Abstract Background: BCR-ABL inhibitors such as imatinib and nilotinib exhibit multi-kinase activity that extends beyond oncology, offering significant potential for drug repurposing. Objectives: This study aims to systematically evaluate and prioritize the repurposing potential of BCR-ABL inhibitors, particularly imatinib and nilotinib. Methods: An integrated pharmacoinformatics framework was applied to analyze seven BCR-ABL inhibitors. Structural clustering, cheminformatics analysis, and transcriptomic profiling using the Connectivity Map were employed to evaluate structural relationships, target profiles, and gene expression signatures associated with non-oncology indications. Results: Structurally, imatinib and nilotinib clustered closely, while HY-11007 exhibited distinct features. Nilotinib’s high selectivity correlated with strong transcriptional effects in neurodegeneration-related pathways (e.g., HSP90 and LYN), whereas imatinib’s broader kinase profile (PDGFR and c-KIT) was linked to fibrosis and metabolic regulation. Connectivity Map analysis identified more than 30 non-cancer indications, including known off-target uses (e.g., imatinib for pulmonary hypertension) and novel hypotheses (e.g., nilotinib for Alzheimer’s via HSPA5 modulation). A substantial portion of these predictions aligned with the existing literature, underscoring the translational relevance of the approach. Conclusions: These findings highlight the importance of integrating structure–activity relationships and transcriptomic signatures to guide rational repurposing. We propose prioritizing nilotinib for CNS disorders and imatinib for systemic fibrotic diseases, supporting their advancement into preclinical and clinical evaluation. More broadly, this framework offers a versatile platform for uncovering hidden therapeutic potential across other drug classes with complex polypharmacology. Keywords: BCR-ABL inhibitors, drug repurposing, imatinib, kinase inhibitors, nilotinib, pharmacoinformatics, polypharmacology 1. Introduction Breakpoint Cluster Region–Abelson Murine Leukemia Viral Oncogene Fusion Protein (BCR-ABL) inhibitors, such as imatinib, nilotinib, and related compounds, were originally developed for the treatment of chronic myeloid leukemia (CML) and other cancers expressing the BCR-ABL fusion oncogene [[32]1]. More recently, these inhibitors have demonstrated promising potential in non-cancer indications, owing to their multi-kinase activity and off-target inhibition of kinases such as PDGFR, c-KIT, FLT3, and native ABL1 [[33]2]. Moreover, their adverse effects, including cardiovascular toxicity, fluid retention, and cytopenias, further highlight their polypharmacological profiles [[34]3]. Clinical investigations have also explored the use of nilotinib and bosutinib in neurodegenerative diseases, providing further evidence of their therapeutic versatility beyond oncology [[35]4]. Imatinib (Gleevec®) was the first tyrosine kinase inhibitor (TKI) designed to selectively inhibit the BCR-ABL fusion protein, which results from the oncogenic Philadelphia chromosome translocation occurring in chronic myeloid leukemia (CML) and some cases of acute lymphoblastic leukemia (ALL) [[36]5]. It has moderate selectivity towards BCR-ABL. It also targets ABL1, ARG, c-KIT (CD117), DDR1, NQO2, PDGFRα, and PDGFRβ. Nilotinib is a highly selective BCR-ABL inhibitor with minimal off-target kinase activity compared to imatinib and other BCR-ABL inhibitors targeting the ATP site, but it is less selective than asciminib (ABL001), which binds allosterically to the myristoyl pocket of ABL1 [[37]6]. Imatinib and nilotinib bind the ATP-binding site of BCR-ABL; both bind the inactive form of the ABL protein. Despite their primary use in oncology, BCR-ABL inhibitors have shown emerging promise in non-cancer indications, largely attributable to their inhibition of kinases implicated in fibrosis, neurodegeneration, immune modulation, and metabolic regulation [[38]7]. Therefore, BCR-ABL inhibitors are not only historically significant for their pioneering role in targeted cancer therapy via well-characterized mechanisms, but are also pharmacologically versatile, making them unique tools for both precision oncology and the expanding field of drug repurposing and systems pharmacology. This expanding therapeutic scope is supported not only by preclinical studies but also by the clinical observations of their off-target activities. Understanding the structural determinants, transcriptional effects, and network pharmacology underlying the broader impact of BCR-ABL inhibitors is therefore critical for rational drug repurposing and precision medicine initiatives. In this study, we present a comprehensive informatics workflow to explore the structural, biological, and therapeutic diversity of BCR-ABL inhibitors, with a particular focus on the distinct transcriptomic profiles of imatinib and nilotinib. We begin by applying cheminformatics approaches to evaluate the chemical and structural diversity across the BCR-ABL inhibitor class. Subsequently, we employ a chemogenomics strategy to characterize the transcriptional effects of these compounds and generate gene expression signatures. These signatures are then used to query the Connectivity Map (CMap) [[39]8] database, the gene expression L1000 platform version 1.0, to identify compounds and genetic perturbations that elicit similar gene expression responses to BCR-ABL inhibitors. Finally, the matched compounds and gene perturbations are prioritized and examined to generate polypharmacology hypotheses, which are further supported through evidence mined from the biomedical literature. This integrative and cost-effective strategy provides a scalable tool for investigating the polypharmacology of other drug classes and guiding hypothesis-driven drug repurposing projects. 