compartment model with first-order absorption and elimination successfully described the data. The final model included total body weight, age and comedication with phenytoin, phenobarbital and carbamazepine with a significant impact on VPA elimination. Internal and external evaluations demonstrated the good predictability of the model. (4) Conclusions: A PopPK model of VPA in Caucasian patients was successfully established, which will be helpful for model-informed precision dosing approaches in clinical patient care.

Population pharmacokinetics of phenobarbital in Caucasian patients with epilepsy.

The present study aimed to establish a population pharmacokinetic (PopPK) model of Phenobarbital (PB) in Caucasian patients with epilepsy included in a Therapeutic Drug Monitoring program. In total, 855 PB serum concentrations (steady-state trough concentrations) were retrospectively collected during routine clinical monitoring of 395 patients over 15 years of age with epilepsy. The PopPK analysis was performed with NONMEM using a non-linear mixed-effect modelling approach. The influence of demographic, anthropometric, treatment, and comedication variables on the apparent clearance (CL/F) of PB were analysed. Goodness of fit plots and the bootstrap method were performed to evaluate the final model. External validation was carried out using an independent group of patients (107 patients, 178 blood samples). A one-compartment model with first-order absorption and elimination successfully described the data. In the final model, CL/F included body surface area (BSA) and comedication with phenytoin (PHT) and valproic acid (VPA), resulting in the following equation: CL/F[L/h]=(0.236+(0.115×(BSA-1.7)))×(0.822PHT)×(0.711VPA) The model presents acceptable estimation errors in the parameters of fixed (<12%) and random effects (<13%), and of the shrinkage values (<21%). Internal and external validations demonstrated the good predictability of the final model. A PopPK model of PB in Caucasian patients over 15 years of age was successfully established, which can be used to estimate phenobarbital CL/F. BSA and drug-drug interactions with PHT and VPA should be incorporated into dosing decisions. This PopPK, using Bayesian algorithms, could help establish an optimal dosage regimen in routine patient care.

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

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 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.

Population pharmacokinetics of doxorubicin and doxorubicinol in patients diagnosed with non-Hodgkin's lymphoma.

AIMS: The 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. METHODS: Population 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. RESULTS: A 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 (CLm ) clearance were 62 l h-1 and 27 l h-1 , respectively. The fraction metabolized to DOXol (Fm ) was estimated at 0.22. While bilirubin and aspartate aminotransferase showed an influence on the CL and CLm , the objective function value decrease was not statistically significant. A trend towards an association between the total area under the concentration-time curve (AUCtotal ), the area under the concentration-time curve for DOX (AUC) plus the area under the concentration-time curve for DOXol (AUCm ), and the neutropenia grade (P = 0.068) and the neutrophil counts (P = 0.089) was observed, according to an exponential relationship. CONCLUSIONS: The 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.

Population pharmacokinetic/pharmacogenetic model of lopinavir/ritonavir in HIV-infected patients.

AIM: This study aims to develop a population pharmacokinetic/pharmacogenetic model for lopinavir/ritonavir (LPV/r) in European HIV-infected patients. MATERIALS & METHODS: A total of 693 LPV/r plasma concentrations were assessed and 15 single-nucleotide polymorphisms were genotyped. The population pharmacokinetic/pharmacogenetic model was created using a nonlinear mixed-effect approach (NONMEM® v.7.2.0., ICON Development Solutions, Dublin, Ireland). RESULTS: Covariates significantly related to LPV/r apparent clearance (CL/F) were ritonavir trough concentration (RTC), BMI, high-density lipoprotein cholesterol (HDL-C) and certain single-nucleotide polymorphisms in genes encoding for metabolizing enzymes, which are representable as follows: CL/F = (0.216BMI + 0.0125HDL-C) × 0.713RTC × 1.26rs28371764[C/T] × 0.528rs6945984[C/C] × 0.302 CYP3A4[1461insA/del] Conclusion: The LPV/r standard dose appears to be appropriate for the rs28371764[C/T] genotype. However, lower doses should be recommended for the rs6945984[C/C] and CYP3A4[1461insA/del] genotypes and even for those patients without any of these variants, as the standard dose seems to be higher than that which is required in order to achieve therapeutic levels.

Population pharmacokinetics of lopinavir/ritonavir (Kaletra) in HIV-infected patients.

