Skip to main content
Download PDF

Identification of Epinastine as CD96/PVR inhibitor for cancer immunotherapy

BMC Biology volume 23, Article number: 27 (2025) Cite this article

Abstract

Background

Poliovirus receptor (PVR) and its receptor system, including TIGIT, CD226, and CD96, play a pivotal role in orchestrating tumor immune evasion. Upon engagement with PVR on tumor cells, CD96 exerts inhibitory effects on the function of T cells and NK cells, thereby fostering tumor immune evasion. Therefore, screening of immune checkpoint inhibitors (ICIs) targeting the CD96/PVR pathway will provide promising candidates for tumor immunotherapy.

Results

In this investigation, we employed MOE software to conduct virtual screening of small molecules from the FDA-approved drug library. Our results demonstrated that Epinastine exhibited high affinity for CD96, thereby effectively disrupting the interaction between CD96 and PVR. In vitro co-culture experiments further revealed that Epinastine effectively restored the ability of Jurkat cells to secrete IL-2. In the MC38 tumor-bearing model, Epinastine significantly enhanced the infiltration of T cells and NK cells into the tumor site and augmented their secretion of IFN-γ, leading to effective suppression of tumor growth.

Conclusions

Our results demonstrated that the development of small molecule inhibitor Epinastine targeting CD96/PVR pathway, which proposed a promising strategy and drug candidate for cancer immunotherapy.

Graphical Abstract

Background

Immune checkpoints play a critical role in the impairment of natural killer (NK) cells and CD8+ T cells, enabling tumor cells to evade immune responses. Immune checkpoint blockade (ICB), particularly through Programmed death 1 (PD-1) and Programmed death ligand 1 (PD-L1) inhibitors, has demonstrated remarkable efficacy in activating antigen-specific T cell responses and suppressing tumor growth [1, 2]. However, a subset of patients treated with PD-1/PD-L1 antibodies experience inadequate clinical responses or tumor relapse. This outcome could be attributed to the existence of alternative inhibitory signals within the tumor microenvironment [3,4,5]. Therefore, it is essential to identify novel therapeutic targets or explore combination strategies to enhance the effectiveness of cancer immunotherapy.

Within the tumor microenvironment, immune checkpoints exhibit variable expression levels and spatial-temporal disparities, leading to diverse functional roles [6, 7]. Consequently, it is crucial to discover drugs targeting different immune checkpoints, such as T cell immunoreceptor with Ig and ITIM domains (TIGIT), Lymphocyte activation gene-3 (LAG-3), and CD96 [8,9,10,11]. CD96, a transmembrane glycoprotein Ig superfamily receptor belonging to the poliovirus receptor-nectin family, operates within a network comprising TIGIT and CD226 to regulate anti-tumor immune responses [12]. CD96 is highly expressed in various tumors, including cervical cancer and lung adenocarcinoma, and its overexpression is associated with an unfavorable prognosis in breast cancer patients [5, 13]. Furthermore, studies have revealed a correlation between high CD96 expression on tumor cells and their stemness, as observed in breast cancer and acute myeloid leukemia cells [14, 15]. Additionally, elevated CD96 expression is implicated in chemotherapy resistance [16]. These findings highlight the unique and promising potential of CD96 as a target for cancer immunotherapy.

CD96 is predominantly presented on different immune cells, including T cells, Treg cells, and NK cells [17]. Through its interaction with PVR, CD96 inhibits the function of NK cells and T cells, thereby promoting tumor growth [17]. Targeting CD96 has emerged as a promising strategy to activate the anti-tumor immune response. Monoclonal antibodies against CD96 have demonstrated efficacy in blocking the CD96/PVR interaction, activating NK cells and CD8+ T cells, and effectively suppressing tumor growth and metastasis in multiple mouse tumor models [18]. Remarkably, in the B16F10 mice tumor model, CD96 antibodies showed superior efficacy in inhibiting lung metastasis compared to PD-1 and CTLA-4 antibodies [19]. Furthermore, combining CD96 antibodies with other immune checkpoint inhibitors, such as anti-CTLA-4, anti-PD-1, or anti-PD-L1 antibodies, resulted in enhanced anti-tumor effects [19].

While monoclonal antibodies have achieved significant success as immune checkpoint inhibitors in cancer therapy, small molecules offer distinct advantages, including improved tumor penetration, lower immunogenicity, and simplified synthesis [20,21,22]. Currently, there are no small molecule drugs targeting CD96 available. The availability of Food and Drug Administration (FDA) approved drugs with established pharmacokinetic, toxicity, and ADME (absorption, distribution, metabolism, and excretion) profiles [23] provides an opportunity to screen small molecule inhibitors targeting CD96. This approach holds the potential to redefine the therapeutic role of existing FDA drugs and expand their applications in disease treatment. Therefore, the identification of novel small molecule inhibitors targeting CD96 through virtual screening of the FDA-approved drug library represents a pivotal step in advancing cancer immunotherapy.

In this study, we employed a multifaceted approach combining virtual screening against the FDA-approved drug library, in vitro blocking experiments, and T cell functional recovery assays to unravel a novel small molecule Epinastine with remarkable specificity towards CD96. Through subsequent investigation in mice tumor models, we assessed the efficacy of Epinastine in restoring T cell and NK cell functions, which ultimately led to significant tumor growth inhibition. Our results shed light on the therapeutic potential of Epinastine as a small molecule drug candidate for cancer treatment.

Results

Virtual screening of small molecules targeting CD96

The crystal structure of the CD96/PVR complex (PDB Code: 6ARQ) serves as a foundational framework for the identification of potential small molecules with the capacity to proficiently impede CD96/PVR interactions (Fig. 1A). Residues localized within the CD96/PVR interaction interface have been identified as crucial contributors in engendering the CD96/PVR binding dynamics (Fig. 1B), and this region was selected as the target site for virtual screening. As outlined in the screening procedure (Additional file 1: Fig. S1), structural refinement, three-dimensional protonation, and optimization of CD96 IgV domain, along with the energy minimization of 1729 small molecules in FDA-approved drug library, were performed by molecular operating environment (MOE) software. Molecular docking of FDA-approved drugs with CD96 was subsequently carried out. Afterwards, 1347 small molecules with molecular weights ranging between 250 and 700, and S-value of ≤ -5, were selected (Fig. 1C). Based on their binding modes, 565 small molecules interacting with CD96/PVR interaction interface were identified (Fig. 1D). Previous studies have shown that mutating Q57 and Y75 of CD96 to A (Alanine) significantly weakened its affinity for PVR, which indicated that Q57 and Y75 on CD96 are key interacting residues for CD96/PVR interaction [24]. Further analysis yielded 20 small molecules that specifically interacted with Q57 and Y75 of CD96 (Fig. 1E), while an additional 11 small molecules, which did not interact with Q57 and Y75 but were fully situated within the CD96/PVR binding surface, were also identified (Fig. 1F). Finally, the top 15 candidate small molecules were screened according to the interaction position and overall S-values (Fig. 1G). The general information for 15 small molecule candidates were presented (Additional file 1: Table S1).

