Population pharmacokinetics of valproic acid in epileptic children: Effects of clinical and genetic factors

Fixed parameters in the population pharmacokinetic modeling of valproic acid might not be suitable: external validation in Chinese adults with epilepsy or after neurosurgery

PURPOSE

This study aims to assess the predictive performance of published valproic acid (VPA) population pharmacokinetic (PPK) models using an external data set in Chinese adults with epilepsy or after neurosurgery.

METHODS

A total of 384 concentrations from 290 Chinese adults with epilepsy or after neurosurgery were used for external validation. Data on published VPA PPK models were extracted from the literature. Prediction-based diagnostics (such as F20 and F30), simulation-based diagnostics, and Bayesian forecasting were used to evaluate the predictability of models.

RESULTS

The results of prediction-based diagnostics of all models were unsatisfactory. Models B, F, and H showed the best prediction performance in simulation-based diagnostics and Bayesian forecasting, demonstrating superior precision and accuracy. Bayesian forecasting demonstrated significant improvements in the model predictability.

CONCLUSION

The published PPK models showed extensive variation in predictive performance for extrapolation among Chinese adults with epilepsy or after neurosurgery patients. Fixed parameters of Vd and Ka in the PPK modeling of VPA might be the reason for the unsatisfied predictive performance. Bayesian forecasting significantly improved model predictability and may help to individualize VPA dosing.

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.

Distribution pattern of UGT1A6 and UGT2B7 gene polymorphism and its impact on the pharmacokinetics of valproic acid and carbamazepine: Prospective genetic association study conducted in Pakistani patients with epilepsy

BACKGROUND

Ethnic variation is one of the important factors in clinical practice that may affect the pharmacokinetics of drugs. The present study aims to determine the distribution pattern of UGT1A6 and UGT2B7 gene polymorphism and its possible impact on the metabolism of valproic acid (VPA) and carbamazepine (CBZ) in patients with epilepsy from Khyber Pakhtunkhwa region of Pakistan.

METHODS

After the enrollment of targeted patients, blood was collected for genotype analysis through Sanger sequencing. Plasma concentrations of VPA and CBZ were determined by reversed-phase high-performance liquid chromatography (HPLC) at the follow-up visit of third month from the initiation of therapy. The drug plasma levels were correlated with different genotypes of UGT1A6 and UGT2B7 to determine the impact of genetic polymorphism on the drug metabolism.

RESULTS

Of the total 178 epileptic patients, 120 subjects were prescribed VPA monotherapy while 58 subjects were given CBZ monotherapy. The mean age of the subjects was recorded as 26.1 ± 13.5 years with a predominance of the male gender. Generalized tonic-clonic (GTC) was the most prevalent type of seizure (82%) followed by partial seizure. Genotype analysis revealed that the frequency of homozygous and heterozygous variants of the targeted UGT genes were exceptionally high in the Khyber Pakhtunkhwa population compared to the ethnic groups of other countries. In UGT1A6-A552C and UGT1A6-A541G, AC and AG were the most prevalent genotypes with respective frequencies of 43.2% and 41.1% whereas, in UGT2B7-T161C and UGT2B7-G211T, TC and GG were the most prevalent genotypes with respective frequencies of 42.7% and 99.4%. In the VPA-treated group, the homozygous and heterozygous variants of UGT1A6-A552C and UGT1A6-A541G were significantly associated with lower drug plasma concentrations (p < 0.05). However, none of the genotypes of UGT2B7-T161C revealed any significant association with VPA plasma concentration (p greater than 0.05). In the CBZ-treated group, UGT gene polymorphisms were not recognized to cause alteration in the drug plasma concentrations (p greater than 0.05).

CONCLUSION

The genetic polymorphisms of UGT1A6, but not UGT2B7 significantly affected the plasma levels of valproic acid. The chosen SNPs did not reveal a role in determining the plasma levels of carbamazepine.