2. Results An informatics workflow ([40]Figure 1) was developed and applied to study the network pharmacology of BCR-ABL inhibitors, based on methods developed by Hajjo et al. [[41]9,[42]10,[43]11,[44]12], to formulate testable hypotheses regarding non-cancer indications and polypharmacological mechanisms. Figure 1. [45]Figure 1 [46]Open in a new tab Integrative informatics workflow to study the polypharmacology of BCR-ABL inhibitors. 2.1. Chemical Dissimilarity All seven BCR-ABL inhibitors analyzed contained heterocyclic cores (e.g., pyrimidine, quinoline, and thiazole), which is essential for ATP-binding site interactions; this is a feature that is linked to cross-pharmacology with kinases like SRC and aurora kinases. Cheminformatics analysis using pairwise Tanimoto distances from binary fingerprints ([47]Figure 2A) showed that nilotinib and imatinib were the most similar (distance = 0.48), while bosutinib, dasatinib, and tozasertib formed a moderately similar subcluster (~0.7–0.8); HY-11007 and AT-9283 were more structurally distinct (>0.85). Pairwise Euclidean distances and 2D alvaDesc descriptors ([48]Figure 2B) captured physicochemical dissimilarity, with tozasertib–dasatinib and tozasertib–imatinib showing the greatest similarity (distances ~8.6–8.8), and nilotinib and HY-11007 showing the greatest divergence (≥11.5). This broader range (~8.6 to 12.1) indicated that Euclidean metrics detected subtler variations compared to Tanimoto distances. A total of 3874 2D molecular descriptors ([49]Supplementary Table S1) were calculated out of 5305 available descriptors, after excluding all 3D descriptors. Of these, 1889 descriptors were used for the principal component analysis (PCA) following the elimination of descriptors with constant or near-constant values, descriptors with at least one missing value (or all missing values), and descriptors with a standard deviation less than 0.0001. Figure 2. [50]Figure 2 [51]Figure 2 [52]Open in a new tab Cheminformatics analysis of BCR-ABL inhibitors. (A) Heatmap for the distance matrix using Tanimoto coefficients and Morgan fingerprints. (B) Heatmap for the distance matrix using Euclidean distances and 2D alvaDesc molecular descriptors. (C) Principal component analysis of seven BCR-ABL inhibitors using 2D alvaDesc molecular descriptors and Euclidean distances. The color gradient reflects DLS_cons—the consensus drug-likeness score—providing an additional interpretive layer grounded in the pharmacokinetic and physicochemical profile of the molecules. The plot reveals the principal component scores for PC1 (50.79%) and PC2 (19.12%), which together explain ~70% of the total variance, suggesting good dimensional reduction and data representation fidelity. The principal component analysis (PCA) of the descriptors ([53]Figure 2C) further confirmed chemical diversity, i.e., nilotinib and HY-11007 were well-separated along the Y-axis, while imatinib, bosutinib, dasatinib, and tozasertib clustered together. AT-9283 appeared isolated along the X-axis, reflecting unique physicochemical features. The DLS_cons color scale (red = high consensus similarity) visualized relative drug-likeness. Overall, complementary molecular representations effectively revealed structural and physicochemical diversity among the inhibitors. 2.2. Biological Dissimilarity The seven BCR-ABL inhibitors analyzed, identified from the Connectivity Map (CMap) based on their annotated mechanisms of action ([54]Figure 3A), exhibited distinct pharmacological profiles. Evidence from the literature ranks nilotinib as the most selective BCR-ABL inhibitor, followed by imatinib. AT-9283 and tozasertib also target aurora kinases, while bosutinib and dasatinib more potently inhibit SRC family kinases. HY-11007, in contrast, primarily targets FLT3 and JAK2, distinguishing it mechanistically. CMap data further supported these differences at the transcriptional level. As shown in [55]Figure 3B, the inhibitors displayed diverse transcriptional activity scores (TASs) and connectivity patterns across nine cancer cell lines. Tozasertib, dasatinib, and nilotinib induced strong, consistent responses (TAS ≥ 0.5), reflected by thick black bars and dense red connectivity chords, indicating broad-spectrum activity. In contrast, imatinib and AT-9283 showed moderate, cell-line-specific responses, while HY-11007 exhibited minimal transcriptional activity, suggesting limited cellular