Introduction

Tacrolimus is a calcineurin inhibitor extensively utilized as a primary immunosuppressive therapy after organ transplantation to prevent graft rejection1. With the rising number of solid organ transplants in recent years, The Lancet reports that the global population of kidney transplant recipients has surpassed 25 million and is projected to double to 54 million by 20302. While the use of tacrolimus has significantly enhanced graft survival rates, it is associated with several adverse effects, including neurotoxicity, nephrotoxicity, and post-transplant diabetes1,3,4.

Tremor is among the most prevalent symptoms of tacrolimus-induced neurotoxicity, typically presenting as fine resting and action tremors in the limbs. This condition severely impacts the quality of life and mental health of transplant recipients5,6. Human tremor pathophysiology generally involves dysregulation of neuronal circuits, such as the basal ganglia-thalamocortical network, and imbalances in neurotransmitters (such as dopamine, GABA) or ion channels (such as voltage-gated sodium/potassium channels)7,8,9. The specific mechanisms underlying tacrolimus-induced tremor remain unclear. It is possible that the inhibition of calcineurin disrupts calcium-dependent signaling pathways, thereby leading to neuronal hyperexcitability3. A three-year follow-up study involving 689 solid organ transplant recipients indicated that 41.7% reported mild to severe tremors and identified tacrolimus plasma concentrations as an independent factor influencing mild tremor6. Research suggests that the neurotoxic effects of tacrolimus may arise from the high expression of calcineurin in the cerebral cortex, striatum, and substantia nigra. The lipophilic nature of tacrolimus allows it to cross the blood-brain barrier and enter the central nervous system, potentially leading to damage in the brain’s white matter10. Moreover, tacrolimus can disrupt mitochondrial function, activating proteases and generating free radicals that contribute to neuronal apoptosis or death, thereby causing neurotoxicity11. However, the pathophysiology and molecular mechanisms underlying tacrolimus-induced neurotoxicity, including tremor, remain poorly understood.

Current management approaches for tacrolimus-induced tremor encompass dose reduction, replacement with alternative immunosuppressants like cyclosporine, and the use of symptomatic therapies including β-blockers, such as propranolol and anticonvulsants such as gabapentin3,12,13. However, these approaches are often suboptimal. Dose reduction risks graft rejection, alternative agents carry their own toxicity profiles, and symptomatic treatments lack specificity. The absence of targeted therapies underscores an urgent need to delineate the molecular drivers of tremor, which could enable early intervention, biomarker development, and personalized dosing strategies to mitigate neurotoxicity while preserving immunosuppressive efficacy.

Network toxicology, as an emerging interdisciplinary field, integrates molecular docking technology to investigate the toxicity mechanisms of compounds effectively14. It is grounded in systems biology, genomics, proteomics, and multi-dimensional pharmacology, employing technologies such as omics, high-throughput screening, network visualization, and analysis to delineate the complex relationships among “drug-target-gene-gene” biological networks15. Initially employed in computer-aided drug design, molecular docking technology predicts the binding modes and affinities of small molecules to proteins16. Recently, this technology has gained traction in network toxicology for modeling interactions between compounds and target proteins, permitting simulations of these interactions and calculations of binding affinities and modes17. The superior performance of CB-Dock2 in ligand-receptor interaction studies stems from its knowledge-driven docking framework, which integrates structural bioinformatics principles to enhance binding cavity localization and conformational pose optimization18. Compared to conventional docking tools, this platform achieves higher predictive accuracy in binding site determination through its curvature-based cavity detection algorithm and template-guided homology modeling18. Such advancements have established CB-Dock2 as a benchmark tool in computational pharmacology, with widespread adoption for structure-based drug discovery and toxicity prediction across the scientific community. Integrating network toxicology with molecular docking combines systems-level network analysis with atomic-resolution binding insights, enabling comprehensive toxicity mechanism studies and target prediction.