Modeling the protein binding non-linearity in population pharmacokinetic model of valproic acid in children with epilepsy: a systematic evaluation study

Background: Several studies have investigated the population pharmacokinetics (popPK) of valproic acid (VPA) in children with epilepsy. However, the predictive performance of these models in the extrapolation to other clinical environments has not been studied. Hence, this study evaluated the predictive abilities of pediatric popPK models of VPA and identified the potential effects of protein binding modeling strategies. Methods: A dataset of 255 trough concentrations in 202 children with epilepsy was analyzed to assess the predictive performance of qualified models, following literature review. The evaluation of external predictive ability was conducted by prediction- and simulation-based diagnostics as well as Bayesian forecasting. Furthermore, five popPK models with different protein binding modeling strategies were developed to investigate the discrepancy among the one-binding site model, Langmuir equation, dose-dependent maximum effect model, linear non-saturable binding equation and the simple exponent model on model predictive ability. Results: Ten popPK models were identified in the literature. Co-medication, body weight, daily dose, and age were the four most commonly involved covariates influencing VPA clearance. The model proposed by Serrano et al. showed the best performance with a median prediction error (MDPE) of 1.40%, median absolute prediction error (MAPE) of 17.38%, and percentages of PE within 20% (F20, 55.69%) and 30% (F30, 76.47%). However, all models performed inadequately in terms of the simulation-based normalized prediction distribution error, indicating unsatisfactory normality. Bayesian forecasting enhanced predictive performance, as prior observations were available. More prior observations are needed for model predictability to reach a stable state. The linear non-saturable binding equation had a higher predictive value than other protein binding models. Conclusion: The predictive abilities of most popPK models of VPA in children with epilepsy were unsatisfactory. The linear non-saturable binding equation is more suitable for modeling non-linearity. Moreover, Bayesian forecasting with prior observations improved model fitness.

Population Pharmacokinetics and Exposure-Safety of Lipophilic Conjugates Prodrug DP-VPA in Healthy Chinese Subjects for Dose Regime Exploring

Phospholipid-valproic acid (DP-VPA)is a prodrug for treating epilepsy. The present study explored the pharmacokinetics (PK) and exposure safety of DP-VPA to provide a basis for future studies exploring the safe dosage and therapeutic strategies for epilepsy. The study included a randomized placebo-controlled dose-escalation tolerance evaluation trial and a randomized triple crossover food-effect trial in healthy Chinese volunteers. A population pharmacokinetic (PopPK) model was established to analyze the PK of DP-VPA and active metabolite VPA. The exposure safety was assessed with the adverse drug reaction (ADR) in CNS. The PopPK of DP-VPA and metabolite VPA fitted a two-compartment model coupling one-compartment with Michaelis-Menten metabolite kinetics and first-order elimination. The absorption processes after single oral administration of DP-VPA tablet demonstrated nonlinear characteristics, including 0-order kinetic phase and time-dependent phase fitting Weibull distribution. The final model indicated that the DP-VPA PK was significantly affected by dosage and food. The exposure-safety relationship demonstrated a generalized linear regression; mild/moderate ADRs occurred in some subjects with 600 mg and all subjects with 1500 mg of DP-VPA, and no severe ADRs were reported up to 2400 mg. In conclusion, the study established a PopPK model describing the processing of DP-VPA and VPA in healthy Chinese subjects. DP-VPA showed good tolerance after a single dose of 600-2400 mg with nonlinear PK and was affected by dosage and food. Based on the association between neurological ADRs and higher exposure to DP-VPA by exposure-safety analysis, 900-1200 mg was recommended for subsequent study of safety and clinical effectiveness.

Effect of ANKK1 Polymorphisms on Serum Valproic Acid Concentration in Chinese Han Adult Patients in the Early Postoperative Period

INTRODUCTION

This study aimed to investigate the relationship between gene polymorphisms and clinical factors with the concentrations of valproic acid (VPA) in adult patients who underwent neurosurgery in China.