engagement despite structural similarity to the other inhibitors. Figure 3. [56]Figure 3 [57]Figure 3 [58]Open in a new tab Structural and transcriptional signatures underlying the polypharmacology of BCR-ABL inhibitors. (A) Chemical structures, target profiles, and mechanisms of action of BCR-ABL inhibitors. (B) Cell-line-specific transcriptional responses to BCR-ABL inhibitors. Thick black bars signify transcriptional activity scores (TASs) greater than or equal to 0.5; thinner black bars indicate scores less than 0.5. Red lines (chords) denote similar positive connectivity scores between cell lines, which range from 80 to 100 (pale to intense color according to the score). Chords are shown only when TASs are ≥0.5, indicating strong or biologically relevant transcriptional activity. The absence of red chords either means that the compound’s TAS is very low, or that no data are available. Source: CMap database. 2.3. Hypothesis Generation Polypharmacological Effects Results from the CMap analysis ([59]Figure 4) suggest that BCR-ABL inhibitors exert broader effects beyond ABL inhibition, consistent with their profiles as multi-target kinase inhibitors. Predicted compound and genetic connections could support the generation of strong hypotheses regarding mechanisms of action, off-target effects, and drug repurposing opportunities by leveraging transcriptomic similarities identified through high-scoring positive CMap connections. Figure 4. [60]Figure 4 [61]Figure 4 [62]Open in a new tab CMap connections with BCR-ABL inhibitors across perturbagen class, compound, and genes. (A) Connections with seven BCR-ABL inhibitors. (B) Connections with imatinib. (C) Connections with nilotinib. 2.3.1. Non-Cancer Indications of BCR-ABL Inhibitors The CMap analysis revealed distinct transcriptional profiles among the seven BCR-ABL inhibitors ([63]Figure 4A). Compounds such as dasatinib, bosutinib, tozasertib, and AT-9283 exhibited widespread positive and negative connections across multiple classes, compounds, and genes. A strong connectivity with unrelated perturbagen classes (e.g., aurora kinases, EGFR inhibitors, VEGFR inhibitors, PDGFR inhibitors, and HIV protease inhibitors), which are not directly tied to BCR-ABL signaling, indicated a broad polypharmacology and a reduced selectivity of inhibitors, consistent with their known multi-kinase activity. Compound connections shown in [64]Table 1 further confirmed strong similarities to EGFR inhibitors (e.g., afatinib, HG-6-64-01, and canertinib) and other multi-kinase inhibitors (e.g., tetrindole, SU-11652, and WZ-4-145). Negative compound connections, such as cholic acid, BRD-A80383043, and BAS-09104376, further suggested opposing transcriptional effects, possibly linked to metabolic or unknown pathways. Table 1. Top CMap positive-scoring perturbagen connections to BCR-ABL inhibitors and their known non-cancer indications. # Perturbagen Type Score Non-Cancer Indications and Validity 1 Aurora kinase inhibitor Class 99.46 Alzheimer’s Disease (Preclinical) [[65]13]; Pulmonary Fibrosis (Preclinical) [[66]14]; Inflammatory Bowel Diseases (Preclinical but weak) [[67]15]; Arthritis, Psoriasis (Preclinical) [[68]16]; Arthritis, Rheumatoid (In silico) [[69]17]. 2 PDGFR\KIT inhibitor Class 99.34 Alzheimer’s Disease (Preclinical) [[70]18]; Atherosclerosis (Preclinical) [[71]19]; Gastrointestinal Diseases (Preclinical) [[72]18]; Liver Cirrhosis, Experimental (Preclinical) [[73]18] Pulmonary Fibrosis (Preclinical) [[74]20]; Wound Healing (Preclinical) [[75]18]. 3 EGFR inhibitor Class 99.02 Pulmonary Fibrosis (Preclinical) [[76]21]; Psoriasis (Preclinical) [[77]22]. 4 VEGFR inhibitor Class 98.73 Eye Diseases (Clinical/Approved) [[78]23]; Arthritis, Rheumatoid (Preclinical) [[79]24]; Hypertension, Portal [[80]25]; Psoriasis (Preclinical) [[81]26]. 5 RAF inhibitor Class 98.67 Arthritis, Rheumatoid (Preclinical) [[82]27]; Lung Injury and Pulmonary Fibrosis (Preclinical) [[83]28]. 6 Afatinib Compound 99.44 Skin Diseases (Preclinical) [[84]29]. 7 HG-6-64-01 Compound 99.40 Heart failure (Preclinical) [[85]30]. 8 Carnertinib Compound 99.26 Liver Cirrhosis, Experimental (Preclinical) [[86]31]. 9 Tetrindole Compound 99.19 Depression (Preclinical/Early clinical) [[87]32]. 10 WZ-4-145 Compound 99.19 Unknown. 11 SU-11652 Compound 99.12 Unknown. Multi-targeted TKI. 12 Mibefradil Compound 98.98 Pain, Neuropathic (Preclinical) [[88]33]. 13 NVP-TAE684 Compound 99.98 Neuroprotection; Alzheimer Disease (Preclinical) [[89]34]. 14 Cediranib Compound 98.94 Liver Cirrhosis, Experimental (Preclinical) [[90]25]. 15 AZD-7762 Compound 98.93 Osteoporosis (Preclinical) [[91]35]. 16 TERF1 Gene (kd) 98.77 Aging (Preclinical) [[92]36]; Alzheimer’s Disease (Preclinical) [[93]37]; Infertility, Male (Preclinical) [[94]38]. 17 C9ORF96 Gene (kd) 98.62 Unknown. 18 UGCG Gene (oe) 98.10 Gaucher Disease (Clinical) [[95]39]; Keloid (Preclinical) [[96]40]; Myocardial Fibrosis (Preclinical) [[97]41]; Vascular Malformations (Preclinical) [[98]42]; Parkinson’s Disease (Preclinical) [[99]43], Depressive Disorder (Preclinical) [[100]43]. 