This study hypothesizes that tacrolimus-induced tremor arises from perturbations in specific molecular targets and signaling pathways. To investigate this hypothesis, we employed network toxicology and molecular docking to identify the core molecular targets and pathways underlying tacrolimus-associated tremor. Network toxicology enables the construction of a complex “drug-target-pathway” network, facilitating the prioritization of key nodes, while molecular docking provides atomic-level insights into ligand-receptor interactions. By integrating these two approaches, we aimed to comprehensively elucidate the neurotoxic mechanisms of tacrolimus and offer a theoretical foundation for optimizing therapeutic strategies for transplant recipients.

Methods

Network analysis of tacrolimus toxicity

To gather toxicity information on tacrolimus, the SMILES representation and molecular structure were obtained from the PubChem database (https://pubchem.ncbi.nlm.nih.gov/). The SMILES formula was then imported into ProTox-3.0 (https://tox.charite.de/protox3/) and ADMETlab 3.0 (https://admetlab3.scbdd.com/), and the results from these platforms were integrated to predict the toxicity of tacrolimus19,20. ProTox-3.0 is a machine learning-based toxicity prediction platform specializing in organ-specific toxicity endpoints and adverse outcome pathway analysis21. Its capability to predict toxicity targets, including neurotoxicity, aligns with our focus on tacrolimus-induced tremor mechanisms, providing critical hypotheses for elucidating the drug’s mode of action. ADMETlab 3.0, a multi-parameter ADMET prediction system, quantitatively evaluates absorption, distribution, metabolism, excretion, and toxicity profiles22. By evaluating pharmacokinetic and toxicity endpoints, including blood-brain barrier permeability, cytochrome P450 interactions, and hepatotoxicity, this tool provides crucial data for assessing the neurotoxic risk of tacrolimus and its trans-barrier distribution kinetics.

Collection of tacrolimus targets

Targets of tacrolimus were predicted using ChEMBL (https://www.ebi.ac.uk/chembl/), Similarity Ensemble Approach (SEA, https://sea.bkslab.org/), TargetNet (http://targetnet.scbdd.com/), and SwissTargetPrediction (STP, http://www.swisstargetprediction.ch/) databases23,24,25,26. The inclusion criteria were: (1) targets annotated for Homo sapiens; (2) STP probability score ≥ 0.5 (high confidence); (3) ChEMBL activity values (IC50/Ki) ≤ 10 µM. Low-confidence predictions (probability < 0.5) and non-human targets were excluded. Gene names were standardized using UniProt, and duplicates were removed to construct a tacrolimus-related target library.

Screening of tremor-related targets

Tremor-related targets were identified using three authoritative databases: GeneCards (https://www.genecards.org/), Online Mendelian Inheritance in Man (OMIM, https://omim.org/), and the Therapeutic Target Database (TTD, http://db.idrblab.net/ttd/). To identify genes closely associated with tremor, a median value was established as the minimum threshold, and genes with values equal to or exceeding this threshold were selected to create a tremor-related target gene library27. A Venn diagram was utilized to illustrate the overlap between tacrolimus targets and the tremor-related target gene library, with the intersecting genes considered potential targets for tacrolimus-induced tremor.

Protein interaction network analysis and core target screening

The overlapping target genes associated with tacrolimus-induced tremor were imported into the STRING 12.0 database (https://cn.string-db.org/), with the species restricted to “Homo sapiens,” to map the proteins encoded by these target genes and their interaction networks28. The resulting data from STRING 12.0 were then imported into Cytoscape (v3.10.3) software29, where we calculated the topological properties of the nodes and edges in the network, including degree, weighted degree, closeness centrality, and betweenness centrality, to construct protein-protein interaction (PPI) networks. The top five core targets for molecular docking were selected based on the following criteria: (1) closeness centrality greater than the median; (2) radial direction greater than the median; and (3) two degree values exceeding the median.

