Population Pharmacokinetics of Valproic Acid in Patients with Mania: Implication for Individualized Dosing Regimens

Population Pharmacokinetics and Loading Dose Optimization of Intravenous Valproic Acid in Hospitalized Thai Patients

Our goal is to create a population pharmacokinetic (PK) model and identify the best loading dose (LD) of intravenous valproic acid for hospitalized Thai patients. Data from patients who received intravenous valproic acid and underwent measurement of serum valproic acid concentrations during hospitalization were collected retrospectively. A nonlinear mixed-effects modeling approach was conducted to estimate the PK parameters of valproic acid. Covariates affecting the PK parameters of valproic acid were examined and ranked based on their impact on the model's performance. Monte Carlo simulations of 1000 patients were conducted to estimate the optimal LD of valproic acid. A total of 120 hospitalized patients (51.7% male) with 167 valproic acid concentrations were included in the study. A linear one-compartment model with constant residual error was the best base model. An age-covariate model was the best predictor of valproic acid clearance (CL). The typical values of CL and volume of distribution for valproic acid were 0.77 L/h and 14.56 L, respectively. The LD of 1000-1200 mg intravenous was identified as the pragmatic option as an empirical regimen for hospitalized Thai patients. The recommended time to initiate maintenance dose (MD) is 4-8 h following the LD. The population PK model and optimal LD of valproic acid in hospitalized Thai patients has been established, and it may be advisable to initiate the MD at a later time for the elderly.

Model-Based Bioequivalence Analysis to Assess and Predict the Relative Bioavailability of Valproic Acid Formulations

BACKGROUND AND OBJECTIVE

Model-based bioequivalence (MBBE) encompasses the use of nonlinear mixed effect models supporting the estimation of pharmacokinetic endpoints to assess the relative bioavailability between multi-source drug products. This application emerges as a valuable alternative to the standard non-compartmental analysis (NCA) in bioequivalence (BE) studies in which dense sampling is not possible. In this work, we aimed to assess the application of MBBE compared to traditional methods in evaluating the relative bioavailability of two formulations with different drug release properties. Additionally, we sought to predict the performance of a modified-release formulation in a multiple-dose scenario, leveraging data from a single-dose study.

METHODS

MBBE analysis was implemented to estimate the BE endpoints (90% CI for the Test/Reference geometric mean ratio, T/R GMR) in area under the concentration-time curve (AUC) and maximum concentration (Cmax) using data from a single-dose, 2-period, 2-sequence BE study performed in 14 healthy subjects between a locally developed valproic acid extended-release formulation (Test) and the brand-name delayed-release formulation (Reference).

RESULTS

Results were compared with the standard approach, revealing that MBBE analysis achieved higher discrimination between formulations for Cmax, addressing limitations of the experimental sampling design and highlighting an advantage for this model-based analysis even when rich data are available. Additionally, the bioequivalence outcome under the multiple-dose scenario was predicted through a simulation-based study for both total and unbound valproic acid concentrations, considering the impact of valproic acid saturable binding on BE conclusions.

CONCLUSIONS

The MBBE analysis was superior to the NCA approach in detecting product-related differences, overcoming limitations in the study experimental design. Predictions for the multiple-dose scenario preclude that the extended-release properties of the Test formulation would persist at steady state, resulting in lower peak-to-trough fluctuation and bioequivalent performance in terms of the extent of drug absorption. Overall, these results should discourage unnecessary experimentation in healthy subjects.

Population pharmacokinetics of valproic acid in children with epilepsy: Implications for dose tailoring when switching from oral syrup to sustained-release tablets