19 AKT3 Gene (kd) 97.47 Brain Malformations (Clinical) [[101]44]; Autoimmunity (Preclinical) [[102]45]; Cognitive Dysfunction (Preclinical) [[103]46]; Hemimegaloencephaly (Preclinical) [[104]47]; Wound Healing (Preclinical) [[105]48]. 20 ZNF449 Gene (kd) 97.60 Chondrogenesis/Cartilage (Preclinical) [[106]49]. 21 KRAS Gene (kd) 97.50 Kidney Fibrosis (Preclinical) [[107]50]. 22 FABP4 Gene (oe) 97.32 Diabetes Mellitus (Clinical) [[108]51]; Atherosclerosis (Preclinical) [[109]52]; Kidney Stones (Preclinical) [[110]53]; Obesity (Preclinical) [[111]54]. 23 PTK2 Gene (kd) 97.22 Alzheimer’s Disease (Preclinical) [[112]55]. 24 AK3 Gene (kd) 97.14 Alzheimer’s Disease (In silico) [[113]56]. 25 MRP1L18 Gene (oe) 97.12 Asthma (Preclinical) [[114]57]. [115]Open in a new tab The table lists the top-scoring compounds, drug classes, and gene perturbations based on transcriptomic similarity scores. Entries include their known cancer and non-cancer indications, highlighting potential polypharmacology and repurposing opportunities. Literature-based evidence is provided for non-cancer indications. Non-cancer indications not identified in the literature or publicly available sources, as of 28 April 2025, are denoted as ‘None found’. Validity levels are described in the Methods section. Gene-level connections showed strong similarity with ERF1, C9ORF96, UGC, and others, with a notable overlap with the PI3K/AKT and RAS signaling pathways (e.g., AKT3, KRAS, and FBP4), reinforcing the involvement of oncogenic signaling. Few negative gene connections (e.g., PIK3C3, XRCC4, CTSK, and P2RY12) pointed to possible effects on autophagy, DNA repair, and inflammation. 2.3.2. Non-Cancer Indications of Imatinib Imatinib connected strongly to other tyrosine kinase inhibitors, but also showed connectivity to the diverse drug classes shown in [116]Table 2, including serotonin and dopamine receptor antagonists, indicating off-target transcriptional effects. Gene connectivity revealed a partial overlap with relevant signaling components to BCR-ABL signaling effects but also included non-BCR-ABL-related genes, suggesting a less-focused transcriptional footprint than nilotinib. The non-cancer indications connected to imatinib via CMap positive-scoring connections are diverse, covering neurological, cardiovascular, metabolic, inflammatory, and infectious diseases. There is a strong neuropsychiatric signal, broader than seen with nilotinib ([117]Table 3), but also a clear footprint in immune modulation and vascular pathology. Table 2. Top CMap positive-scoring perturbagen connections to imatinib and their known non-cancer indications. # Perturbagen Type Score Non-Cancer Indications 1 Mineralocorticoid Agonist Class 98.22 Orthostatic Hypotension (FDA-approved); Addison Disease (FDA-approved). 2 PLK Inhibitor Class 97.63 Inflammation (Preclinical) [[118]58]; HIV Infections (Preclinical) [[119]59]. 3 Dopamine Receptor Grp1 Class 96.93 Parkinson’s Disease (Clinical) [[120]60]; Depressive Disorder (Preclinical) [[121]61]. 4 PKC Activator Class 95.54 Alzheimer’s Disease (Clinical) [[122]62]; HIV infection (Clinical) [[123]62]. 5 VEGFR Inhibitor Class 95.45 Uveitis (FDA-approved); Atherosclerosis (Preclinical) [[124]63]. 6 Opioid Receptor Agonist Class 95.09 Pain (FDA-approved); Cough (FDA-approved). 7 Dopamine Receptor Grp2 Class 92.62 Schizophrenia (FDA-approved); Parkinson’s Disease (Clinical) [[125]60]; Alzheimer’s Disease (Clinical) [[126]64]. 8 ROCK Inhibitor Class 90.26 Glaucoma (FDA-approved) [[127]65]; Hypertension, Pulmonary (Clinical) [[128]66]; Parkinson’s Disease (Clinical) [[129]67]. 9 Na-K-Cl Transporter Inhibitor Class 89.83 Kidney Diseases; Hypertension (FDA-approved for non-selective inhibitors); Epilepsy (Clinical for selective inhibitors) [[130]68]; Autistic Disorder (Clinical for selective inhibitors) [[131]69]. 10 Phosphodiesterase Inhibitor Class 89.61 Erectile dysfunction (FDA-approved); Pulmonary Disease, Chronic Obstructive (FDA-approved); Heart Diseases (FDA-approved); Schizophrenia (Clinical) [[132]70]. 11 Imatinib Compound 99.97 Heart Diseases (Clinical) [[133]3]; Asthma (Clinical) [[134]71]; Insulin Resistance (Clinical) [[135]72]. 12 Lorazepam Compound 99.61 Anxiety Disorders (FDA-approved). 13 Parecoxib Compound 99.56 Pain (FDA-approved); Inflammation (FDA-approved). 14 KUC103904N Compound 99.51 Not detected. 15 Cefalexin Compound 99.51 Surgical Wound Infection (FDA-approved); Urinary Tract Infections (FDA-approved); Respiratory Tract Infections (FDA-approved); Skin Diseases, Infectious (FDA-approved); Otitis Media (FDA-approved). 16 SNS-314 Compound 99.37 Pulmonary Fibrosis (Clinical) [[136]73]; Arthritis, Rheumatoid (Clinical) [[137]74]; Alzheimer’s Disease (Preclinical) [[138]13]; Psoriasis (Preclinical) [[139]75]. 17 Pioglitazone Compound 99.28 Diabetes Mellitus; Insulin Resistance (FDA-approved). 18 GSK-1904529A Compound 99.12 None found. 19 BRD-A97035593 Compound 99.09 None found. 20 Tandutinib Compound 99.07 Parkinson’s Disease (Preclinical) [[140]2]. 21 STAT4 Gene (kd) 99.34 Autoimmune Diseases (Clinical) [[141]76]; Inflammation (Clinical); (Preclinical) [[142]77]; Hypersensitivity (Preclinical) [[143]77]. 