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. 2025 Aug 6;22(6):e00714. doi: 10.1016/j.neurot.2025.e00714

Shared and unique therapeutic targets of KarXT and clozapine for schizophrenia treatment revealed by network pharmacology and molecular docking analyses: Implications for differential clinical responses

Chuanjun Zhuo a,b,⁎, Chao Li a,b,⁎, Hongjun Tian c, Lina Wang a,b, Xiaoyan Ma a,b, Ranli Li a,b, Ximing Chen a,b, Yachen Li a,b, Qiuyu Zhang a,b, Lei Yang a,b
PMCID: PMC12664458  PMID: 40774882

Abstract

Xanomeline plus trospium (KarXT) is a combination drug targeting muscarinic receptors with demonstrated efficacy against positive, negative, and cognitive symptoms of schizophrenia, although therapeutic effects on positive and negative symptoms do not differ significantly from risperidone and olanzapine. Clozapine remains the most effective treatment for schizophrenia unresponsive to other antipsychotics and demonstrates superior efficacy for positive and negative symptoms compared to risperidone and olanzapine. However, the common and distinct molecular targets underlying these different clinical responses to KarXT and clozapine are not fully understood. Potential xanomeline and clozapine targets were identified by searching PharmMapper, SwissTargetPrediction, GeneCards, and SuperPred, and schizophrenia-related targets by searching GeneCards, OMIM, and TTD. Protein–protein interaction (PPI) networks were constructed to identify hub targets, and GO and KEGG pathway enrichment analyses were conducted for the top 25 targets using DAVID. Cytoscape was used to build a network linking drugs, pathways, targets, and disease. Molecular docking simulations were conducted to assess drug binding affinities to core targets. Combined database searches identified 103 overlapping targets for xanomeline and schizophrenia, and 285 overlapping targets for clozapine and schizophrenia. PPI network and KEGG pathway analyses identified FOS, CASP3, NFKB1, AKT1, IGF1, KDR, and CDC42, proteins related to apoptosis, inflammation, neuroprotection, and MAPK signaling, as core xanomeline targets, and FOS, CASP3, NFKB1, TNF, IL6, IFNG, and CXCL8, proteins involved in apoptosis, inflammation, immune responses, and IL-17 signaling, as core clozapine targets. Molecular docking confirmed strong binding between drugs and core targets. KarXT and clozapine share core targets FOS, CASP3, and NFKB1. Distinct KarXT targets such as AKT1, IGF1, KDR, and CDC42, and clozapine targets including TNF, IL6, IFNG, and CXCL8 may explain differences in therapeutic efficacy. These bioinformatics findings support recent meta-analyses and provide guidance for more appropriate drug selection.

Keywords: KarXT, Clozapine, Schizophrenia, Network pharmacology, Molecular docking

Introduction

Schizophrenia is a severe and debilitating mental disorder that affects approximately 1 ​% of the global population [1]. The Global Burden of Disease Study 2019 found that while schizophrenia affected a smaller proportion of the population than depressive and anxiety disorders, the acute psychotic state carried a higher disability weight [2]. Most individuals with schizophrenia do not fully recover, and even those with relatively favorable outcomes often experience lifelong adverse consequences, including social isolation, relationship difficulties, and employment challenges [3]. Individuals with schizophrenia also have a reduced life expectancy of 15–25 years, and about 5 ​% die by suicide [[4], [5], [6]]. Approximately 30 ​% of patients show little to no clinical response to antipsychotic agents except for clozapine, widely used as the first-line option for otherwise treatment-resistant cases [7]. Most currently approved antipsychotic agents block D2 dopamine receptors, but more recent theories on disease mechanisms implicate multiple additional transmitter systems. Therefore, D2 antagonist fail to adequately address clinical needs, necessitating the development of new treatments.