Significant pharmacokinetic (PK) differences exist between different forms of valproic acid (VPA), such as syrup and sustained-release (SR) tablets. This study aimed to develop a population pharmacokinetic (PopPK) model for VPA in children with epilepsy and offer dose adjustment recommendation for switching dosage forms as needed. The study collected 1411 VPA steady-state trough concentrations (Ctrough) from 617 children with epilepsy. Using NONMEM software, a PopPK model was developed, employing a stepwise approach to identify possible variables such as demographic information and concomitant medications. The final model underwent internal and external evaluation via graphical and statistical methods. Moreover, Monte Carlo simulations were used to generate a dose tailoring strategy for typical patients weighting 20-50 kg. As a result, the PK characteristics of VPA were described using a one-compartment model with first-order absorption. The absorption rate constant (ka) was set at 2.64 and 0.46 h-1 for syrup and SR tablets. Body weight and sex were identified as significant factors affecting VPA's pharmacokinetics. The final PopPK model demonstrated acceptable prediction performance and stability during internal and external evaluation. For children taking syrup, a daily dose of 25 mg/kg resulted in the highest probability of achieving the desired target Ctrough, while a dose of 20 mg/kg/day was appropriate for those taking SR tablets. In conclusion, we established a PopPK model for VPA in children with epilepsy to tailor VPA dosage when switching between syrup and SR tablets, aiming to improve plasma VPA concentrations fluctuations.

Predictors of valproic acid steady-state serum levels in adult and pediatric psychiatric inpatients: a comparative analysis

RATIONALE

Valproic acid (VPA) is commonly used as a second-line mood stabilizer or augmentative agent in severe mental illnesses. However, population pharmacokinetic studies specific to psychiatric populations are limited, and clinical predictors for the precision application of VPA remain undefined.

OBJECTIVES

To identify steady-state serum VPA level predictors in pediatric/adolescent and adult psychiatric inpatients.

METHODS

We analyzed data from 634 patients and 1,068 steady-state therapeutic drug monitoring (TDM) data points recorded from 2015 to 2021. Steady-state VPA levels were obtained after tapering during each hospitalization episode. Electronic patient records were screened for routine clinical parameters and co-medication. Generalized additive mixed models were employed to identify independent predictors.

RESULTS

Most TDM episodes involved patients with psychotic disorders, including schizophrenia (29.2%) and schizoaffective disorder (17.3%). Polypharmacy was common, with the most frequent combinations being VPA + quetiapine and VPA + promethazine. Age was significantly associated with VPA levels, with pediatric/adolescent patients (< 18 years) demonstrating higher dose-adjusted serum levels of VPA (β = 7.6±2.34, p < 0.001) after accounting for BMI. Women tended to have higher adjusted VPA serum levels than men (β = 5.08±1.62, p < 0.001). The formulation of VPA (Immediate-release vs. extended-release) showed no association with VPA levels. Co-administration of diazepam exhibited a dose-dependent decrease in VPA levels (F = 15.7, p < 0.001), suggesting a potential pharmacokinetic interaction.

CONCLUSIONS

This study highlights the utility of population-specific pharmacokinetic data for VPA in psychiatric populations. Age, gender, and co-administration of diazepam were identified as predictors of VPA levels. Further research is warranted to establish additional predictors and optimize the precision application of VPA in psychiatric patients.

Retrospective Analysis of Steady-State Sodium Valproate Plasma Concentrations in Chinese Patients With Bipolar Disorder: Impact of Demographic and Clinical Characteristics

BACKGROUND

This study comprehensively examined the demographic and clinical characteristics of patients undergoing valproic acid therapy and explored their potential impact on plasma valproic acid concentrations. All enrolled patients were administered the extended-release formulation. An in-depth investigation of factors, including dose, age, sex, body mass index, co-administered medications, and laboratory test findings, was conducted to evaluate their potential influence on study outcomes.

METHODS

In total, 164 patients met the inclusion criteria and were included in the analysis. The patient age ranged from 13 to 60 years, with a median age of 25.71 years. Most patients (89%) received a daily dose of 1 g valproic acid. Co-administered psychiatric medications included aripiprazole, quetiapine, and lorazepam. Laboratory test results, such as hemoglobin and transaminase levels, were also collected as part of the study.

RESULTS

The average plasma valproic acid plasma concentration was 79.8 mg/L. The dose significantly affected valproic acid concentrations, as a higher percentage of measurements exceeded the therapeutic range at a daily dose of 1 g. Furthermore, females exhibited significantly higher valproic acid concentrations compared with males at the same dose (P < 0.05). However, different age groups showed no statistically significant differences in valproic acid concentrations (P > 0.05). The co-administered antipsychotic and antidepressant medications significantly affected valproate concentrations, as reflected in the multiple regression model (P < 0.01).

