Evidence map›Paper›PMID 42829528›Full record

ArticleDepression and anxiety2026

Comparative Analysis of Antidepressant Mechanisms and Side Effects: Network Pharmacology and Molecular Docking Study of Amitriptyline, Mirtazapine, and Escitalopram for Treating Major Depressive Disorder.

Chuanjun Zhuo, Qiuyu Zhang, Haitao Song, Jiatong Zou, Ranli Li, Xiaoyan Ma, Ximing Chen, Yachen Li, Lei Yang, Lina Wang and 2 more

Abstract readComparative Study
In one paragraph

Article in Depression and anxiety, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

12 authors.

Chuanjun ZhuoLaboratory of Computational and Computational Psychiatry (CBCP_Lab) Tianjin Anding Hospital Mental Health Center of Tianjin Medical University Tianjin China.ORCID https://orcid.org/0000-0002-3793-550X
Qiuyu ZhangLaboratory of Computational and Computational Psychiatry (CBCP_Lab) Tianjin Anding Hospital Mental Health Center of Tianjin Medical University Tianjin China.ORCID https://orcid.org/0009-0000-2794-6777
Haitao SongLaboratory of Computational and Computational Psychiatry (CBCP_Lab) Tianjin Anding Hospital Mental Health Center of Tianjin Medical University Tianjin China.
Jiatong ZouLaboratory of Computational and Computational Psychiatry (CBCP_Lab) Tianjin Anding Hospital Mental Health Center of Tianjin Medical University Tianjin China.
Ranli LiLaboratory of Computational and Computational Psychiatry (CBCP_Lab) Tianjin Anding Hospital Mental Health Center of Tianjin Medical University Tianjin China.
Xiaoyan MaLaboratory of Computational and Computational Psychiatry (CBCP_Lab) Tianjin Anding Hospital Mental Health Center of Tianjin Medical University Tianjin China.
Ximing ChenLaboratory of Computational and Computational Psychiatry (CBCP_Lab) Tianjin Anding Hospital Mental Health Center of Tianjin Medical University Tianjin China.ORCID https://orcid.org/0009-0000-8349-1551
Yachen LiLaboratory of Computational and Computational Psychiatry (CBCP_Lab) Tianjin Anding Hospital Mental Health Center of Tianjin Medical University Tianjin China.
Lei YangLaboratory of Computational and Computational Psychiatry (CBCP_Lab) Tianjin Anding Hospital Mental Health Center of Tianjin Medical University Tianjin China.
Lina WangLaboratory of Computational and Computational Psychiatry (CBCP_Lab) Tianjin Anding Hospital Mental Health Center of Tianjin Medical University Tianjin China.
Chao LiLaboratory of Computational and Computational Psychiatry (CBCP_Lab) Tianjin Anding Hospital Mental Health Center of Tianjin Medical University Tianjin China.
Hongjun TianLaboratory of Computational and Computational Psychiatry (CBCP_Lab) Tianjin Anding Hospital Mental Health Center of Tianjin Medical University Tianjin China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Amitriptyline, mirtazapine, and escitalopram are effective antidepressants for treating major depressive disorder (MDD). Our study aimed to use computational biology to understand the mechanisms for the similarities and differences in efficacies and side effects of these drugs. Methods: Potential targets for MDD and the three drugs were obtained using public databases. Venn diagrams and protein-protein interaction (PPI) networks were constructed. Drug-target-pathway (D-T-P) networks were built using Cytoscape, and core targets were filtered using CytoNCA and CytoHubba plugins. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses were conducted using the Database for Annotation, Visualization, and Integrated Discovery. Ligand compounds and receptor proteins were processed with PyMOL and AutoDock. The binding of amitriptyline, mirtazapine, and escitalopram with core targets was predicted by molecular docking using AutoDock Vina. The results were visualized using PyMOL and protein-ligand interaction profiler (PLIP). Results: Forty-eight overlapping targets and 66, 50, and 30 individual targets for MDD treatment were identified for amitriptyline, mirtazapine, and escitalopram, respectively. Critical pathways included "Neuroactive ligand-receptor interaction," "Calcium signaling pathway," and "Serotonergic synapse." Key targets, indicating a wide range of effects on cellular activities and signaling mechanisms, included D(2) dopamine receptor (DRD2), CYP2D6, monoamine oxidase type B (MAOB), solute carrier family 6 member 3 (SLC6A3), solute carrier family 6 member 4 (SLC6A4), 5-hydroxytryptamine receptor 2C (HTR2C), and CHRNA4. MAOB had the highest binding affinity with amitriptyline, followed by SLC6A4 and DRD2. Binding stability was enhanced by salt bridges and π-stacking interactions. Mirtazapine formed strong binding affinities with HTR2C, SLC6A4, and CHRNA4 through hydrogen bonds and π-stacking interactions. Escitalopram exhibited the highest affinity for SLC6A3, followed by HTR2C. Conclusion: All three drugs regulated serotonin, norepinephrine (NE), and dopamine, but target specificity, pathways, and binding patterns differed. Amitriptyline exhibited stronger antidepressant effects and broader mechanisms but a higher risk of side effects. Mirtazapine's dual mechanism regulates broad targets with reliable efficacy and fewer side effects. Escitalopram selectively acts on serotonin-related targets, with narrow actions and potentially fewer side effects. These findings align with the clinical profiles of the antidepressant efficacy and side effects.

Indexed as

AmitriptylineAntidepressive AgentsEscitalopramMajor Depressive DisorderMolecular Docking SimulationAntidepressive Agents, TricyclicHumansMirtazapineNetwork PharmacologyProtein Interaction MapsAmitriptylineAntidepressive AgentsAntidepressive Agents, TricyclicEscitalopramMirtazapineamitriptylineescitaloprammajor depressive disordermirtazapinemolecular dockingnetwork pharmacology

Identifiers

PMID42829528
PMCPMC13633451

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.