Schizophrenia is a complex neuropsychiatric disorder with positive, negative, and cognitive symptoms. In rats, sub-chronic administration of ketamine is used for the induction of schizophrenia model. Increased locomotor activity is one of the most important features of psychotic-like symptoms in rodents. On the other hand, risperidone is a potent antipsychotic medication that is approved for the treatment of schizophrenia and bipolar disorder. In the present research, we aimed to investigate the effect of sub-chronic treatment of ketamine on cognitive and behavioral functions, and brain-derived neurotrophic factor (BDNF) expression level in the prefrontal cortex. Also, we assessed the efficacy of risperidone on cognitive and behavioral impairments induced by ketamine. Possible sex differences were also measured. Ketamine was intraperitoneally injected at the dose of 30 mg/kg for five consecutive days. Risperidone was also intraperitoneally injected at the dose of 2 mg/kg. Novel object recognition memory, pain threshold, locomotor activity, rearing behavior, and BDNF level were evaluated. The results showed that ketamine injection for five consecutive days impaired the acquisition of long-term recognition memory and decreased BDNF level in the prefrontal cortex in both sexes. Also, it decreased pain threshold in females, increased rearing behavior in males, and induced hyperlocomotion with greater effect in females. On the other hand, risperidone restored or attenuated the effect of ketamine on all the behavioral effects and BDNF level. In conclusion, we suggested that there were sex differences in the effects of ketamine on pain perception, locomotion, and rearing behavior in a rat model of schizophrenia.

BACKGROUND: Many articles suggest that clozapine was strongly associated with a higher incidence of new-onset diabetes mellitus, and the issue has remained unsettled. Many articles have compared clozapine with FGAs, but few have compared clozapine with SGAs. We aimed to compare the risk of new-onset diabetes mellitus in adults with schizophrenia treated with clozapine and other SGAs.
METHODS: We conducted a comprehensive search of databases from their inception up until August 26, 2023. The specific databases include PubMed, Embase and others. We included non-randomized controlled trials involving the use of SGAs such as clozapine, olanzapine, risperidone, quetiapine, amisulpride, and zotepine, with a focus on new-onset diabetes mellitus as an outcome. We utilized odds ratio with 95 % credible intervals (95 % CI) as our effect size measures. The study protocol is registered with PROSPERO, number CRD42024511280.
RESULTS: We included 7 studies with sufficient data to include in the meta-analysis. A total of eight studies with 641,48 participants met the eligibility criteria. The OR of the incidence rates of new-onset diabetes between clozapine and olanzapine was 0.95 (95 % CI:[0.82-1.09]), between clozapine and risperidone was 1.25 (95 % CI: [1.09-1.44]), between clozapine and quetiapine was 1.44 (95 % CI: [0.92-2.25]).
CONCLUSIONS: In patients with schizophrenia, clozapine has been found to have a higher rate of new-onset diabetes mellitus compared to risperidone. However, there was no significant difference in incidence rate between clozapine versus olanzapine and quetiapine. These findings can assist clinicians in balancing the risks and benefits of those drugs.

Risperidone is useful for the treatment of schizophrenia symptoms; however, it also has side effects, and an overdose can be harmful. The metabolic effects of risperidone at high therapeutic doses and its metabolites have not been elucidated. Endogenous cellular metabolites may be comprehensively analyzed using untargeted metabolomics-based liquid chromatography-mass spectrometry (LC-MS), which can reveal changes in cell regulation and metabolic pathways. By identifying the metabolites and pathway changes using a nontargeted metabolomics-based LC-MS approach, we aimed to shed light on the potential toxicological effects of high-dose risperidone on brain microvascular endothelial cells (MVECs) associated with the human blood brain barrier. A total of 42 metabolites were selected as significant putative metabolites of the toxicological response of high-dose risperidone in MVECs. Six highly correlated pathways were identified, including those involving diacylglycerol, fatty acid, ceramide, glycerophospholipid, amino acid, and tricarboxylic acid metabolism. We demonstrated that methods focused on metabolomics are useful for identifying metabolites that may be used to clarify the mechanism of drug-induced toxicity.