CONCLUSIONS

This study offers valuable insights into the demographic and clinical characteristics of patients undergoing valproic acid therapy. It highlights the influence of dose, sex, and concomitant medications on plasma valproic acid concentrations. Overall, these findings can help guide dose adjustments and implement personalized treatment strategies in valproic acid therapy.

Current Status of Therapeutic Drug Monitoring in Mental Health Treatment: A Review

Therapeutic drug monitoring (TDM) receives growing interest in different psychiatric clinical settings (emergency, inpatient, and outpatient services). Despite its usefulness, TDM remains underemployed in mental health. This is partly due to the need for evidence about the relationship between drug serum concentration and efficacy and tolerability, both in the general population and even more in subpopulations with atypical pharmacokinetics. This work aims at reviewing the scientific literature published after 2017, when the most recent guidelines about the use of TDM in mental health were written. We found 164 pertinent records that we included in the review. Some promising studies highlighted the possibility of correlating early drug serum concentration and clinical efficacy and safety, especially for antipsychotics, potentially enabling clinicians to make decisions on early laboratory findings and not proceeding by trial and error. About populations with pharmacokinetic peculiarities, the latest studies confirmed very common alterations in drug blood levels in pregnant women, generally with a progressive decrease over pregnancy and a very relevant dose-adjusted concentration increase in the elderly. For adolescents also, several drugs result in having different dose-related concentration values compared to adults. These findings stress the recommendation to use TDM in these populations to ensure a safe and effective treatment. Moreover, the integration of TDM with pharmacogenetic analyses may allow clinicians to adopt precise treatments, addressing therapy on an individual pharmacometabolic basis. Mini-invasive TDM procedures that may be easily performed at home or in a point-of-care are very promising and may represent a turning point toward an extensive real-world TDM application. Although the highlighted recent evidence, research efforts have to be carried on: further studies, especially prospective and fixed-dose, are needed to replicate present findings and provide clearer knowledge on relationships between dose, serum concentration, and efficacy/safety.

Machine learning advances the integration of covariates in population pharmacokinetic models: Valproic acid as an example

Background and Aim: Many studies associated with the combination of machine learning (ML) and pharmacometrics have appeared in recent years. ML can be used as an initial step for fast screening of covariates in population pharmacokinetic (popPK) models. The present study aimed to integrate covariates derived from different popPK models using ML.

Methods: Two published popPK models of valproic acid (VPA) in Chinese epileptic patients were used, where the population parameters were influenced by some covariates. Based on the covariates and a one-compartment model that describes the pharmacokinetics of VPA, a dataset was constructed using Monte Carlo simulation, to develop an XGBoost model to estimate the steady-state concentrations (Css) of VPA. We utilized SHapley Additive exPlanation (SHAP) values to interpret the prediction model, and calculated estimates of VPA exposure in four assumed scenarios involving different combinations of CYP2C19 genotypes and co-administered antiepileptic drugs. To develop an easy-to-use model in the clinic, we built a simplified model by using CYP2C19 genotypes and some noninvasive clinical parameters, and omitting several features that were infrequently measured or whose clinically available values were inaccurate, and verified it on our independent external dataset.

Results: After data preprocessing, the finally generated combined dataset was divided into a derivation cohort and a validation cohort (8:2). The XGBoost model was developed in the derivation cohort and yielded excellent performance in the validation cohort with a mean absolute error of 2.4 mg/L, root-mean-squared error of 3.3 mg/L, mean relative error of 0%, and percentages within ±20% of actual values of 98.85%. The SHAP analysis revealed that daily dose, time, CYP2C19*2 and/or *3 variants, albumin, body weight, single dose, and CYP2C19*1*1 genotype were the top seven confounding factors influencing the Css of VPA. Under the simulated dosage regimen of 500 mg/bid, the VPA exposure in patients who had CYP2C19*2 and/or *3 variants and no carbamazepine, phenytoin, or phenobarbital treatment, was approximately 1.74-fold compared to those with CYP2C19*1/*1 genotype and co-administered carbamazepine + phenytoin + phenobarbital. The feasibility of the simplified model was fully illustrated by its performance in our external dataset.

Conclusion: This study highlighted the bridging role of ML in big data and pharmacometrics, by integrating covariates derived from different popPK models.