Fig. 1
figure 1

Virtual screening of small molecules from the FDA-approved drug library. A Crystal structure of CD96 in complex with PVR (PDB Code: 6ARQ), with the binding interface highlighted in blue. B Detailed depiction of amino acids involved in the CD96/PVR interaction, with residues shown in stick representation. C Relationship between molecular weight and S-value for selected small molecules. Red indicates molecules with molecular weights between 250 and 700 and S-values of -5 or less. D-F Results of further screening of small molecules interacting with the CD96/PVR binding interface. G Distribution of S-values for the final 15 selected small molecules

Full size image

In vitro assessment of candidate small molecules' inhibitory activity

To investigate the potential of these small molecules to obstruct the CD96/PVR pathway via in vitro experiments, we established a HEK-293T-hCD96 cell line, overexpressing human CD96, as depicted in Fig. 2A. For assessing the blocking activity of the candidate small molecules on the CD96/PVR pathway at the cellular level, the human PVR-Fc protein was employed as a positive control. The detailed information and the corresponding blocking rates of 15 small molecules were shown in Additional file 1: Table S1. The results of blocking assay revealed that SMC587, SMC588 (Epinastine), and SMC589 exhibited notable blocking activity at a concentration of 100 µM, as illustrated in Fig. 2B. The results of multi-concentration blocking experiments showed that the inhibition of the CD96/PVR pathway exhibited a concentration-dependent gradient, as demonstrated in Fig. 2C, E, G, with calculated IC50 values of 12.4 ± 2.7 µM for SMC587, 7.40 ± 0.86 µM for SMC588 (Epinastine), and 22.5 ± 1.1 µM for SMC589, respectively. Binding mode predictions revealed that SMC587, Epinastine, and SMC589 occupied the CD96/PVR interaction interface (Fig. 2D, F, H). Notably, SMC587 established a hydrogen bond with Q55, located within the conserved region of CD96 (Figs. 2D, 3C). Epinastine occupied the binding interface of CD96 to PVR through forming a hydrogen bond with G76 and an H-π bond with V70, consequently hindering PVR binding (Fig. 2F). Importantly, G76 is located in the conserved region of CD96, and the V70 action site is located in the human-mouse similar amino acid region (Fig. 3C), which might provide theoretical support for Epinastine with the highest saturation point blocking rate (> 60%) alongside the lowest semi-inhibitory concentration. SMC589 formed hydrogen bonds with Y75, F77, and Y78 on CD96 (Fig. 2H). Both Y75 and Y78 reside in the conserved region of CD96, while F77 is located in a region with similar amino acid sequences between humans and mice (Fig. 3C). Compared to Epinastine, SMC587 formed fewer hydrogen bonds and exhibited relatively lower blocking effects on the hCD96/hPVR interaction (Fig. 2C-F). In contrast, SMC589 formed more hydrogen bonds with hCD96 than Epinastine; however, its binding occurred at the periphery of the hCD96/hPVR interface, which may partially account for its reduced blocking efficiency (Fig. 2E-H). These findings suggested that Epinastine bound more effectively to the CD96/PVR interaction interface, thereby inhibiting CD96/PVR binding more efficiently.

Fig. 2
figure 2

Analysis of the blocking efficiency of small molecules for hCD96/hPVR interaction and their binding modes with hCD96. A Flow cytometry analysis detecting CD96 expression on the HEK-293T-hCD96 cell line. B Evaluation of blocking effects of the 15 small molecules on hCD96/hPVR interaction at a concentration of 100 µM. n = 3. C, E, G Dose-response curves illustrating the efficacy of SMC587 (C), Epinastine (E), and SMC589 (G) in disrupting the hCD96/hPVR interaction. The IC50 values represented the semi-inhibitory concentrations. n = 3. Data are presented as mean ± SEM and individual data values are provided in Additional file 2. D, F, H Predicted binding modes of SMC587 (D), Epinastine (F), and SMC589 (H) to hCD96. Contact residues of hCD96 were indicated in blue, with the backbone of hCD96 shown in light grey. SMC587, Epinastine, and SMC589 were depicted in green, purple, and red, respectively

Full size image
Fig. 3
figure 3

The binding affinity and blocking activity of Epinastine on CD96/PVR pathway. A Surface plasmon resonance (SPR) analysis of the interaction between Epinastine and hCD96. Left: SPR sensorgrams showing the 120 s association and 120 s dissociation phases of Epinastine with hCD96. Right: SPR data were analyzed using a steady-state model. Results represent one of three independent measurements. B Binding affinity of Epinastine for human CD96 as determined by microscale thermophoresis (MST). The KD value represents the binding dissociation constant. Data are expressed as mean ± SEM and are representative of three independent experiments. C Human-murine homology analysis in the CD96 D1 region. The red-shaded area highlighted conserved amino acid sites, while similar amino acids were enclosed in blue boxes. Amino acids involved in the CD96/PVR interaction were marked with an asterisk (*), and the black arrow indicated the interaction site between Epinastine and CD96. D The dose-response curve of Epinastine in disrupting mCD96/mPVR interaction, where IC50 denotes the semi-inhibitory concentration. Data are presented as mean ± SEM and are representative of three independent biological experiments. Individual data values are provided in Additional file 2

Full size image

Epinastine is able to bind CD96 and effectively block CD96/PVR binding

To further validate the binding affinity of Epinastine for CD96, a surface plasmon resonance (SPR) assay was conducted. Human CD96 protein was immobilized on a CM5 chip, and Epinastine, in two-fold serial dilutions ranging from 2.5 to 0.078 μM, was introduced to interact with the immobilized hCD96 using the Biacore T200 system (Fig. 3A). Across three independent experiments, the binding affinity of Epinastine to hCD96 was determined to be 0.77 ± 0.4 μM. Additionally, microscale thermophoresis (MST) was employed to assess the interaction between Epinastine and human CD96, revealing the binding affinity with the KD value of 0.28 ± 0.2 μM. These results indicated that Epinastine exhibited high binding affinity to human CD96.The analysis of CD96 homology in humans and mice showed that CD96 (D1) had high homology in human and murine, and its proportion of conserved and similar amino acids could reach 61.8% (Fig. 3C). The murine blocking experiments convincingly demonstrated Epinastine's ability to disrupt the binding of murine CD96 to murine PVR in a concentration-dependent manner, with an IC50 value of 8.40 ± 1.7 μM (Fig. 3D). Epinastine also exhibited high binding affinity for murine CD96 with the KD values of 7.05 ± 4.3 μM (Additional file 1: Fig. S2). These results suggested that Epinastine could efficiently bind with CD96 and block the CD96/PVR interaction.

