Novel Compounds in the Treatment of Schizophrenia—A Selective Review

Brain Sciences, Journal Year: 2023, Volume and Issue: 13(8), P. 1193 - 1193

Published: Aug. 11, 2023

Schizophrenia is a chronic neuropsychiatric syndrome that significantly impacts daily function and quality of life. All the available guidelines suggest combined treatment approach with pharmacologic agents psychological interventions. However, one in three patients non-responder, effect on negative cognitive symptoms limited, many drug-related adverse effects complicate clinical management. As result, discovering novel drugs for schizophrenia presents significant challenge psychopharmacology. This selective review literature aims to outline current knowledge aetiopathogenesis present recently approved newly discovered pharmacological substances treating schizophrenia. We discuss ten drugs, which have been by FDA (Olanzapine/Samidorphan, Lumateperone, Pimavanserin). The rest are under trial investigation (Brilaroxazine, Xanomeline/Trospium, Emraclidine, Ulotaront, Sodium Benzoate, Luvadaxistat, Iclepertin). additional basic research required not only improve our understanding neurobiology potential targets schizophrenia, but also establish more effective therapeutical interventions syndrome, including attenuation avoiding dopamine blockade-related effects.

Language: Английский

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QSPR Analysis of Drugs for Treatment of Schizophrenia Using Topological Indices

ACS Omega, Journal Year: 2023, Volume and Issue: 8(44), P. 41417 - 41426

Published: Oct. 24, 2023

Schizophrenia is a chronic psychotic disorder characterized primarily by cognitive deficits. Drugs and therapies are helpful in managing the symptoms, mostly with long-term compliance. There pressing need to design more efficient drugs fewer adverse effects. Solubility, metabolic stability, toxicity, permeability, transporter effects important parameters efficacy of drug design, which turn depend upon different physical chemical characteristics drugs. In recent years, there has been growing interest developing computational tools for discovery development schizophrenia. Some these methods use machine learning algorithms predict side potential Other studies have used computer simulations understand molecular mechanisms underlying disease identify new targets development. Topological indices numeric quantities linked structure properties, reactivity, stability through quantitative structure–property relationship (QSPR). This work aimed at using statistical techniques link QSPR correlating properties connectivity linear regression. The model gives quite better estimation drugs, such as melting point, boiling enthalpy, flash molar refractivity, refractive index, complexity, weight, refractivity. Results validated comparing actual values estimated

Language: Английский

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The Long and Winding Road of Schizophrenia Drug Development

Journal of Pharmacology and Experimental Therapeutics, Journal Year: 2025, Volume and Issue: 392(3), P. 103391 - 103391

Published: Jan. 20, 2025

Language: Английский

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Quetiapine Fumarate-Loaded Mixed Micelles in Nasal In-Situ Gels: Enhancing Efficacy for Schizophrenia Management

Next research., Journal Year: 2025, Volume and Issue: unknown, P. 100234 - 100234

Published: March 1, 2025

Language: Английский

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IUPHAR Review on muscarinic M1 and M4 receptors as drug treatment targets relevant to the molecular pathology of schizophrenia.

Pharmacological Research, Journal Year: 2024, Volume and Issue: 210, P. 107510 - 107510

Published: Nov. 19, 2024

Language: Английский

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The trace amine-associated receptor 1 agonists – non-dopaminergic antipsychotics or covert modulators of D2 receptors?

Journal of Psychopharmacology, Journal Year: 2024, Volume and Issue: 38(6), P. 503 - 506

Published: April 23, 2024

A major effort of the pharmaceutical industry has been to identify and market drug treatments that are effective in ameliorating symptoms psychotic illness but without limitations current acting at dopamine D2 receptors. These include induction a range adverse effects, inadequate treatment response substantial proportion people with schizophrenia, generally poor negative cognitive features disease. Recently introduced have gone some way avoiding first these, reduced propensity for weight gain, cardiovascular risk extrapyramidal motor effects. Despite claims small improvements symptoms, these drugs not demonstrated increases efficacy. Of currently development as antipsychotic agents, several misleadingly described having novel ‘non-dopaminergic’ mechanisms may offer addressing effects It will be argued, using trace amine-associated receptor 1 agonist an example, new still act primarily through modulation dopaminergic neurotransmission and, primary pathology therefore unlikely much-needed efficacy required address unmet need associated resistance treatments.