Function and pathway enrichment analysis of potential targets

Functional and pathway enrichment analyses of the screened target genes related to tacrolimus-induced tremor were conducted using Gene Ontology (GO, https://www.geneontology.org/) and Kyoto Encyclopedia of Genes and Genomes (KEGG, https://www.genome.jp/kegg/)30,31. Initially, GO functional analysis was performed to evaluate the biological functions of these target genes, categorizing them into biological processes (BP), cellular components (CC), and molecular functions (MF). Subsequently, KEGG functional enrichment analysis was conducted to identify pathways associated with tacrolimus-induced tremor. The top 10 and 5 GO functions were ranked by their -log10(p-value) from highest to lowest, and the top 10 and 5 KEGG pathways were shown in both bar and bubble charts. Plots were generated using “pheatmap” and “ggplot2” packages to visualize the GO and KEGG results. Analysis utilized the hypergeometric test with Benjamini-Hochberg correction (FDR < 0.05).

Molecular docking

Molecular docking was performed using CB-Dock2 to elucidate the molecular interactions and binding modes between tacrolimus and the predicted key target proteins. The 2D structure of tacrolimus was retrieved from the PubChem database and saved in SDF format. Open Babel (v2.4.1) software was utilized for file conversion to PDB format. This study employed the novel blind docking tool CB-Dock2, which predicts the binding regions of proteins using a curvature-based cavity detection method to determine the center and size of the binding site. This tool integrates with the state-of-the-art docking software AutoDock Vina (v1.2.3), enhancing the accuracy of binding site identification and binding mode predictions18. The online CB-Dock2 database (http://clab.labshare.co.uk/cb-dock/php/index.php) was used to obtain the PDB format of the protein structures from the PDB database (https://www.rcsb.org/) for verification and visual analysis. Each docking experiment was repeated three times under identical conditions. The flow chart of this study is shown in Fig. 1.

Fig. 1
figure 1

Flow chart of the study.

Full size image

Results

Toxicity assessment of tacrolimus

In this study, we retrieved the standard structure of tacrolimus (Fig. 2A) and its molecular details (Table 1) from the PubChem database. We assessed the toxicity of tacrolimus using ProTox-3.0 and ADMETlab 3.0 databases, which allowed us to obtain a comprehensive overview of its toxicological profile (Fig. 2B, Supplementary Information 1). The toxicity assessment revealed that tacrolimus is primarily associated with neurotoxicity, aligning with previous reports indicating that neurotoxicity is one of the most prevalent adverse effects of this drug32. Given the significant prevalence of tacrolimus-induced neurotoxicity, this finding underscores the importance of investigating tacrolimus-induced tremor, a notable component of post-transplant neurotoxicities.

Fig. 2
figure 2

(A) Structure of the tacrolimus compound sourced from the National Center for Biotechnology Information (NCBI) PubChem Compound ID: 445,643. (B) Toxicity radar chart illustrating the toxicity profile of tacrolimus.

Full size image
Table 1 Details of the tacrolimus molecule.
Full size table

Identification of toxic targets for tacrolimus-induced post-transplantation tremor

In this study, we identified a total of 2,428 tacrolimus target genes from the ChEMBL, SEA, TargetNet, and SwissTargetPrediction databases. Additionally, we obtained 359 tremor-related target genes from GeneCards, OMIM, and the Therapeutic Target Database. After removing duplicate targets, we identified 43 intersecting targets through Venn diagram analysis (Fig. 3A,B), which were considered potential targets for tacrolimus-induced post-transplantation tremor.

Fig. 3
figure 3

(A) Venn diagram displaying the common targets between tacrolimus and tremor-associated targets. (B) Network representation of the interactions among Tacrolimus, its targets, and tremor-related genes.