22 YTHDF2 Gene (kd) 99.29 Alzheimer’s Disease (Preclinical) [[144]78]. 23 ZNF92 Gene (kd) 99.21 Alzheimer’s Disease (Preclinical) [[145]79]; Parkinson’s Disease (Preclinical) [[146]79]. 24 C2 Gene (oe) 99.12 Systemic Lupus Erythematosus (Clinical) [[147]80]; Sjögren’s Syndrome (Clinical) [[148]80]. 25 CLPB Gene (oe) 99.07 Neutropenia (Clinical) [[149]81]; Parkinson’s Disease (Preclinical) [[150]82]. 26 KIF5C Gene (kd) 99.03 Neurodegenerative Diseases (Preclinical) [[151]83]. 27 TKT Gene (oe) 98.98 Diabetes Mellitus (Preclinical) [[152]84]; Alzheimer’s Disease (Preclinical) [[153]85]. 28 ACAT1 Gene (kd) 98.82 Hypertension (Preclinical) [[154]86]; Dementia (Preclinical) [[155]87]. 29 SLC3A2 Gene (oe) 98.74 Wound Healing (Preclinical) [[156]88]. 30 YTHDF1 Gene (kd) 98.74 Diabetes Mellitus (Clinical) [[157]89]; Alzheimer’s Disease (Preclinical) [[158]90]. [159]Open in a new tab Non-cancer indications not identified in the literature or publicly available sources, as of 28 April 2025, are denoted as ‘None found’. Validity levels are described in the Methods section. The frequency analysis of the non-cancer indications of strong CMap positive-scoring connections indicated that imatinib exhibits a broad systemic pharmacology, consistent with its known off-target actions affecting immune cells, vasculature, and metabolism. This analysis highlighted Alzheimer’s disease (seven times), Parkinson’s disease (six times), diabetes mellitus (four times), hypertension (four times), inflammation (three times), schizophrenia (two times), heart diseases (two times), and insulin resistance (two times). Other non-cancer indications that appeared only once are listed in [160]Table 2. These findings are consistent with imatinib’s multi-kinase inhibition profile, particularly against PDGFR, c-KIT, and ABL kinases, and suggest broad repositioning opportunities across CNS, cardiovascular, metabolic, and autoimmune conditions. 2.3.3. Non-Cancer Indications of Nilotinib Nilotinib showed a tight clustering of connections, specifically with aurora kinase inhibitors, heat shock protein inhibitors, and other BCR-ABL or tyrosine kinase-related perturbagens. Its compound connectivity profile is extremely specific, with perfect (score = 100) or near-perfect similarity to related kinase inhibitors. Gene-level connectivity includes genes directly involved in cell cycle regulation and kinase signaling, reinforcing its focused biological activity and minimal off-target effects. The frequency analysis of non-cancer indications (using MeSH terms) indicated that nilotinib’s top CMap positive-scoring perturbagen connections are strongly enriched for neurological, autoimmune, fibrotic, and inflammatory diseases ([161]Table 3). This analysis highlighted Alzheimer’s disease (six times), autoimmune diseases (three times), diabetes mellitus (three times), Parkinson’s disease (three times), and pulmonary fibrosis (three times). Other notable indications included amyotrophic lateral sclerosis, rheumatoid arthritis, COVID-19, ulcerative colitis, inflammation, leukoencephalopathy, nervous system diseases, neurodegenerative diseases, and psoriasis, reflecting a diverse enrichment across neurodegenerative, autoimmune, inflammatory, and infectious disease categories. These findings regarding nilotinib’s polypharmacology are consistent with previous evidence, which highlighted neurodegeneration and immunomodulation as key non-cancer repositioning opportunities [[162]91]. However, this study suggests more repurposing opportunities and underscores the importance of multi-pathway inhibitors in identifying therapeutic strategies for complex diseases. Table 3. Top CMap positive-scoring perturbagen connections to nilotinib and their known non-cancer indications. # Perturbagen Type Score Non-Cancer Indications 1 Aurora Kinase inhibitor Grp1 Class 99.90 Alzheimer’s Disease (Preclinical) [[163]13]; Liver Cirrhosis, Experimental (Preclinical) [[164]92]; Pulmonary Fibrosis (Preclinical) [[165]14]. 2 JAK inhibitor Class 99.28 Alopecia Areata (FDA-approved); Colitis, Ulcerative (FDA-approved); Crohn’s Disease (FDA-approved); Vitiligo (FDA-approved); Arthritis, Rheumatoid (Clinical) [[166]93]; Autoimmune Diseases (Clinical) [[167]94]; Arthritis (Clinical) [[168]95]; Psoriasis (Clinical) [[169]95]; Dermatitis, Atopic (Clinical) [[170]96]; Diabetes Mellitus (Clinical) [[171]97]. 3 Vesicular transport Class 99.28 None found. 4 EGFR inhibitor Class 99.09 Psoriasis (Preclinical) [[172]98]; Pulmonary Fibrosis (Preclinical) [[173]99]; Atherosclerosis (Preclinical) [[174]100]; Neovascularization, Pathologic (Preclinical) [[175]101]. 5 BRAF RAF1 inhibitor Class 98.91 Pulmonary Fibrosis (Preclinical) [[176]28]; Arthritis, Rheumatoid (Preclinical) [[177]27]. 6 EIF proteins Class 98.73 Leukoencephalopathy (Preclinical) [[178]102]; Diabetes Mellitus (In silico) [[179]103]. 7 NFKB activation Class 98.57 Inflammation (Clinical) [[180]104]; Neurodegenerative Diseases (Preclinical) [[181]105]. 8 BCL2 and related protein inhibitor Class 98.33 Alzheimer’s Disease (Preclinical) [[182]106]; Lupus Erythematosus, Cutaneous (Preclinical) [[183]107]; 9 GSK3 inhibitor Class 98.23 Psychiatric Disorders (Clinical) [[184]108]; Alzheimer’s Disease (Preclinical) [[185]109]; Diabetes Mellitus (Clinical) [[186]110]; Inflammation (Preclinical) [[187]111]. 