Effect of CYP2C19 polymorphisms on serum valproic level acid in Chinese Han patients with schizophrenia

Wang, S.; Li, Jinlong; Song, Meijuan; Pan, Yan; Ju, Xu; Liu, J.; Wang, C.; Shi, Jianfei

doi:10.1038/s41598-021-02628-x

Cited by 11 publications

(12 citation statements)

References 32 publications

(27 reference statements)

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“…Nevertheless, cytochrome P450 2C19 (CYP2C19) also participates in VPA metabolism ( Hiemke et al, 2018 ; Song et al, 2022 ). Multiple studies reported that CYP2C19 polymorphisms/genotypes significantly influenced the pharmacokinetic variability of VPA in Chinese Han subjects ( Jiang et al, 2009 ; Guo et al, 2020 ; Wang et al, 2021 ). Given the limitations of the genetic test items in our hospital (no CYP2C9 genotype testing), the reported references about the impact of CYP2C19 polymorphisms on VPA, and the goal of validation of the simplified XGBoost model using our external dataset, we selected two previously published popPK models of VPA in Chinese epileptic patients for simulations [i.e., Model-A including the covariate CYP2C19 genotypes ( Guo et al, 2020 ) and Model-B including the covariates BW and daily dose of VPA (Daily Dose) ( Lin et al, 2015 )], both of which involved one-compartment models and Chinese epileptic patients aged 14 years and above.…”

Section: Methodsmentioning

confidence: 99%

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

et al. 2022

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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.

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Section: Methodsmentioning

confidence: 99%

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

et al. 2022

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“…Finally, a 2020 study [29] found an upregulation of miR-1343-3p (upregulated in MNR) in the presence of the mutant allele rs4244285 of the CYP2C19 gene, which encodes a member of the cytochrome P450 enzyme superfamily. This rs4244285 allele encodes the CYP2C19*2 variant, which is known to be related to poor metabolism of compounds, such as some antidepressants, including fluvoxamine or sertraline hydrochloride [58,59]; anticonvulsants, such as valproic acid [60]; and benzodiazepines, such as phenazepam [61], among others. Moreover, CYP2C19 product is an enzyme responsible for the metabolization of clozapine [62,63].…”

Section: Mirnas Potentially Related To Drug Resistancementioning

confidence: 99%

MiRNA Differences Related to Treatment-Resistant Schizophrenia

Pérez-Rodríguez

Penedo

Rivera-Baltanás

et al. 2023

IJMS

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Schizophrenia (SZ) is a serious mental disorder that is typically treated with antipsychotic medication. Treatment-resistant schizophrenia (TRS) is the condition where symptoms remain after pharmacological intervention, resulting in long-lasting functional and social impairments. As the identification and treatment of a TRS patient requires previous failed treatments, early mechanisms of detection are needed in order to quicken the access to effective therapy, as well as improve treatment adherence. In this study, we aim to find a microRNA (miRNA) signature for TRS, as well as to shed some light on the molecular pathways potentially involved in this severe condition. To do this, we compared the blood miRNAs of schizophrenia patients that respond to medication and TRS patients, thus obtaining a 16-miRNA TRS profile. Then, we assessed the ability of this signature to separate responders and TRS patients using hierarchical clustering, observing that most of them are grouped correctly (~70% accuracy). We also conducted a network, pathway analysis, and bibliography search to spot molecular pathways potentially altered in TRS. We found that the response to stress seems to be a key factor in TRS and that proteins p53, SIRT1, MDM2, and TRIM28 could be the potential mediators of such responses. Finally, we suggest a molecular pathway potentially regulated by the miRNAs of the TRS profile.

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“…Because VPA is also a CYP2C19 substrate, the question arises to what extent these results can be applied to the CBD‐VPA interaction. In two independent pharmacogenetic studies in China, significant increases in concentration‐dose ratios were observed in carriers of inactive CYP2C19 alleles 18,19 . In contrast, the combination of CBD with VPA had previously been described as clinically irrelevant 20 …”

Section: Figurementioning

confidence: 99%

“…In two independent pharmacogenetic studies in China, significant increases in concentrationdose ratios were observed in carriers of inactive CYP2C19 alleles. 18,19 In contrast, the combination of CBD with VPA had previously been described as clinically irrelevant. 20 Thus, the extent to which a drug-drug, drug-gene, or drug-drug-gene interaction 21 contribute to the serious observed increases in ALT under VPA and CBD has not yet been elucidated.…”

mentioning

confidence: 99%

Clinical Pharmacology of Cannabinoid Therapeutics: Drug Interactions and Side Effects

Cascorbi

2023

Clin Pharma and Therapeutics

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Effect of CYP2C19 polymorphisms on serum valproic level acid in Chinese Han patients with schizophrenia

Cited by 11 publications

References 32 publications

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

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

MiRNA Differences Related to Treatment-Resistant Schizophrenia

Clinical Pharmacology of Cannabinoid Therapeutics: Drug Interactions and Side Effects

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