Population pharmacokinetic analysis for dose regimen optimization of vancomycin in Southern Chinese children.

Changes in physiological factors may result in large pharmacokinetic variability of vancomycin in pediatric patients, thereby leading to either supratherapeutic or subtherapeutic exposure and potentially affecting clinical outcomes. This study set out to characterize the disposition of vancomycin, quantify the exposure target and establish an optimal dosage regimen among the Southern Chinese pediatric population. Routine therapeutic drug monitoring data of 453 patients were available. We performed a retrospective population pharmacokinetic analysis of hospitalized children prescribed intravenous vancomycin using NONMEM® software. A one-compartment PPK model of vancomycin with body weight and renal functions as covariates based on a cutoff of 2 years old children was proposed in this study. Both internal and external validation showing acceptable and robust predictive performance of the model to estimate PK parameters. The value of area under the curve over 24 h to minimum inhibitory concentration ratio (AUC0-24/MIC) ≥ 260 was a significant predictor for therapeutic efficacy. Monte Carlo simulations served as a model-informed precision dosing approach and suggested that different optimal dose regimens in various scenarios should be considered rather than flat dosing. The evaluation of vancomycin exposure-efficacy relationship indicated that lower target level of AUC0-24/MIC may be needed to achieve clinical effectiveness in children, which was used to derive the recommended dosing regimen. Further prospective studies will be needed to corroborate and elucidate these results.

Association of ABCB1 Polymorphisms with Efficacy and Adverse Drug Reactions of Valproic Acid in Children with Epilepsy.

Genetic polymorphisms in ATP-binding cassette subfamily B member 1 (ABCB1, also known as MDR1) have been reported to be possibly associated with the regulation of response to antiseizure medications. The aim of this study was to investigate the association of ABCB1 polymorphisms with the efficacy of and adverse drug reactions to valproic acid among Chinese children with epilepsy. A total of 170 children from southern China with epilepsy treated with valproic acid for more than one year were recruited, including 61 patients with persistent seizures and 109 patients who were seizure-free. Two single nucleotide polymorphisms of ABCB1, rs1128503 and rs3789243, were genotyped using the Sequenom MassArray system. The two single nucleotide polymorphisms of ABCB1 were found to be significantly associated with treatment outcomes of valproic acid in children with epilepsy. Carriers with the TT genotype of ABCB1 rs1128503 were more inclined to exhibit persistent seizures after treatment with valproic acid (p = 0.013). The CC genotype of rs3789243 was observed to be a potential protective factor for valproic acid-induced gastrointestinal adverse drug reactions (p = 0.018), but possibly increased the risk of valproic acid-induced cutaneous adverse drug reactions (p = 0.011). In contrast, the CT genotype of rs3789243 was associated with a lower risk of valproic acid-induced cutaneous adverse drug reactions (p = 0.011). Haplotype analysis showed that CC haplotype carriers tended to respond better to valproic acid treatment (p = 0.009). Additionally, no significant association was found between ABCB1 polymorphisms and serum concentrations of valproic acid. This study revealed that the polymorphisms and haplotypes of the ABCB1 gene might be associated with the treatment outcomes of valproic acid in Chinese children with epilepsy.

SCN1A Polymorphisms and Haplotypes Are Associated With Valproic Acid Treatment Outcomes in Chinese Children With Epilepsy.

BACKGROUND: Sodium channel genes, especially SCN1A, were reported to play an important role in the treatment outcomes of antiseizure medications. The aim of this study was to explore the association of SCN1A polymorphisms with efficacy and adverse drug reactions (ADRs) related to valproic acid (VPA) among Chinese children with epilepsy. METHODS: A total of 126 children with epilepsy treated with VPA for at least 12 months were enrolled in this study. Three single nucleotide polymorphisms (SNPs) of SCN1A including rs2298771, rs10167228, and rs3812718 were genotyped using Sequenom MassArray system. Bioinformatics tools were used to explore the potential targets and pathways of SCN1A in VPA-related ADRs. RESULTS: The three SNPs in this study were found to be closely associated with treatment outcomes for VPA. Carriers of SCN1A rs3812718 TT genotype tended to be seizure-free with VPA treatment (P = 0.007). AA genotype of rs10167228 and TT genotype of rs2298771 might be protective factors for weight gain induced by VPA, whereas TA genotype of rs10167228 and CT genotype of rs2298771 increased the risk. TAT haplotype carriers were found to respond better to VPA treatment (P = 0.017), whereas CTC haplotype might be a risk factor for VPA-induced weight gain (P = 0.035). Bioinformatics analysis suggested that SCN1A might play a role in VPA-induced weight gain by regulating gated channel activity and GABAergic synapse pathway. CONCLUSION: This study revealed that SCN1A rs2298771, rs10167228, and rs3812718 polymorphisms and haplotypes might affect the treatment outcomes of VPA in Chinese children with epilepsy.

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.

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.

Computational Approaches in Preclinical Studies on Drug Discovery and Development.

Because undesirable pharmacokinetics and toxicity are significant reasons for the failure of drug development in the costly late stage, it has been widely recognized that drug ADMET properties should be considered as early as possible to reduce failure rates in the clinical phase of drug discovery. Concurrently, drug recalls have become increasingly common in recent years, prompting pharmaceutical companies to increase attention toward the safety evaluation of preclinical drugs. In vitro and in vivo drug evaluation techniques are currently more mature in preclinical applications, but these technologies are costly. In recent years, with the rapid development of computer science, in silico technology has been widely used to evaluate the relevant properties of drugs in the preclinical stage and has produced many software programs and in silico models, further promoting the study of ADMET in vitro. In this review, we first introduce the two ADMET prediction categories (molecular modeling and data modeling). Then, we perform a systematic classification and description of the databases and software commonly used for ADMET prediction. We focus on some widely studied ADMT properties as well as PBPK simulation, and we list some applications that are related to the prediction categories and web tools. Finally, we discuss challenges and limitations in the preclinical area and propose some suggestions and prospects for the future.