BACKGROUND: A relationship between plasma concentrations and viral suppression in patients receiving lopinavir (LPV)/ritonavir (RTV) has been observed. Therefore, it is important to increase our knowledge about factors that determine interpatient variability in LPV pharmacokinetics (PK). METHODS: The study, designed to develop and validate population PK models for LPV and RTV, involved 263 ambulatory patients treated with 400/100 mg of LPV/RTV twice daily. A database of 1110 concentrations of LPV and RTV (647 from a single time-point and 463 from 73 full PK profiles) was available. Concentrations were determined at steady state using high-performance liquid chromatography with ultraviolet detection. PK analysis was performed with NONMEM software. Age, gender, height, total body weight, body mass index, RTV trough concentration (RTC), hepatitis C virus coinfection, total bilirubin, hospital of origin, formulation and concomitant administration of efavirenz (EFV), saquinavir (SQV), atazanavir (ATV), and tenofovir were analyzed as possible covariates influencing LPV/RTV kinetic behavior. RESULTS: Population models were developed with 954 drug plasma concentrations from 201 patients, and the validation was conducted in the remaining 62 patients (156 concentrations). A 1-compartment model with first-order absorption (including lag-time) and elimination best described the PK. Proportional error models for interindividual and residual variability were used. The final models for the drugs oral clearance (CL/F) were as follows: CL/F(LPV)(L/h)=0.216·BMI·0.81(RTC)·1.25(EFV)·0.84(ATV); CL/F(RTV)(L/h) = 8.00·1.34(SQV)·1.77(EFV)·1.35(ATV). The predictive performance of the final population PK models was tested using standardized mean prediction errors, showing values of 0.03 ± 0.74 and 0.05 ± 0.91 for LPV and RTV, and normalized prediction distribution error, confirming the suitability of both models. CONCLUSIONS: These validated models could be implemented in clinical PK software and applied to dose individualization using a Bayesian approach for both drugs.

Vancomycin dosage optimization in patients with malignant haematological disease by pharmacokinetic/pharmacodynamic analysis.

BACKGROUND: The use of vancomycin against Staphylococcus aureus is currently debated because of the increasing resistance developed by this pathogen. Nevertheless, antibacterial effectiveness is a limited resource that must be protected and restored. Novel dosage strategies based on pharmacokinetic/pharmacodynamic analyses are needed to retain effectiveness that could improve drug exposure in patients infected with such pathogens. OBJECTIVE: The aim of this study was to assess whether standard or higher vancomycin dosages are required to increase the probability of attaining a target pharmacokinetic/pharmacodynamic index for several staphylococcal strains and thus to estimate the minimum vancomycin daily dose related to a high probability of effective treatment in patients with malignant haematological disease. METHODS: Monte Carlo simulation was performed to calculate the cumulative fraction of response (CFR) for different vancomycin daily dosages, using a population pharmacokinetic model previously defined in patients with malignant haematological disease and the minimum inhibitory concentration (MIC) distribution for vancomycin against several staphylococcal species (vancomycin-susceptible S. aureus and vancomycin-intermediate S. aureus [VISA], S. epidermidis, S. haemolyticus and coagulase-negative Staphylococcus [CNS] species) obtained from the European Committee on Antimicrobial Susceptibility Testing (EUCAST) in order to predict the dose that would achieve the pharmacokinetic/pharmacodynamic index value associated with efficacy (the area under the concentration-time curve from 0 to 24 hours divided by the MIC [AUC(24)/MIC >/=400]). RESULTS: CFR values showed dependence on the renal function of the patient and the causative pathogen. Only in patients with a creatinine clearance (CL(CR)) <60 mL/min did the standard vancomycin dosage (2000 mg/day) induce CFRs >60% for all staphylococci, except the VISA strains. CFRs for S. aureus of 90.6%, 47.3% and 31.2% for CL(CR) values of <60, 60-120 and >120 mL/min, respectively, were obtained, whereas for the VISA strains, the corresponding values were only 14.0%, 0.3% and 0%. The impact of potential pathogens on CFRs is also significant. According to our pharmacokinetic/pharmacodynamic analysis, in patients with normal renal function (CL(CR) between 60 and 120 mL/min) vancomycin 2000 mg/day leads to a risk of not achieving the recommended AUC(24)/MIC breakpoint of 52.7%, 70.4%, 74.9% and 80.3% for S. aureus, S. haemolyticus, CNS and S. epidermidis, respectively. Application of our results to clinical practice graphically allows us to obtain the recommended dose for any a priori-selected probability of attaining the AUC(24)/MIC ratio of >/=400 and to evaluate the CFRs for any dosing regimen used in this population group, depending on the patients' renal function. CONCLUSIONS: Application of pharmacokinetic/pharmacodynamic analysis based on Monte Carlo simulation offers an excellent tool for selecting the therapeutic option with the highest probability of clinical success in patients with malignant haematological disease. Thus, for vancomycin-susceptible S. aureus, if a CFR >/=80 is assumed as clinically acceptable, vancomycin doses of 1500, 3000 and 4000 mg/day for a CL(CR) of <60, 60-120 and >120 mL/min, respectively, will be required.

Immunosuppressive therapy for paediatric transplant patients: pharmacokinetic considerations.

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.