Search Results (701)

Gelatin is a natural biopolymer derived from collagen and is widely employed in biomedical applications because of its biocompatibility, biodegradability, low toxicity, water solubility, and intrinsic thermo-responsive gelation behavior. Owing to the presence of bioactive motifs and its ability to form hydrogels under mild conditions, gelatin has emerged as a promising material for tissue engineering, drug delivery, and in vitro modeling. However, the poor mechanical stability and rapid dissolution of native gelatin under physiological conditions limit its direct use in advanced biomedical systems. To overcome these drawbacks, several chemical modification strategies have been developed, among which gelatin methacryloyl (GelMA) is one of the most investigated derivatives. GelMA combines the biological advantages of gelatin with photo-crosslinkable methacryloyl groups, enabling the fabrication of stable hydrogels with tunable mechanical, rheological, and degradation properties. This review adopts a source-to-performance perspective, systematically examining how gelatin origin, processing history, molecular characteristics, and Bloom strength may influence GelMA functionalization and subsequent hydrogel network formation. Particular attention is given to Type A and Type B gelatin, while recognizing that this classification does not fully capture the variability in the gelatin precursor. The review critically discusses how precursor characteristics interact with key synthesis and formulation parameters, including the degree of substitution/functionalization (DS/DoF), polymer concentration, photoinitiator content, and photo-crosslinking conditions, ultimately affecting network formation and the mechanical, rheological, swelling, porosity, degradation, and biological properties of GelMA hydrogels. This interconnected view highlights how variability introduced at the precursor level may propagate through functionalization and crosslinking, contributing to differences in GelMA performance and limiting direct comparison among reported formulations. The implications of these material-dependent properties are examined across three major biomedical application areas: tissue engineering, controlled drug delivery, and physiologically relevant three-dimensional in vitro models. Finally, current challenges and emerging opportunities related to GelMA standardization, biofabrication, multifunctional hydrogel design, personalized medicine, and clinical translation are considered. Overall, GelMA is presented not simply as a versatile biomaterial, but as a tunable protein-derived platform whose performance depends on the interconnected effects of precursor characteristics, functionalization, formulation, and network formation. Full article

The transport mechanisms for substrates and nutrients within the heme pathway of the Toxoplasma gondii apicoplast remain poorly understood. However, studies on heme metabolic enzymes have employed disparate genetic manipulation approaches, limiting direct phenotypic comparisons among different enzymes. This research involved screening potential apicoplast proteins in Toxoplasma gondii by cross-referencing and analyzing protein–protein interaction networks. Within the heme enzyme pathway of the apicoplast, eight enzymes were found to be predominantly conserved in the Sarcocystide family. Utilizing the CRISPR-Cas9 system alongside a U1 snRNP-mediated gene-silencing approach, we developed inducible knockdown strains—iKD-PBGD, iKD-UROS, and iKD-UROD—targeting three key metabolic enzymes crucial for the parasite lytic cycle, as demonstrated through replication experiments. To investigate the transport mechanisms for heme-related nutrients or substrates, we knocked down these three enzymes, using TgGRA12 as an initial marker. Continuous fluorescence signals highlighted the parasitophorous vacuole (PV) membrane surrounding tachyzoites during both early and late replication stages, particularly at 48 h post-rapamycin treatment, indicating a transformation of the cyst-like PV resembling that in Toxoplasma gondii. Phenotypically, knockdown of these heme enzymes led to the formation of slowly replicating, cyst-like parasitophorous vacuoles. However, this morphological change did not significantly affect the acute virulence of the parasites in vivo, as determined by mouse survival assays. This study explored the functional roles of the three intermediate metabolic enzymes, offering a novel viewpoint on the gradual demise of Toxoplasma gondii as a potential target for drug development. Full article

