Impact of Venlafaxine on Platelet Count and Activity—Case Report and Narrative Review

Personalized venlafaxine dose prediction using artificial intelligence technology: a retrospective analysis based on real-world data

BACKGROUND

Venlafaxine dose regimens vary considerably between individuals, requiring personalized dosing.

AIM

This study aimed to identify dose-related influencing factors of venlafaxine through real-world data analysis and to construct a personalized dose model using advanced artificial intelligence techniques.

METHOD

We conducted a retrospective study on patients with depression treated with venlafaxine. Significant variables were selected through a univariate analysis. Subsequently, the predictive performance of seven models (XGBoost, LightGBM, CatBoost, GBDT, ANN, TabNet, and DT) was compared. The algorithm that demonstrated optimal performance was chosen to establish the dose prediction model. Model validation used confusion matrices and ROC analysis. Additionally, a dose subgroup analysis was conducted.

RESULTS

A total of 298 patients were included. TabNet was selected to establish the venlafaxine dose prediction model, which exhibited the highest performance with an accuracy of 0.80. The analysis identified seven crucial variables correlated with venlafaxine daily dose, including blood venlafaxine concentration, total protein, lymphocytes, age, globulin, cholinesterase, and blood platelet count. The area under the curve (AUC) for predicting venlafaxine doses of 75 mg, 150 mg, and 225 mg were 0.90, 0.85, and 0.90, respectively.

CONCLUSION

We successfully developed a TabNet model to predict venlafaxine doses using real-world data. This model demonstrated substantial predictive accuracy, offering a personalized dosing regimen for venlafaxine. These findings provide valuable guidance for the clinical use of the drug.

A Hematoma in the Anterior Abdominal Muscle in a Woman Receiving Venlafaxine: A Literature Review of the Reports on Similar Cases

INTRODUCTION

Venlafaxine (VEN) is a selective norepinephrine reuptake inhibitor (SNRI) that mainly helps treat major depressive disorder and anxiety and panic disorders. It works by inhibiting the reuptake of serotonin (5-hydroxytryptamine, 5-HT) and noradrenaline (NA) by presynaptic neurons. Additionally, VEN administration has been linked with a bleeding predisposition that may be due to the inhibition of NA and 5-HT uptake by platelets which have their own receptors on their surface and are implicated in platelet aggregation.

CASE PRESENTATION

Herein, we report a case of a 54-year-old patient treated with VEN, who presented with a hematoma in the anterior abdominal muscle. We also present the observational studies and case reports highlighting the association of SNRIs use with various hemorrhagic complications ranging from gastrointestinal hemorrhage or vaginal bleeding to bleeding during or after surgery due to either thrombocytopenia or impaired platelet aggregation.

CONCLUSION

Given the cases of either reductions in the platelet count or impairment of platelet activity accompanied by bleeding events, every clinician should be aware of these possible adverse effects when prescribing SNRIs.

Molecular docking and biochemical validation of (-)-syringaresinol-4-O-β-D-apiofuranosyl-(1→2)-β-D-glucopyranoside binding to an allosteric site in monoamine transporters

Albizia julibrissin Durazz is one of the most common herbs used for depression and anxiety treatment, but its mechanism of action as an antidepressant or anxiolytic drug have not been fully understood. We previously isolated and identified one lignan glycoside compound from Albizia Julibrissin Durazz, (-)-syringaresinol-4-O-β-D-apiofuranosyl-(1→2)-β-D-glucopyranoside (SAG), that inhibited all three monoamine transporters with a mechanism of action different from that of the conventional antidepressants. In this study, we generated homology models for human dopamine transporter and human norepinephrine transporter, based on the X-ray structure of Drosophila dopamine transporter, and conducted the molecular docking of SAG to all three human monoamine transporters. Our computational results indicated that SAG binds to an allosteric site (S2) that has been demonstrated to be formed by an aromatic pocket positioned in the scaffold domain in the extracellular vestibule connected to the central site (S1) in these monoamine transporters. In addition, we demonstrated that SAG stabilizes a conformation of serotonin transporter with both the extracellular and cytoplasmic pathways closed. Furthermore, we performed mutagenesis of the residues in both the allosteric and orthosteric sites to biochemically validate SAG binding in all three monoamine transporters. Our results are consistent with the molecular docking calculation and support the association of SAG with the allosteric site. We expect that this herbal molecule could become a lead compound for the development of new therapeutic agents with a novel mechanism of action.