Epinastine can relieve immunosuppression of immune cells

CD96, located on immune cells, has been reported to exert immunosuppressive effects when engaged with its ligand PVR. The above results indicated that Epinastine could bind with CD96 to block CD96/PVR binding at the protein level. In order to detect whether Epinastine could bind to CD96 on the cell surface, the cellular thermal shift assay (CETSA) was performed and the results showed that Epinastine treatment significantly increased CD96 thermal stability (Fig. 4A, B, Additional file 1: Fig. S3), suggesting that Epinastine targets CD96 on cell surface. Thus, we conducted co-culture experiments to investigate the impact of Epinastine on immune cell functional restoration. Firstly, we established a Jurkat-hCD96 cell line that overexpressed human CD96, as evidenced in Fig. 4C. Subsequently, Jurkat-hCD96 was co-cultured with CHO-K1-hPVR cells. Epinastine significantly augmented IL-2 secretion of Jurkat-hCD96 cells, with a comparable effect to that of anti-hCD96 antibody (Fig. 4D, E). Our results underscore the capability of Epinastine to target CD96 on immune cells, thereby inhibiting the binding of CD96 to PVR and alleviating the immunosuppressive impact on immune cells. In conclusion, our results showed the ability of Epinastine to target CD96 on immune cells, consequently inhibiting CD96/PVR binding and ameliorating the immunosuppressive implications on immune cells.

Fig. 4
figure 4

Effects of Epinastine on immune cell activity in vitro. A, B CETSA analysis illustrating the interaction between Epinastine and CD96. Representative western blotting results (A) and quantitative analysis (B) were shown. n = 3. ns: no significance, * P < 0.05 and *** P < 0.001. C Expression of CD96 detected on Jurkat-hCD96 cells detected by flow cytometry. D, E Influence of Epinastine on IL-2 secretion within the co-culture system of Jurkat-hCD96 cells and CHO-K1-hPVR cells, using a 5:1 effector-to-target ratio. Jurkat-hCD96 cells were pre-stimulated with 1 μg/ml PHA and 0.25 ng/ml PMA for 24 h. Representative flow cytometry images (D) and corresponding statistical plots (E) are presented. Data are presented as mean ± SEM and represent three independent biological replicates. Individual data values are provided in Additional file 2. ** P < 0.01

Full size image

Epinastine effectively inhibited the growth of MC38 tumors and increased CD8+ T cells and NK cells function at the tumor site

Previous studies have highlighted the substantial anti-tumor responses resulting from the blockade of CD96/PVR interactions in mouse tumor models. In order to investigate the in vivo anti-tumor efficacy of Epinastine, we established a subcutaneous mouse MC38 tumor model, as depicted in Fig. 5A. Following continuous intraperitoneal administration of Epinastine at two doses for a duration of 14 days, a noteworthy reduction in tumor volume was observed in mice when compared to the normal saline group (NS), as illustrated in Fig. 5B. Importantly, the administration of Epinastine did not exert any discernible impact on mouse body weight, as indicated in Fig. 5C. CD96 is predominantly expressed on T cells and NK cells. During tumorigenesis, influenced by the tumor microenvironment, the heightened presence of PVR on tumor cell surfaces leads to the binding of CD96 on immune cells, resulting in the inhibition of immune cells' tumor-killing functions. In this study, we investigated the impact of Epinastine administration on mouse T cells and NK cells. Our findings demonstrated that Epinastine significantly enhanced the infiltration of CD8+ T cells at the tumor site (Fig. 5D) and elevated the secretion of CD8+ T cell IFN-γ (Fig. 5E). Notably, the high-dose Epinastine group also exhibited substantial improvements in NK cell infiltration and IFN-γ secretion at the tumor site (Fig. 5F, G).

Fig. 5
figure 5

Anti-tumor efficacy of Epinastine in MC38-bearing mouse model. A Diagram illustrating the construction of the MC38 mouse tumor-bearing model and the dosing regimen pattern. B Change curve depicting the alteration in MC38 tumor volume in tumor-bearing mice. n = 5, * P < 0.05. C Curve illustrating the change in body weight of tumor-bearing mice with MC38 tumors. n = 5. D, E Flow cytometry analysis of T cells proportion (D) and intracellular IFN-γ levels in T cells (E) at the tumor site in tumor-bearing mice. n = 5 and * P < 0.05. F, G Flow cytometry assessment of NK cells proportion (F) and intracellular IFN-γ levels in NK cells (G) at the tumor sites of tumor-bearing mice. Data are presented as mean ± SEM for n = 5 samples and individual data values are provided in Additional file 2. ns: no significance, and * P < 0.05

Full size image

Epinastine is a potent and selective histamine receptor H1 (HRH1) antagonist [25]. In this study, we identified Epinastine as a potential immune checkpoint inhibitor of the CD96/PVR pathway, capable of eliciting anti-tumor effects (Fig. 5). To delineate the specific anti-tumor mechanism of Epinastine, it was crucial to isolate and exclude its effects as an HRH1 antagonist on tumor growth. We utilized histamine to activate HRH1 and further examined the anti-tumor efficacy of Epinastine. A subcutaneous MC38 tumor model in mice was established to evaluate the anti-tumor activity of Epinastine (Additional file 1: Fig. S4A). Treatment with Epinastine, either alone or in combination with histamine, did not affect the body weight of MC38 tumor-bearing mice (Additional file 1: Fig. S4B). Drug treatment results demonstrated that Epinastine effectively inhibited MC38 tumor growth (Additional file 1: Fig. S4C). Consistent with previous studies, histamine treatment alone promoted tumor growth [26, 27]. However, Epinastine, when administered with histamine, still exhibited significant anti-tumor effects, suggesting that Epinastine can inhibit tumor growth even in the presence of histamine (Additional file 1: Fig. S4C). Tumor weight measurements post-treatment confirmed these findings (Additional file 1: Fig. S4D). These results indicate that Epinastine acts as a histamine antagonist, countering histamine's effects, and also functions as an inhibitor of the CD96/PVR pathway, thereby contributing to its anti-tumor effects.

Through immunohistochemical analysis, it was found that the Epinastine treatment illustrated lower Ki67 expression and higher Caspase-3 expression compared with NS treatment, indicating that Epinastine inhibited cell proliferation and induced apoptosis for tumor cells (Fig. 6A). In order to evaluate the drug safety of Epinastine, we performed hematoxylin and eosin (H&E) staining and the Epinastine showed no obvious toxicity for heart, liver, spleen, lung and kidney, demonstrating the safety of Epinastine for anti-tumor treatment (Fig. 6B).