Polycystic ovary syndrome (PCOS) is a multifactorial endocrine disorder characterized by chronic inflammation, insulin resistance, and reproductive dysfunction. Although empagliflozin, a sodium-glucose cotransporter-2 inhibitor, has demonstrated anti-inflammatory and metabolic benefits, its molecular mechanisms in PCOS remain poorly understood. This study investigated the anti-inflammatory mechanisms of empagliflozin in PCOS using network pharmacology, molecular docking, and molecular dynamics simulations. Potential drug targets were identified using SwissTargetPrediction and SuperPred, while PCOS- and inflammation-related genes were obtained from GeneCards. Overlapping targets were subjected to Gene Ontology, Kyoto Encyclopedia of Genes and Genomes, protein–protein interaction network, and hub gene analyses. Molecular docking and 50 ns molecular dynamics simulations were performed to evaluate binding affinity and complex stability. Key inflammatory targets identified included TNF, IL6, IL1B, TLR4, STAT3, and PTGS2, with significant enrichment in cytokine-mediated signaling, TNF signaling, and NF-κB pathways. Empagliflozin showed strong binding affinities for PTGS2 (−9.0 kcal/mol) and TLR4 (−8.8 kcal/mol), while molecular dynamics simulations demonstrated stable protein–ligand complexes throughout the simulation. These findings suggest that empagliflozin may alleviate PCOS-associated inflammation by modulating the TLR4/NF-κB/PTGS2 signaling axis, supporting its potential as a repurposed therapeutic agent for PCOS and providing a foundation for future experimental validation. Full article

Network pharmacology has become a central paradigm in modern drug discovery, replacing the reductionist “one drug, one target” view with a systems-level understanding of how compounds engage networks of proteins that are linked to disease. Despite its impact, a typical network-pharmacology study remains fragmented and technically demanding: researchers must query several independent databases, install and reconcile multiple standalone tools for target collection, network construction, hub-gene ranking and pathway enrichment, and then manually bridge the results into structure-based follow-up such as molecular docking. This fragmentation is a persistent barrier, particularly for experimental and non-specialist users. Here, we present the network-pharmacology module of Drug Design Studio (DDS) 2.0, a unified, user-friendly platform that streamlines the entire workflow—disease target retrieval, compound–target prediction, shared-target identification, protein–protein interaction (PPI) network construction, hub-gene ranking and Gene Ontology/pathway enrichment—within a single guided interface, consolidating steps that otherwise require several separate tools. Crucially, DDS 2.0 links the resulting hub genes directly to the docking and virtual-screening engine introduced in the previous DDS releases: representative experimental structures and mutant forms—for instance, resistance-conferring variants found in drug-resistant strains—of the target proteins are selected and streamed into a docking-ready workspace, with dedicated support for covalent binders. We validate the module against four independent published network-pharmacology studies spanning diverse diseases; DDS reproduces the reported hub genes with a mean recovery (recall) of 0.85 (range 0.80–0.90) and a mean Jaccard index of 0.74, and recovers the corresponding target sets and enriched pathways. DDS 2.0 thus delivers an integrated route from systems-level analysis to structure-based drug design. DDS 2.0 is freely and publicly accessible. Comprehensive user documentation is built directly into DDS and can be accessed at any time from the Documentation panel. Full article

Background: Dystonia is a severe neurological disorder with enigmatic pathogenesis. Current treatment options are limited in preventing the disease progression, underscoring the urgent need for new targeted therapeutic agents to develop more effective therapies. Methods: We performed a proteome-wide Mendelian randomization (MR) study and sensitivity analyses to evaluate the causal relationships between dystonia and proteins. GO and KEGG enrichment analysis of dystonia-associated proteins was conducted. Then, we built PPI network and identified the expression of hub-genes in specific brain neurons in single-cell sequencing data. Additionally, we performed drug enrichment analysis of hub-genes, and employed network pharmacology and molecular docking methods to identify potential drugs for dystonia. Results: Our study identified genetically predicted associations consistent with a potential causal effect between 51 proteins and risk of dystonia. GO and KEGG enrichment analyses revealed that these proteins are involved cellular response to transforming growth factor-β stimulation and cytokine-cytokine receptor interaction. Notably, the PPI network exhibited 21 community relationships within the regulatory network among the 51 dystonia-associated proteins identified. The single-cell RNA annotations for brain cluster specificity revealed Tumor necrosis factor (TNF) was highly expressed in microglia cells. Drug enrichment analysis identified five traditional Chinese medicine monomers (paeoniflorin, artesunate, ginsenoside Rh1, psoralen, and quercetin dihydrate) as candidates for molecular docking analysis. Among these, paeoniflorin-TNF, quercetin dihydrate-TNF, and artesunate-TNF exhibited the highest binding energy (−9.1 kcal/mol). Conclusions: Our molecular-docking analysis suggested that traditional Chinese medicine monomers including paeoniflorin, quercetin dihydrate, and artesunate may serve as promising candidates for future drug development. Full article

