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Bondrescu M, Dehelean L, Farcas S, Dragan PA, Podaru CA, Popa L, Andreescu N. Into a Deeper Understanding of CYP2D6's Role in Risperidone Monotherapy and the Potential Side Effects in Schizophrenia Spectrum Disorders. Int J Mol Sci 2024; 25:6350. [PMID: 38928058 PMCID: PMC11204263 DOI: 10.3390/ijms25126350] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2024] [Revised: 06/03/2024] [Accepted: 06/06/2024] [Indexed: 06/28/2024] Open
Abstract
Schizophrenia spectrum disorders (SSD) are a group of diseases characterized by one or more abnormal features in perception, thought processing and behavior. Patients suffering from SSD are at risk of developing life-threatening complications. Pharmacogenetic studies have shown promising results on personalized treatment of psychosis. In the current study, 103 patients diagnosed with SSD treated with risperidone as antipsychotic monotherapy were enrolled. Socio-demographics and clinical data were recorded, and laboratory tests and genotyping standard procedure for cytochrome P450 (CYP) 2D6*4 were performed. Patients were evaluated by the Positive and Negative Syndrome Scale (PANSS) on admission and at discharge. Based on the reduction in the PANSS total score, subjects were divided into non-responders, partial responders and full responders. Only 11 subjects had a full response to risperidone (10.67%), 53 subjects (51.45%) had a partial response, and 39 participants (37.86%) were non-responders. Patients at first episode psychosis showed significantly higher levels of blood glucose and prolactin levels, while chronic patients showed significantly higher LDL levels. Adverse drug reactions (ADR) such as tremor and stiffness significantly correlated with genetic phenotypes (p = 0.0145). While CYP2D6 showed no impact on treatment response, ADR were significantly more frequent among poor and intermediate metabolizers.
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Affiliation(s)
- Mariana Bondrescu
- Department of Neurosciences-Psychiatry, “Victor Babes” University of Medicine and Pharmacy, Eftimie Murgu Square 2, 300041 Timisoara, Romania;
- Timis County Emergency Clinical Hospital “Pius Brinzeu”, Liviu Rebreanu 156, 300723 Timisoara, Romania;
- Doctoral School, “Victor Babes” University of Medicine and Pharmacy, Eftimie Murgu Square 2, 300041 Timisoara, Romania;
| | - Liana Dehelean
- Department of Neurosciences-Psychiatry, “Victor Babes” University of Medicine and Pharmacy, Eftimie Murgu Square 2, 300041 Timisoara, Romania;
- Timis County Emergency Clinical Hospital “Pius Brinzeu”, Liviu Rebreanu 156, 300723 Timisoara, Romania;
| | - Simona Farcas
- Discipline of Medical Genetics, Department of Microscopic Morphology, Center of Genomic Medicine “Victor Babes” University of Medicine and Pharmacy, Eftimie Murgu Square 2, 300041 Timisoara, Romania; (S.F.); (N.A.)
| | - Patricia Alexandra Dragan
- Timis County Emergency Clinical Hospital “Pius Brinzeu”, Liviu Rebreanu 156, 300723 Timisoara, Romania;
| | | | - Laura Popa
- Doctoral School, “Victor Babes” University of Medicine and Pharmacy, Eftimie Murgu Square 2, 300041 Timisoara, Romania;
| | - Nicoleta Andreescu
- Discipline of Medical Genetics, Department of Microscopic Morphology, Center of Genomic Medicine “Victor Babes” University of Medicine and Pharmacy, Eftimie Murgu Square 2, 300041 Timisoara, Romania; (S.F.); (N.A.)
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Wang X, Huang J, Lu J, Li X, Tang H, Shao P. Risperidone plasma level, and its correlation with CYP2D6 gene polymorphism, clinical response and side effects in chronic schizophrenia patients. BMC Psychiatry 2024; 24:41. [PMID: 38200532 PMCID: PMC10782740 DOI: 10.1186/s12888-023-05488-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/18/2023] [Accepted: 12/29/2023] [Indexed: 01/12/2024] Open
Abstract
BACKGROUND To explore the influence of CYP2D6 genetic polymorphism on risperidone metabolism, thereby affecting risperidone's effects and safeties in patients with chronic schizophrenia. METHODS Sixty-nine subjects with chronic schizophrenia treated with risperidone were recruited. CYP2D6 genotypes was determined using targeted sequencing and translated into phenotype using activity system. Risperidone plasma concentrations were measured using HPLC. Positive and Negative Symptom Scale (PANSS) and Brief Psychiatric Rating Scale (BPRS) were used to evaluate the existence and severity of psychiatric symptoms, Barnes Akathisia Scale (BAS) and Extrapyramidal Symptom Rating Scale (ESRS) for neurological side effects. Metabolic and endocrine status assess were also included. RESULTS The plasma drug concentrations varied hugely among individuals. Intermediate metabolizer (IM) group had higher plasma levels of RIP and dose corrected RIP concentration, RIP/9-OH-RIP ratio and C/D ratio than normal metabolizer (NM) group (p < 0.01). There was no statistic difference between responders and non-responders in dose-adjusted plasma concentrations and ratios of RIP/9-OH-RIP and C/D. The occurrence of EPS was related to active moiety levels in 4th week (p < 0.05). The prolactin (PRL) levels in two follow-ups were both significantly higher than baseline (p < 0.01). PRL change from baseline to week 4 and week 8 were both positively associated with active moiety concentration detected in week 4 (p < 0.05). CONCLUSIONS The risperidone plasma levels have great inter- and intraindividual variations, and are associated with the CYP2D6 phenotypes, as well as the changes in serum prolactin in patients diagnosed with chronic schizophrenia.