Xanomeline plus trospium chloride or KarXT (marketed as COBENFY™) is a first-in-class, fixed-dose, oral muscarinic agonist/antagonist combination developed for schizophrenia and Alzheimer's disease psychosis [8]. Xanomeline is thought to address a wide spectrum of schizophrenia symptoms by acting as an agonist at M1 and M4 muscarinic acetylcholine receptors in the brain, while trospium chloride reduces the adverse events associated with xanomeline by blocking peripheral cholinergic receptors [8]. Successful Phase 3 trials of KarXT for individuals with schizophrenia experiencing acute psychosis represent a breakthrough in treatment. In Phase 3 EMERGENT-2 (NCT04659161) [8] and EMERGENT-3 (NCT04738123) trials [9], KarXT significantly improved positive, negative, and cognitive symptoms of schizophrenia compared to placebo [9]. However, a recent meta-analysis including 33 trials with 7193 participants [10] concluded that while KarXT and three other antipsychotics studied, risperidone, olanzapine, and aripiprazole, were significantly more efficacious against total, positive, and negative symptoms than placebo (mean difference [MD] vs placebo ranges: −10.67 to −8.05 for positive, −3.46 to −2.53 for negative, and −1.99 to −1.44 for cognitive symptoms), there were no significant differences in efficacy among these drugs. KarXT was considered the least likely to result in weight gain and the difference was significant versus risperidone (−2.06 ​kg; 95 ​% CrI: −3.28, −0.87) and olanzapine (−2.86 ​kg; 95 ​% CrI: −3.97, −1.82). However, KarXT was also ranked highest for all-cause discontinuation (vs. risperidone, risk ratio [RR] ​= ​0.64, 95 ​% CrI 0.46, 0.89; vs. olanzapine, RR ​= ​0.6; 95 ​% CrI 0.44, 0.83) [10]. Further, KarXT has not demonstrated superior short-term efficacy compared to risperidone and olanzapine, although the lower risk of weight gain is an important outcome as patients with schizophrenia are at elevated risk of cardiovascular disease [[10], [11], [12]]. Alternatively, short-term data did not permit evaluation of the risk for tardive dyskinesia, a major motor symptom associated with antipsychotics [10].

Clozapine, a tricyclic dibenzodiazepine classified as an atypical antipsychotic agent, is the only medication approved by the United States Food and Drug Administration (FDA) for the treatment of schizophrenia resistant to other available antipsychotics [13]. Although agranulocytosis and sudden cardiac death are two potentially fatal side effects, clozapine remains an irreplaceable therapeutic for these treatment-resistant cases [14,15]. Therapeutic effects of KarXT for treatment-resistant schizophrenia are anticipated but as yet unverified. Further, it is unclear whether KarXT has overlapping or unique effects therapeutic compared to clozapine.

First introduced by Hopkins [16], network pharmacology combines systems biology and network biology to identify the mechanisms underlying drug actions and to discover potential new therapeutic targets and agents. Advances in bioinformatics have made network pharmacology an essential computational tool for predicting the molecular mechanisms underlying complex diseases and drug responses. These predictions can be further supported by molecular docking simulations exploring the binding sites and interactions of small molecules such as clinical drugs with macromolecule targets. Our recent studies have employed combined network pharmacology and molecular docking approaches to explore the potential mechanisms underlying clozapine-induced agranulocytosis and cardiac arrest [17,18]. In the current study, we employed these analytic techniques to predict potential shared and drug-specific molecular mechanisms underlying the effects of xanomeline, the psychoactive component of KarXT, and clozapine on schizophrenia. The technological roadmap is shown in Fig. 1.

Fig. 1.

Fig. 1

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Technological roadmap of the study.

Methods

Identification of potential targets of xanomeline and clozapine related to schizophrenia

The chemical structures of xanomeline and clozapine were downloaded from DrugBank (https://go.drugbank.com) [19], and potential targets were predicted using PharmMapper (http://www.lilab-ecust.cn/pharmmapper/) [20], SwissTargetPrediction (http://www.swisstargetprediction.ch/) [21], GeneCards (https://www.genecards.org/) [22], and SuperPred (https://prediction.charite.de/) [23]. Target lists for the two drugs were compiled based on preset screening criteria and matching scores from these databases with duplicates removed. The screened targets were compiled and input into UniProt (https://www.uniprot.org/) [24] for standardization and validation of the target names. Disease-related targets were obtained from GeneCards, Online Mendelian Inheritance in Man (OMIM) (https://www.omim.org/) [25], and Therapeutic Target Database (TTD) (https://db.idrblab.net/ttd/) [26] using “schizophrenia” as a keyword. Again, disease targets were compiled after removing duplicates. Subsequently, Venny 2.1.0 (https://bioinfogp.cnb.csic.es/tools/venny) was used to identify the intersecting targets, and the online bioinformatics platform (http://www.bioinformatics.com.cn/) was employed to visualize the Venn diagrams.

Protein-protein interaction (PPI) network construction and hub target screening

The intersecting targets were imported into the STRING database (https://string-db.org) [27], filtered for Homo sapiens, and further screened based on a confidence score threshold of ≥0.4. Disconnected nodes were excluded, and the resulting protein–protein interaction (PPI) network was constructed. Cytoscape 3.10.0 (https://www.cytoscape.org/) [28] was used to visualize the PPI network and perform network analysis, and the CytoHubba plug-in for Cytoscape was employed to identify the top 25 hub targets using the maximum clique centrality (MCC) method.