Full size image

PPI network analysis and core target identification

We constructed a PPI network diagram (Fig. 4A) using the overlapping targets via the STRING database, resulting in a highly interconnected network comprising 43 nodes and 73 edges, with a PPI enrichment p-value of 1.63e-14. The topological properties of the network nodes, including degree, weighted degree, closeness centrality, and betweenness centrality, were analyzed using Cytoscape 3.10.3 software. Additionally, a PPI diagram was created to illustrate the interactions among the 43 core targets associated with tacrolimus-induced post-transplantation tremor (Fig. 4B). Key findings include the identification of five core targets based on betweenness centrality: serine and threonine kinase 1 (AKT1), glucocerebrosidase (GBA), sodium channel voltage-gated 8 (SCN8A), sodium channel voltage-gated 2 (SCN2A), and sodium channel voltage-gated 4 (SCN4A). The size of the nodes and the intensity of their red color corresponded to the number of connections, indicating their significance within the network. Current research suggests that these targets play crucial roles in processes such as cell apoptosis, glucose metabolism, cell membrane lipid metabolism, and nerve signal transmission33,34,35,36.

Fig. 4
figure 4

(A) Protein-protein interaction (PPI) network diagram derived from the STRING database. (B) Filtered PPI network diagram generated using Cytoscape software.

Full size image

Function and pathway enrichment analysis of potential targets

We conducted GO analysis on the 43 potential targets, restricting the species to Homo sapiens. A total of 589 statistically significant GO terms were analyzed, comprising 528 BPs, 33 CC, and 28 MFs. We selected the top 10 terms with the lowest false discovery rate (FDR) from each category to present them as classification histograms and bubble charts (Fig. 5A,B).

Fig. 5
figure 5

GO enrichment analysis of potential targets (top 10). (A) Histogram showcasing the top 10 enriched terms across BP, CC, and MF. (B) Bubble chart where the size of each bubble corresponds to gene expression levels within the identified pathways.

Full size image

Additionally, we performed KEGG analysis on these 43 potential targets to identify the signaling pathways in which they are involved. This analysis revealed 10 enriched signaling pathways, which were illustrated using significance bubble plots and histograms (Fig. 6A and B)37.

Fig. 6
figure 6

KEGG enrichment analysis of potential targets (top 10). (A) Histogram displaying the frequency and significance of enrichment for each pathway. (B) Bubble chart visualizing the top 10 enriched KEGG signaling pathways, ordered by FDR value.

Full size image

GO analysis demonstrated that the potential targets are enriched in biosynthetic processes and stress responses, including conditions of low oxygen availability like hypoxia. Moreover, these targets are associated with cation channel complexes, particularly at neuron projection termini, as well as neuromuscular regulation. KEGG enrichment analysis highlighted the involvement of these targets in several critical signaling pathways, including the dopaminergic synapse, Parkinson’s disease, Rap1 signaling pathway, spinocerebellar ataxia, and apoptosis.

Further, we conducted GO and KEGG pathway analysis for the top five core targets (Fig. 7A,B). In the GO-BP analysis, key biological processes such as membrane depolarization during action potential, neuronal action potential propagation, and sodium ion transmembrane transport were significantly enriched, all of which are critical for regulating neuronal excitability and synaptic signaling. The enrichment of transmission of nerve impulse and regulation of membrane potential further suggests that tacrolimus may disrupt the equilibrium of neuronal polarization-depolarization, leading to abnormal oscillations in membrane potential. GO-CC results revealed a significant enrichment of voltage - gated sodium channel complexes, as well as sodium channel complexes, at the nodes of Ranvier and within cation channel complexes. This finding suggests that dysfunction of sodium channels may disrupt the velocity and rhythmic propagation of action potentials, ultimately leading to abnormal neuronal synchronization, which is a characteristic feature of the pathophysiology of tremor35. At the molecular functional level (GO-MF), the strong association with voltage-gated sodium channel activity and sodium ion transmembrane transporter activity implies that tacrolimus or its metabolites may directly or indirectly delay sodium channel inactivation or prolong channel opening, resulting in sustained depolarization and repetitive action potential firing.

Fig. 7
figure 7

GO and KEGG enrichment analysis of five core targets. (A) Bubble chart visualizing the enriched GO results. (B) Bubble chart visualizing the enriched KEGG signaling pathways, ordered by FDR value.