Language: Английский

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Current emerging therapeutic targets and clinical investigational agents for schizophrenia: Challenges and opportunities

Medicinal Research Reviews, Journal Year: 2024, Volume and Issue: unknown

Published: Sept. 19, 2024

Abstract Since the first discovery of antipsychotics in 1950s, targeting dopaminergic drugs has manifested to well manage positive symptoms schizophrenia with limited efficacy for negative and cognitive symptoms. In past decades, extensive efforts have been undertaken towards development innovative agents that can effectively stabilize dopamine serotonin systems or target nondopaminergic pathways, leading various promising drug candidates entering into clinical trials. Notably, sigma‐2, 5‐HT 2A , α 1A receptor antagonist roluperidone, as a fixed‐dose combination M 1/4 agonist KarXT, submitted NDA applications. The dual ulotaront, which targets TAAR1 receptors, GlyT1 inhibitor iclepertin advanced phase 3 Nevertheless, satisfactory therapeutic strategies remain elusive. This review highlights current endeavors developing novel chemical small‐molecule entities combinations treatment since 2017, thus facilitating efficient next generation antipsychotics.

Language: Английский

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Brexpiprazole in patients with schizophrenia with or without substance use disorder: an observational study

Frontiers in Psychiatry, Journal Year: 2023, Volume and Issue: 14

Published: Dec. 4, 2023

Partial dopamine D2 receptor agonists are used for psychotic symptoms in adults with schizophrenia spectrum disorders. Recently, interest surged partial substance use disorders (SUDs). Since it is believed that SUDs decrease the efficacy of pharmacotherapy underlying psychiatric disorders, we tested agonist brexpiprazole patients who were either comorbid a SUD (SUD group) or not (non-SUD) to assess treatment response and effect on craving SUD.We included DSM-5/DSM-5-TR (using SCID-5-CV) aged 18-66 years non-SUD treat 4 mg/day 6 months during February-October 2022. Patients assessed Clinical Global Impressions-Severity (CGI-S) scale, 24-item Brief Psychiatric Rating Scale (BPRS), Positive And Negative Syndrome (PANSS) at baseline, weekly first 2 monthly next four. Furthermore, visual analog scale (VAScrav) same timepoints.The total sample was 86 (85 analysable) 18- 64-year-old (mean 39.32 ± 14.09) [51 men (59.3%) 35 women (40.7%)], whom 48 (55.8%) (37 11 women) 38 (44.2%) (14 24 women). No serious persistent adverse events developed over study period, but one patient dropped out subjective akathisia. Results indicated main effects time improvements course CGI-S, BPRS, PANSS both groups entire sample, VAScrav SUD. Brexpiprazole associated similar significant month endpoint compared baseline.Treatment improved schizophrenia, independently from whether they belonged group; hence, comorbidity did confer resistance brexpiprazole. group, observed reduced craving.

Language: Английский

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Novel Compounds for the Treatment of Schizophrenia – A Narrative Review

Published: July 4, 2023

Schizophrenia is a chronic neuropsychiatric syndrome with significant impact on daily function and quality of life. All available guidelines suggest combined treatment approach pharmacologic agents psychological interventions. However, one in three patients non-responder, the effect negative cognitive symptoms limited, many drug-related adverse effects complicate clinical management. As result, discovering novel drugs for schizophrenia presents challenge psychopharmacology. This narrative review literature aims to present recently approved newly discovered pharmacological substances as well their suggested mechanism action. We discuss seven drugs, which have been by FDA (Olanzapine/Samidorphan, Lumateperone, Pimavanserin). The rest are under trial investigation (Ulotaront, CVL-231, Xanomeline/Trospium, Brilaroxazine). Additional basic research is, however, required not only improve our understanding neurobiology potential targets but also establish modern more effective therapeutical interventions friendlier side-effect profiles.

Language: Английский

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Enhancing drug discovery in schizophrenia: a deep learning approach for accurate drug-target interaction prediction – DrugSchizoNet

Computer Methods in Biomechanics & Biomedical Engineering, Journal Year: 2024, Volume and Issue: unknown, P. 1 - 18

Published: Feb. 20, 2024

Drug discovery relies on the precise prognosis of drug–target interactions (DTI). Due to their ability learn from raw data, deep learning (DL) methods have displayed outstanding performance over traditional approaches. However, challenges such as imbalanced noise, poor generalization, high cost, and time-consuming processes hinder progress in this field. To overcome above challenges, we propose a DL-based model termed DrugSchizoNet for drug interaction (DI) prediction Schizophrenia. Our leverages drug-related data DrugBank repoDB databases, employing three key preprocessing techniques. First, cleaning eliminates duplicate or incomplete entries ensure integrity. Next, normalization is performed enhance security reduce costs associated with acquisition. Finally, feature extraction applied improve quality input data. The layers are input, hidden output layers. In layer, employ dropout regularization mitigate overfitting generalization. fully connected (FC) layer extracts relevant features, while LSTM captures sequential nature DIs. our provides confidence scores potential optimize accuracy, utilize hyperparameter tuning through OB-MOA optimization. Experimental results demonstrate that achieves superior accuracy 98.70%. existing models, including CNN-RNN, DANN, CKA-MKL, DGAN, CNN, across various evaluation metrics recall, specificity, precision, F1 score, AUPR, AUROC compared proposed model. By effectively addressing cost processes, offers promising approach accurate DTI Its demonstrates DL advancing development processes.

Language: Английский

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