METHODS

A total of 531 serum concentration samples at steady state were collected from 313 patients to develop a population pharmacokinetic (PPK) model. Data analysis was performed using nonlinear mixed effects modeling. Covariates included demographic parameters, biological characteristics, and genetic polymorphism. Bootstrap evaluation showed that the final model was stable. Sensitive analysis was performed to verify the relationship between gene polymorphisms and concentrations of VPA. Linear regression was used to analyze the relationship between VPA concentration, ANKK1, and daily dosage.

RESULTS

In the recruited patients, 17 of 25 single-nucleotide polymorphism distributions were consistent with the Hardy-Weinberg equilibrium. A one-compartment model with first-order absorption and elimination was developed for VPA injections. VPA clearance was significantly influenced by three variables: sex (17.41% higher in male than female patients), body weight, and the ANKK1 gene. Typical values for the elimination clearance and the volume of central compartment were 0.614 L/min and 23.5 L, respectively. The model evaluation indicated the stable and precise performance of the final model. After sensitive analysis using Kruskal-Wallis and Mann-Whitney U tests, we found that patients with AA alleles had higher VPA concentrations than those with GG and AG alleles. Linear regression models showed that gene polymorphisms of ANKK1 had little effects on VPA concentration.

CONCLUSION

A PPK model of VPA in Chinese Han patients was successfully established; this can be helpful for model-informed precision-dosing approaches in clinical patient care, and for exploring the mechanism of VPA-induced weight gain.

Pharmacogenetic Aspects of Drug Metabolizing Enzymes and Transporters in Pediatric Medicine: Study Progress, Clinical Practice and Future Perspectives

As the activity of certain drug metabolizing enzymes or transporter proteins can vary with age, the effect of ontogenetic and genetic variation on the activity of these enzymes is critical for the accurate prediction of treatment outcomes and toxicity in children. This makes pharmacogenetic research in pediatrics particularly important and urgently needed, but also challenging. This review summarizes pharmacogenetic studies on the effects of genetic polymorphisms on pharmacokinetic parameters and clinical outcomes in pediatric populations for certain drugs, which are commonly prescribed by clinicians across multiple therapeutic areas in a general hospital, organized from those with the most to the least pediatric evidence among each drug category. We also further discuss the research status of the gene-guided dosing regimens and clinical implementation of pediatric pharmacogenetics. More and more drug-gene interactions are demonstrated to have clinical validity for children, and pharmacogenomics in pediatrics have shown evidence-based benefits to enhance the efficacy and precision of existing drug dosing regimens in several therapeutic areas. However, the most important limitation to the implementation is the lack of high-quality, rigorous pediatric prospective clinical studies, so adequately powered interventional clinical trials that support incorporation of pharmacogenetics into the care of children are still needed.

Pharmacogenetics-based population pharmacokinetic analysis and dose optimization of valproic acid in Chinese southern children with epilepsy: Effect of ABCB1 gene polymorphism

Objective: The aim of this study was to establish a population pharmacokinetic (PPK) model of valproic acid (VPA) in pediatric patients with epilepsy in southern China, and provide guidance for individualized medication of VPA therapy. Methods: A total of 376 VPA steady-state trough concentrations were collected from 103 epileptic pediatric patients. The PPK parameter values for VPA were calculated by using the nonlinear mixed-effects modeling (NONMEM) method, and a one-compartment model with first-order absorption and elimination processes was applied. Covariates included demographic information, concomitant medications and selected gene polymorphisms. Goodness-of-fit (GOF), bootstrap analysis, and visual predictive check (VPC) were used for model evaluation. In addition, we used Monte Carlo simulations to propose dose recommendations for different subgroup patients. Results: A significant effect of the patient age and ABCB1 genotypes was observed on the VPA oral clearance (CL/F) in the final PPK model. Compared with patients with the ABCB1 rs3789243 AA genotype, CL/F in patients with GG and AG genotypes was increased by 8% and reduced by 4.7%, respectively. The GOF plots indicated the satisfactory predictive performance of the final model, and the evaluation by bootstrap and VPC showed that a stable model had been developed. A table of individualized dosing regimens involving age and ABCB1 genotype was constructed based on the final PPK model. Conclusion: This study quantitatively investigated the effects of patient age and ABCB1 rs3789243 variants on the pharmacokinetic variability of VPA. The PPK models could be beneficial to individual dose optimization in epileptic children on VPA therapy.