Full size image

KEGG pathway (Fig. 7B) analysis revealed perturbations in sphingolipid metabolism, which may contribute to compromised neuronal membrane stability and altered synaptic plasticity. Dysregulation of the VEGF signaling pathway and GnRH secretion pathway could exacerbate tremors through impaired neurovascular unit function or neuroendocrine modulation. Of particular interest, functional abnormalities in voltage-gated sodium channels have been directly linked to tacrolimus-induced tremors, potentially mediated by calcineurin inhibition-induced imbalances in ion channel phosphorylation states, which alter sodium channel gating dynamics38. These findings are consistent with the mechanisms underlying tacrolimus-induced post-transplantation tremor.

Molecular docking of tacrolimus and tremor-related core targets

To investigate the interactions between tacrolimus and the five core targets, we conducted molecular docking using AutoDock Vina. The docking results indicated that the binding energies of the five target proteins ranged from - 7.8 to - 50.3 kcal/mol: AKT1 (-7.8 kcal/mol), GBA (-8.9 kcal/mol), SCN8A (-11.6 kcal/mol), SCN2A (-11.0 kcal/mol), and SCN4A (-7.8 kcal/mol) (Fig. 8). While all binding energies were below - 7.5 kcal/mol, indicating spontaneous binding, we further evaluated the binding affinity and selectivity of tacrolimus across these targets to assess their biological relevance. Binding affinity, quantified as equilibrium dissociation constants (Kd), was estimated using the relationship ΔG ≈− RTln(Kd), where lower Kd values correlate with higher binding stability. The Kd values of these five core targets are as follows: AKT1 = 3.1 µM, GBA = 0.53 µM, SCN8A = 6.7 nM, SCN2A = 27 nM, and SCN4A = 3.1 µM. Among them, SCN8A and SCN2A exhibit the highest affinity. This finding highlights the potential significance of these interactions in the molecular mechanisms underlying tacrolimus-induced neurotoxicity related to tremor.

Fig. 8
figure 8

Molecular docking results for core targets. (A) Interaction between tacrolimus and AKT1; (B) Interaction between tacrolimus and GBA; (C) Interaction between tacrolimus and SCN8A; (D) Interaction between tacrolimus and SCN2A; (E) Interaction between tacrolimus and SCN4A.

Full size image

Discussion

Tacrolimus is widely used to prevent rejection following transplantation; however, its neurotoxic side effects, such as tremor, significantly impact patients’ quality of life. Currently, the specific mechanisms underlying tacrolimus-induced tremor remain unclear. Therefore, this study utilized multiple databases—including ChEMBL, Similarity Ensemble Approach, TargetNet, SwissTargetPrediction, OMIM, and the Therapeutic Target Database—to identify 43 potential targets associated with tacrolimus-induced tremor. Subsequently, we constructed a PPI network diagram for these targets using the STRING and Cytoscape platforms, employing the betweenness centrality method to extract five key nodes: AKT1, GBA, SCN8A, SCN2A, and SCN4A. These nodes are considered the core targets of tacrolimus-induced tremor. Finally, we employed molecular docking technology to explore the interactions between these five core targets and tacrolimus, providing a theoretical basis for studying tremor in the context of tacrolimus-induced neurotoxicity.