Gene ontology (GO) and Kyoto encyclopedia of genes and genomes (KEGG) enrichment analyses of hub targets

Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were then performed using the Database for Annotation, Visualization, and Integrated Discovery (DAVID) (https://david.ncifcrf.gov/) [29] to identify the biological functions of the top 25 core targets from the PPI network. GO annotations (terms) encompass three categories: biological processes (BPs), cellular components (CCs), and molecular functions (MFs). The top 10 GO terms for each category and the top 10 KEGG pathways were selected for further investigation based on a significance level of P ​< ​0.05. The bioinformatics platform (http://www.bioinformatics.com.cn/) was then used to visualize the enrichment results.

Drug–pathway–target–disease network construction and molecular docking simulations

To explore the intricate interactions among drugs, targets, pathways, and disease, drug-pathway-target-disease networks were constructed and analyzed using Cytoscape 3.10.0. To validate the network pharmacology results, molecular docking simulations were performed using AutoDockTools 1.5.6 and AutoDock Vina.26 [30]. The ligand preparation involved retrieving sdf structure files for xanomeline and clozapine from the PubChem database (https://pubchem.ncbi.nlm.nih.gov/) [31], converting them into 3D models using ChemBio3D Ultra 14.0 for energy minimization, processing these models using AutoDockTools 1.5.6, and saving the results in pdbqt format. For receptor preparation, crystal structures of core target proteins were obtained from the Protein Data Bank (PDB) (http://www.rcsb.org) [32] based on gene symbols. Water molecules and initial ligands were removed using PyMOL 2.4.1, followed by hydrogenation, charge calculation, and atom type assignment using AutoDockTools 1.5.6. The processed structures were then saved in pdbqt format. Grid preparation involved adjusting the docking box size and coordinates based on binding sites predicted by DoGSiteScorer (https://proteins.plus/). Molecular docking and binding affinities were predicted using AutoDock Vina, and interactions between the drugs and core protein receptors were visualized using PyMOL 2.4.1 and Discovery Studio 2021.

Results

Identification of intersecting targets for xanomeline or clozapine and schizophrenia

A PharmMapper search yielded 292 xanomeline targets, SwissTargetPrediction 109 xanomeline targets, GeneCards 20 targets, and SuperPred 132 targets, while these same database searches yielded 166, 108, 602, and 187 clozapine targets, respectively. After removing duplicates, searches yielded 473 potential targets for xanomeline and 904 for clozapine. A GeneCards search yielded 981 schizophrenia targets, while OMIM identified 59, and TTD 54. After removing duplicates, 1040 potential targets for schizophrenia were identified. Venn diagrams identified 103 overlapping targets for xanomeline and schizophrenia (Fig. 2A) and 285 overlapping targets for clozapine and schizophrenia (Fig. 3A).

Fig. 2.

Fig. 2

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Identification of xanomeline hub targets via PPI network analysis.(A) Venn diagram showing the shared targets between xanomeline and schizophrenia. (B) PPI network of common targets retrieved from the STRING database. (C) PPI network visualized using Cytoscape, where redder colors and larger node sizes indicate higher degrees. (D) Top 25 hub targets ranked by the MCC method using the CytoHubba plugin in Cytoscape.

Fig. 3.

Fig. 3

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Identification of hub targets of clozapine via PPI network analysis.(A) Venn diagram showing the shared targets between clozapine and schizophrenia. (B) PPI network of common targets retrieved from the STRING database. (C) PPI network visualized using Cytoscape, where redder colors and larger node sizes indicate higher degrees. (D) Top 25 hub targets ranked by the MCC method using the CytoHubba plugin in Cytoscape.

PPI network construction and hub target identification

The drug targets overlapping with schizophrenia involvement were imported into the STRING database to construct PPI networks (Fig. 2, Fig. 3B). The PPI network for overlapping xanomeline and schizophrenia targets included 102 nodes and 765 edges (Fig. 2C), whereas the PPI network for clozapine and schizophrenia comprised 280 nodes and 4675 edges (Fig. 3C). The top 25 hub targets were identified by the MCC method using the CytoHubba plug-in for Cytoscape (Fig. 2, Fig. 3D).