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Affiliation(s)
- Xiaoyi Wang
- Department of Psychiatry, National Clinical Research Center for Mental Disorders, and National Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha, 410011, Hunan, China
| | - Jing Huang
- Department of Psychiatry, National Clinical Research Center for Mental Disorders, and National Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha, 410011, Hunan, China
| | - Jianjun Lu
- The Third People's Hospital of Jiangyin City, Wuxi, Jiangsu, China
| | - Xuemei Li
- People's Hospital of Dali Prefecture, Dali, Yunnan, China
| | - Hui Tang
- Department of Psychiatry, National Clinical Research Center for Mental Disorders, and National Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha, 410011, Hunan, China.
| | - Ping Shao
- Department of Psychiatry, National Clinical Research Center for Mental Disorders, and National Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha, 410011, Hunan, China.
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Singh S, Kumar PVSNK, Kumar JP, Tomo S, Yadav D, Sharma P, Rao M, Banerjee M. Genetic and Epigenetic Basis of Drug-Induced Liver Injury. Semin Liver Dis 2023; 43:163-175. [PMID: 37225145 DOI: 10.1055/a-2097-0531] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 05/26/2023]
Abstract
Drug-induced liver injury (DILI) is a rare but severe adverse drug reaction seen in pharmacotherapy and a major cause of postmarketing drug withdrawals. Advances in genome-wide studies indicate that genetic and epigenetic diversity can lead to inter-individual differences in drug response and toxicity. It is necessary to identify how the genetic variations, in the presence of environmental factors, can contribute to development and progression of DILI. Studies on microRNA, histone modification, DNA methylation, and single nucleotide polymorphisms related to DILI were retrieved from databases and were analyzed for the current research and updated to develop this narrative review. We have compiled some of the major genetic, epigenetic, and pharmacogenetic factors leading to DILI. Many validated genetic risk factors of DILI, such as variants of drug-metabolizing enzymes, HLA alleles, and some transporters were identified. In conclusion, these studies provide useful information in risk alleles identification and on implementation of personalized medicine.
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Affiliation(s)
- Snigdha Singh
- Department of Biochemistry, All India Institute of Medical Sciences, Jodhpur, Rajasthan, India
| | - P V S N Kiran Kumar
- Department of Biochemistry, All India Institute of Medical Sciences, Jodhpur, Rajasthan, India
| | - J Pradeep Kumar
- Department of Biochemistry, All India Institute of Medical Sciences, Jodhpur, Rajasthan, India
| | - Sojit Tomo
- Department of Biochemistry, All India Institute of Medical Sciences, Jodhpur, Rajasthan, India
| | - Dharamveer Yadav
- Department of Biochemistry, All India Institute of Medical Sciences, Jodhpur, Rajasthan, India
| | - Praveen Sharma
- Department of Biochemistry, All India Institute of Medical Sciences, Jodhpur, Rajasthan, India
| | - Mahadev Rao
- Department of Pharmacy Practice, Manipal College of Pharmaceutical Sciences, Manipal, Karnataka, India
| | - Mithu Banerjee
- Department of Biochemistry, All India Institute of Medical Sciences, Jodhpur, Rajasthan, India
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Hongkaew Y, Gaedigk A, Wilffert B, Ngamsamut N, Kittitharaphan W, Limsila P, Sukasem C. Relationship between CYP2D6 genotype, activity score and phenotype in a pediatric Thai population treated with risperidone. Sci Rep 2021; 11:4158. [PMID: 33603025 PMCID: PMC7892547 DOI: 10.1038/s41598-021-83570-w] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2020] [Accepted: 02/04/2021] [Indexed: 12/29/2022] Open
Abstract