10 RAR agonist Grp2 Class 98.21 Acne Vulgaris (Clinical) [[188]112]. 11 Nilotinib Compound 99.99 Parkinson’s Disease (Clinical) [[189]7]; Alzheimer’s Disease (Clinical) [[190]113]; Amyotrophic Lateral Sclerosis (Preclinical) [[191]114]. 12 AT-9283 Compound 99.79 Hypertension, Pulmonary (Preclinical) [[192]115]; COVID-19 (In silico) [[193]116]. 13 Alisertib Compound 99.68 None found. 14 ZM-447439 Compound 99.61 None found. 15 Avrainvillamide-analog-2 Compound 99.58 Antibacterial Activity (Preclinical) [[194]117]. 16 Tozasertib Compound 99.40 Optic Nerve Injuries (Preclinical) [[195]118]. 17 Crizotinib Compound 99.38 None found. 18 Erastin Compound 99.30 None found. 19 KI-8751 Compound 99.19 Cystitis (Preclinical) [[196]119]. 20 MK-5108 Compound 99.12 Kidney Fibrosis (Preclinical) [[197]120]. 21 HSPA5 Gene (kd) 99.84 Fatty Liver, Nonalcoholic (Clinical) [[198]121]; Alzheimer’s Disease (Preclinical) [[199]122]; Parkinson’s Disease (Preclinical) [[200]123]; Myositis (Preclinical) [[201]124]; COVID-19 (Preclinical) [[202]125]. 22 COPA Gene (kd) 99.84 Autoimmune Diseases (Clinical) [[203]126]. 23 GMDS Gene (oe) 99.81 Glaucoma, Open-Angle (Preclinical) [[204]127]. 24 SFPQ Gene (oe) 99.77 HIV Infection (Clinical) [[205]128]; Nervous System Diseases (Preclinical) [[206]129]; Congenital Structural Myopathies (Preclinical) [[207]130]; Amyotrophic Lateral Sclerosis (Preclinical) [[208]131]. 25 HSP90B1 Gene (kd) 99.73 Tuberculosis (Preclinical) [[209]132]; Polycystic Ovary Syndrome (Preclinical) [[210]133]. 26 HRSP12 Gene (oe) 99.68 Osteoarthritis (Preclinical) [[211]134]; Diabetic Nephropathies (In silico) [[212]135]. 27 EIF2B2 Gene (kd) 99.66 Leukoencephalopathy (Preclinical) [[213]136]; Nervous System Diseases (Preclinical) [[214]137]. 28 NIT1 Gene (kd) 99.63 None found. 29 NFE2L2 Gene (oe) 99.63 Parkinson’s Disease (Preclinical) [[215]138]; Heart Failure (Preclinical) [[216]139]. 30 LYN Gene (oe) 99.63 Autoimmune Diseases (Preclinical) [[217]140]; Neurodegenerative Diseases (Preclinical) [[218]140]; Alzheimer’s Disease (Preclinical) [[219]141]. [220]Open in a new tab If no non-cancer indications were identified in the published literature or open-source databases, as of 28 April 2025, the designation “None found” was assigned. Validity levels are described in the Methods section. 2.4. Supporting Evidence Supporting evidence from network biology analyses and/or the biomedical literature is a valuable layer of validation for computational hypotheses. The reliability of such an approach depends on the source quality, context, and convergence with other evidence. Thus, manual curation and critical evaluations are essential. 2.4.1. BCR-ABL PPI Network and Annotated Enriched Pathways BCR-ABL is a fusion oncogene that is characteristic of CML, whereas BCR and ABL1 are separate genes. Most pathway databases treat BCR-ABL as a functional complex. As shown in [221]Figure 5, BCR-ABL inhibitors not only modulate leukemic signaling but also key hubs such as STAT5, GRB2, and CBL. Enrichment in pathways like ErbB, insulin signaling, and focal adhesion aligns with CMap predictions, suggesting potential for repurposing these inhibitors in neurodegenerative diseases, fibrosis, and metabolic disorders. These findings underscore the broader therapeutic relevance of BCR-ABL beyond CML. Figure 5. [222]Figure 5 [223]Open in a new tab BCR-ABL protein–protein interaction (PPI) network and pathway enrichment. The network was generated using BCR-ABL as the input. Nodes represent proteins directly interacting with BCR-ABL, and edges indicate known or predicted protein–protein associations. Node segments are color-coded based on the top 10 enriched KEGG pathways identified through over-representation analysis in Cytoscape. White colors indicate that the corresponding pathways are not enriched in that node. A totally grey node indicate that this node was not enriched in any of the top pathways shown in this figure. 2.4.2. Literature-Based Validation Most BCR-ABL inhibitors, including imatinib, nilotinib, dasatinib, bosutinib, tozasertib, and AT-9283, are FDA-approved for the treatment of hematological malignancies such as leukemia—myelogenous, chronic, BCR-ABL-positive (CML); acute lymphocytic leukemia (ALL); mastocytosis; gastrointestinal stromal tumors; hypereosinophilic syndrome; and other related conditions. In addition to their established oncological uses, these inhibitors have also been associated with non-cancer indications at various stages of validation, including clinical, preclinical, and hypothesis-driven studies as reported in [224]Table 4. These emerging applications span across areas such as neurodegeneration, inflammation, fibrosis, and metabolic dysfunction. Thus, these literature-based findings confirm that BCR-ABL inhibitors exhibit strong non-cancer repositioning potential toward neurodegenerative, autoimmune, fibrotic, metabolic, and infectious diseases. Therefore, this serves as additional proof that some CMap-predicted indications align closely with known clinical and preclinical observations, supporting the robustness of connectivity mapping for drug repurposing hypotheses. Other unreported indications predicted from the CMap could serve as robust novel hypotheses. Table 4. Cancer and non-cancer indications for BCR-ABL inhibitors and drug targets. # Perturbagen Cancer Indications and Validity Non-Cancer Effects and Validity 1 AT-9283 Leukemia, Lymphoid (Clinical) [[225]142]; Multiple Myeloma (Clinical) [[226]143]; Neoplasms (Clinical) [[227]144]; Lymphoma, B-Cell (Preclinical) [[228]145]. Myeloproliferative Disorders (Preclinical) [[229]146]. 