OBJECTIVE: This study aimed to create and validate robust machine-learning-based prediction models for antipsychotic drug (risperidone) continuation in children and teenagers suffering from mania over one year and to discover potential variables for clinical treatment.
METHODS: The study population was collected from the national claims database in China. A total of 4,532 patients aged 4-18 who began risperidone therapy for mania between September 2013 and October 2019 were identified. The data were randomly divided into two datasets: training (80%) and testing (20%). Five regularly used machine learning methods were employed, in addition to the SuperLearner (SL) algorithm, to develop prediction models for the continuation of atypical antipsychotic therapy. The area under the receiver operating characteristic curve (AUC) with a 95% confidence interval (CI) was utilized.
RESULTS: In terms of discrimination and robustness in predicting risperidone treatment continuation, the generalized linear model (GLM) performed the best (AUC: 0.823, 95% CI: 0.792-0.854, intercept near 0, slope close to 1.0). The SL model (AUC: 0.823, 95% CI: 0.791-0.853, intercept near 0, slope close to 1.0) also exhibited significant performance. Furthermore, the present findings emphasize the significance of several unique clinical and socioeconomic variables, such as the frequency of emergency room visits for nonmental health disorders.
CONCLUSIONS: The GLM and SL models provided accurate predictions regarding risperidone treatment continuation in children and adolescents with episodes of mania and hypomania. Consequently, applying prediction models in atypical antipsychotic medicine may aid in evidence-based decision-making.

Patient response to antipsychotic drugs varies and may be related to clinical and genetic heterogeneity. This study aimed to determine the performance of clinical, genetic, and hybrid models to predict the response of first episode of psychosis (FEP). patients to the antipsychotic risperidone. We evaluated 141 antipsychotic-naïve FEP patients before and after 10 weeks of risperidone treatment. Patients who had a response rate equal to or higher than 50% on the Positive and Negative Syndrome Scale were considered responders (n = 72; 51%). Analyses were performed using a support vector machine (SVM), k-nearest neighbors (kNN), and random forests (RF). Clinical and genetic (with single-nucleotide variants [SNVs]) models were created separately. Hybrid models (clinical+genetic factors) with and without feature selection were created. Clinical models presented greater balanced accuracy 63.3% (confidence interval [CI] 0.46-0.69) with the SVM algorithm than the genetic models (balanced accuracy: 58.5% [CI 0.41-0.76] - kNN algorithm). The hybrid model, which included duration of untreated psychosis, Clinical Global Impression-Severity scale scores, age, cannabis use, and 406 SNVs, showed the best performance (balanced accuracy: 72.9% [CI 0.62-0.84] - RF algorithm). A hybrid model, including clinical and genetic predictors, can provide enhanced predictions of response to antipsychotic treatment.

This study investigated the usage patterns and impact of therapeutic drug monitoring (TDM) for risperidone and paliperidone in patients diagnosed with schizophrenia, utilizing retrospective real-world data sourced from a single center\'s Clinical Data Warehouse. Our study cohort comprised patients diagnosed with schizophrenia undergoing treatment with either risperidone or paliperidone. Data on demographic characteristics, comorbidities, medication utilization, and clinical outcomes were collected. Patients were categorized into two groups: those undergoing TDM and those not undergoing TDM. Additionally, within the TDM group, patients were further stratified based on their risperidone and paliperidone concentrations relative to the reference range. The findings revealed that patients in the TDM group received higher risperidone and paliperidone doses (320 mg/day and 252 mg/day, p = 0.0045) compared to their non-TDM counterparts. Nevertheless, no significant disparities were observed in hospitalization rates, duration of hospital stays, or compliance between the two groups (p = 0.9082, 0.5861, 0.7516, respectively). Subgroup analysis within the TDM cohort exhibited no notable distinctions in clinical outcomes between patients with concentrations within or surpassing the reference range. Despite the possibility of a selection bias in assigning patients to the groups, this study provides a comprehensive analysis of TDM utilization and its ramifications on schizophrenia treatment outcomes.