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.

Combined cell grafting and VPA administration facilitates neural repair through axonal regeneration and synaptogenesis in traumatic brain injury

Neuronal regeneration and functional recovery are severely compromised following traumatic brain injury (TBI). Treatment options, including cell transplantation and drug therapy, have been shown to benefit TBI, although the underlying mechanisms remain elusive. In this study, neural stem cells (NSCs) are transplanted into TBI-challenged mice, together with olfactory ensheathing cells (OECs) or followed by valproic acid (VPA) treatment. Both OEC grafting and VPA treatment facilitate the differentiation of NSCs into neurons (including endogenous and exogenous neurons) and significantly attenuate neurological functional defects in TBI mice. Combination of NSCs with OECs or VPA administration leads to overt improvement in axonal regeneration, synaptogenesis, and synaptic plasticity in the cerebral cortex in TBI-challenged mice, as shown by retrograde corticospinal tract tracing, electron microscopy, growth-associated protein 43 (GAP43), and synaptophysin (SYN) analyses. However, these beneficial effects of VPA are reversed by local delivery of N-methyl-D-aspartate (NMDA) into tissues surrounding the injury epicenter in the cerebral cortex, accompanied by a pronounced drop in axons and synapses in the brain. Our findings reveal that increased axonal regeneration and synaptogenesis evoked by cell grafting and VPA fosters neural repair in a murine model of TBI. Moreover, VPA-induced neuroprotective roles are antagonized by exogenous NMDA administration and its concomitant decrease in the number of neurons of local brain, indicating that increased neurons induced by VPA treatment mediate axonal regeneration and synaptogenesis in mice after TBI operation. Collectively, this study provides new insights into NSC transplantation therapy for TBI.

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.

Genetic Polymorphism of GABRG2 rs211037 is Associated with Drug Response and Adverse Drug Reactions to Valproic Acid in Chinese Southern Children with Epilepsy

Background

Valproic acid (VPA) is recommended as a first-line treatment for children with epilepsy. GABRG2 polymorphism is found to be associated with epilepsy susceptibility and therapeutic response of anti-seizure medications (ASM); however, the role of GABRG2 in VPA treatment still remains unknown.

Objective

The purpose of this study was to explore the association of GABRG2 gene polymorphism with the drug response and adverse drug reactions (ADRs) related to VPA.

Methods

A retrospective study including 96 Chinese children with epilepsy treated by VPA was carried out. The ADRs were collected during VPA therapy and GABRG2 rs211037 in enrolled patients was genotyped using Sequenom MassArray system. A network pharmacological analysis involved protein–protein interaction and enrichment analysis was constructed to investigate the potential targets and pathways of GABRG2 on VPA-related ADRs.

Results

Among 96 patients, 41 individuals were defined as seizure together with 49 patients with seizure-free and 6 patients unclassified. Carriers of homozygote GABRG2 rs211037 CC genotype exhibited seizure-free to VPA (P = 0.042), whereas those with CT genotype showed seizure. Furthermore, CC genotype had predisposition to digestive ADRs (P = 0.037) but was a protective factor for VPA-associated weight gain (P = 0.013). Ten key genes related to digestive ADRs and weight gain induced by VPA were identified by network pharmacological analysis and mainly involved in “GABAergic synaptic signaling”, “GABA receptor signaling”, and “taste transduction” pathways/processes through enrichment analysis.

Conclusion

This study revealed that GABRG2 variation exerted a predictable role in the efficacy and safety of VPA treatment for Chinese children with epilepsy.