Background/Objectives: Chronic apical periodontitis (CAP) is a persistent periapical inflammatory lesion whose treatment still fails in a substantial fraction of cases, and no molecularly targeted adjunct exists. We aimed to identify druggable hub genes and small-molecule candidates suitable for local intracanal delivery using an integrative computational pipeline. Methods: CAP-associated genes were retrieved from GeneCards and the Open Targets Platform. A protein–protein interaction network, MCODE modules, and three CytoHubba centrality algorithms were used to define hub genes. DrugClip virtual screening and ADMET filtering calibrated for intracanal safety selected candidates, which were assessed by molecular docking with AutoDock Vina, 100 ns molecular dynamics simulations, and per-snapshot binding free-energy analysis. Results: Retrieval yielded 110 non-redundant genes; inflammation was the most enriched process. Eighteen hub genes emerged, with CCL5 and CCR2 among the most central. Screening prioritized MCULE-9834903214 for CCL5 and MCULE-9117306970 for CCR2. The CCR2 complex remained stably engaged, whereas the CCL5 complex was only metastable. Per-snapshot binding free energies over 35–100 ns overlapped in central tendency (−30.5 ± 35.4 vs. −21.6 ± 66.7 kJ·mol⁻¹ for CCR2 and CCL5) but separated by dispersion, with the CCR2 energy trace being nearly twofold tighter. Conclusions: Neither docking affinity nor the mean binding free energy distinguished the leads; the consistency of the binding-energy trace did. MCULE-9117306970 is the priority candidate for experimental validation as a locally delivered anti-inflammatory adjunct against CCR2; MCULE-9834903214 requires redesign to achieve a stable polar anchor. This pipeline yields experimentally testable hypotheses rather than validated therapeutics. Full article

Background and Objectives: Alzheimer’s disease (AD) is the most common neurodegenerative disorder, and current therapies provide only limited symptomatic relief without effectively slowing its progression. Increasing evidence suggests that aberrant activation of the Janus kinase/signal transducer and activator of transcription (JAK/STAT) signaling cascade contributes to AD-associated neuroinflammation. This study investigated the therapeutic potential and molecular mechanisms of JAK inhibitors in AD using integrated bioinformatics and network pharmacology approaches. Materials and Methods: Potential anti-AD targets of JAK inhibitors were identified using the SwissTargetPrediction and GeneCards databases. Functional enrichment, protein–protein interaction (PPI) analysis, transcriptomic validation using public datasets, regulatory network construction, molecular docking, normal mode analysis (NMA), and absorption, distribution, metabolism, excretion, and toxicity (ADMET) prediction were performed to investigate the potential mechanisms of action of these drugs in AD. Results: Our analysis identified 163 shared targets between JAK inhibitors and AD. Enrichment analysis revealed that these genes were primarily involved in protein phosphorylation and were enriched in key signaling pathways, including the neurotrophin, phosphoinositide 3-kinase/protein kinase B (PI3K/Akt), and mitogen-activated protein kinase (MAPK) signaling pathways. PPI analysis identified AKT1, BCL2, SRC, STAT3, and TNF as five highly ranked hub targets across multiple topological algorithms. Transcriptomic validation confirmed significantly higher expression of these targets in the prefrontal cortex of individuals with AD compared with normal subjects. Molecular docking indicated that pacritinib and momelotinib showed relatively favorable predicted interactions with the hub proteins, while NMA revealed differences in the predicted flexibility of the docked complexes. Furthermore, ADMET prediction showed that pacritinib possesses favorable pharmacokinetic properties for the treatment of AD. Conclusions: Collectively, these findings provide mechanistic insights into the potential effects of JAK inhibitors in AD and identify pacritinib as a computationally prioritized candidate that warrants experimental validation in appropriate AD models. However, as this study is based solely on computational analyses without wet-lab validation, the findings should be considered hypothesis-generating in silico evidence, and the potential safety concerns of pacritinib require further investigation. Full article