Comparative pharmacokinetics of valproic acid among Pakistani and South Korean patients: A population pharmacokinetic study

Purpose

The pharmacokinetics of valproic acid have been evaluated in a variety of populations however, the comparison in two different populations was yet to be reported. This study is aimed to compare the pharmacokinetics of valproic acid in Pakistani and South Korean patients.

Method

The therapeutic drug monitoring (TDM) data of valproic acid from 92 Pakistani patients with 218 samples was combined with the data of 99 South Korean patients with 335 samples in order to form a pooled dataset of 191 patients with 553 samples. Population pharmacokinetic model was developed on NONMEM® software by using first order conditional estimation method for estimation of pharmacokinetic parameters. The influence of different covariates including ethnicity was evaluated the stepwise covariate modelling. The final model was evaluated for predictive performance and robustness by using goodness of fit plots and bootstrap analysis respectively.

Results

The data was better described by one compartment model with first order elimination. The value for clearance (CL) of valproic in pooled data was 0.931 L/h with 43.4% interindividual variability (IIV) while volume of distribution (Vd) was 16.6 L with 22.3% IIV. In covariate analysis, ethnicity and body weight were significant covariates for CL while body weight was also significant for Vd.

Conclusion

A significant difference in CL of valproic acid among Pakistani and South Korean patients was observed. The model can be used for the dose tailoring of valproic acid based on ethnicity and body weight of Pakistani and South Korean patients.

Published population pharmacokinetic models of valproic acid in adult patients: a systematic review and external validation in a Chinese sample of inpatients with bipolar disorder

BACKGROUND

This study reviewed all published valproic acid (VPA) population pharmacokinetic (PPK) models in adult patients and assessed them using external validation methods to determine predictive performance.

METHODS

Thirteen published PPK models (labeled with letters A to M) not restricted to children were identified in PubMed, Embase, and Web of Science databases. They were evaluated in a sample totaling 411 serum concentrations from 146 adult inpatients diagnosed with bipolar disorder in a Chinese hospital. Serum concentrations of VPA were analyzed by validated ultra-performance liquid chromatography-tandem mass spectrometry. Performance was assessed by 4 tests (prediction-based diagnostics, visual predictive checks, normalized prediction distribution error, and Bayesian forecasting).

RESULTS

Models K and L, developed in large samples of Chinese and Thai patients, showed good performance in our Chinese dataset. Models H and J demonstrated good performance in Tests 2 and 3 of the 4 tests, respectively. Another 7 models exhibited intermediate performance. The models with the worst performance, F and M, could not be improved by Bayesian forecasting.

CONCLUSION

In our validation study the most important factors contributing to good performance were absence of children, Asian ethnicity, one-compartment models and inclusion of body weight and VPA dose in previously published models.

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 valproic acid in adult Chinese patients with bipolar disorder

PURPOSE

To develop and validate a population pharmacokinetic (PPK) model of valproic acid (VPA) in adult Chinese patients with bipolar disorder, and provide guidance for individualized therapy in this population.

METHODS

A total of 1104 serum concentrations from 272 patients were collected in this study. The data analysis was performed using a nonlinear mixed-effects modeling approach. Covariates included demographic parameters, biological characteristics, and concomitant medications. Bootstrap validation (1000 runs), normalized prediction distribution error (NPDE), and external validation of 50 patients were employed to evaluate the final model.

RESULTS

A one-compartment model with first-order absorption and elimination was developed for VPA extended-release tablets. VPA clearance was significantly influenced by three variables: sex (12% higher in male patients), daily dose (increasing with the 0.13 exponent), and body weight (increasing with the 0.56 exponent). Typical values for the absorption rate constant (K_a), apparent clearance (CL/F), and apparent distribution volume (V/F) for a female patient weighing 70 kg administered VPA 1000 mg/day were 0.18 h^-1, 0.46 L/h, and 12.84 L, respectively. The results of model evaluation indicated a good stable and precise performance of the final model.

CONCLUSIONS

A qualified PPK model of VPA was developed in Chinese patients with bipolar disorder. This model could be used as a suitable tool for the personalization of VPA dosing for bipolar patients.

Population pharmacokinetics of unbound valproic acid in pediatric epilepsy patients in China: a protein binding model

PURPOSE

The purpose of this study was to establish a protein binding model of unbound valproic acid (VPA) based on Chinese pediatric patients with epilepsy and provide a reference for clinical medication.

