This study was conducted to explore the influence of genetic variations on responsiveness to valproic acid (VPA) monotherapy among Chinese children with epilepsy. One hundred and forty epileptic children taking VPA as monotherapy were enrolled, and at least one-year follow-up was obtained to assess the therapeutic outcome. Twenty-seven single nucleotide polymorphisms (SNPs) within twelve candidate genes correlated with the metabolic enzymes, transporters and targets of VPA were genotyped. The effects of selected polymorphisms on VPA efficacy were identified by binary logistic regression analysis adjusting for potential confounders. SCN2A rs2304016 (A > G) AG genotype was more common among the VPA-resistant patients in comparison with the VPA-responsive patients (OR = 3.18, 95 % CI = 1.10-9.14, P = 0.032), and in subgroup of focal seizure, lower frequency of VPA resistance was found in epileptic children with SCN1A rs2298771 AG genotype than those with AA genotype (OR = 0.11, 95 % CI = 0.01-0.91, P = 0.040). Our study indicated that SCN2A rs2304016 and SCN1A rs2298771 polymorphisms might be associated with the response to VPA monotherapy in Chinese epileptic children. Further and larger researches are required to validate these results.

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.

Background: Busulfan is known for its high inter- and intra-individual pharmacokinetics/pharmacodynamics (PK/PD) variability, especially in children. Therefore, we aimed to identify factors affecting PK variability of busulfan in pediatric allogeneic hematopoietic cell transplantation (HCT) recipients and investigate the effect of glutathione S-transferase (GST) activity on busulfan metabolism using a semi-mechanistic population PK model.Methods: Overall, 636 whole-blood busulfan concentrations from 65 pediatric HCT recipients were analyzed using nonlinear mixed-effects modeling. A semi-mechanistic population PK model was developed to describe busulfan metabolism in response to glutathione (GSH) depletion. The effects of potential covariates were selected based on previous study and physiologically-based theoretical mechanisms. Virtual clinical trials were conducted to compare different dosing strategies, and model-based optimal dosing regimen was recommended.Results: A two-compartment model with first-order absorption was selected to describe busulfan PK. A GSH compartment was added to represent the relative amount of GSH available at any time. The estimated mean clearance of busulfan was 9.57 L h⁻¹ (relative standard error: 10.8%). Busulfan disposition was best described by including normal fat mass (NFM) allometrically and GST enzyme activity on S_GSH exponentially. The S_GSH increased by 40.6% as GST enzyme activity increased from 0.9 nmol/min/mL to 20.7 nmol/min/mL. Patients with weights (WT) of 9–16 kg are at high risk of sinusoidal obstructive syndrome (SOS) when receiving WT-based dosing strategy.Conclusion: NFM, age-dependent maturation function, and GST enzyme activity may contribute to busulfan PK variability. The WT-based dosing strategy showed a higher risk of SOS than the age-based dosing strategy in 9–16 kg patients.

Background: Population pharmacokinetic (popPK) models can optimise linezolid dosage regimens in patients with multidrug-resistant tuberculosis (MDR-TB); however, unknown cross-centre precision and poor adherence remain problematic. This study aimed to assess the predictive ability of published models and use the most suitable model to optimise dosage regimens and manage compliance. Methods: One hundred fifty-eight linezolid plasma concentrations from 27 patients with MDR-TB were used to assess the predictive performance of published models. Prediction-based metrics and simulation-based visual predictive checks were conducted to evaluate predictive ability. Individualised remedial dosing regimens for various delayed scenarios were optimised using the most suitable model and Monte Carlo simulations. The influence of covariates, scheduled dosing intervals, and patient compliance were assessed. Results: Seven popPK models were identified. Body weight and creatinine clearance were the most frequently identified covariates influencing linezolid clearance. The model with the best performance had a median prediction error (PE%) of -1.62 %, median absolute PE of 29.50%, and percentages of PE within 20% (F20, 36.97%) and 30 % (F30, 51.26 %). Monte Carlo simulations indicated that a twice-daily 300 mg linezolid dose may be more efficient than 600 mg once daily. For the 'typical' patient treated with 300 mg twice daily, half the dosage should be taken after a delay of >= 3 h.