The association of adjusted plasma valproic acid concentration with CYP2C9 gene polymorphism in patients with epilepsy: a systematic review and meta-analysis

Background

Valproic acid (VPA) is a common antiepileptic drug used to treat both generalized and partial epilepsy. Although there is increasing evidence to suggest that CYP2C9 gene polymorphisms are associated with interindividual variability of VPA metabolism, the results are debatable. Therefore, in the present study, we conducted a meta-analysis to evaluate the correlation between CYP2C9 gene polymorphisms and adjusted plasma VPA concentration.

Methods

The EMBASE, MEDLINE, and Cochrane Library databases were searched to obtain relevant studies. Eligible articles were reviewed, and data extraction was performed. We calculated 95% confidence intervals (CIs) and mean differences (MDs) to assess the strength of the relationship of CYP2C9 gene polymorphisms with adjusted plasma VPA concentration.

Results

The meta-analysis included 6 studies involving 847 patients with epilepsy. The pooled analysis showed that the CYP2C9 A1075C (AA vs. AC) polymorphism was related to the adjusted plasma concentration of VPA (P=0.02, I²= 82%). Additionally, the AC phenotype statistically significantly increased the adjusted plasma VPA concentration in children compared with the mixed age subgroup (P=0.04, I²= 48%). A similar association was observed between the AC phenotype for Asians (P<0.00001, I²=0%) but not for Caucasians (P=0.34, I²=87%).

Discussion

Age might be a crucial covariate influencing the dosage-adjusted VPA concentration in patients with epilepsy. A reduced VPA dosage may be recommendable for children, particularly Asian children, who are CYP2C9 A1075C AC carriers. Further studies could provide high-quality evidence to confirm the correlation between VPA pharmacokinetics and CYP2C9 A1075C polymorphisms.

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.

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.

Pharmacogenomics of Cognitive Dysfunction and Neuropsychiatric Disorders in Dementia

Symptomatic interventions for patients with dementia involve anti-dementia drugs to improve cognition, psychotropic drugs for the treatment of behavioral disorders (BDs), and different categories of drugs for concomitant disorders. Demented patients may take >6-10 drugs/day with the consequent risk for drug-drug interactions and adverse drug reactions (ADRs >80%) which accelerate cognitive decline. The pharmacoepigenetic machinery is integrated by pathogenic, mechanistic, metabolic, transporter, and pleiotropic genes redundantly and promiscuously regulated by epigenetic mechanisms. CYP2D6, CYP2C9, CYP2C19, and CYP3A4/5 geno-phenotypes are involved in the metabolism of over 90% of drugs currently used in patients with dementia, and only 20% of the population is an extensive metabolizer for this tetragenic cluster. ADRs associated with anti-dementia drugs, antipsychotics, antidepressants, anxiolytics, hypnotics, sedatives, and antiepileptic drugs can be minimized by means of pharmacogenetic screening prior to treatment. These drugs are substrates, inhibitors, or inducers of 58, 37, and 42 enzyme/protein gene products, respectively, and are transported by 40 different protein transporters. APOE is the reference gene in most pharmacogenetic studies. APOE-3 carriers are the best responders and APOE-4 carriers are the worst responders; likewise, CYP2D6-normal metabolizers are the best responders and CYP2D6-poor metabolizers are the worst responders. The incorporation of pharmacogenomic strategies for a personalized treatment in dementia is an effective option to optimize limited therapeutic resources and to reduce unwanted side-effects.

Efficacy of antiepileptic drugs in the era of pharmacogenomics: A focus on childhood

BACKGROUND

In recent years advances in the field of pharmacogenomics have expanded the concept for more individualized treatments. Our aim is to provide literature data about the relationship between genetic polymorphisms and efficacy of antiepileptic drugs in children.

METHODS

Pubmed was used as the main medical database source. Only original research papers were considered. No year-of-publication restriction was placed. Quality of evidence was assessed according to American Academy of Neurology guidelines.

