Amitriptyline 25mg / Perphenazine 2mg tablets

This review aimed to investigate the metabolic alterations associated with psychopharmacological treatment of neuropsychiatric disorders, which can significantly impact patients' physical health and overall quality of life. The study utilized the PRISMA methodology and included cross-sectional, retrospective studies, and randomized clinical trials from reputable databases like SCOPUS, CLARIVATE, SCIENCE DIRECT, and PUBMED. Out of the 64 selected studies, various psychotropic drug classes were analyzed, including antidepressants, anticonvulsants, and antipsychotics. Among the antidepressants, such as amitriptyline, Imipramine, and clomipramine, weight gain, constipation, and cardiovascular effects were the most commonly reported metabolic adverse effects. SSRI antidepressants like Fluoxetine, Sertraline, Citalopram, Escitalopram, and Paroxetine exhibited a high prevalence of gastrointestinal and cardiac alterations. Regarding anticonvulsants, valproic acid and Fosphenytoin were associated with adverse reactions such as weight gain and disturbances in appetite and sleep patterns. As for antipsychotics, drugs like Clozapine, Olanzapine, and Risperidone were linked to weight gain, diabetes, and deterioration of the lipid profile. The findings of this review emphasize the importance of continuous monitoring for adverse effects, particularly considering that the metabolic changes caused by psychopharmacological medications may vary depending on the age of the patients. Future research should focus on conducting field studies to further expand knowledge on the metabolic effects of other commonly prescribed psychotropic drugs. Overall, the study highlights the significance of understanding and managing metabolic alterations induced by psychopharmacological treatment to enhance patient care and well-being.

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BackgroundThere are no recommendations based on the efficacy of specific drugs for the treatment of psychotic depression. To address this evidence gap, we did a network meta-analysis to assess and compare the efficacy and safety of pharmacological treatments for psychotic depression.MethodsIn this systematic review and network meta-analysis, we searched ClinicalTrials.gov, CENTRAL, Embase, PsycINFO, PubMed, Scopus, and Web of Science from inception to Nov 23, 2023 for randomised controlled trials published in any language that assessed pharmacological treatments for individuals of any age with a diagnosis of a major depressive episode with psychotic features, in the context of major depressive disorder or bipolar disorder in any setting. We excluded continuation or maintenance trials. We screened the study titles and abstracts identified, and we extracted data from relevant studies after full-text review. If full data were not available, we requested data from study authors twice. We analysed treatments for individual drugs (or drug combinations) and by grouping them on the basis of mechanisms of action. The primary outcomes were response rate (ie, the proportion of participants who responded to treatment) and acceptability (ie, the proportion who discontinued treatment for any reason). We calculated risk ratios and did separate frequentist network meta-analyses by using random-effects models. The risk of bias of individual studies was assessed with the Cochrane risk-of-bias tool and the confidence in the evidence with the Confidence-In-Network-Meta-Analysis (CINeMA). This study was registered with PROSPERO, CRD42023392926.FindingsOf 6313 reports identified, 16 randomised controlled trials were included in the systematic review, and 14 were included in the network meta-analyses. The 16 trials included 1161 people with psychotic depression (mean age 50·5 years [SD 11·4]). 516 (44·4%) participants were female and 422 (36·3%) were male; sex data were not available for the other 223 (19·2%). 489 (42·1%) participants were White, 47 (4·0%) were African American, and 12 (1·0%) were Asian; race or ethnicity data were not available for the other 613 (52·8%). Only the combination of fluoxetine plus olanzapine was associated with a higher proportion of participants with a treatment response compared with placebo (risk ratio 1·91 [95% CI 1·27-2·85]), with no differences in terms of safety outcomes compared with placebo. When treatments were grouped by mechanism of action, the combination of a selective serotonin reuptake inhibitor with a second-generation antipsychotic was associated with a higher proportion of treatment responses than was placebo (1·89 [1·17-3·04]), with no differences in terms of safety outcomes. In head-to-head comparisons of active treatments, a significantly higher proportion of participants had a response to amitriptyline plus perphenazine (3·61 [1·23-10·56]) and amoxapine (3·14 [1·01-9·80]) than to perphenazine, and to fluoxetine plus olanzapine compared with olanzapine alone (1·60 [1·09-2·34]). Venlafaxine, venlafaxine plus quetiapine (2·25 [1·09-4·63]), and imipramine (1·95 [1·01-3·79]) were also associated with a higher proportion of treatment responses overall. In head-to-head comparisons grouped by mechanism of action, antipsychotic plus antidepressant combinations consistently outperformed monotherapies from either drug class in terms of the proportion of participants with treatment responses. Heterogeneity was low. No high-risk instances were identified in the bias assessment for our primary outcomes.InterpretationAccording to the available evidence, the combination of a selective serotonin reuptake inhibitor and a second-generation antipsychotic-and particularly of fluoxetine and olanzapine-could be the optimal treatment choice for psychotic depression. These findings should be taken into account in the development of clinical practice guidelines. However, these conclusions should be interpreted cautiously in view of the low number of included studies and the limitations of these studies.FundingNone.