Fig. 6
figure 6

Immunohistochemical and H&E staining of tissue sections in MC38 tumor-bearing mice. A Immunohistochemical assessment of Ki67 and Caspase-3 levels at the tumor site. Scale bar: 50 μm. B H&E analysis for major organs including heart, liver, spleen, lung and kidney in MC38 tumor-bearing mice. Scale bar: 25 μm

Full size image

Epinastine improved the immune response of CD8+ T cells and NK cells in spleen and lymph nodes

Given the significance of spleen and lymph nodes as key immune organs, we further explored the influence of Epinastine on their functions. Our results revealed that Epinastine substantially augmented IFN-γ secretion in T cells and NK cells within the lymph nodes (Fig. 7A, C). Additionally, in the spleen region, the high-dose Epinastine group displayed a noteworthy increase in IFN-γ secretion by T cells, and similarly elevated IFN-γ secretion levels by NK cells (Fig. 7B, D). Collectively, our results suggested that Epinastine's anti-tumor potential may be attributed to its capacity to alleviate immunosuppression in the tumor microenvironment, alongside its immune-modulating impact within T cells and NK cells localized in the spleen and lymph node environs.

Fig. 7
figure 7

The effects of Epinastine on T cells and NK cells in spleen and lymph nodes. A, B Flow cytometry detection of the IFN-γ secretion level in lymph nodes (A) and spleen (B) CD8+ T cells of tumor-bearing mice. n = 5. ns: no significance, and * P < 0.05. C, D Flow cytometry measurement of the IFN-γ secretion level in NK cells at the lymph node-bearing site (C) and spleen (D) of tumor-bearing mice. Data are presented as mean ± SEM for n = 5 samples and individual data values are provided in Additional file 2. * P < 0.05

Full size image

Discussion

The functional role of CD96 in T cells, whether it assumes an inhibitory or stimulatory function, remains to be investigated. Co-expression of CD96 with TIGIT and/or PD-1 in tumor-infiltrating lymphocytes (TILs) suggests its inhibitory role [13, 19, 28, 29]. Notably, blockade of CD96 alone or in combination with PD-1/PD-L1 inhibitors has shown significant tumor control through CD8+ T cell-dependent mechanisms in experimental mouse models [19, 30]. Conversely, studies have indicated that CD96 on CD8+ T cells could induce their proliferation, and stimulates cytokine production [31]. Interestingly, CD96 deficiency leads to a reduction in the frequency of tumor-specific CD8+ T cells in a murine model of colorectal cancer, suggesting a co-stimulatory function for CD96 [31]. The multifaceted functionality of CD96 within the tumor microenvironment may arise from the specific tumor models employed, necessitating further exploration of its underlying mechanisms. In this study, we report the discovery that Epinastine, a small molecule that targets CD96 and inhibits its interaction with CD155, effectively suppresses the growth of MC38 tumors, providing compelling evidence for the inhibitory role of CD96 within the tumor microenvironment of MC38-bearing mouse model.

CD96, along with CD226 and TIGIT, belongs to the immunoglobulin (Ig) superfamily and interacts with nectin and nectin-like proteins [17, 32]. Disruption of CD96/TIGIT/CD226 signaling axis has yielded favorable therapeutic outcomes in tumor treatment [17]. However, clinical trials involving TIGIT antibodies for stage III melanoma have encountered setbacks, potentially attributable to patient selection issues within the clinical setting. Given the heterogeneity observed among tumors, specific interventions aimed at immune checkpoint molecules with divergent expression patterns in tumor patients can significantly enhance treatment outcomes [33]. Clinical data from patients with colorectal cancer reveal elevated expression levels of CD96 with a discernible positive correlation with PD-1 expression. Notably, the inhibitor Epinastine effectively suppresses the growth of the MC38 tumor model, underscoring its potential utility in the treatment of colorectal cancer.

Drug repurposing represents a strategic approach aimed at identifying new therapeutic indications for existing or investigational drugs beyond their original approvals, thereby expanding their clinical utility and relevance [34, 35]. Successful examples of repurposed drugs include aspirin, metformin, and azithromycin [36,37,38]. Drug repurposing offers advantages such as reduced development costs and accelerated timelines [39]. Consequently, the repurposing of existing drugs for the treatment of both common and rare diseases has gained increasing attention. Systematic screening of approved drug libraries enables the identification of novel mechanisms of action and the discovery of therapeutic potential in diverse disease contexts, thereby expanding the scope of clinical applications for these drugs [40, 41]. FDA-approved drugs possess well-characterized pharmacokinetic profiles, known toxicity profiles, and comprehensive ADME properties. The application of virtual screening methodologies to FDA-approved drugs can facilitate the identification of novel drug-target interactions, elucidate mechanisms underlying adverse effects, and capitalize on the established synthetic routes and ready availability of these drugs [42,43,44]. Epinastine, previously primarily used for the treatment of allergic rhinitis, atopic eczema, allergic conjunctivitis and asthma, is a FDA-approved drug [45,46,47]. In this study, through virtual screening and functional investigations involving FDA-approved drugs, we discover that Epinastine targets CD96, exerting inhibitory effects on tumor growth and broadening its potential applications in cancer immunotherapy.

Conclusions

In conclusion, Epinastine, a novel small molecule, exhibiting specific targeting towards CD96 were identified. Through comprehensive in vitro and in vivo investigations, we demonstrated the ability of Epinastine to effectively restore T cell and NK cell functions, leading to notable suppression of tumor growth. The findings presented herein position Epinastine as a promising candidate for further development as a small molecule drug for cancer treatment.

Methods

Virtual screening

The three-dimensional crystal structure of the hCD96-hPVR complex was retrieved from the Protein Data Bank (PDB) and used as a reference for virtual screening. The individual crystal structure of the hCD96 protein was generated using the Molecular Operating Environment (MOE) software (version 2020) and prepared for docking analysis. 1729 small molecules were obtained from FDA-approved drug library (Topscience, Shanghai, China). The binding surface information between the hCD96 and hPVR was analyzed using the MOE protein contacts module. Subsequently, induced-fit docking of the hCD96 protein with the small molecules from the drug library was performed, and the docking results were processed using MOE and StarDrop software. All the small molecule candidates were procured from Topscience.

