Defeating cancer with antidepressants

Elucidation of escitalopram oxalate and related antidepressants as putative inhibitors of PTP4A3/PRL-3 protein in hepatocellular carcinoma: A multi-computational investigation

Hepatocellular carcinoma (HCC) persists to be one of the most devastating and deadliest malignancies globally. Recent research into the molecular signaling networks entailed in many malignancies has given some prominent insights that can be leveraged to create molecular therapeutics for combating HCC. Therefore, in the current communication, an in-silico drug repurposing approach has been employed to target the function of PTP4A3/PRL-3 protein in HCC using antidepressants: Fluoxetine hydrochloride, Citalopram, Amitriptyline, Imipramine, and Escitalopram oxalate as the desired ligands. The density function theory (DFT) and chemical absorption, distribution, metabolism, excretion, and toxicity (ADMET) parameters for the chosen ligands were evaluated to comprehend the pharmacokinetics, drug-likeness properties, and bioreactivity of the ligands. The precise interaction mechanism was explored using computational methods such as molecular docking and molecular dynamics (MD) simulation studies to assess the inhibitory effect and the stability of the interactions against the protein of interest. Escitalopram oxalate exhibited a comparatively significant docking score (-7.4 kcal/mol) compared to the control JMS-053 (-6.8 kcal/mol) against the PRL-3 protein. The 2D interaction plots exhibited an array of hydrophobic and hydrogen bond interactions. The findings of the ADMET forecast confirmed that it adheres to Lipinski's rule of five with no violations, and DFT analysis revealed a HOMO-LUMO energy gap of -0.26778 ev, demonstrating better reactivity than the control molecule. The docked complexes were subjected to MD studies (100 ns) showing stable interactions. Considering all the findings, it can be concluded that Escitalopram oxalate and related therapeutics can act as potential pharmacological candidates for targeting the activity of PTP4A3/PRL-3 in HCC.

Selecting Antidepressants Based on Medical History and Stress Mechanism

Purpose At present, clinicians typically prescribe antidepressants based on the widely accepted "serotonin hypothesis." This study explores an alternative mechanism, the stress mechanism, for selecting antidepressants based on patients' medical history. Methods This study investigated clinicians' prescribing patterns for the 15 most common antidepressants, including amitriptyline, bupropion, citalopram, desvenlafaxine, doxepin, duloxetine, escitalopram, fluoxetine, mirtazapine, nortriptyline, paroxetine, ropinirole, sertraline, trazodone, and Venlafaxine. The least absolute shrinkage and selection operator (LASSO) logistic regression was used to identify factors that affect the remission of depression symptoms after receiving an antidepressant. Results The study found that a wide range of factors influenced the propensity of clinicians to prescribe antidepressants, with the number of predictors ranging from 51 to 206 variables. The prevalence of prescribing an antidepressant ranged from 0.5% for doxepin to 24% for the combination of more than one antidepressant. The area under the receiver operating curves (AROC) ranged from 77.2% for venlafaxine to 90.5% for ropinirole, with an average AROC of 82% for predicting the propensity of medications. A variety of diagnoses and prior medications affected remission, in agreement that the central mechanism for the impact of medications on the brain is through stress reduction. For example, psychotherapy, whether done individually or in a group, whether done for a short or long time, and whether done with evaluation/assessment or not, had an impact on remission. Specifically, teenagers and octogenarians were less likely to benefit from bupropion, citalopram, escitalopram, fluoxetine, and sertraline compared to patients between 40 and 65 years old. The findings of this study suggest that considering a patient's medical history and individual characteristics is crucial for selecting the most effective antidepressant treatment. Conclusions Many studies have raised doubt about the serotonin hypothesis as the central mechanism for depression treatment. The identification of a wide range of predictors for prescribing antidepressants highlights the complexity of depression treatment and the need for individualized approaches that consider patients' comorbidities and previous treatments. The significant impact of comorbidities on the response to treatment makes it improbable that the mechanism of action of antidepressants is solely based on the serotonin hypothesis. It is hard to explain how comorbidities lead to the depletion of serotonin. These findings open up a variety of courses of action for the clinical treatment of depression, each addressing a different source of chronic stress in the brain. Overall, this study contributes to a better understanding of depression treatment and provides valuable insights for clinicians in selecting antidepressants based on patients' medical history.

