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

Factors Influencing Plasma Concentrations of Valproic Acid in Pediatric Patients With Epilepsy and the Clinical Significance of CYP2C9 Genotypes in Personalized Valproic Acid Therapy

BACKGROUND

The aim of this study was to investigate the factors affecting plasma valproic acid (VPA) concentration in pediatric patients with epilepsy and the clinical significance of CYP2C9 gene polymorphisms in personalized dosing using therapeutic drug monitoring and pharmacogenetic testing.

METHODS

The medical records of children with epilepsy who underwent therapeutic drug monitoring at our institution between July 2022 and July 2023 and met the inclusion criteria were reviewed. Statistical analysis was performed to determine whether age, sex, blood ammonia, liver function, kidney function, and other characteristics affected the concentration-to-dose ratio of VPA (CDRV) in these patients. To investigate the effect of CYP2C9 polymorphisms on CDRV, DNA samples were collected from patients and the CYP2C9 genotypes were identified using real-time quantitative PCR.

RESULTS

The mean age of 208 pediatric patients with epilepsy was 5.50 ± 3.50 years. Among these patients, 182 had the CYP2C9 *1/*1 genotype, with a mean CDRV (mcg.kg/mL.mg) of 2.64 ± 1.46, 24 had the CYP2C9 *1/*3 genotype, with a mean CDRV of 3.28 ± 1.74, and 2 had the CYP2C9 *3/*3 genotype, with a mean CDRV of 6.46 ± 3.33. There were statistical differences among these 3 genotypes ( P < 0.05). The CDRV in these patients were significantly influenced by age, aspartate aminotransferase, total bilirubin, direct bilirubin, globulin, albumin/globulin ratio, prealbumin, creatinine, and CYP2C9 polymorphisms. In addition, multivariate linear regression analysis identified total bilirubin, direct bilirubin, and CYP2C9 polymorphisms as independent risk factors for high CDRV.

CONCLUSIONS

Liver problems and mutations in the CYP2C9 gene increase VPA levels. This underscores the importance of considering these factors when prescribing VPA to children with epilepsy, thereby enhancing the safety and efficacy of the therapy.

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.

Sources of pharmacokinetic and pharmacodynamic variability and clinical pharmacology studies of antiseizure medications in the pediatric population

Multiple treatment options exist for children with epilepsy, including surgery, dietary therapies, neurostimulation, and antiseizure medications (ASMs). ASMs are the first line of therapy, and more than 30 ASMs have U.S. Food and Drug Administration (FDA) approval for the treatment of various epilepsy and seizure types in children. Given the extensive FDA approval of ASMs in children, it is crucial to consider how the physiological and developmental changes throughout childhood may impact drug disposition. Various sources of pharmacokinetic (PK) variability from different extrinsic and intrinsic factors such as patients' size, age, drug-drug interactions, and drug formulation could result in suboptimal dosing of ASMs. Barriers exist to conducting clinical pharmacological studies in neonates, infants, and children due to ethical and practical reasons, limiting available data to fully characterize these drugs' disposition and better elucidate sources of PK variability. Modeling and simulation offer ways to circumvent traditional and intensive clinical pharmacology methods to address gaps in epilepsy and seizure management in children. This review discusses various physiological and developmental changes that influence the PK and pharmacodynamic (PD) variability of ASMs in children, and several key ASMs will be discussed in detail. We will also review novel trial designs in younger pediatric populations, highlight the role of extrapolation of efficacy in epilepsy, and the use of physiologically based PK modeling as a tool to investigate sources of PK/PD variability in children. Finally, we will conclude with current challenges and future directions for optimizing the efficacy and safety of these drugs across the pediatric age spectrum.

Effects of Sex Differences and Combined Use of Clozapine on Initial Dosage Optimization of Valproic Acid in Patients with Bipolar Disorder

BACKGROUND

Due to the narrow therapeutic window and large pharmacokinetic variation of valproic acid (VPA), it is difficult to make an optimal dosage regimen. The present study aims to optimize the initial dosage of VPA in patients with bipolar disorder.

METHODS

A total of 126 patients with bipolar disorder treated by VPA were included to construct the VPA population pharmacokinetic model retrospectively. Sex differences and combined use of clozapine were found to significantly affect VPA clearance in patients with bipolar disorder. The initial dosage of VPA was further optimized in male patients without the combined use of clozapine, female patients without the combined use of clozapine, male patients with the combined use of clozapine, and female patients with the combined use of clozapine, respectively.