Cell culture

The human T lymphocyte cell line Jurkat, Chinese hamster ovary cell line CHO-K1, and mouse MC38 cell line were generously provided by Professor Shengdian Wang from the Institute of Biology, Chinese Academy of Sciences. Lentiviral transfection experiments were conducted to generate HEK-293T-hCD96, CHO-K1-mCD96 and Jurkat-hCD96 stable overexpression cell lines. CHO-K1-hPVR stable overexpression cell lines were obtained from our laboratory's preserved stocks [48]. Jurkat cells, Jurkat-hCD96 cells, CHO-K1-mCD96 cells, and CHO-K1-hPVR cells were cultured in RPMI 1640 medium (Gibco, USA), while MC38 cells, HEK-293T cells, and HEK-293T-hCD96 cells were cultured in DMEM (Gibco, USA). Both culture media were supplemented with 10% fetal bovine serum (FBS, BI, Israel), 100 U/mL penicillin (Solarbio, China), and 100 μg/mL streptomycin (Solarpio, China). All the cell lines were maintained in an incubator with 5% CO2 at 37 °C and validated to be negative of mycoplasma by using the EZ-PCR Mycoplasma Test Kit (Cat. #20–700-20, Biological Industries).

Blocking assay

HEK-293 T-hCD96 cells in optimal growth condition were collected and washed with PBS (pH 7.2). The cells (2 × 105) were then incubated with small molecules (TargetMol, USA) at a final concentration of 100 μM for 30 min. After the incubation, anti-human PVR-Fc protein (Sino Biological Inc., China) was added to each tube and incubated at room temperature for an additional 30 min. Finally, anti-human IgG Fc antibody-tagged protein (12-4998-82, eBioscience, USA) was added and incubated for another 30 min. Following the antibody incubation, the cells were washed with FACS buffer (PBS plus 2% FBS) and analyzed using flow cytometry.

Microscale thermophoresis (MST)

The binding affinity between small molecules and CD96 protein was assessed using the Monolith NT.115 microscale thermophoresis (MST). The hCD96-His protein (ACRO Biosystem, USA) or mCD96-His protein (Sino Biological Inc., China) was labeled with a fluorescent dye by binding it to the histidine (His) tag. The labeled protein was mixed with different concentrations of small molecules, and the interactions were analyzed using the MO.Affinity Analysis software to determine the binding affinity.

Surface plasmon resonance (SPR)

Human CD96 protein (ACRO Biosystem, USA) was diluted in 10 mM sodium acetate buffer at pH values of 4.0, 4.5, 5.0, and 5.5, and pre-enriched on the surface of a CM5 chip (Cytiva, USA) to determine the optimal conditions for maximum enrichment. The protein demonstrated the highest enrichment efficiency at pH 4.0. It was then covalently coupled to the activated CM5 chip via amino coupling. Epinastine (TargetMol, USA) was prepared in a two-fold serial dilution ranging from 2.5 μM to 0.078 μM in PBS at pH 7.2. These analyte samples were sequentially injected over the immobilized human CD96 protein using a Biacore T200 (GE Healthcare, USA), with a binding time of 120 s and a dissociation time of 120 s. The binding affinity of Epinastine for human CD96 protein was assessed using Biacore T200 Evaluation Software.

Cellular thermal shift assay

HEK-293T-hCD96 cells (5 × 105) were collected and treated with 25 μM small molecule Epinastine (TargetMol, USA) or PBS buffer with 1% DMSO for 1 h. The cells were washed by PBS buffer and resuspended with protease inhibitor buffer. The samples were heated in a metal bath at 52, 55, 58, 61, and 64 °C for 3 min. Hereafter, the heat-treated cells were quickly frozen in liquid nitrogen and thawed on ice, and this cycle was repeated three times. Cell Lysates were centrifuged at 25,000 g for 15 min at 4 °C, and equal amounts of collected supernatant were detected by immunoblotting.

In vitro co-culture experiment

CHO-K1-hPVR cells (5 × 104 cells/mL) and Jurkat-hCD96 cells (2.5 × 105 cells/mL) were seeded into 24-well plates and co-cultured for 48 h under various experimental conditions. Prior to co-culture, Jurkat-hCD96 cells were pre-stimulated with 1 μg/mL PHA and 0.25 ng/mL PMA for 24 h. The human CD96 functional antibody anti-hCD96 (NK92.39, Biolegend, USA) was used as a positive control. Protein transport inhibitors were added 4 h before the end of the culture. After 4 h, cells were collected for IL-2 secretion analysis using flow cytometry.

Anti-tumor experiment

C57BL/6N mice aged 6-8 weeks were obtained from the Vital River Laboratory (Beijing, China) and sustained under pathogen-free conditions. Mice were subcutaneously inoculated with 1 × 106 MC38 cells, and once the tumor volume reached 60 ~ 80 mm3, the mice were randomly divided into different treatment groups. To evaluate the anti-tumor activity of Epinastine, mice were divided into three groups: the control group, which received normal saline; the low-dose group, which received Epinastine at 2 mg/kg; and the high-dose group, which received Epinastine at 6 mg/kg. In the combined treatment experiment, mice were intraperitoneally (i.p.) injected with normal saline, 6 mg/kg Epinastine, or 6 mg/kg histamine. Intraperitoneal administration was carried out daily. Tumor dimensions, including length, width, and height, were measured using vernier calipers and rulers, and the tumor volume were calculated using the formula V = 1/2 × A (length) × B (width) × C (height). The day after the administration was completed, mice were euthanized, tumor tissues were excised, weighed, and subjected to further analysis.

Flow cytometry was employed for the detection of immune infiltration and intracellular factor secretion. Tumor cells, spleen cells, and lymph node cells were isolated from tumor-bearing mice, and a single-cell suspension was obtained through grinding digestion. The cells were then stimulated with 20 ng/mL of 12-myristic ester-13-acetate (PMA, Sigma, USA) and 1 μM ionomycin (Sigma, USA) in the culture plates. Protein transport inhibitors were added during plating, and after 4 h, the cells were collected for antibody staining. The following antibodies were used: anti-mouse CD3 PerCP-eFluor710 (17A2, eBioscience, USA), anti-mouse CD8α eFluor450 (53–6.7, eBioscience, USA), anti-mouse NK1.1 (PK136, Biolegend, USA) staining, followed by staining with the intracellular marker anti-mouse IFN-γ APC (XMG1.2, eBioscience, USA) or isotype control antibodies.

Immunohistochemistry and H&E staining analysis

Upon completion of the treatment, the mice were sacrificed, and tumor tissue as well as major organs including the heart, liver, spleen, lung, and kidney were harvested. The tissues were fixed in 10% formalin for 24 h, followed by paraffin embedding and sectioning. Hematoxylin-eosin (H&E) and Ki67/Caspase-3 staining analysis were performed on the tissue sections, and the staining results were analyzed using Image J software (National Institutes of Health, Bethesda, MD).