Background: Orientothele washanensis is a venomous spider with considerable ecological and scientific importance. Its venom, characterized by complex composition and ease of collection, serves as a valuable resource for the discovery of natural peptide drugs. Conventional wet-lab screening methods are limited by rigorous experimental conditions, high resource consumption, and long research cycles, which hinder the efficient identification of functional peptides from the venom gland transcriptome of this spider. Methods: To address these technical bottlenecks, this study developed a novel deep learning model named PepPI-DRN for peptide-protein interaction prediction. The model integrated a residual equivariant graph neural network, a residual 1-dimensional convolutional neural network, and a dual-modal attention mechanism by leveraging both sequence and structural features of peptides and proteins. Results: Results on an independent test set indicated that PepPI-DRN achieved the competitive or superior performance compared with state-of-the-art methods on multiple key evaluation metrics. Candidate peptides with high interaction probabilities against targets were obtained from the venom gland transcriptome of Orientothele washanensis. Furthermore, lead peptides with high binding strength and good structural stability were identified from candidate peptides by molecular docking, and molecular dynamics simulation. Conclusions: These results showed that the pipeline with PepPI-DRN, molecular docking and molecular dynamics simulation enabled efficient and reliable identification of lead peptides from the venom gland transcriptome of Orientothele washanensis, providing a robust and effective strategy for the discovery and development of natural peptide drugs from the spider venom. Full article

Background/Objectives: Osteoarthritis (OA) is a whole-joint disease, and cellular senescence is one of several processes associated with cartilage degeneration. This study aimed to identify senescence-associated differentially expressed genes in established OA and to examine checkpoint-related candidates without assuming a causal checkpoint-imbalance mechanism. Methods: Five Gene Expression Omnibus (GEO) datasets spanning articular-cartilage tissue and primary cartilage-derived chondrocytes (GSE57218, GSE117999, GSE114007, GSE246425, and GSE169077) were integrated as a training cohort; the meniscus dataset GSE98918 was reserved as an independent cross-tissue validation cohort. OA-associated differentially expressed genes (DEGs) were intersected with CELLAGE genes. Enrichment, protein–protein interaction, transcription-factor, competing endogenous RNA, drug-enrichment, and molecular-docking analyses were performed. CDK6 and WEE1 expression was evaluated in IL-1β-treated human C28/I2 chondrocytes by RT-qPCR and representative Western blotting. Results: Forty-one senescence-associated DEGs were identified, and seven network-central genes (CDKN1A, CDK6, WEE1, NFKB2, ID1, RBL2, and IGFBP7) were prioritized. CDKN1A, CDK6, and WEE1 were reduced in OA-associated meniscal samples in GSE98918; within-dataset ROC analyses yielded AUCs of 0.931, 0.882, and 0.792, respectively. In IL-1β-treated C28/I2 cells, WEE1 mRNA decreased whereas CDK6 mRNA increased; representative immunoblots showed concordant qualitative trends. Berberine- and folic acid-related docking findings were computational only. Conclusions: Checkpoint-related gene expression is associated with senescence-linked transcriptomic changes in established OA. The discordant CDK6 results between clinical tissue datasets and an acute inflammatory cell model do not establish stage-dependent regulation. The present data also do not demonstrate p53-mediated CDKN1A activation, checkpoint failure, cell-cycle arrest, or cellular senescence; these hypotheses require dedicated functional experiments. Full article