2 Bosutinib Leukemia, Myelogenous, Chronic, BCR-ABL Positive (FDA-approved). Lewy Body Disease (Clinical) [[230]147]. 3 Dasatinib Leukemia, Myelogenous, Chronic, BCR-ABL Positive (FDA-approved). Alzheimer’s Disease (Clinical) [[231]148]; COVID-19 (Clinical) [[232]149]; Hepatitis B, Chronic (Clinical) [[233]150]; Pulmonary Fibrosis (Clinical) [[234]151]; Obesity (Preclinical) [[235]152]. 4 HY-1107 Liver Neoplasms (Preclinical) None found. 5 Imatinib Leukemia, Lymphoid (FDA-approved); Mastocytosis (FDA-approved); Leukemia, Myelogenous, Chronic, BCR-ABL Positive (FDA-approved); Dermatofibrosarcoma Protuberans (FDA-approved); Hypereosinophilic Syndrome (FDA-approved); Leukemia, Eosinophilic, Chronic (FDA-approved); Gastrointestinal Stromal Tumors (FDA-approved); Myeloproliferative Disorders (FDA-approved). Anemia, Sickle Cell (Clinical) [[236]153]; Diabetes Mellitus, Type 1 (Clinical) [[237]154]; Pulmonary Fibrosis (Clinical) [[238]155]; Stroke (Clinical) [[239]156]; Liver Cirrhosis (Clinical) [[240]157]. 6 Nilotinib Leukemia, Myelogenous, Chronic, BCR-ABL Positive (FDA-approved). Alzheimer’s Disease (Clinical) [[241]158]; Parkinson’s Disease (Clinical) [[242]7]. 7 Tozasertib Glioma (Clinical) [[243]159]; Melanoma (Preclinical) [[244]160]. Hypersensitivity (Preclinical) [[245]161]; Neuralgia (Preclinical) [[246]162]. 8 BCR-ABL Leukemia, Myelogenous, Chronic, BCR-ABL Positive (Clinical) [[247]163]. None found. 9 BCR None found. Autoimmune Diseases (Clinical) [[248]164]. 10 ABL None found. Developmental Disabilities (Preclinical) [[249]165]; Parkinson’s Disease (Preclinical) [[250]166]. [251]Open in a new tab If no indications were identified in the published literature or open-source databases, as of 28 April 2025, the designation “None found” was assigned. Validity levels are described in the Methods section. The strong positive CMap associations of imatinib and nilotinib with non-oncological indications suggest high-confidence repurposing opportunities warranting clinical investigation. Imatinib’s robust connectivity with PDGFR and c-KIT indicates its potential efficacy in treating pulmonary fibrosis. PDGFR-α/β signaling is known to drive fibroblast proliferation, migration, and extracellular matrix production, processes which are central to fibrotic progression; thus, its inhibition can attenuate these fibrotic responses [[252]167]. Similarly, c-KIT facilitates the recruitment of bone marrow-derived progenitor cells that differentiate into myofibroblasts, contributing to fibrosis; inhibiting c-KIT may reduce this pathogenic cell population [[253]168]. Nilotinib’s strong positive connectivity with HSP90, HSPA5, and LYN suggests repurposing potential for Alzheimer’s disease. HSP90, HSPA5, and LYN are implicated in key Alzheimer’s disease pathologies, including neuroinflammation and disrupted protein homeostasis. Evidence indicates that nilotinib may exert neuroprotective effects by promoting the autophagic clearance of misfolded proteins, reducing endoplasmic reticulum (ER) stress, and modulating the unfolded protein response (UPR) pathways [[254]169,[255]170]. Nilotinib’s inhibition of HSP90 could reduce tau phosphorylation and amyloid-β accumulation, further supporting its therapeutic potential in Alzheimer’s disease. It is known that HSP90 activation drives the production of pathological tau aggregates [[256]171]. Furthermore, it has been reported that the administration of Hsp90 inhibitors could prevent Aβ-induced neurotoxicity by increasing the levels of HSP70 and Hsp90 [[257]172]. 2.5. Mechanistic Insight The analysis of strong positive gene connections with CMap scores ≥ 90.00 for both imatinib and nilotinib ([258]Supplementary Tables S2 and S3), using protein–protein interaction networks and pathway enrichment analysis, revealed distinct biological signatures for each compound ([259]Figure 6). A complete list of enriched pathways is provided in [260]Supplementary Tables S4 and S5. Figure 6. [261]Figure 6 [262]Open in a new tab Comparative PPI networks and enriched pathways of imatinib’s and nilotinib’s positive CMAP connections, revealing divergent polypharmacological profiles. Networks were created using all positive gene connections with CMap scores ≥ 90.00. Imatinib’s associated network is comparatively sparse and modular, with a lower connectivity among gene products. This pattern suggests a broader and less convergent transcriptional response, potentially involving multiple biological domains without a single dominant signaling axis. The top KEGG-enriched pathways linked to imatinib include EGFR signaling, glioma, neurotrophin signaling, and various metabolic processes. In contrast, the PPI network associated with nilotinib is highly interconnected, indicating a focused and coherent biological response. Its strong enrichment in KEGG immune-related pathways, including TNF, NFκB, Toll-like receptor, IL17, and NOD-like receptor signaling, highlights alignment with core immunoregulatory mechanisms. 