Data analysis

All data were statistically analyzed and presented as means ± S.E.M. Statistical significance was determined using one-tailed paired or unpaired Student's t-test, with * P < 0.05, ** P < 0.01, and *** P < 0.001 considered as statistically significant.

Data availability

No datasets were generated or analysed during the current study.

Abbreviations

ICIs:

Immune checkpoint inhibitors

ICB:

Immune checkpoint blockade

PVR:

Poliovirus receptor

TIGIT:

T cell immunoreceptor with Ig and ITIM domains

LAG-3:

Lymphocyte activation gene-3

PD-1:

Programmed death 1

PD-L1:

Programmed death ligand 1

NK:

Natural killer cells

ADME:

Absorption, distribution, metabolism, and excretion

TILs:

Tumor-infiltrating lymphocytes

FDA:

Food and Drug Administration

MOE:

Molecular operating environment

MST:

Microscale thermophoresis

FBS:

Fetal bovine serum

H&E:

Hematoxylin-eosin staining

References

  1. Li H, Seeram NP, Liu C, Ma H. Further investigation of blockade effects and binding affinities of selected natural compounds to immune checkpoint PD-1/PD-L1. Front Oncol. 2022;12:995461.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. Juárez-Salcedo LM, Sandoval-Sus J, Sokol L, Chavez JC, Dalia S. The role of anti-PD-1 and anti-PD-L1 agents in the treatment of diffuse large B-cell lymphoma: The future is now. Crit Rev Oncol Hematol. 2017;113:52–62.

    Article  PubMed  Google Scholar 

  3. Koyama S, Akbay EA, Li YY, Herter-Sprie GS, Buczkowski KA, Richards WG, et al. Adaptive resistance to therapeutic PD-1 blockade is associated with upregulation of alternative immune checkpoints. Nat Commun. 2016;7:10501.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Thommen DS, Schreiner J, Müller P, Herzig P, Roller A, Belousov A, et al. Progression of Lung Cancer Is Associated with Increased Dysfunction of T Cells Defined by Coexpression of Multiple Inhibitory Receptors. Cancer Immunol Res. 2015;3(12):1344–55.

    Article  CAS  PubMed  Google Scholar 

  5. Wang Y, Wang C, Qiu J, Qu X, Peng J, Lu C, et al. Targeting CD96 overcomes PD-1 blockade resistance by enhancing CD8+ TIL function in cervical cancer. J Immunother Cancer. 2022;10(3):e003667.

    Article  PubMed  PubMed Central  Google Scholar 

  6. Pardoll DM. The blockade of immune checkpoints in cancer immunotherapy. Nat Rev Cancer. 2012;12(4):252–64.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Liu G, Rui W, Zhao X, Lin X. Enhancing CAR-T cell efficacy in solid tumors by targeting the tumor microenvironment. Cell Mol Immunol. 2021;18(5):1085–95.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Dougall WC, Kurtulus S, Smyth MJ, Anderson AC. TIGIT and CD96: new checkpoint receptor targets for cancer immunotherapy. Immunol Rev. 2017;276(1):112–20.

    Article  CAS  PubMed  Google Scholar 

  9. Zhai W, Zhou X, Zhai M, Li W, Ran Y, Sun Y, et al. Blocking of the PD-1/PD-L1 interaction by a novel cyclic peptide inhibitor for cancer immunotherapy. Sci China Life Sci. 2021;64(4):548–62.

    Article  CAS  PubMed  Google Scholar 

  10. Zhou X, Li Y, Zhang X, Li B, Jin S, Wu M, et al. Hemin blocks TIGIT/PVR interaction and induces ferroptosis to elicit synergistic effects of cancer immunotherapy. Sci China Life Sci. 2024;67(5):996–1009.

    Article  CAS  PubMed  Google Scholar 

  11. Zhai W, Zhou X, Wang H, Li W, Chen G, Sui X, et al. A novel cyclic peptide targeting LAG-3 for cancer immunotherapy by activating antigen-specific CD8(+) T cell responses. Acta Pharm Sin B. 2020;10(6):1047–60.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Georgiev H, Ravens I, Papadogianni G, Bernhardt G. Coming of Age: CD96 Emerges as Modulator of Immune Responses. Front Immunol. 2018;9:1072.

    Article  PubMed  PubMed Central  Google Scholar 

  13. Ye W, Luo C, Liu F, Liu Z, Chen F. CD96 Correlates With Immune Infiltration and Impacts Patient Prognosis: A Pan-Cancer Analysis. Front Oncol. 2021;11:634617.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Hosen N, Park CY, Tatsumi N, Oji Y, Sugiyama H, Gramatzki M, et al. CD96 is a leukemic stem cell-specific marker in human acute myeloid leukemia. Proc Natl Acad Sci U S A. 2007;104(26):11008–13.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Staudinger M, Humpe A, Gramatzki M. Strategies for purging CD96(+) stem cells in vitro and in vivo: New avenues for autologous stem cell transplantation in acute myeloid leukemia. Oncoimmunology. 2013;2(6):e24500.

    Article  PubMed  PubMed Central  Google Scholar 

  16. Li J, Xia Q, Di C, Li C, Si H, Zhou B, et al. Tumor Cell-Intrinsic CD96 Mediates Chemoresistance and Cancer Stemness by Regulating Mitochondrial Fatty Acid β-Oxidation. Adv Sci (Weinh). 2023;10(7):e2202956.

    Article  PubMed  Google Scholar 

  17. Conner M, Hance KW, Yadavilli S, Smothers J, Waight JD. Emergence of the CD226 Axis in Cancer Immunotherapy. Front Immunol. 2022;13:914406.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Blake SJ, Stannard K, Liu J, Allen S, Yong MC, Mittal D, et al. Suppression of Metastases Using a New Lymphocyte Checkpoint Target for Cancer Immunotherapy. Cancer Discov. 2016;6(4):446–59.

    Article  CAS  PubMed  Google Scholar 

  19. Mittal D, Lepletier A, Madore J, Aguilera AR, Stannard K, Blake SJ, et al. CD96 Is an Immune Checkpoint That Regulates CD8(+) T-cell Antitumor Function. Cancer Immunol Res. 2019;7(4):559–71.

    Article  PubMed  PubMed Central  Google Scholar 

  20. Wu Q, Jiang L, Li SC, He QJ, Yang B, Cao J. Small molecule inhibitors targeting the PD-1/PD-L1 signaling pathway. Acta Pharmacol Sin. 2021;42(1):1–9.