Recently, the Clinical Pharmacogenetics Implementation Consortium (CPIC) have revised recommendations for the translation of CYP2D6 genotype to phenotype. Changes affect phenotype grouping, as well as the value used to calculate activity score for the CYP2D6*10 allele to better reflect the substantially decreased activity of this allele which is the most frequent allele found in Asian populations. This study aimed to evaluate whether the lower value for CYP2D6*10 as recommended, and the revised phenotype groupings improve the relationship between CYP2D6 genotype and risperidone measures. One hundred and ninety-nine children and adolescents with autism treated with a risperidone-based regimen for at least four weeks were included. CYP2D6 genotype was determined using the Luminex xTAG CYP2D6 Kit assay and translated into phenotype using different translation methods. Plasma concentrations of risperidone and 9-hydroxyrisperidone were measured using LC/MS/MS. Plasma levels of risperidone, risperidone concentration/dose ratio, and risperidone/9-hydroxyrisperidone ratio in patients with an activity score < 1 were significantly higher than those ≥ 1 (P value < 0.001 for all three parameters). Plasma risperidone levels and risperidone concentration/dose ratios were significantly higher in intermediate metabolizers (defined as AS = 0.25–0.75) than normal metabolizer (defined as AS = 1–2) patients (1.44 vs. 0.23 ng/ml, P < 0.001 and 1.63 vs. 0.29 ng/ml/ng, P < 0.001, respectively) as well as risperidone/9-hydroxyrisperidone ratio (0.20 vs. 0.04, P < 0.001). This is the first study in an Asian population utilizing the revised CPIC-recommended method for translating the CYP2D6 genotype to phenotype. In addition to validating that CYP2D6 genetic variation significantly impacts risperidone metabolism, we demonstrated that revised value for the CYP2D6*10 was superior for genotype to phenotype translation. However, at least for risperidone, subjects with an activity score of 1 presented as phenotypic normal, and not intermediate metabolizers, suggesting that phenotype classification is substrate dependent.
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Affiliation(s)
- Yaowaluck Hongkaew
- Division of Pharmacogenomics and Personalized Medicine, Department of Pathology, Faculty of Medicine Ramathibodi Hospital, Mahidol University, Bangkok, 10400, Thailand.,Laboratory for Pharmacogenomics, Somdech Phra Debaratana Medical Center (SDMC), Ramathibodi Hospital, Bangkok, Thailand.,Advanced Research and Development Laboratory, Bumrungrad International Hospital, Bangkok, Thailand
| | - Andrea Gaedigk
- Division of Clinical Pharmacology, Toxicology and Therapeutic Innovation, Children's Mercy Kansas City, Kansas City, MO, USA.,School of Medicine, University of Missouri-Kansas City, Kansas City, MO, USA
| | - Bob Wilffert
- Unit of PharmacoTherapy, -Epidemiology and -Economics, Groningen Research Institute of Pharmacy, University of Groningen, Groningen, The Netherlands.,Department of Clinical Pharmacy and Pharmacology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
| | - Nattawat Ngamsamut
- Yuwaprasart Waithayopathum Child and Adolescent Psychiatric Hospital, Department of Mental Health Services, Ministry of Public Health, Samut Prakan, Thailand
| | - Wiranpat Kittitharaphan
- Yuwaprasart Waithayopathum Child and Adolescent Psychiatric Hospital, Department of Mental Health Services, Ministry of Public Health, Samut Prakan, Thailand
| | - Penkhae Limsila
- Yuwaprasart Waithayopathum Child and Adolescent Psychiatric Hospital, Department of Mental Health Services, Ministry of Public Health, Samut Prakan, Thailand
| | - Chonlaphat Sukasem
- Division of Pharmacogenomics and Personalized Medicine, Department of Pathology, Faculty of Medicine Ramathibodi Hospital, Mahidol University, Bangkok, 10400, Thailand. .,Laboratory for Pharmacogenomics, Somdech Phra Debaratana Medical Center (SDMC), Ramathibodi Hospital, Bangkok, Thailand.