AKT1 is a member of the phosphorylase family and serves as a crucial cell signaling protein. It plays a pivotal role in the metabolism of various intracellular substances and is closely associated with oxidative stress, apoptosis, and protein synthesis39,40,41. Research in patients with Parkinson’s disease has demonstrated that activation of the AKT1-CREB pathway can enhance the expression of RNF146, thereby inhibiting PARP1-mediated neuronal death42 and protecting dopaminergic neurons, which may help alleviate tremor symptoms. Additionally, tacrolimus’s effect on calcineurin is notably expressed in brain regions such as the cerebral cortex, striatum, substantia nigra, cerebellum, and hippocampus. Calcineurin has been implicated in Parkinson’s disease pathology, where its dysregulation contributes to dopaminergic neuron loss via aberrant protein aggregation and mitochondrial dysfunction43,44,45. Due to its high lipophilicity, tacrolimus can cross the blood-brain barrier, potentially damaging white matter10 and leading to neurotoxicity. Considering that Parkinson’s disease, often referred to as shaking palsy, is linked to the pathogenesis of the nigrostriatal pathway, it is suggested that the mechanism by which tacrolimus affects the nervous system may resemble the pathological mechanisms of Parkinson’s disease. Interestingly, while tacrolimus primarily inhibits calcineurin, our findings suggest that AKT1 is also implicated in tremor pathogenesis. This apparent paradox may arise from indirect regulatory mechanisms. Calcineurin inhibition by tacrolimus could disrupt phosphatase activity-dependent signaling pathways, leading to compensatory hyperphosphorylation of downstream substrates or altered cross-talk between calcineurin and AKT1-mediated pathways. For instance, calcineurin has been shown to dephosphorylate AKT1 substrates in certain contexts, and its inhibition might indirectly stabilize phosphorylated AKT1 targets, amplifying pro-survival or excitability-related signals in neurons46,47. This compensatory interaction can account for the significance of AKT1 in the PPI network, and tacrolimus directly binds to AKT1 with high-affinity.

GBA (glucocerebrosidase) is a lysosomal enzyme responsible for hydrolyzing glucosylceramide into ceramide and glucose48. Studies have shown that GBA-knockout Drosophila exhibit mitochondrial dysfunction characterized by oxidative stress, reduced ATP levels, neuroinflammation, and alterations in mitochondrial morphology49,50. Furthermore, GBA deficiency may impair autophagy and proteasome pathways, leading to the accumulation of dysfunctional mitochondria51. This aligns with the potential mechanism of tacrolimus-induced neurotoxicity, which involves mitochondrial dysfunction and oxidative stress in glial cells, providing a basis for studying the role of GBA in tacrolimus-induced tremor52.

The voltage-gated sodium channel alpha subunit gene family comprises 10 genes in the human genome, with SCN8A, SCN2A, and SCN4A being key players in the initiation and propagation of neuronal action potentials. One proposed mechanism of tacrolimus-induced neurotoxicity involves the modulation of excitatory and inhibitory neurotransmitter receptor activity53,54, resulting in alterations in neuronal excitability and membrane depolarization due to sodium ion influx, which is closely related to the voltage-gated sodium channel family. Studies have indicated that mutations in the SCN8A and SCN2A genes are associated with sodium channel mutation diseases and the onset of epileptic encephalopathy, characterized by intractable seizures, intellectual disability, movement disorders, and an increased risk of sudden unexpected death in epilepsy55,56. These findings suggest that SCN8A and SCN2A may play important roles in tacrolimus-induced tremor, although their specific mechanisms warrant further investigation. Notably, AKT1 was identified as a highly connected hub in the PPI network. However, molecular docking results showed that SCN2A and SCN8A had the strongest binding affinities for tacrolimus. This difference likely reflects the distinct contributions of two different mechanisms. The central role of AKT1 in the network may indicate its involvement in downstream signaling cascades, such as those related to apoptosis and synaptic plasticity, which exacerbate tremor - related pathological processes. In contrast, the high-affinity binding of SCN2A and SCN8A to tacrolimus may directly disrupt sodium channel gating, resulting in immediate neuronal hyperexcitability. These dual mechanisms, namely direct ion channel modulation and indirect kinase pathway dysregulation, may work together to drive tremor phenotypes, underscoring the complexity of tacrolimus-induced neurotoxicity.

GO and KEGG enrichment analyses of potential tacrolimus targets revealed that pathways related to Parkinson’s disease and dopaminergic synapses may be significantly involved in tacrolimus-induced tremor. This suggests that tacrolimus-induced tremor may resemble idiopathic or parkinsonian tremor, consistent with previous findings57. Moreover, studies indicate that oral administration of tacrolimus in post-transplantation patients can induce neurotoxic reactions such as tremor, headache, insomnia, and epileptic seizures58,59,60, further validating the reliability of our research methodology.