RESULTS

A total of 12 cross-sectional and case-control studies fulfilled our selection criteria. ABCB1 gene was associated with drug responsiveness in 2 out of 6 studies and ABCC2 gene in 1 out of 1 studies. SCN1A gene was also associated with seizure control in 4 out of 5 studies. Cytochrome P450 genes were found to significantly affect drug responsiveness in 2 out of 4 studies, while polymorphisms of uridinediphosphateglucuronosyltransferaseUGT2B7 gene predisposed to drug-resistance in 1 out of 2 studies.

CONCLUSION

Variability in genes coding for sodium channels, drug transporters and cytochrome P450 enzymes can have a significant impact on response to antiepileptic drugs. Larger prospective studies with better stratification of samples are needed to shed light on these associations.

Pharmacist impact on adherence of valproic acid therapy in pediatric patients with epilepsy using active education techniques

There is limited information on the impact of active education by a pharmacist in the population of pediatric patients with epilepsy (PWE) in China. The objective of this study was to assess the effect of education by pharmacists on medication adherence and percentage of valproic acid (VPA) samples reaching therapeutic reference range in these patients. This study was conducted at two teaching hospitals in Changsha, China. Patients were retrospectively identified from January 2016 to December 2017. Active education by a pharmacist in both oral and written formats was provided at the intervention hospital whereas standard passive pharmacist service (dispensing and answering questions) was provided at the control hospital. Medication adherence was assessed by the simplified medication adherence questionnaire (SMAQ), and serum concentrations of VPA were collected. The correlation between pharmacist education and medication adherence and percentage of VPA samples reaching therapeutic reference range were analyzed. A total of 2165 patients and 4343 serum VPA concentrations were included in the analysis. For the first therapeutic drug monitoring (TDM) measurement, there was no statistical difference between the two hospitals: 41.3% of VPA samples reached therapeutic range at the intervention hospital compared with 45.4% at the control hospital (χ² = 3.686, P > 0.05). After pharmacist intervention at the intervention hospital, however, there were significant differences in the percentage of therapeutic VPA samples reaching therapeutic range between the first and the second, third, fourth, and fifth TDM measurements (χ² = 9.756, P < 0.01; χ² = 22.840, P < 0.01; χ² = 15.816, P < 0.01; χ² = 27.613, P < 0.01). Based on the SMAQ adherence assessment, adherence increased from a minimum of 56.0% to a maximum of 73.9% with stabilization during the last six months of follow-up at the intervention hospital. Both the medication adherence rate and the percentage of VPA samples reaching therapeutic range increased as the result of active education by a pharmacist, suggesting that continuous pharmacist intervention had a positive impact in outpatient pediatric PWE.

Pharmacokinetic considerations for anti-epileptic drugs in children

INTRODUCTION

Epilepsy is a chronic and debilitating neurological disease, with a peak of incidence in the first years of life. Today, the vast armamentarium of antiepileptic drugs (AEDs) available make even more challenging to select the most appropriate AED and establish the most effective dosing regimen. In fact, AEDs pharmacokinetics is under the influence of important age-related factors which cannot be ignored. Areas covered: Physiological changes occurring during development age (different body composition, immature metabolic patterns, reduced renal activity) can significantly modify the pharmacokinetic profile of AEDs (adsorption, volume of distribution, half-life, clearance), leading to an altered treatment response. We reviewed the main pharmacokinetic characteristics of AEDs used in children, focusing on age-related factors which are of relevance when treating this patient population. Expert opinion: To deal with this pharmacokinetic variability, physicians have at their disposal two tools: 1) therapeutic drug concentration monitoring, which may help to set the optimal therapeutic regimen for each patient and to monitor eventual fluctuation, and 2) the use of extended-release drug formulations, when available. In the next future, the development of 'ad-hoc' electronic dashboard systems will represent relevant decision-support tools making the AED therapy even more individualized and precise, especially in children.