GO enrichment analyses

The top 25 hub targets associated with each drug and schizophrenia were then subjected to GO enrichment analysis. The top 10 BP, CC, and MF category terms for each set of hub genes are shown in Fig. 4A and B. For xanomeline, 140 BPs, 26 ​CCs, and 42 ​MFs were identified. Highly enriched BP terms included ‘regulation of gene expression’, ‘positive regulation of transcription by RNA polymerase II’, ‘neuron apoptotic process’, and ‘response to xenobiotic stimulus’. Highly enriched CC terms included ‘extracellular region’, ‘nucleus’,’ chromatin’, ‘cytosol’, and ‘glutamatergic synapse’, and highly enriched MF terms included ‘nuclear receptor activity’, ‘identical protein binding’, ‘enzyme binding’, and ‘estrogen response element binding’. For clozapine, 203 BPs, 20 ​CCs, and 36 ​MFs were identified. Highly enriched BP terms included ‘regulation of gene expression’, ‘positive regulation of transcription by RNA polymerase II’, ‘positive regulation of tyrosine phosphorylation of STAT protein’, and ‘apoptotic process regulation’. Highly enriched CC terms included ‘extracellular region’, ‘endoplasmic reticulum lumen’, and ‘endosome lumen’, and highly enriched MF terms included ‘growth factor activity’, ‘identical protein binding’, and ‘cytokine activity’. While the BP terms are similar (related to gene transcription and apoptosis), the unique core target enrichment terms suggest that xanomeline differentially influences nuclear signaling and enzyme activity while clozapine differentially influences protein processing, immune signaling, and growth factor signaling.

Fig. 4.

Fig. 4

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GO and KEGG pathway enrichment analyses.(A and B) Bar charts of GO enrichment analyses. (A) The top 10 enriched biological process (BP), cellular component (CC), and molecular function (MF) terms from GO enrichment analysis of the top 25 hub genes for xanomeline and schizophrenia. (B) The top 10 enriched BP, CC, and MF terms from GO enrichment analysis of the top 25 hub genes for clozapine and schizophrenia. (C and D) Bubble charts of KEGG pathway enrichment analysis. (C) The top 10 enriched pathways for the top 25 hub genes common to xanomeline and schizophrenia. (D) The top 10 enriched pathways for the top 25 hub genes common to clozapine and schizophrenia. The color and size of each bubble indicate the P value and gene count, respectively.

KEGG pathway analyses and drug-pathway-target-disease network construction

The top 10 enriched KEGG pathways involved in the effects of xanomeline on schizophrenia are presented in Table 1 and Fig. 4C. Of these, ‘MAPK signaling pathway’ was the most significant. The drug-pathway-target-disease network diagram (Fig. 5A) illustrating the effects of xanomeline on schizophrenia contained 49 nodes (1 drug, 10 pathways, 37 targets, and 1 disease) and 144 edges. The top 10 enriched KEGG pathways associated with clozapine and schizophrenia are shown in Table 2 and Fig. 4D. Of these, the IL-17 signaling pathway was identified as the most significant (lowest P-value). The drug-pathway-target-disease network diagram (Fig. 5B) illustrating the effects of clozapine on schizophrenia included 92 nodes (1 drug, 10 pathways, 80 targets, and 1 disease) and 197 edges. The positions of core targets within the MAPK signaling pathway are shown for the xanomeline network in Fig. 6A, and the positions of IL-17 signaling pathway targets for the clozapine network in Fig. 6B.

Table 1.

KEGG pathway enrichment analysis of the top 25 core targets of xanomeline implicated in schizophrenia.

ID Term Gene Count P-value
hsa05417 Lipid and atherosclerosis CDC42, HSPA8, GSK3B, NOS3, CASP3, AKT1, PPARG, FOS, MAPK14, MMP9, NFKB1, BCL2L1 12 2.64E-12
hsa05200 Pathways in cancer GSK3B, FOS, IGF1, ESR1, MMP9, IL2, ESR2, NFKB1, CDC42, CDK4, CASP3, KIT, AKT1, PPARG, BCL2L1 15 7.67E-12
hsa05162 Measles HSPA8, GSK3B, CDK4, CASP3, AKT1, FOS, IL2, NFKB1, BCL2L1 9 1.78E-09
hsa04010 MAPK signaling pathway CDC42, HSPA8, CASP3, KIT, KDR, AKT1, FOS, IGF1, NGF, MAPK14, NFKB1 11 2.19E-09
hsa04932 Non-alcoholic fatty liver disease CDC42, GSK3B, CASP3, AKT1, PPARG, FOS, MAPK14, PPARA, NFKB1 9 4.68E-09
hsa01522 Endocrine resistance CDK4, AKT1, FOS, IGF1, MAPK14, ESR1, MMP9, ESR2 8 5.18E-09
hsa04151 PI3K-Akt signaling pathway GSK3B, CDK4, NOS3, KIT, KDR, AKT1, IGF1, NGF, IL2, NFKB1, BCL2L1 11 1.34E-08
hsa04660 T cell receptor signaling pathway CDC42, GSK3B, CDK4, AKT1, FOS, MAPK14, IL2, NFKB1 8 2.24E-08
hsa04917 Prolactin signaling pathway GSK3B, AKT1, FOS, MAPK14, ESR1, ESR2, NFKB1 7 2.56E-08
hsa05224 Breast cancer GSK3B, CDK4, KIT, AKT1, FOS, IGF1, ESR1, ESR2 8 8.56E-08
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KEGG, Kyoto Encyclopedia of Genes and Genomes.