Cannabidiol (CBD), the principal non-psychoactive phytocannabinoid of Cannabis sativa, exhibits diverse pharmacological activities through interactions with multiple molecular targets. Thus, breast, colorectal, and lung cancers arise from distinct molecular mechanisms. This study investigated the potential multi-target pharmacological mechanisms of CBD using an integrated approach combining network pharmacology and molecular docking. CBD-associated targets from three prediction platforms were intersected with disease-associated genes for each cancer type, yielding 143 overlapping targets that formed a significantly enriched protein–protein interaction network. Maximal Clique Centrality (MCC) analysis identified 10 hub proteins, including SRC, SIRT1, PTGS2 (COX-2), PPARG, NFKB1, MMP2, IGF1R, ESR2, ESR1, and EGFR, which represent key regulators of hormone signaling, inflammation, cell proliferation, and tumor progression. Molecular docking against these targets, benchmarked using each protein’s authentic co-crystallized ligand, predicted predominantly moderate binding affinities for CBD. Compared with the corresponding reference ligands, CBD generally exhibited lower predicted binding affinity, although comparable or slightly stronger scores were observed for PTGS2, ESR2, and EGFR. Independent validation using AutoDock Vina demonstrated overall agreement with the MOE docking results, supporting the robustness of the predicted binding profiles. Collectively, these findings suggest that CBD may exert its biological activity through coordinated modulation of multiple cancer-related signaling pathways rather than a single molecular target. By integrating pooled cancer-associated network pharmacology with co-crystallized ligand benchmarking, this study provides a computational framework for prioritizing biologically relevant CBD targets for future experimental validation. These findings should be regarded as hypothesis-generating rather than evidence of clinical efficacy. Full article

Background/Objectives: Hepatocellular carcinoma (HCC) is the most prevalent primary liver tumor and is often diagnosed at advanced stages with very poor therapeutic response, leading to high mortality. Thus, new therapeutic strategies and biomarkers are urgently needed. We previously showed that the combination of loratadine, raloxifene, and sorafenib exerts synergistic cytotoxicity on HCC cells. Here, we explored potential molecular mechanisms underlying the anticancer effects of this combination using multiomics analyses. Methods: We performed proteomic analyses based on mass spectrometry, transcriptomic analyses using the Clariom D Plus human microarray (Affymetrix), and metabolomic analyses based on nuclear magnetic resonance to investigate the profile changes induced by the drug combination in HuH7 cells. Bioinformatic analyses were applied to associate the omics changes with biological functions, molecular interactions, and clinical relevance in terms of patient survival. Results: We identified several molecules whose expression changed in response to treatment across the three omics profiles analyzed. Some of them were found to be involved in hallmarks of cancer, including sustained proliferation, evasion of growth suppressors, and resistance to cell death. Integrated multi-omics analyses revealed that the drug combination suppresses critical oncogenic drivers (C7orf50, NUP188, and HS2ST1) and that the mitotic cell cycle process, DNA synthesis and cholesterol biosynthesis are the primary pathways affected. Protein–protein interaction analysis revealed five key hubs (KIF2C, PCNA, TRIP13, NDC80, and RPA3), whose expression in HCC is associated with poor clinical prognosis. Conclusions: The combined treatment rewired molecular networks involved in HCC progression. These findings identify clinically relevant molecular targets associated with poor prognosis and provide mechanistic insights into the synergistic anticancer activity of this drug combination. Full article