METHODS

A total of 313 patients were included and both their total and unbound VPA concentrations (375 pairs of concentrations) were measured. NONMEM software was used for population pharmacokinetic modeling. The stepwise method was used to screen the potential covariates. Goodness-of-fit plot, bootstrap, and visual predictive check were used for model evaluation. In addition, dose recommendations for typical patients aged 0 to 16 years were proposed by Monte Carlo simulations.

RESULTS

A one-compartment model of first-order absorption and first-order elimination was used to describe the pharmacokinetic characteristics of unbound VPA, and the linear non-saturable binding equation was introduced to describe the protein binding. Body weight, age-based maturation, and co-medicated with lamotrigine could affect the CL/F of unbound and bound VPA. Model evaluation showed satisfactory robustness of the final model. The dosing regimens for children aged 0 to 16 years were proposed based on the final established model.

CONCLUSION

We developed a population pharmacokinetic model of unbound and bound VPA that took account of protein binding. The VPA dosing regimen in pediatric patients with epilepsy needs to be optimized by the body weight, age, and co-medications.

Predictive ability of published population pharmacokinetic models of valproic acid in Thai manic patients

WHAT IS KNOWN AND OBJECTIVE

Population pharmacokinetic (PopPK) models of valproic acid (VPA) have been developed to aid individualized drug dosing, but most of these have been based on the treatment of epileptic patients and recent evidence shows that VPA clearance (CL_VPA ) in manic patients differs from that in epileptic patients. In the light of this, the predictive ability of selected VPA PopPK models based on epileptic patients was assessed to determine whether they could be used with patients with mania.

METHODS

VPA PopPK models that were based on the treatment of epileptic patients and developed using a non-linear mixed-effect approach with a one-compartment structure were selected and used to predict the VPA concentrations of a validation data set. The mean absolute prediction error (MAPE) and root mean square error (RMSE) were used to assess the accuracy and precision of the model.

RESULTS

The validation data set consisted of 235 Thai manic patients with a mean age of 39.6 years and a mean weight of 62.8 kg. Five models were selected to predict VPA concentrations in patients suffering from mania, and these were labelled A, C, E, F and G. The results showed that all models sufficiently predicted VPA concentrations in patients with mania, and of the models studied, G provided the most accurate and precise predictions, with MAPE and RMSE of 23% and 29.75, respectively.

WHAT IS NEW AND CONCLUSION

VPA PopPK models developed using patients with epilepsy can also be used for individualized dosing of patients with mania, but before implementation, the accuracy of these models' predictions should be assessed in the target population.

Impact of gender, albumin, and CYP2C19 polymorphisms on valproic acid in Chinese patients: a population pharmacokinetic model

OBJECTIVE

This prospective study aimed to establish the valproic acid (VPA) population pharmacokinetic model in Chinese patients and realise personalised medication on the basis of population pharmacokinetics.

METHODS

The patients' clinical information and VPA plasma concentrations were collected from The General Hospital of Taiyuan Iron & Steel (Group) Corporation (TISCO). Nonlinear mixed-effect modelling was used to build the population pharmacokinetic model. To characterise the pharmacokinetic data, a one-compartment pharmacokinetic model with first-order absorption and elimination was used. The first-order conditional estimation with η-ε interaction was applied throughout the model-developing procedure. The absorption rate constant (Ka) was fixed at 2.38 hour^-1, and the impact of covariates on clearance and apparent volume of distribution were also explored. Medical records of 60 inpatients were reviewed prospectively and the objective function value (OFV) of the base model and final model were 851.813 and 817.622, respectively.

RESULTS

Gender was identified as the covariate that had a significant impact on the volume of distribution, and albumin and CYP2C19 genotypes influenced clearance.

CONCLUSION

Bootstrap and VPC indicated that a reliable model had been developed that was based on the simulation results, and a simple-to-use dosage regimen table was created to guide clinicians for VPA drug dosing.