Fig. 5.

Fig. 5

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Drug–pathway–target–disease network diagrams.(A) Network illustrating the effects of xanomeline on schizophrenia. (B) Network illustrating the effects of clozapine on schizophrenia. In both (A) and (B), orange inverted triangles indicate drugs and diseases, green diamonds represent the top ten pathways, blue circles denote targets, and red triangles highlight core targets and key pathways.

Table 2.

KEGG pathway enrichment analysis of the top 25 core targets of clozapine implicated in schizophrenia.

ID Term Gene Count P-value
hsa04657 IL-17 signaling pathway IL4, IL6, CXCL8, IFNG, CASP3, FOS, TNF, MMP9, NFKB1 9 8.24E-11
hsa05200 Pathways in cancer CXCL8, FOS, IGF1, ESR1, MMP9, IL2, NFKB1, IL4, IL6, IFNG, CASP3, CTNNB1, PPARG 13 2.75E-09
hsa04932 Non-alcoholic fatty liver disease IL6, CXCL8, LEP, CASP3, PPARG, FOS, TNF, NFKB1, INS 9 4.68E-09
hsa05142 Chagas disease IL10, IL6, CXCL8, IFNG, FOS, TNF, IL2, NFKB1 8 6.85E-09
hsa05321 Inflammatory bowel disease IL10, IL4, IL6, IFNG, TNF, IL2, NFKB1 7 1.64E-08
hsa05133 Pertussis IL10, IL6, CXCL8, CASP3, FOS, TNF, NFKB1 7 4.53E-08
hsa05417 Lipid and atherosclerosis IL6, CXCL8, NOS3, CASP3, PPARG, FOS, TNF, MMP9, NFKB1 9 5.74E-08
hsa05161 Hepatitis B IL6, CXCL8, CREB1, CASP3, FOS, TNF, MMP9, NFKB1 8 1.66E-07
hsa04151 PI3K-Akt signaling pathway IL4, IL6, CREB1, BDNF, NOS3, IGF1, NGF, IL2, NFKB1, INS 10 2.17E-07
hsa05146 Amebiasis IL10, IL6, CXCL8, IFNG, CASP3, TNF, NFKB1 7 2.41E-07
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KEGG, Kyoto Encyclopedia of Genes and Genomes.

Fig. 6.

Fig. 6

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Distribution of hub targets within key signaling pathways.(A) Distribution of overlapping hub genes in the MAPK signaling pathway. (B) Distribution of overlapping hub genes in the IL-17 signaling pathway. Red dashed ellipses and arrows highlight the top seven core targets involved in each pathway.

Core target identification and molecular docking simulations

Based on statistical analysis of the PPI network and KEGG pathways, the top seven core targets for each drug were identified. Core targets of xanomeline linked to schizophrenia included AKT1, FOS, NFKB1, IGF1, KDR, and CDC42, while core targets of clozapine linked to schizophrenia included TNF, IL6, FOS, CASP3, IFNG, CXCL8, and NFKB1. Thus, xanomeline shares at least three core targets (FOS, NFKB1, and CASP3) with clozapine (Fig. 7A). Detailed information on these potential core targets is provided in Table 3. The docking scores for xanomeline binding ranged from −5.2 to −6.8 ​kcal/mol, while those for clozapine binding ranged from −6.2 to −9.4 ​kcal/mol, supporting these targets as likely binding partners (Table 4). The binding affinities of shared targets were generally stronger for clozapine than xanomeline (FOS, −6.2 ​kcal/mol vs. −5.2 ​kcal/mol; CASP3, −8.6 ​kcal/mol vs. −6.3 ​kcal/mol; NFKB1, −8.6 ​kcal/mol vs. −6.3 ​kcal/mol). The simulated binding interactions with these common core targets are shown in Fig. 7B–G.

