A Prospective Study to Evaluate the Effect of CYP2D6 Polymorphism on Plasma level of Risperidone and its Metabolite in North Indian Patients with Schizophrenia

Into a Deeper Understanding of CYP2D6's Role in Risperidone Monotherapy and the Potential Side Effects in Schizophrenia Spectrum Disorders

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.

Risperidone plasma level, and its correlation with CYP2D6 gene polymorphism, clinical response and side effects in chronic schizophrenia patients

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.

Genetic and Epigenetic Basis of Drug-Induced Liver Injury

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.

Relationship between CYP2D6 genotype, activity score and phenotype in a pediatric Thai population treated with risperidone

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.

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

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.

The Challenge to Search for New Nervous System Disease Biomarker Candidates: the Opportunity to Use the Proteogenomics Approach

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.