Estimation of apparent clearance of valproic acid in adult Saudi patients

Background Valproic acid is one of several antiepileptic medications requiring therapeutic drug monitoring due to its complex and wide pharmacokinetic interindividual variability. Objective The objective of this study was to determine the population pharmacokinetics of valproic acid in adult Saudi patients and to identify factors that explain its pharmacokinetic variability. Setting Tertiary referral teaching hospital, Riyadh, Saudi Arabia. Method A retrospective chart review was performed at King Saud University Medical City of patients who received oral valproic acid. The population pharmacokinetic models were developed using Monolix 4.4. After development of the base model, we investigated several covariates including age, sex, weight, total daily dose, and cotherapy with carbamazepine and phenytoin. Main outcome measures the pharmacokinetic parameters of valproic acid and the variables that contributing towards its inter-individual variability. Results The analysis included a total of 54 valproic acid plasma concentrations from 54 patients (42.5% male). The data were sufficiently described by a one-compartment model with linear absorption and elimination processes. Average parameter estimates for valproic acid apparent clearance (CL/F) and apparent volume of distribution (V/F) were 0.14 L/h and 37.7 L (fixed), respectively. The inter-individual variability (coefficients of variation) in CL/F was 12%. The most significant covariates for valproic acid CL/F were age, body weight, total daily dose, and cotherapy with carbamazepine and phenytoin. Conclusion This model showed significant inter-individual variability between subjects. Our findings showed that patient age, body weight, total daily dose, and cotherapy with carbamazepine and phenytoin are the most significant covariates of valproic acid clearance. Collectively, healthcare providers should take these factors in consideration for optimal valproic acid dosage regimen.

External evaluation of a published population pharmacokinetic model of valproic acid in Thai manic patients

Objective

To evaluate external predictability of a population pharmacokinetic model of valproic acid in Thai patients with mania to ensure its appropriateness for use in other clinical settings.

Methods

The published population pharmacokinetic model was evaluated for its predictive ability (at both individual and population levels) and its precision by means of mean absolute prediction error (MAPE), root mean square error (RMSE) and normalised prediction distribution error (NPDE).

Results

Forty-six steady-state serum valproic acid concentration levels from 30 manic patients were retrospectively collected from routine therapeutic drug monitoring at Srithanya Hospital, Thailand. For the prediction-based diagnostics, the MAPE and RMSE were 10.44% (95% CI 8.12% to 12.76%) and 12.99% (95% CI 9.51% to 15.72%), respectively, suggesting that the proposed model was relatively predictive and had a good precision. In simulation-based diagnostics, the NPDE results also showed that the model appropriately predicted valproic acid concentration levels, as indicated by a normal distribution of NPDEs with a mean and a variance of 0 and 1, respectively.

Conclusion

The predictability of the population pharmacokinetic model of valproic acid in Thai patients with mania was confirmed. This model could be applied in other clinical settings.

A systematic review of population pharmacokinetics of valproic acid

AIMS

Population pharmacokinetics is an essential tool that helps guide individualized dosing regimens. The aims of this systematic review are to provide knowledge concerning population pharmacokinetics of valproic acid (VPA) and to identify factors influencing VPA pharmacokinetic variability.

METHODS

PubMed and Embase databases were systematically searched from inception to June, 2017. Relevant articles from reference lists were also included. All population pharmacokinetic studies of VPA conducted in humans and that employed a nonlinear mixed effect modelling approach were included in this review.

RESULTS

Twenty-six studies were included in this review. Most studies characterized VPA pharmacokinetics as a one-compartment model. Three studies reported a two-compartment model. Body weight, dose and age were significant predictors for VPA volume of distribution (V_d ). The estimated V_d for one-compartment models ranged from 8.4 to 23.3 l. For two-compartment models, peripheral volumes of distribution ranged from 4.08 to 42.1 l. Frequently reported significant predictors for VPA clearance (CL_VPA ) included body weight, VPA dose, concomitant medications, gender and age. The estimated CL_VPA ranged from 0.206 to 1.154 l h^-1 and the inter-individual variability ranged from 13.40 to 35.90%. Two studies reported population pharmacokinetics/pharmacodynamics of VPA in patients with epilepsy. Seventeen studies evaluated the performance of their final models.

CONCLUSIONS

Significant predictors influencing VPA pharmacokinetics as well as model methodologies are highlighted in this review. For clinical application, CL_VPA could be predicted using body weight, VPA dose, concomitant medications, gender or age. For future research, there is a knowledge gap regarding population pharmacokinetics/pharmacodynamics of VPA in a population other than epileptic patients.