Fig. 7.

Fig. 7

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Docking simulation results for xanomeline and clozapine with common core targets. (A) Venn diagram showing the common core targets. (B–D) xanomeline binding to CASP3 (B), FOS (C), and NFKB1 (D). (E–G) Clozapine binding to CASP3 (E), FOS (F), and NFKB1 (G).

Table 3.

Summary of the 14 core targets.

Drug Disease NO. Gene symbol PDB ID Protein name Degree
Xanomeline Schizophrenia 1 AKT1 3CQU AKT Serine/Threonine Kinase 1 49
2 FOS 6UCI Fos Proto-Oncogene, AP-1 transcription factor subunit 42
3 CASP3 1RHJ Caspase 3 35
4 NFKB1 9BOR Nuclear factor Kappa B subunit 1 35
5 IGF1 3O23 Insulin like growth factor 1 32
6 KDR 3VO3 Kinase Insert Domain receptor 28
7 CDC42 6FMC Cell division Cycle 42 27
Clozapine Schizophrenia 1 TNF 2TUN Tumor necrosis factor 114
2 IL6 1ALU Interleukin 6 111
3 FOS 6UCI Fos Proto-Oncogene, AP-1 transcription factor subunit 101
4 CASP3 1RHJ Caspase 3 79
5 IFNG 7LTT Interferon gamma 76
6 CXCL8 6WZM C-X-C Motif chemokine ligand 8 68
7 NFKB1 9BOR Nuclear factor Kappa B subunit 1 67
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PDB, Protein Data Bank.

Table 4.

Molecular docking scores (kcal/mol) for the core targets.

Ligand Receptor Residue involved in H bonding H-bond length (Å) Docking score
Xanomeline AKT1 ALA-230; THR-291 3.6; 3.3 −8.2
Xanomeline FOS ASN-165; ASN-165 3.1; 3.3 −5.2
Xanomeline CASP3 GLU-240 3.7 −6.3
Xanomeline NFKB1 PHE-295; THR-313 3.4; 3.5 −6.3
Xanomeline IGF1 LYS-1033; GLY-1152 3.2; 3.3 −6.0
Xanomeline KDR HIS-1026; HIS-1026; ASP-1046; ASP-1046 3.6; 3.7; 3.5; 3.7 −6.8
Xanomeline CDC42 TYR-353; TYR-297; ASP-320 2.4; 3.1; 3.5 −5.3
Clozapine TNF ARG-103; GLU-104 2.9; 3.0 −7.6
Clozapine IL6 ASP-160 2.9 −7.2
Clozapine FOS ALA-168; ARG-173; ASN-165; ASN-165 3.3; 3.3; 3.8; 3.7 −6.2
Clozapine CASP3 GLU-240 3.3 −8.6
Clozapine IFNG TYR-456; GLU-459 3.6; 3.3 −9.4
Clozapine CXCL8 NA NA −7.1
Clozapine NFKB1 PHE-295 3.4 −8.6
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H bonding, hydrogen bonding; NA, not available.

Discussion

Schizophrenia is a heterogenous psychiatric disorder featuring positive symptoms such as hallucinations, negative symptoms such as flat affect, and a variety of cognitive symptoms including thought disorder. Although antipsychotic medications are often effective against positive symptoms, they demonstrate limited therapeutic benefits for negative symptoms and cognitive impairments [33]. The majority of antipsychotics in current use are D2 antagonists or partial agonists developed based on theories proposed in the 1950s that schizophrenia results from excessive activity within dopaminergic transmission pathways (‘the dopamine hypothesis’) [34]. However, more recent theories implicate other transmitter systems such as glutamatergic pathways. KarXT is the first FDA approved antipsychotic targeting cholinergic transmission, and not surprisingly our database searches identified numerous distinct molecular targets compared to clozapine, a drug with broad spectrum effects on dopamine, muscarinic, serotonergic, adrenergic, and histaminergic receptors [35]. The top seven core targets for xanomeline associated with schizophrenia based on PPI network and KEGG pathway analyses include three common to clozapine (FOS, CASP3, NFKB1) and 4 unique targets (AKT1, IGF1, KDR, and CDC42), while unique core targets of clozapine include TNF, IL6, IFNG, and CXCL8. These shared and distinct molecular targets may help predict common as well as differential clinical effects for better treatment guidance.