The interest in the development and therapeutic application of long-acting injectable products for chronic or long-term treatments has experienced exponential growth in recent decades. TV-46000 (Uzedy, Teva) is a long-acting subcutaneous (sc) injectable formulation of risperidone, approved for the treatment of schizophrenia in adults. Following sc injection, the copolymers together with risperidone precipitate to form a sc depot under the skin to deliver therapeutic levels of risperidone over a prolonged period of either 1 month or 2 months, depending upon the dose. This work presents the strategy and the results of the physiologically-based pharmacokinetic (PBPK) modeling and establishing of in vitro-in vivo correlation (IVIVC) for the prediction of TV-46000 pharmacokinetic profile in humans, using in vitro release, intravenous (iv), and sc single-dose pharmacokinetic data in beagle dogs. The resulting simulated TV-46000 PK profile in humans showed that the shape of the predicted risperidone and its active metabolite 9-OH-risperidone PK profiles was different from the observed one, thus suggesting that the TV-46000 release profile was species-dependent and cannot be directly extrapolated from dog to human. In conclusion, while level A IVIVC cannot be claimed, this work combining PBPK and IVIVC modeling represents an interesting alternative approach for complex injectable formulations where classical methods are not applicable.

Several studies indicate the impact of antipsychotics like risperidone and paliperidone on oxidative stress parameters, yet data remain inconsistent. We investigated the link between these medications, hyperprolactinemia (HPRL), and oxidative stress. This study was conducted at the Psychiatry Clinic, University Clinical Center, Kragujevac, between November 2022 and August 2023. Inclusion criteria comprised diagnosed psychotic disorders from the ICD-10-based F20-F29 spectrum and clinical stability on risperidone/paliperidone for ≥12 weeks with no recent dose adjustments. Exclusion criteria included pregnancy, breastfeeding, relevant medical conditions, or co-therapy with prolactin-secreting drugs. Data encompassed drug choice, administration method, therapy duration, and daily dose. Prolactin (PRL) levels, oxidative stress parameters (TBARS, H2O2, O2-, NO2-), and antioxidant system (CAT, GSH, SOD) were assessed. Of 155 subjects, women exhibited significantly higher PRL levels (p < 0.001) and symptomatic HPRL (p < 0.001). Drug choice and regimen significantly influenced TBARS (p < 0.001), NO2- (p < 0.001), O2- (p = 0.002), CAT (p = 0.04), and GSH (p < 0.001) levels. NO2- levels were affected by drug dose (p = 0.038). TBARS (p < 0.001), O2- (p < 0.001), and SOD (p = 0.022) inversely correlated with PRL levels, suggesting PRL\'s protective role against oxidative stress. The female sex association with higher PRL levels implies additional factors influencing PRL\'s antioxidant role. Antipsychotic choice and dosage impact PRL and oxidative stress markers, necessitating further exploration.

OBJECTIVE: The constant emergence and broad toxicological effects of synthetic cannabinoids create a discernible public health threat. The synthetic cannabinoid AMB-FUBINACA (AMB-FUB) is a potent agonist at the CB1 receptor and has been associated with numerous fatalities. Synthetic cannabinoids are commonly abused alongside other drugs and medications, including a \"party pill\" drug, para-fluorophenylpiperazine (pFPP), and the antipsychotic risperidone. This research aimed to investigate the mechanisms underpinning AMB-FUB toxicity and the impact of clinically relevant co-exposures in vivo.
METHODS: Male and female C57Bl/6 mice received a single dose of AMB-FUB (3 or 6 mg kg-1), pFPP (10 or 20 mg kg-1) or vehicle intraperitoneally. Mice were co-exposed to AMB-FUB (3 mg kg-1) and pFPP (10 mg kg-1) or risperidone (0.5 mg kg-1) to investigate these drug combinations. To study receptor-dependency and potential rescue of AMB-FUB toxicity, rimonabant (3 mg kg-1) was administered both pre- and post-AMB-FUB. Adverse effects caused by drug administration, including hypothermia and convulsions, were recorded.
RESULTS: AMB-FUB induced CB1-dependent hypothermia and convulsions in mice. The combination of AMB-FUB and pFPP significantly potentiated hypothermia, as did risperidone pre-treatment. Interestingly, risperidone provided significant protection from AMB-FUB-induced convulsions in female mice. Pre- and post-treatment with rimonabant was able to significantly attenuate both hypothermia and convulsions in mice administered AMB-FUB.
CONCLUSIONS: Factors such as dose, CB1 signalling, and substance co-exposure significantly contribute to the toxicity of AMB-FUBINACA. Mechanistic understanding of synthetic cannabinoid toxicity and fatality can help inform overdose treatment strategies and identify vulnerable populations of synthetic cannabinoid users.