KEGG enrichment analysis of tacrolimus’s potential targets identified several important signaling pathways related to tremor, including the dopaminergic synapse, Parkinson’s disease, Rap1 signaling pathway, gap junctions, apoptosis, and spinocerebellar ataxia. The interplay among these signaling pathways is closely related to tremor, indicating that the mechanism underlying tacrolimus-induced tremor toxicity may align with the tremor mechanisms observed in Parkinson’s disease and could also be linked to apoptotic processes in nerve cells, ultimately leading to tremor development. Molecular docking analysis revealed that these five core targets bind stably to tacrolimus with binding energies lower than − 7.8 kcal/mol, further underscoring their significance in tacrolimus-induced tremor. Further studies are needed to confirm whether the mechanisms of tacrolimus-induced tremor resemble those of parkinsonian tremor.

This study not only elucidates the molecular mechanisms underlying tacrolimus-induced tremor but also proposes an innovative strategy that integrates network toxicology with molecular docking. While network toxicology enables a comprehensive analysis of drug toxicity mechanisms by constructing “toxicity-gene-toxic component-target” networks, it is important to acknowledge that computational models inherently simplify biological complexity. Experimental validation remains critical to confirm predictions, as biological systems are dynamic and influenced by factors such as genetic variability, epigenetic modifications, and environmental interactions61. Nevertheless, these tools offer advantages such as high efficiency and cost-effectiveness, enabling rapid prioritization of hypotheses for further investigation. By utilizing computational technology and bioinformatics tools, drug toxicity can be preliminarily screened, significantly reducing experimental costs and time. Molecular docking, conversely, serves as a complementary method to predict interactions between small molecules (such as tacrolimus) and biomolecular targets (such as ion channels, kinases), providing atomic-level insights into binding affinities and modes62,63. The combination of network toxicology and molecular docking can provide a more comprehensive evaluation of drug toxicity and introduce novel methodologies for investigating the toxicological mechanisms of compounds.

While this study offers valuable insights into the mechanisms of tacrolimus-induced tremor, it also has certain limitations. First, the identification of compounds and disease targets primarily relies on searches through several large databases, which may lead to less comprehensive results, as not all possible compounds and disease targets are included. Second, public databases such as GeneCards, OMIM, the Therapeutic Target Database, Similarity Ensemble Approach, TargetNet, and SwissTargetPrediction may undergo updates over time, potentially affecting the accuracy of core target identification and functional enrichment analyses. Lastly, this study does not provide an in-depth exploration of cross-research related to the five core targets implicated in tacrolimus-induced tremor. Future research should consider employing animal and cell experiments to further investigate the specific mechanisms of tacrolimus-induced tremor, ultimately aiding transplant clinicians in developing more targeted therapeutic strategies.

Conclusion

This study employs an innovative integration of network toxicology and molecular docking to elucidate the molecular mechanisms underlying tacrolimus-induced neurotoxic tremor, a previously underexplored area in post-transplant complication research. By systematically identifying 43 potential targets and prioritizing five core targets (AKT1, GBA, SCN8A, SCN2A, SCN4A), we provide the first evidence linking tacrolimus-induced tremor to perturbations in Dopaminergic synapse pathways, Parkinson’s disease-related signaling, and voltage-gated sodium channel dysfunction. Notably, our discovery of high-affinity interactions between tacrolimus and sodium channels (SCN8A/SCN2A) unveils a novel mechanistic axis distinct from classical calcineurin inhibition, offering a paradigm shift in understanding neurotoxicity associated with immunosuppressants. These findings hold significant translational implications. The identified targets and pathways not only serve as potential biomarkers for early tremor detection but also lay a foundation for developing targeted therapies to mitigate neurotoxicity without compromising immunosuppressive efficacy. While further experimental validation is warranted, this study advances personalized immunosuppression strategies and underscores the need for precision medicine in organ transplant care.