Numerous physiological factors and pharmacological agents can induce expression and translation of the immediate early gene c-Fos and its phosphorylated product Fos. In fact, both c-Fos and Fos are widely used markers for acute neuronal activation patterns, and Fos expression has also been used to map the action sites of various drugs, including antipsychotics [36]. For instance, both xanomeline and clozapine increased c-Fos expression in the prefrontal cortex and nucleus accumbens [[37], [38], [39]], structures critical for executive functions such as working memory and response inhibition, and for reward-contingent learning. These findings may explain the efficacy of clozapine against negative and cognitive symptoms of schizophrenia. Caspase-3 (CASP3) is the main effector protease in the mitochondrial apoptosis pathway involved in regulated cell death. The expression of CASP3 was elevated in fibroblasts from antipsychotic drug-naïve first-episode schizophrenia patients, while expression of the anti-apoptotic mitochondrial protein BCL2 was reduced [40], Clozapine at relatively low concentrations inhibited X-irradiation-induced apoptosis in human lymphoma U937 ​cells by suppressing CASP8 and CASP3 activation and preventing loss of the mitochondrial membrane potential required to sustain oxidative phosphorylation [41]. Thus, both clozapine and KarXT may protect against neurological damage by maintaining efficient metabolism. The NFKB1 protein is the active subunit of the stress-responsive transcription factor NF-kB, considered a master regulator of inflammation. A genome-wide association study in Han Chinese populations identified a link between the rs28362691 single nucleotide polymorphism of NFKB1 and treatment-resistant schizophrenia [42]. Several atypical antipsychotics, including clozapine, induced the expression of NFKB1 downstream target genes in adipocytes, including the proinflammatory cytokines TNF-α, IL-1β, IL-8, and MCP-1 [43]. Thus, by interacting with these three common core targets, KarXT and clozapine may control neuroinflammation as well as processes resulting directly in neuron damage and death.

In the brain, AKT1 acts as a key downstream signaling intermediate of the D2 dopamine receptor with notable functions in the modulation of synaptic plasticity, which is essential for various cognitive processes [44]. Insulin-like growth factor 1 (IGF1) may also contribute to clinical effects against cognitive impairment and negative symptoms of schizophrenia [45,46] through promotion of neurogenesis, myelination, and dendritic branching during brain development [47]. Vascular endothelial growth factor receptor 2 (VEGFR2/KDR) was reported to be significantly downregulated in the prefrontal cortex of schizophrenia patients, which may contribute to cognitive deficits through dysregulated angiogenesis [48]. Moreover, overexpression of cell division control protein 42 (CDC42) was found to counteract neurotoxicity and alleviate cognitive impairment induced by isoflurane [49]. In schizophrenia, however, the CDC42 signaling pathway may disrupt actin dynamics, causing dendritic spine deficits (mainly in deep layer 3 pyramidal cells) that could impair synaptic plasticity and associated cognitive processes [50]. Collectively, these four unique core targets of KarXT—AKT1, IGF1, KDR, and CDC42—may contribute to improvements in negative and cognitive symptoms by promoting synaptic plasticity, neurogenesis, stress resistance, and immune modulation. However, it is important to emphasize that the associations between these KarXT-specific targets and antipsychotic effects are solely based on computational analyses, and as such they remain hypothetical. Although biologically plausible, further functional validation, including transcriptomic profiling, cellular assays, and animal studies, is required to confirm their mechanistic involvement in KarXT's therapeutic effects.

In contrast, all unique core targets of clozapine implicated in schizophrenia encode cytokines or chemokines involve in immune responses. Imbalances in cytokine levels and concomitant neuroinflammation and immune dysfunction are strongly implicated in the pathogenesis of schizophrenia [51,52]. In fact, several meta-analyses have concluded that interleukin (IL)-6, IL-8, IL-10, interferon-gamma (IFNG), and tumor necrosis factor (TNF) are elevated in both drug-naïve and chronically medicated schizophrenia patients [[53], [54], [55]]. Activated NF-κB dimers translocate to the nucleus, where they induce the expression of proinflammatory cytokines such as TNF, IL-6, and IFNG and chemokines such as CXCL1, CXCL2, and CXCL8 [56]. Clozapine significantly upregulated the expression of IL-1β, IL-6, and TNF-α in the pancreatic tissue of mice [57]. Thus, these four unique core targets of clozapine may address symptoms by regulating cytokine signaling and neuroinflammation.