    Article  PubMed  Google Scholar 

  21. Chen S, Lee LF, Fisher TS, Jessen B, Elliott M, Evering W, et al. Combination of 4–1BB agonist and PD-1 antagonist promotes antitumor effector/memory CD8 T cells in a poorly immunogenic tumor model. Cancer Immunol Res. 2015;3(2):149–60.

    Article  CAS  PubMed  Google Scholar 

  22. Zhan MM, Hu XQ, Liu XX, Ruan BF, Xu J, Liao C. From monoclonal antibodies to small molecules: the development of inhibitors targeting the PD-1/PD-L1 pathway. Drug Discov Today. 2016;21(6):1027–36.

    Article  CAS  PubMed  Google Scholar 

  23. Migliorati JM, Liu S, Liu A, Gogate A, Nair S, Bahal R, et al. Absorption, Distribution, Metabolism, and Excretion of US Food and Drug Administration-Approved Antisense Oligonucleotide Drugs. Drug Metab Dispos. 2022;50(6):888–97.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Deuss FA, Watson GM, Fu Z, Rossjohn J, Berry R. Structural Basis for CD96 Immune Receptor Recognition of Nectin-like Protein-5, CD155. Structure. 2019;27(2):219–28.e3.

    Article  CAS  PubMed  Google Scholar 

  25. Amon U, Gibbs BF, Buss G, Nitschke M. In vitro investigations with the histamine H1 receptor antagonist, epinastine (WAL 801 CL), on isolated human allergic effector cells. Inflamm Res. 2000;49(3):112–6.

    Article  CAS  PubMed  Google Scholar 

  26. Zhu X, Wang X, Zhu B, Ding S, Shi H, Yang X. Disruption of histamine/H(1)R-STAT3-SLC7A11 axis exacerbates doxorubicin-induced cardiac ferroptosis. Free Radic Biol Med. 2022;192:98–114.

    Article  CAS  PubMed  Google Scholar 

  27. Li H, Xiao Y, Li Q, Yao J, Yuan X, Zhang Y, et al. The allergy mediator histamine confers resistance to immunotherapy in cancer patients via activation of the macrophage histamine receptor H1. Cancer Cell. 2022;40(1):36–52.e9.

    Article  CAS  PubMed  Google Scholar 

  28. Blake SJ, Dougall WC, Miles JJ, Teng MW, Smyth MJ. Molecular Pathways: Targeting CD96 and TIGIT for Cancer Immunotherapy. Clin Cancer Res. 2016;22(21):5183–8.

    Article  CAS  PubMed  Google Scholar 

  29. Harjunpää H, Blake SJ, Ahern E, Allen S, Liu J, Yan J, et al. Deficiency of host CD96 and PD-1 or TIGIT enhances tumor immunity without significantly compromising immune homeostasis. Oncoimmunology. 2018;7(7): e1445949.

    Article  PubMed  PubMed Central  Google Scholar 

  30. Boch C, Reschke M, Igney F, Maier P, Müller P, Danklmaier S, et al. Evaluating Antibody Pharmacokinetics as Prerequisite for Determining True Efficacy as Shown by Dual Targeting of PD-1 and CD96. Biomedicines. 2022;10(9):2146.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Chiang EY, de Almeida PE, de Almeida Nagata DE, Bowles KH, Du X, Chitre AS, et al. CD96 functions as a co-stimulatory receptor to enhance CD8(+) T cell activation and effector responses. Eur J Immunol. 2020;50(6):891–902.

    Article  CAS  PubMed  Google Scholar 

  32. O’Donnell JS, Madore J, Li XY, Smyth MJ. Tumor intrinsic and extrinsic immune functions of CD155. Semin Cancer Biol. 2020;65:189–96.

    Article  CAS  PubMed  Google Scholar 

  33. Tu L, Guan R, Yang H, Zhou Y, Hong W, Ma L, et al. Assessment of the expression of the immune checkpoint molecules PD-1, CTLA4, TIM-3 and LAG-3 across different cancers in relation to treatment response, tumor-infiltrating immune cells and survival. Int J Cancer. 2020;147(2):423–39.

    Article  CAS  PubMed  Google Scholar 

  34. Pushpakom S, Iorio F, Eyers PA, Escott KJ, Hopper S, Wells A, et al. Drug repurposing: progress, challenges and recommendations. Nat Rev Drug Discov. 2019;18(1):41–58.

    Article  CAS  PubMed  Google Scholar 

  35. Jana D, Zhao Y. Strategies for enhancing cancer chemodynamic therapy performance. Exploration (Beijing). 2022;2(2):20210238.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Saengboonmee C, Sanlung T, Wongkham S. Repurposing Metformin for Cancer Treatment: A Great Challenge of a Promising Drug. Anticancer Res. 2021;41(12):5913–8.

    Article  CAS  PubMed  Google Scholar 

  37. Palazzolo G, Mollica H, Lusi V, Rutigliani M, Di Francesco M, Pereira RC, et al. Modulating the Distant Spreading of Patient-Derived Colorectal Cancer Cells via Aspirin and Metformin. Transl Oncol. 2020;13(4):100760.

    Article  PubMed  PubMed Central  Google Scholar 

  38. Takano N, Hiramoto M, Yamada Y, Kokuba H, Tokuhisa M, Hino H, et al. Azithromycin, a potent autophagy inhibitor for cancer therapy, perturbs cytoskeletal protein dynamics. Br J Cancer. 2023;128(10):1838–49.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Lu C, Li X, Ren Y, Zhang X. Disulfiram: a novel repurposed drug for cancer therapy. Cancer Chemother Pharmacol. 2021;87(2):159–72.

    Article  CAS  PubMed  Google Scholar 

  40. Wu J, Hu B, Lu S, Duan R, Deng H, Li L, et al. Identification of raloxifene as a novel α-glucosidase inhibitor using a systematic drug repurposing approach in combination with cross molecular docking-based virtual screening and experimental verification. Carbohydr Res. 2022;511:108478.

    Article  CAS  PubMed  Google Scholar 

  41. Wang Y, He X, Li C, Ma Y, Xue W, Hu B, et al. Carvedilol serves as a novel CYP1B1 inhibitor, a systematic drug repurposing approach through structure-based virtual screening and experimental verification. Eur J Med Chem. 2020;193:112235.

    Article  CAS  PubMed  Google Scholar 

  42. Zhang X, Li K, Zhong S, Liu S, Liu T, Li L, et al. Immunotherapeutic Value of MAP1LC3C and Its Candidate FDA-Approved Drugs Identified by Pan-Cancer Analysis, Virtual Screening and Sensitivity Analysis. Front Pharmacol. 2022;13:863856.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Umesh, Prerna K, Dubey VK. Virtual screening and repurposing of FDA-approved drugs from ZINC database to identify potential autophagy inhibitors exploiting autophagy related 4A cysteine peptidase as a target: potential as novel anti-cancer molecule. J Biomol Struct Dyn. 2022;40(12):5266–82.