3. Discussion This study presents a comprehensive informatics workflow for investigating the network pharmacology of BCR-ABL inhibitors, leveraging cheminformatics, transcriptomics, and literature mining processes to uncover their polypharmacological potential beyond oncology. The results highlight structural and biological diversity among these inhibitors, their broad transcriptional effects, and their potential repurposing for non-cancer indications. A cheminformatics analysis revealed significant chemical diversity among the seven BCR-ABL inhibitors, with distinct structural clusters identified by Tanimoto fingerprint and Euclidean distance metrics. Nilotinib and imatinib showed the highest similarity, while HY-11007 and AT-9283 were structurally distinct, as confirmed by PCA. Integrating fingerprint- and descriptor-based metrics uncovered orthogonal dimensions of similarity, supporting the hypothesis that structural diversity underlies the polypharmacologic and off-target profiles of these inhibitors. Compounds with high DLS_cons values clustered within favorable drug-like chemical spaces, whereas structurally distant compounds, though potentially potent, may require formulation or ADMET optimization. These findings highlight the value of integrated cheminformatics approaches in modern drug design, network pharmacology, and precision medicine. Biologically, the BCR-ABL inhibitors exhibited varying selectivity and polypharmacology. Nilotinib was the most selective, while dasatinib and bosutinib showed broader kinase inhibition, including SRC family targets. Despite structural similarity, HY-11007 displayed minimal transcriptional activity, indicating limited cellular engagement. CMap analysis corroborated these patterns, with dasatinib and nilotinib inducing strong transcriptional responses, whereas HY-11007 showed weak activity. These findings highlight the importance of integrative approaches that consider both structural and functional profiles when repurposing kinase inhibitors. The CMap analysis revealed extensive polypharmacology among BCR-ABL inhibitors, with strong connections to unrelated drug classes (e.g., EGFR inhibitors and VEGFR inhibitors) and genes involved in diverse pathways (e.g., PI3K/AKT and RAS signaling). These findings align with their known multi-kinase activities and suggest potential repurposing opportunities for non-cancer diseases. Key non-cancer indications for BCR-ABL inhibitors include neurodegenerative diseases, with Alzheimer’s and Parkinson’s diseases frequently being associated, particularly for nilotinib and dasatinib, which have shown preclinical and clinical neuroprotective effects. Fibrotic disorders such as pulmonary and liver fibrosis also emerged, likely reflecting the inhibition of PDGFR and other pro-fibrotic kinases. Additionally, links to autoimmune and inflammatory diseases, including rheumatoid arthritis and psoriasis, were supported by connections to JAK inhibitors and immune-modulatory genes like STAT4. Selective JAK inhibitors such as baricitinib have been primarily developed and approved for rheumatoid arthritis, COVID-19 (emergency use) [[263]173], and atopic dermatitis. Metabolic disorders, notably diabetes mellitus and insulin resistance, were also associated, possibly through the modulation of insulin signaling pathways. Notably, both imatinib and nilotinib are BCR-ABL inhibitors; their non-cancer profiles diverged markedly. Imatinib exhibited broad systemic effects, with strong associations to cardiovascular (hypertension), metabolic (diabetes), and neuropsychiatric (schizophrenia and depression) conditions, consistent with its multi-kinase inhibition profile, targeting PDGFR and c-KIT. In contrast, nilotinib showed more focused activity in neurodegeneration (Alzheimer’s and Parkinson’s) and autoimmune diseases, reflecting its higher selectivity and unique off-target interactions, such as HSP90 inhibition. These differences underscore how structural features and target selectivity shape repurposing potential, with imatinib offering versatility but greater off-target risks, and nilotinib presenting a more favorable profile for CNS-focused applications. These predictions serve as repurposing hypotheses for BCR-ABL inhibitors. Predictions were supported by evidence from the literature, as reported in [264]Table 1, [265]Table 2 and [266]Table 3, where many CMap-identified indications matched known preclinical or clinical findings. For example, imatinib’s anti-fibrotic effects in pulmonary fibrosis and nilotinib’s neuroprotective role in Parkinson’s disease are well-documented (references are cited in [267]Table 4). This