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BackgroundDepression is widespread global problem that not only severely impacts individuals' physical and mental health but also imposes a heavy disease burden on nations and societies. The role of inflammation in the pathogenesis and pathophysiology of depression has received much attention, but the precise relationship between the two remains unclear. This study aims to investigate the correlation between depression and inflammation using a network medicine approach.MethodsWe utilized a degree-preserving approach to identify the large connected component (LCC) of all depression-related proteins in the human interactome. The LCC was deemed as the disease module for depression. To measure the association between depression and other diseases, we calculated the overlap between these disease protein modules using the Sab algorithm. A smaller Sab value indicates a stronger association between diseases. Building on the results of this analysis, we further explored the correlation between inflammation and depression by conducting enrichment and pathway analyses of critical targets. Finally, we used a network proximity approach to calculate drug-disease proximity to predict the efficacy of drugs for the treatment of depression. We calculated and ranked the distances between depression disease modules and 6,100 drugs. The top-ranked drugs were selected to explore their potential for treating depression based on the hypothesis that their antidepressant effects are related to reducing inflammation.ResultsIn the human interactome, all depression-related proteins are clustered into a large connected component (LCC) consisting of 202 proteins and multiple small subgraphs. This indicates that depression-related proteins tend to form clusters within the same network. We used the 202 LCC proteins as the key disease module for depression. Next, we investigated the potential relationships between depression and 299 other diseases. Our analysis identified over 18 diseases that exhibited significant overlap with the depression module. Where SAB = -0.075 for the vascular disease and depressive disorders module, SAB = -0.070 for the gastrointestinal disease and depressive disorders module, and SAB = -0.062 for the endocrine system disease and depressive disorders module. The distance between them SAB di = -0.358) in the human protein interaction network. After enrichment and pathway analysis of key genes, we identified the HIF-1 signaling pathway, PI3K-Akt signaling pathway, Th17 cell differentiation, hepatitis B, and inflammatory bowel disease as key to the inflammatory response in depression. Finally, we calculated the Z-score to determine the proximity of 6,100 drugs to the depression disease module. Among the top three drugs identified by drug-disease proximity analysis were Perphenazine, Clomipramine, and Amitriptyline, all of which had a greater number of targets in the network associated with the depression disease module. Notably, these drugs have been shown to exert both anti-inflammatory and antidepressant effects, suggesting that they may modulate depression through an anti-inflammatory mechanism. These findings demonstrate a correlation between depression and inflammation at the network medicine level, which has important implications for future elucidation of the etiology of depression and improved treatment outcomes.ConclusionNeuroimmune signaling pathways play an important role in the pathogenesis of depression, and many classes of antidepressants exhibiting anti-inflammatory properties. The pathogenesis of depression is closely related to inflammation.

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Therapeutic drug monitoring is a tool for optimising the pharmacological treatment of diseases where the therapeutic effect is difficult to measure or monitor. Therapeutic reference ranges and dose-effect relation are the main requirements for this drug titration tool. Defining and updating therapeutic reference ranges are difficult, and there is no standardised method for the calculation and clinical qualification of these. The study presents a basic model for validating and selecting routine laboratory data. The programmed algorithm was applied on data sets of antidepressants and antipsychotics from three public hospitals in Denmark. Therapeutic analytical ranges were compared with the published therapeutic reference ranges by the Arbeitsgemeinschaft für Neuropsychopharmakologie und Pharmakopsychiatrie (AGNP) and in additional literature. For most of the drugs, the calculated therapeutic analytical ranges showed good concordance between the laboratories and to published therapeutic reference ranges. The exceptions were flupentixol, haloperidol, paroxetine, perphenazine, and venlafaxine + o-desmethyl-venlafaxine (total plasma concentration), where the range was considerably higher for the laboratory data, while the calculated range of desipramine, sertraline, ziprasidone, and zuclopenthixol was considerably lower. In most cases, we identified additional literature supporting our data, highlighting the need of a critical re-examination of current therapeutic reference ranges in Denmark. An automated approach can aid in the evaluation of current and future therapeutic reference ranges by providing additional information based on big data from multiple laboratories.

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Amitriptyline was first introduced as a medication to treat depression. Over time, this substance has been used to treat other conditions, such as gastrointestinal disorders, fibromyalgia, neuropathic pain, and analgesia, among others. However, there are no published studies that provide a broad view of the possible motivations that have led to changes in the use of amitriptyline. In this study, we have identified the landscape of use for amitriptyline based on knowledge mapping of the 100 most-cited articles about this drug. We searched Web of Science Core Collection without time and language restrictions. We obtained 14,446 results, but we only used the 100 most-cited articles that had amitriptyline as the object of study. We collected the following information from each article: authors, country of the corresponding authors, year of publication, citation count, citation density (number of citations per year), and keywords. In addition, we seek to map in the chosen articles study design and research findings. We found that since 1980, the use of amitriptyline has expanded beyond depression, moving to off-label use to treat a variety of diseases and conditions, including post-herpetic neuralgia, neuropathic pain, primary fibrosis, fibromyalgia, and migraine, can be considered a drug with more clinical applicability than its original clinical indication.