Leucine-rich repeat kinase 2 (LRRK2) has emerged as a central molecular node linking genetic risk, membrane trafficking, lysosomal homeostasis, and immune signalling in Parkinson’s disease (PD). Rather than functioning as a conventional protein kinase, LRRK2 operates as a conformationally regulated, Rab-directed signalling machine whose activity is governed by long-range interdomain communication, membrane recruitment, and cooperative interactions with small GTPases. Converging advances in cryo-electron microscopy, quantitative phosphoproteomics, and human genetics indicate that pathogenic mutations, lysosomal stress, and pharmacological inhibitors do not simply alter catalytic output, but reshape the conformational landscape of LRRK2, biasing it toward distinct structural states with divergent cellular consequences. A defining feature of this system is the selective phosphorylation of Rab GTPases at low stoichiometry—most prominently Rab8 and Rab10—yet with disproportionate functional impact on vesicle trafficking, ciliogenesis, autophagy, and organelle positioning. The identification of Rab-directed phosphatases, particularly PPM1H, further establishes that LRRK2 signalling is governed by a dynamically balanced kinase–phosphatase circuit operating in space and time. These observations, together with emerging evidence linking LRRK2 activation to lysosomal damage and immune pathways, support a unifying hypothesis: PD-associated LRRK2 dysfunction arises from maladaptive stabilization of specific conformational and spatial states within a membrane-responsive signalling network, leading to persistent misregulation of Rab-dependent trafficking and organelle homeostasis, rather than from kinase hyperactivity alone. In this review, we integrate structural, biochemical, and cellular evidence to advance this framework and discuss its implications for disease mechanisms and therapy. We highlight key unresolved challenges—including conformation-selective drug targeting, spatial control of Rab phosphorylation, and context-dependent immune–neuronal crosstalk—and propose that restoring physiological regulation of LRRK2, rather than simply inhibiting its activity, will be essential for achieving mechanism-based disease modification in Parkinson’s disease. Full article

Drug discovery is still an expensive and time-consuming process where finding the right drug associations is important for therapeutic development. In this paper, a new system is proposed for drug design called PharmaGraphFormer (PGF). It consists of five stages: (i) Data acquisition and preprocessing (DAP), (ii) Feature extraction and feature fusion (FEF), (iii) Molecular representation (MR), (iv) Multi-task prediction, and (v) Explainable artificial intelligence (XAI). This study employs a hybrid graph neural network (GNN)-transformer architecture that combines structural and sequence-based representations. Through DAP, several processes are executed, including the imputation or removal of missing values, outlier rejection, and class balancing. Next, through FEF1, features are extracted to represent the input data efficiently. Initially, compound-protein features are generated to document the interactions and relationships between chemical compounds and their corresponding target proteins. Secondly, drug characterizations are computed to encapsulate the physical, chemical, and structural attributes of each drug. After that, MR is performed using a graph-based molecule representation. Then, a novel model integrating GNNs and graph transformers, termed GNN-T, is proposed. Initially, GNNs represent the most promising deep learning models adept at processing non-Euclidean data. The Graph Transformer layer enhances atom representations by consolidating the representations of adjacent atoms through an attention mechanism. Finally, XAI is applied to explain the internal mechanisms of AI systems, rendering them comprehensible and interpretable. Across five independent runs, the proposed model achieved an accuracy of $0.963\pm 0.002$, a precision of $0.971\pm 0.002$, a recall of $0.958\pm 0.003$, an F1-score of $0.964\pm 0.002$, and a ROC-AUC of $0.993\pm 0.001$. These results demonstrate an outstanding performance when compared with all other models and emphasize that the proposed model is reliable in solving the problems of prioritizing compounds in line with the latest developments in AI-powered virtual screening and drug–target interaction modeling. Full article

