Pharmacogenomic Study Reveals New Variants of Drug Metabolizing Enzyme and Transporter Genes Associated with Steady-State Plasma Concentrations of Risperidone and 9-Hydroxyrisperidone in Thai Autism Spectrum Disorder Patients

Medhasi, Sadeep; Pinthong, Darawan; Pasomsub, Ekawat; Vanwong, Natchaya; Ngamsamut, Nattawat; Puangpetch, Apichaya; Chamnanphon, Montri; Hongkaew, Yaowaluck; Pratoomwun, Jirawat; Limsila, Penkhae; Sukasem, Chonlaphat

doi:10.3389/fphar.2016.00475

Cited by 20 publications

(16 citation statements)

References 38 publications

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“…The same transporter seems also to be implicated in the metabolism of drugs such as Risperidone given for limiting the symptoms in ASD patients. Depending on the polymorphism of the gene, the drug can be differently catabolized[47]. One might notice that the transporter SLC7A5 is not purely a transporter of carnitine but a protein able to transport various amino acids.…”

Section: Links Between Autism and Carnitinementioning

confidence: 99%

Autism and carnitine: A possible link

Demarquoy¹,

Demarquoy²

2019

WJBC

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Patients with autism spectrum disorders (ASD) present deficits in social interactions and communication, they also show limited and stereotypical patterns of behaviors and interests. The pathophysiological bases of ASD have not been defined yet. Many factors seem to be involved in the onset of this disorder. These include genetic and environmental factors, but autism is not linked to a single origin, only. Autism onset can be connected with various factors such as metabolic disorders: including carnitine deficiency. Carnitine is a derivative of two amino acid lysine and methionine. Carnitine is a cofactor for a large family of enzymes: the carnitine acyltransferases. Through their action these enzymes (and L-carnitine) are involved in energy production and metabolic homeostasis. Some people with autism (less than 20%) seem to have L-carnitine metabolism disorders and for these patients, a dietary supplementation with L-carnitine is beneficial. This review summarizes the available information on this topic.

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Section: Links Between Autism and Carnitinementioning

confidence: 99%

Autism and carnitine: A possible link

Demarquoy¹,

Demarquoy²

2019

WJBC

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“…CYP2C19, CYP3A5, SLCO1B1, UGT1A1, ABCB11, and ADH7 are all involved in the metabolism of risperidone into 9-hydroxyrisperidone. Inhibitors or inducers of these liver drug enzymes and transporters can affect risperidone and 9-hydroxyrisperidone concentrations (29)(30)(31). Some showing drug-drug interactions in previous research, such as high-dose sertraline, may elevate the serum concentration of risperidone (32,33).…”

Section: Discussionmentioning

confidence: 99%

A Machine Learning Model to Predict Risperidone Active Moiety Concentration Based on Initial Therapeutic Drug Monitoring

Guo

Yu²,

Gao³

et al. 2021

Front. Psychiatry

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Risperidone is an efficacious second-generation antipsychotic (SGA) to treat a wide spectrum of psychiatric diseases, whereas its active moiety (risperidone and 9-hydroxyrisperidone) concentration without a therapeutic reference range may increase the risk of adverse drug reactions. We aimed to establish a prediction model of risperidone active moiety concentration in the next therapeutic drug monitoring (TDM) based on the initial TDM information using machine learning methods. A total of 983 patients treated with risperidone between May 2017 and May 2018 in Beijing Anding Hospital were collected as the data set. Sixteen predictors (the initial TDM value, dosage, age, WBC, PLT, BUN, weight, BMI, prolactin, ALT, MECT, Cr, AST, Ccr, TDM interval, and RBC) were screened from 26 variables through univariate analysis (p < 0.05) and XGBoost (importance score >0). Ten algorithms (XGBoost, LightGBM, CatBoost, AdaBoost, Random Forest, support vector machine, lasso regression, ridge regression, linear regression, and k-nearest neighbor) compared the model performance, and ultimately, XGBoost was chosen to establish the prediction model. A cohort of 210 patients treated with risperidone between March 1, 2019, and May 31, 2019, in Beijing Anding Hospital was used to validate the model. Finally, the prediction model was evaluated, obtaining R2 (0.512 in test cohort; 0.374 in validation cohort), MAE (10.97 in test cohort; 12.07 in validation cohort), MSE (198.55 in test cohort; 324.15 in validation cohort), RMSE (14.09 in test cohort; 18.00 in validation cohort), and accuracy of the predicted TDM within ±30% of the actual TDM (54.82% in test cohort; 60.95% in validation cohort). The prediction model has promising performance to facilitate rational risperidone regimen on an individualized level and provide reference for other antipsychotic drugs' risk prediction.

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“…miRNAs are a transcriptional inhibitor, which recognizes the specific seed regions in the 3 UTR sequences [27], thereby suppressing gene expression [28]. rs1060253 (SLC7A5) is located in the 3 UTR [29,30]. Therefore, genotypic variations in rs1060253 could change the target sites for hsa-miR-301a-3p and -301b-3p in SLC7A5 3 UTR (Figure 2), which could contribute to the altered metabolism of tacrolimus.…”

Section: Discussionmentioning

confidence: 99%

A Machine Learning-Based Identification of Genes Affecting the Pharmacokinetics of Tacrolimus Using the DMETTM Plus Platform

Gim

Kwon

Lee

et al. 2020

IJMS

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Tacrolimus is an immunosuppressive drug with a narrow therapeutic index and larger interindividual variability. We identified genetic variants to predict tacrolimus exposure in healthy Korean males using machine learning algorithms such as decision tree, random forest, and least absolute shrinkage and selection operator (LASSO) regression. rs776746 (CYP3A5) and rs1137115 (CYP2A6) are single nucleotide polymorphisms (SNPs) that can affect exposure to tacrolimus. A decision tree, when coupled with random forest analysis, is an efficient tool for predicting the exposure to tacrolimus based on genotype. These tools are helpful to determine an individualized dose of tacrolimus.

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Pharmacogenomic Study Reveals New Variants of Drug Metabolizing Enzyme and Transporter Genes Associated with Steady-State Plasma Concentrations of Risperidone and 9-Hydroxyrisperidone in Thai Autism Spectrum Disorder Patients

Cited by 20 publications

References 38 publications

Autism and carnitine: A possible link

Autism and carnitine: A possible link

A Machine Learning Model to Predict Risperidone Active Moiety Concentration Based on Initial Therapeutic Drug Monitoring

A Machine Learning-Based Identification of Genes Affecting the Pharmacokinetics of Tacrolimus Using the DMETTM Plus Platform

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