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In the area of drug research, several computational drug repurposing studies have highlighted candidate repurposed drugs, as well as clinical trial studies that have tested/are testing drugs in different phases. To the best of our knowledge, the aggregation of the proposed lists of drugs by previous studies has not been extensively exploited towards generating a dynamic reference matrix with enhanced resolution. To fill this knowledge gap, we performed weight-modulated majority voting of the modes of action, initial indications and targeted pathways of the drugs in a well-known repository, namely the Drug Repurposing Hub. Our method, DReAmocracy, exploits this pile of information and creates frequency tables and, finally, a disease suitability score for each drug from the selected library. As a testbed, we applied this method to a group of neurodegenerative diseases (Alzheimer's, Parkinson's, Huntington's disease and Multiple Sclerosis). A super-reference table with drug suitability scores has been created for all four neurodegenerative diseases and can be queried for any drug candidate against them. Top-scored drugs for Alzheimer's Disease include agomelatine, mirtazapine and vortioxetine; for Parkinson's Disease, they include apomorphine, pramipexole and lisuride; for Huntington's, they include chlorpromazine, fluphenazine and perphenazine; and for Multiple Sclerosis, they include zonisamide, disopyramide and priralfimide. Overall, DReAmocracy is a methodology that focuses on leveraging the existing drug-related experimental and/or computational knowledge rather than a predictive model for drug repurposing, offering a quantified aggregation of existing drug discovery results to (1) reveal trends in selected tracks of drug discovery research with increased resolution that includes modes of action, targeted pathways and initial indications for the investigated drugs and (2) score new candidate drugs for repurposing against a selected disease.

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Antipsychotics are considered a gold standard treatment for schizophrenia. However, there is considerable variation in antipsychotic medication choice. Factors considered involved include symptomatology, prior response, and adverse reactions. This case report presents a 38-year-old male patient with schizophrenia in acute psychosis refractory to several antipsychotics. Hypotheses for the mechanism of action of antipsychotics and psychopharmacology are discussed, and treatment resistance is defined. The patient's psychiatric, medical, and social history and past antipsychotic medications are reviewed. Afterward, the rationale for initiating perphenazine is discussed, and the patient's improvement with this medication is examined. Current literature on perphenazine's efficacy is also reviewed and discussed alongside its limitations.

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Recent years have provided numerous reports on the mechanisms of action of psychiatric medications (antidepressants, antipsychotics, mood stabilizers, and antidementia drugs) that directly inhibit the growth of cancer cells, as well as on their indirect effects on the psyche and immune system, and their supportive effects on chemotherapeutic agents. The mechanisms of the anticancer activity of psychiatric drugs include inhibition of dopamine and N-methyl-D-aspartate receptors that work via signaling pathways (PI3K/AKT/mTOR/NF-κB, ERK, Wnt/ß-catenin, and bcl2), metabolic pathways (ornithine decarboxylase, intracellular cholesterol transport, lysosomal enzymes, and glycolysis), autophagy, Ca2+-dependent signaling cascades, and various other proteins (actin-related protein complex, sirtuin 1, p21, p53, etc.). The anticancer potential of psychiatric drugs seems to be extremely broad, and the most extensive anticancer literature has been reported on antidepressants (fluoxetine, amitriptyline, imipramine, mirtazapine, and St John's Wort) and antipsychotics (chlorpromazine, pimozide, thioridazine, and trifluoperazine). Among mood stabilizers, lithium and valproates have the largest body of literature. Among antidementia drugs, memantine has documented anticancer effects, while there is limited evidence for galantamine. Of the new psychiatric substances, the antipsychotic drug brexpiprazole and the antidepressant vortioxetine have a very interesting body of literature regarding glioblastoma, based on in vitro and in vivo animal survival studies. Their use in brain tumors and metastases is particularly compelling, as these substances readily cross the blood-brain barrier (BBB). Moreover, the synergistic effect of psychiatric drugs with traditional cancer treatment seems to be extremely important in the fight against chemo- and radio-resistance of tumors. Although there are some studies describing the possible carcinogenic effects of psychiatric drugs in animals, the anticancer effect seems to be extremely significant, especially in combination treatment with radio/chemotherapy. The emerging evidence supporting the anticancer properties of psychiatric drugs presents an exciting frontier in oncology. The anticancer properties of psychiatric drugs may prove particularly useful in the period between chemotherapy and radiotherapy sessions to maintain the tumor-inhibitory effect. While further research is necessary to elucidate the mechanisms, clinical implications, dose-dependence of the effect, and clear guidelines for the use of psychiatric medications in cancer therapy, the potential for these commonly prescribed medications to contribute to cancer treatment enhances their value in the management of patients facing the dual challenges of mental health and cancer.

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