RESULTS

The CL/F and V/F of VPA in patients with bipolar disorder were 11.3 L/h and 36.4 L, respectively. It was found that sex differences and combined use of clozapine significantly affected VPA clearance in patients with bipolar disorder. At the same weight, the VPA clearance rates were 1.134, 1, 1.276884, and 1.126 in male patients without the combined use of clozapine, female patients without the combined use of clozapine, male patients with the combined use of clozapine, and female patients with the combined use of clozapine, respectively. This study further optimized the initial dosage of VPA in male patients without the combined use of clozapine, female patients without the combined use of clozapine, male patients with the combined use of clozapine, and female patients with the combined use of clozapine, respectively.

CONCLUSION

This study is the first to investigate the initial dosage optimization of VPA in patients with bipolar disorder based on sex differences and the combined use of clozapine. Male patients had higher clearance, and the recommended initial dose decreased with increasing weight, providing a reference for the precision drug use of VPA in clinical patients with bipolar disorder.

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.

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.

Effects of genetic polymorphism of drug-metabolizing enzymes on the plasma concentrations of antiepileptic drugs in Chinese population

As a chronic brain disease, epilepsy affects ~50 million people worldwide. The traditional antiepileptic drugs (AEDs) are widely applied but showing various problems. Although the new AEDs have partially solved the problems of traditional AEDs, the current clinical application of traditional AEDs are not completely replaced by new drugs, particularly due to the large individual differences in drug plasma concentrations and narrow therapeutic windows among patients. Therefore, it is still clinically important to continue to treat patients using traditional AEDs with individualized therapeutic plans. To date, our understanding of the molecular and genetic mechanisms regulating plasma concentrations of AEDs has advanced rapidly, expanding the knowledge on the effects of genetic polymorphisms of genes encoding drug-metabolizing enzymes on the plasma concentrations of AEDs. It is increasingly imperative to summarize and conceptualize the clinical significance of recent studies on individualized therapeutic regimens. In this review, we extensively summarize the critical effects of genetic polymorphisms of genes encoding drug-metabolizing enzymes on the plasma concentrations of several commonly used AEDs as well as the clinical significance of testing genotypes related to drug metabolism on individualized drug dosage. Our review provides solid experimental evidence and clinical guidance for the therapeutic applications of these AEDs.

Switching Between LC-ESI-MS/MS and EMIT Methods for Routine TDM of Valproic Acid in Pediatric Patients With Epilepsy: What Clinicians and Researchers Need to Know

Background: Valproic acid (VPA) is a widely used antiseizure medication and its dosing needs to be tailored individually through therapeutic drug monitoring (TDM) to avoid or prevent toxicity. Currently, immune-enzymatic assays such as Enzyme Multiplied Immunoassay Technique (EMIT), and Liquid Chromatography (LC)-based techniques, particularly coupled to Electrospray Ionization Tandem Mass Spectrometry (LC-ESI-MS/MS), resulting a potential lack of concordance between laboratories. Methods: In this study, plasma VPA concentrations were determined for 711 pediatric patients with epilepsy by a routine EMIT assay and by a validated in-house LC-ESI-MS/MS method on the same group of samples, aimed to address the aforementioned concern. Consistency between two assays was evaluated using linear regression and Bland-Altman analysis. Results: The calibration curve was linear in the range of 5.00-300 μg/ml for LC-ESI-MS/MS method and 1.00-150 μg/ml for EMIT assay, respectively. The two methods were proven to be accurate with quality control samples. As a result, a significant correlation between two methods was obtained with a regression equation described as [ EMIT ] = 1.214 × [ LC - ESI - MS / MS ] + 3.054 (r ² = 0.9281). Bland-Altman plot showed a mean bias of 14.5 μg/ml (95% confidence interval (CI) (-0.2, 29.2) and a mean increase of 27.8% (95% CI (3.3, 52.4) measured by EMIT assay more than that measured by LC-ESI-MS/MS method. Conclusion: In conclusion, two methods were closely correlated, but EMIT assay overestimate VPA levels in human plasma compared with LC-ESI-MS/MS method. Due to the observed significant discordance between the tested methods, switching from immunoassays to LC-based techniques for TDM of VPA deserves close attention and therapeutic range of 35.0-75.0 μg/ml may be feasible. However, further studies are needed to evaluate the eligibility of this alternative range in the clinical practice. Clinicians should be informed when switching the VPA quantitation methods during the clinical practice.