Effectiveness of common antidepressants: a post market release study

BACKGROUND

This study summarizes the experiences of patients, who have multiple comorbidities, with 15 mono-treated antidepressants.

METHODS

This is a retrospective, observational, matched case control study. The cohort was organized using claims data available through OptumLabs for depressed patients treated with antidepressants between January 1, 2001 and December 31, 2018. The cohort included patients from all states within United States of America. The analysis focused on 3,678,082 patients with major depression who had 10,221,145 antidepressant treatments. Using the robust, and large predictors of remission, and propensity to prescribe an antidepressant, the study created 16,770 subgroups of patients. The study reports the remission rate for the antidepressants within the subgroups. The overall impact of antidepressant on remission was calculated as the common odds ratio across the strata.

FINDINGS

The study accurately modelled clinicians' prescription patterns (cross-validated Area under the Receiver Operating Curve, AROC, of 82.0%, varied from 77% to 90%) and patients' remission (cross-validated AROC of 72.0%, varied from 69.5% to 78%). In different strata, contrary to published randomized studies, remission rates differed significantly and antidepressants were not equally effective. For example, in age and gender subgroups, the best antidepressant had an average remission rate of 50.78%, 1.5 times higher than the average antidepressant (30.30% remission rate) and 20 times higher than the worst antidepressant. The Breslow-Day chi-square test for homogeneity showed that across strata a homogenous common odds-ratio did not exist (alpha<0.0001). Therefore, the choice of the optimal antidepressant depended on the strata defined by the patient's medical history.

INTERPRETATION

Study findings may not be appropriate for specific patients. To help clinicians assess the transferability of study findings to specific patient, the web site http://hi.gmu.edu/ad assesses the patient's medical history, finds similar cases in our data, and recommends an antidepressant based on the experience of remission in our data. Patients can share this site's recommendations with their clinicians, who can then assess the appropriateness of the recommendations.

FUNDING

This project was funded by the Robert Wood Johnson foundation grant #76786. The development of related web site was supported by grant 247-02-20 from Virginia's Commonwealth Health Research Board.

Fluoxetine Induces Apoptosis through Extrinsic/Intrinsic Pathways and Inhibits ERK/NF-κB-Modulated Anti-Apoptotic and Invasive Potential in Hepatocellular Carcinoma Cells In Vitro

The aim of the present study was to verify the effects of fluoxetine on dysregulation of apoptosis and invasive potential in human hepatocellular carcinoma (HCC) SK-Hep1 and Hep3B cells. Cells were treated with different concentrations of fluoxetine for different times. MTT (3-(4,5-Dimethylthiazol-2-yl)-2,5-Diphenyltetrazolium Bromide) assays were used for testing the effects of fluoxetine on cell viability. The regulation of apoptosis signaling, and anti-apoptotic, proliferation, and metastasis-associated proteins after fluoxetine treatment were assayed by flow cytometry and Western blotting assay. The detection of nuclear factor kappa-light-chain-enhancer of activated B cells (NF-κB) activation after fluoxetine treatment was performed by NF-κB reporter gene assay. The results demonstrated that fluoxetine significantly reduced cell viability, cell migration/invasion, NF-κB, extracellular signal-regulated kinases (ERK) activation, and expression of anti-apoptotic (Cellular FLICE (FADD-like IL-1β-converting enzyme)-inhibitory protein (C-FLIP), Myeloid cell leukemia-1 (MCL-1), X-Linked inhibitor of apoptosis protein (XAIP), and Survivin), proliferation (Cyclin-D1), angiogenesis (vascular endothelial growth factor (VEGF)), and metastasis-associated proteins (matrix metalloproteinase-9 (MMP-9)). Fluoxetine also significantly induced apoptosis, unregulated extrinsic (activation of first apoptosis signal protein and ligand (Fas/FasL), and caspase-8) and intrinsic (loss of mitochondrial membrane potential (ΔΨm) pathways and increased Bcl-2 homologous antagonist killer (BAK) apoptosis signaling. Taken together, these results demonstrated that fluoxetine induced apoptosis through extrinsic/intrinsic pathways and diminished ERK/NF-κB-modulated anti-apoptotic and invasive potential in HCC cells in vitro.