Background: Cordyceps militaris (C. militaris) is a medicinal mushroom recognized for its diverse pharmacological activities, largely attributed to its principal bioactive nucleosides, cordycepin and adenosine. Although accumulating evidence supports their neuroprotective potential, the molecular mechanisms underlying their effects against Alzheimer’s disease (AD) remain incompletely understood. This study investigated the neuroprotective effects of cordycepin and adenosine against amyloid-β (Aβ₄₂)-induced neurotoxicity and explored their potential molecular mechanisms using integrated experimental and computational approaches. Methods: SH-SY5Y neuroblastoma cells were pretreated with cordycepin (COR), adenosine (ADE), or donepezil (DNPZ) prior to Aβ₄₂ exposure, and cell viability was assessed using the MTT assay. Drug-likeness and absorption, distribution, metabolism, excretion, and toxicity (ADMET) properties were evaluated in silico, followed by network pharmacology to identify potential therapeutic targets and enriched biological pathways. Molecular docking and molecular dynamics simulations were performed to elucidate the interactions of the compounds with selected Alzheimer’s disease-related proteins. Results: COR and ADE significantly attenuated Aβ₄₂-induced cytotoxicity and improved SH-SY5Y cell viability. Network pharmacology identified 84 shared molecular targets, including 9 AD-associated genes. Protein–protein interaction analysis revealed hub genes involved in signal transduction, epigenetic regulation, and purine metabolism, while Gene Ontology and KEGG enrichment analyses highlighted pathways associated with neuroactive ligand–receptor interaction, calcium signaling, and inflammatory regulation. ADMET analysis predicted favorable pharmacokinetic properties for both compounds, although cordycepin was predicted to be AMES-positive. Molecular docking and molecular dynamics simulations demonstrated stable interactions of COR and ADE with liver X receptors (LXRα and LXRβ), whereas donepezil exhibited stronger binding affinity toward β-secretase (BACE1). Conclusions: COR and ADE exert neuroprotective effects through coordinated modulation of multiple AD-related signaling pathways rather than a single molecular target. These findings provide mechanistic insights into the neuroprotective activities of C. militaris-derived nucleosides and support further investigation of their potential as multi-target therapeutic candidates for AD and other neurodegenerative disorders. Full article

Background/Objectives: The identification of novel therapeutic targets is imperative to overcome the limitations of current anti-toxoplasmosis treatments. This study aims to investigate the potential of repurposing Pentamidine as an inhibitor against the HMG-Box domain-containing protein (TGARI_247020) in Toxoplasma gondii, a protein hypothesized to be essential for the parasite’s genomic stability. Methods: The study utilized a multi-layered in silico approach. First, the biological essentiality of the target gene was validated by analyzing CRISPR-Cas9-based phenomics data from the ToxoDB database. Second, the structural properties of the HMG-Box domain (ID: A0A139YAG1) were characterized using AlphaFold models. Finally, molecular docking simulations were conducted via the SwissDock server to evaluate the binding affinity and interaction dynamics between Pentamidine and the target protein. Results: Genomic analysis revealed a phenotype score of −1.2, confirming the indispensable role of the TGARI_247020 gene for parasite viability. Structural analysis identified a well-defined binding pocket within the HMG-Box domain. Molecular docking results demonstrated a high binding affinity for Pentamidine, yielding an optimal AC Score of −44.93, supported by a FullFitness value of −1134.13 kcal/mol. The interaction was primarily stabilized by a network of hydrogen bonds and favorable steric fits within the catalytic groove of the protein. Toxoplasmosis is widely classified as a neglected parasitic disease, posing persistent public health challenges and veterinary economic concerns globally. Traditional de novo drug discovery is often hindered by high costs and prolonged timelines, making drug repositioning (repurposing) a highly attractive and cost-effective strategy to identify novel therapeutics from established clinical agents over the past decade. Computer-Aided Drug Design (CADD), particularly Structure-Based Drug Design (SBDD), has provided a robust molecular framework to prioritize candidate drugs against essential parasitic targets. In apicomplexan parasites, high-mobility group box (HMGB) proteins, such as TgHMGB1a, serve as critical nuclear architectural factors that bind to distorted DNA structures and modulate genomic transcription, disrupting these essential DNA–protein interactions, representing a promising, yet under-explored, therapeutic target. The hypothesis for evaluating Pentamidine—an aromatic dicationic diamidine traditionally used in African trypanosomiasis—lies in its established ability to interact with nucleic acids and block critical molecular targets in other protozoa, providing a logical biochemical rationale for testing its potential as a structural inhibitor of the T. gondii HMG-box protein. Conclusions: Our findings provide preliminary in silico evidence that Pentamidine targets the HMG-Box protein, suggesting its potential for drug repurposing. However, due to established clinical limitations of Pentamidine (such as nephrotoxicity and poor blood–brain barrier permeability), further experimental in vitro and in vivo validation is strictly required to evaluate its therapeutic efficacy. Full article