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Guo W, Yu Z, Gao Y, Lan X, Zang Y, Yu P, Wang Z, Sun W, Hao X, Gao F. A Machine Learning Model to Predict Risperidone Active Moiety Concentration Based on Initial Therapeutic Drug Monitoring. Front Psychiatry 2021; 12:711868. [PMID: 34867511 PMCID: PMC8637165 DOI: 10.3389/fpsyt.2021.711868] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/16/2021] [Accepted: 10/11/2021] [Indexed: 12/25/2022] Open
Abstract
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 R 2 (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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Affiliation(s)
- Wei Guo
- Beijing Key Laboratory of Mental Disorders, The National Clinical Research Center for Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, China.,Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, China
| | - Ze Yu
- Beijing Medicinovo Technology Co. Ltd., Beijing, China
| | - Ya Gao
- Lugouqiao Community Health Service Center, Beijing, China
| | - Xiaoqian Lan
- Beijing Key Laboratory of Mental Disorders, The National Clinical Research Center for Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, China.,Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, China
| | - Yannan Zang
- Beijing Key Laboratory of Mental Disorders, The National Clinical Research Center for Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, China.,Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, China
| | - Peng Yu
- Beijing Medicinovo Technology Co. Ltd., Beijing, China
| | - Zeyuan Wang
- School of Computer Science, The University of Sydney, Sydney, NSW, Australia
| | - Wenzhuo Sun
- Xi'an Jiaotong-liverpool University, Suzhou, China
| | - Xin Hao
- Dalian Medicinovo Technology Co. Ltd., Dalian, China
| | - Fei Gao
- Beijing Medicinovo Technology Co. Ltd., Beijing, China
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Cacabelos R, Cacabelos N, Carril JC. The role of pharmacogenomics in adverse drug reactions. Expert Rev Clin Pharmacol 2019; 12:407-442. [DOI: 10.1080/17512433.2019.1597706] [Citation(s) in RCA: 27] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Affiliation(s)
- Ramón Cacabelos
- EuroEspes Biomedical Research Center, Institute of Medical Science and Genomic Medicine, Corunna, Spain
| | - Natalia Cacabelos
- EuroEspes Biomedical Research Center, Institute of Medical Science and Genomic Medicine, Corunna, Spain
| | - Juan C. Carril
- EuroEspes Biomedical Research Center, Institute of Medical Science and Genomic Medicine, Corunna, Spain
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Nery TGM, Silva EM, Tavares R, Passetti F. The Challenge to Search for New Nervous System Disease Biomarker Candidates: the Opportunity to Use the Proteogenomics Approach. J Mol Neurosci 2018; 67:150-164. [PMID: 30554402 DOI: 10.1007/s12031-018-1220-1] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2018] [Accepted: 11/18/2018] [Indexed: 12/14/2022]
Abstract
Alzheimer's disease, Parkinson's disease, prion diseases, schizophrenia, and multiple sclerosis are the most common nervous system diseases, affecting millions of people worldwide. The current scientific literature associates these pathological conditions to abnormal expression levels of certain proteins, which in turn improved the knowledge concerning normal and affected brains. However, there is no available cure or preventive therapy for any of these disorders. Proteogenomics is a recent approach defined as the data integration of both nucleotide high-throughput sequencing and protein mass spectrometry technologies. In the last years, proteogenomics studies in distinct diseases have emerged as a strategy for the identification of uncharacterized proteoforms, which are all the different protein forms derived from a single gene. For many of these diseases, at least one protein used as biomarker presents more than one proteoform, which fosters the analysis of publicly available data focusing proteoforms. Given this context, we describe the most important biomarkers for each neurodegenerative disease and how genomics, transcriptomics, and proteomics separately contributed to unveil them. Finally, we present a selection of proteogenomics studies in which the combination of nucleotide and proteome high-throughput data, from cell lines or brain tissue samples, is used to uncover proteoforms not previously described. We believe that this new approach may improve our knowledge about nervous system diseases and brain function and an opportunity to identify new biomarker candidates.
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Affiliation(s)
- Thais Guimarães Martins Nery
- Laboratory of Functional Genomics and Bioinformatics, Oswaldo Cruz Institute, Fundação Oswaldo Cruz (Fiocruz), Manguinhos, Rio de Janeiro, Brazil
- Laboratory of Gene Expression Regulation, Carlos Chagas Institute, Fundação Oswaldo Cruz (Fiocruz), Curitiba, Brazil
| | - Esdras Matheus Silva
- Laboratory of Functional Genomics and Bioinformatics, Oswaldo Cruz Institute, Fundação Oswaldo Cruz (Fiocruz), Manguinhos, Rio de Janeiro, Brazil
- Laboratory of Gene Expression Regulation, Carlos Chagas Institute, Fundação Oswaldo Cruz (Fiocruz), Curitiba, Brazil
| | - Raphael Tavares
- Departamento de Bioquímica e Imunologia, Instituto de Ciências Biológicas, Universidade Federal de Minas Gerais (UFMG), Belo Horizonte, Minas Gerais, Brazil
| | - Fabio Passetti
- Laboratory of Functional Genomics and Bioinformatics, Oswaldo Cruz Institute, Fundação Oswaldo Cruz (Fiocruz), Manguinhos, Rio de Janeiro, Brazil.
- Laboratory of Gene Expression Regulation, Carlos Chagas Institute, Fundação Oswaldo Cruz (Fiocruz), Curitiba, Brazil.
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