GO enrichment analyses revealed that xanomeline influenced schizophrenia via multiple biological processes, including ‘gene expression regulation,’ ‘neuron apoptotic process,’ and ‘response to xenobiotic stimulus.’ KEGG pathway analysis identified MAPK signaling as a key pathway mediating the effects of xanomeline, which aligned with our previous study, showing that fingolimod alleviated cognitive impairment in schizophrenia by regulating apoptosis and inflammation through the PI3K-AKT and MAPK signaling pathways [58]. Haloperidol and olanzapine also modulate the MAPK and VEGF signaling pathways, contributing to their neuroprotective effects [59]. In line with these findings, our current computational analysis indicated that KarXT could influence schizophrenia via the MAPK signaling pathway, which was linked to apoptosis, inflammation, and neuroprotection.

GO enrichment analyses revealed that clozapine affects schizophrenia via multiple biological processes, including ‘gene expression regulation’, ‘positive regulation of tyrosine phosphorylation of STAT protein’, and ‘apoptotic process regulation’. Maximal IL-2 signaling depends on the tyrosine phosphorylation of both STAT5A and STAT5B [60], indicating that regulation of inflammation may be a major contributor to the therapeutic actions of clozapine. Consistent with this notion, KEGG pathway analysis identified the IL-17 signaling pathway as another key mediator of clozapine actions on schizophrenia. Further, a study of 38 cytokines/chemokines in military veterans with schizophrenia found that alterations in certain cytokines (including IL-6, TNF, and IFNG) within the IL-17 signaling pathway contribute to the development of psychotic symptoms [61]. In line with these findings, our computational analysis suggests that clozapine may affect schizophrenia via the IL-17 signaling pathway, which is associated with apoptosis, inflammation, and immune responses.

Molecular docking, a crucial technique in computer-aided drug design, largely validated the network pharmacology analysis results, as binding affinities for core targets ranged from −5.2 to −9.4 ​kcal/mol, suggesting stable interactions. However, additional pharmacological experiments are needed to confirm these interactions and their relevance to therapeutic mechanisms and responses.

This study still has several limitations. First, the reliability and accuracy of the predictions are reliant on the quality of the data submitted to these online databases. Second, while network pharmacology provides qualitative insights into potential drug targets for diseases, it does not provide critical qualitative information such as dose–response data. Third, we did not consider the potential effects of trospium. Although trospium has limited blood-brain barrier permeability [10,39,[62], [63], [64]], its side effects may still contribute to KarXT tolerability and patient compliance. Finally, our findings are based entirely on in silico analyses and lack experimental validation. Therefore, further basic and clinical investigations are warranted. Future studies should aim to validate these computational predictions through transcriptomic and proteomic profiling, the analysis of patient-derived samples, and in vitro or in vivo functional assays. These efforts will help confirm the biological relevance of our findings and facilitate their potential application in schizophrenia treatment.

Conclusions

Our study is the first to apply bioinformatics methods, including network pharmacology and molecular docking, to systematically compare the molecular mechanisms of KarXT to clozapine for the treatment of schizophrenia. KarXT and clozapine share three core targets, FOS, CASP3, and NFKB1, which are associated with immune modulation, neuronal function, and apoptosis. Distinct targets for KarXT, such as AKT1, IGF1, KDR, and CDC42, are associated with the MAPK signaling pathway, while distinct targets for clozapine, such as TNF, IL6, IFNG, and CXCL8, are associated with the IL-17 signaling pathway. These distinct molecular interactions may confer unique therapeutic responses. These findings may aid in more appropriate application of KarXT and clozapine for treating patients with schizophrenia resistant to other agents.

Data availability

The data supporting the results of this study are available upon reasonable request from the corresponding author.

Author contributions

Chuanun Zuo and Chao Li: Designed this study and Original protocol, Methodology, Data curation and Formal analysis. Chuanjun Zhuo and Qiuyu Zhang: Methodology, Formal analysis. Chuanjun Zhuo and Lei Yang: Software, Formal analysis. Chuanjun Zhuo and Yachen Li: Resources, Data curation. Ximing Chen: Resources, Methodology. Ranli Li: Methodology, Data curation. Chuanjun Zhuo and Chao Li: Supervision, Resources. Xiaoyan Ma and Lina Wang: Investigation, Formal analysis. Hongjun Tian: Software, Methodology. Chuanjun Zhuo and Chao Li: Writing–review & editing, Methodology, Investigation, Conceptualization.

Funding

This work was supported by grants from the National Natural Science Foundation of China (Nos. 82171503 and 81871052) to Chuanjun Zhuo.

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgments

We acknowledge publicly available online databases and their contributors for providing meaningful datasets.

Contributor Information

Chuanjun Zhuo, Email: zhuochuanjun@tmu.edu.cn.

Chao Li, Email: lichaotjmh@163.com.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Data Availability Statement

The data supporting the results of this study are available upon reasonable request from the corresponding author.

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