  44. Liang H, Zhao L, Gong X, Hu M, Wang H. Virtual screening FDA approved drugs against multiple targets of SARS-CoV-2. Clin Transl Sci. 2021;14(3):1123–32.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Fraunfelder FW. Epinastine hydrochloride for atopic disease. Drugs Today (Barc). 2004;40(8):677–83.

    Article  CAS  PubMed  Google Scholar 

  46. Wong LS, Yen YT, Lee CH. The Implications of Pruritogens in the Pathogenesis of Atopic Dermatitis. Int J Mol Sci. 2021;22(13):7227.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Vafaee F, Shirzad S, Shamsi F, Boskabady MH. Neuroscience and treatment of asthma, new therapeutic strategies and future aspects. Life Sci. 2022;292:120175.

    Article  CAS  PubMed  Google Scholar 

  48. Zhou X, Du J, Wang H, Chen C, Jiao L, Cheng X, et al. Repositioning liothyronine for cancer immunotherapy by blocking the interaction of immune checkpoint TIGIT/PVR. Cell Commun Signal. 2020;18(1):142.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

Not applicable.

Funding

This work was supported by the National Natural Science Foundation of China (grant number 82272785, 82404497), Natural Science Foundation of Henan Province (grant number 242300421198, 242301420078), Henan provincial Key Research and Development Project (grant number 231111313000), Henan provincial Science and Technology Research Project (grant number 232102310216, 242102311206), Key Scientific Research Projects of Higher Education Institutions in Henan Province (grant number 24B310002, 25A180007), Fostering Project for Young Teachers of Zhengzhou University (grant number JC22851042).

Author information

Author notes

  1. Xiangrui Zhang and Lihan Zhang contributed equally to this work.

Authors and Affiliations

  1. School of Life Sciences, Zhengzhou University, Zhengzhou, 450001, China

    Xiangrui Zhang, Beibei Li, Qingchao Wang, Peixin Chen, Wenjie Zhai, Yahong Wu, Xiaowen Zhou & Wenshan Zhao

  2. Department of Integrated Chinese and Western Medicine, The Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer Hospital, Zhengzhou, 450008, China

    Lihan Zhang

  3. Department of Basic Medical Sciences, Luohe Medical College, Luohe, 462000, China

    Ranran Shi

  4. School of Pharmaceutical Sciences (Shenzhen), Shenzhen Campus of Sun Yat-Sen University, Shenzhen, 518107, China

    Xiuman Zhou & Xiaoshuang Niu

  5. International Joint Laboratory for Protein and Peptide Drugs of Henan Province, Zhengzhou University, Zhengzhou, 450001, China

    Wenjie Zhai & Yahong Wu

  6. Department of Head Neck and Thyroid, The Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer Hospital, Zhengzhou, 450008, China

    Wenhui Shen

Authors
  1. Xiangrui Zhang
    View author publications

    Search author on:PubMed Google Scholar

  2. Lihan Zhang
    View author publications

    Search author on:PubMed Google Scholar

  3. Beibei Li
    View author publications

    Search author on:PubMed Google Scholar

  4. Qingchao Wang
    View author publications

    Search author on:PubMed Google Scholar

  5. Peixin Chen
    View author publications

    Search author on:PubMed Google Scholar

  6. Ranran Shi
    View author publications

    Search author on:PubMed Google Scholar

  7. Xiuman Zhou
    View author publications

    Search author on:PubMed Google Scholar

  8. Xiaoshuang Niu
    View author publications

    Search author on:PubMed Google Scholar

  9. Wenjie Zhai
    View author publications

    Search author on:PubMed Google Scholar

  10. Yahong Wu
    View author publications

    Search author on:PubMed Google Scholar

  11. Wenhui Shen
    View author publications

    Search author on:PubMed Google Scholar

  12. Xiaowen Zhou
    View author publications

    Search author on:PubMed Google Scholar

  13. Wenshan Zhao
    View author publications

    Search author on:PubMed Google Scholar

Contributions

W.Z. and X.Z. conceived and designed the research. X.R.Z., L.Z., B.L., Q.W. and P.C performed the experiments and data analyses. X.R.Z., L.Z., R.S., X.M.Z., X.N. and W.S. collected the sample and interpreted the results. W.Z., X.Z., W.J.Z. and Y.W. wrote the manuscript with input from all authors. All authors discussed the results and gave final approval of the manuscript.

Corresponding authors

Correspondence to Xiaowen Zhou or Wenshan Zhao.

Ethics declarations

Ethics approval and consent to participate

The animal study was reviewed and approved by the Ethics Committee of Zhengzhou university (ZZUIRB: 2021–36).

Consent for publication

All authors read and approved the final manuscript.

Competing interests

The authors declare no competing interests.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

12915_2025_2132_MOESM1_ESM.docx

Additional file 1: Figs. S1–S4 and Tables S1. Fig. S1. Overview of the virtual screening process for identifying 15 candidate small molecules targeting CD96. Fig. S2. The binding affinity of Epinastine to mCD96. The binding affinity of Epinastine with murine CD96 tested by MST, with KD representing the binding dissociation constant. Data are presented as mean ± SEM and represent three independent experiments. Fig. S3. The original images of the Western blotting results for CETSA analysis. (A and B) Original results for the CD96 band (A) and β-actin band (B) for the control group shown in Fig. 5A. (C and D) Original results for the CD96 band (C) and β-actin band (D) for Epinastine treatment group shown in Fig. 5A. Fig. S4. The effects of Epinastine and histamine in regulating tumor growth in MC38 tumor-bearing mice. (A) Diagram illustrating the establishment of the MC38 mouse tumor-bearing model and the corresponding dosing regimen. (B) Body weight changes of mice bearing MC38 tumors throughout the treatment period. n = 5. (C) Curve showing the change in tumor volume of MC38 tumor-bearing mice. n = 5, * P < 0.05 and ** P < 0.01. (D) Tumor weight measurements of MC38 tumor-bearing mice post-treatment. Data are presented as mean ± SEM for n = 5 samples. * P < 0.05 and ** P < 0.01. Table S1. Comprehensive data on the 15 small molecule candidates identified as potential inhibitors of CD96.

Additional file 2. Individual data values for biology repeated experiments were provided as an excel file.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zhang, X., Zhang, L., Li, B. et al. Identification of Epinastine as CD96/PVR inhibitor for cancer immunotherapy. BMC Biol 23, 27 (2025). https://doi.org/10.1186/s12915-025-02132-y

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1186/s12915-025-02132-y

Keywords

BMC Biology

ISSN: 1741-7007

Contact us