In silico screen identifies Pranidipine as a potential inhibitor of the PD-1/PD-L1 immune checkpoint

Abstract

Immune checkpoint blockade targeting the PD-1/PD-L1 axis using monoclonal antibodies has revolutionized cancer therapy by inducing durable antitumor responses across multiple cancer types. However, antibody-based therapeutics suffer from several limitations, including poor tissue permeability, immune-related adverse effects, complex manufacturing processes, and high production costs. These challenges underscore the need for small-molecule inhibitors (SMIs) as alternative therapeutic agents; nevertheless, to date, no FDA-approved small molecule has demonstrated proven in vivo efficacy against the PD-1/PD-L1 axis. Given the prolonged timelines and substantial costs associated with de novo drug discovery, drug repurposing represents an efficient and cost-effective strategy. In this study, we employed an integrated computational approach to identify potential SMIs capable of disrupting the PD-1/PD-L1 interaction. Structure-based virtual screening of 20 dihydropyridine (DHP) calcium channel blockers (CCBs) was performed using AutoDock Vina, along with CA-170 as a reference inhibitor. Several lead compounds were identified and evaluated based on binding affinity, bonding pose, and interaction patterns. Among the screened compounds, Pranidipine, Isradipine, and Efonidipine exhibited the most favorable binding profiles toward PD-1/PD-L1 complex inhibition. Furthermore, molecular dynamics (MD) simulations confirmed the stability of the interactions between these lead compounds and PD-L1, especially Pranidipine, over time. Collectively, our in silico findings suggest that selected DHP-CCBs may represent promising repurposing candidates for targeting the PD-1/PD-L1 immune checkpoint. However, further in vitro studies are required to validate their potential biological activity.

Supplementary Information

The online version contains supplementary material available at 10.1038/s41598-026-54129-4.

Introduction

The programmed cell death-1 (PD-1) receptor is an inhibitory immune receptor expressed on activated T and B lymphocytes. It plays a central role in immune checkpoint regulation by suppressing excessive immune responses and maintaining peripheral tolerance¹. Within the tumor microenvironment (TME), elevated levels of PD-1 ligands, particularly programmed death-ligand 1 and 2 (PD-L1 and PD-L2) are expressed by various tumor cells and antigen-presenting cells. The interaction of these ligands with PD-1 on T cells leads to reduced cytokine secretion, impaired T-cell receptor (TCR) which mediated proliferation, and diminished cytotoxic T-cell activity. Collectively, these effects create an immunosuppressive milieu that enables tumor cells to evade immune surveillance². Although PD-L1 is minimally expressed in most normal tissues, it is markedly upregulated in a wide range of malignancies, making it an attractive and clinically relevant therapeutic target³.

Immune checkpoint inhibitors (ICIs), particularly monoclonal antibodies (mAbs) targeting the PD-1/PD-L1 axis such as nivolumab and pembrolizumab, have revolutionized cancer treatment. These agents have demonstrated remarkable clinical efficacy in melanoma, non-small cell lung cancer (NSCLC), and breast cancer by reinvigorating exhausted T cells and restoring antitumor immune responses with relatively manageable toxicity profiles^4,5. However, despite their success, antibody-based therapies face several limitations, including variable patient response rates, immune-related adverse events, limited tissue penetration, high production costs, and complex manufacturing processes⁶. These challenges have driven growing interest in alternative therapeutic strategies, including de novo drug discovery, computer-aided drug design, and drug repurposing approaches.

Small-molecule inhibitors (SMIs) targeting the PD-1/PD-L1 interaction have emerged as promising alternatives to antibody-based immunotherapies. Although still in the early stages of development, these compounds offer several potential advantages, such as oral bioavailability, improved tumor tissue penetration, reduced immunogenicity, and lower production costs^7,8. Nevertheless, the clinical efficacy of current ICIs remains limited in a subset of patients, highlighting the need for novel therapeutic strategies.

Recent evidence indicates that exosomal PD-L1 plays a critical role in immune evasion and therapeutic resistance. Exosomal PD-L1 mimics the immunosuppressive function of membrane-bound PD-L1 and contributes to reduced responsiveness to PD-1/PD-L1 blockade therapies. Consequently, targeting exosomal PD-L1 has emerged as a promising strategy to enhance immunotherapy efficacy⁹.

Given the central role of calcium (Ca²⁺) signaling in exosome biogenesis, increasing evidence suggests that Ca²⁺ homeostasis critically regulates the expression and secretion of exosomal PD-L1. Notably, studies have established a mechanistic link between PD-1 and PD-L1–mediated immune evasion and Ca²⁺ signaling pathways⁹. Dysregulated Ca²⁺ signaling promotes tumor progression by activating oncogenic pathways, facilitating exosome biogenesis, and modulating immune checkpoint expression¹⁰. Ca²⁺ flux has been shown to regulate both the expression and secretion of exosomal PD-L1, suggesting that inhibition of Ca²⁺ signaling could simultaneously target multiple mechanisms of tumor immune escape. Specifically, blocking Ca²⁺-permeable ion channels may reduce PD-L1 expression, inhibit exosomal PD-L1 release, and restore CD8⁺ T-cell–mediated antitumor immunity⁹.

Ca²⁺ channel antagonists, commonly known as Ca²⁺ channel blockers (CCBs), are widely prescribed for the treatment of cardiovascular disorders. These drugs are generally classified into two main categories: non-dihydropyridines (non-DHPs), such as Verapamil and Diltiazem, and dihydropyridines (DHPs), such as Nifedipine and Amlodipine¹¹. DHP-CCBs are primarily potent vasodilators, whereas non-DHPs exhibit more pronounced negative inotropic effects. Both classes effectively lower blood pressure, although non-DHPs may provide additional benefits in patients with chronic kidney disease and diabetic nephropathy¹². Accumulating evidence suggests that CCBs may also possess anticancer properties, including inhibition of cancer cell proliferation and enhancement of chemotherapy-induced cytotoxicity^13,14. Voltage-gated calcium channels (VGCCs) have been implicated in the progression of various cancers, prompting investigations into the repurposing of VGCC inhibitors for oncological applications¹⁴. Although CCBs have promising anticancer effects in lab studies, especially for overcoming chemoresistance, clinical evidence is limited and inconsistent¹⁵. Early studies in the 1990s demonstrated that CCBs such as Nifedipine, Verapamil, and Diltiazem inhibited the growth of U-373 MG human astrocytoma cells and SK-N-MC human neuroblastoma cells¹⁶. Nifedipine, a DHP-CCB, has been shown to suppress colorectal cancer proliferation and metastasis by inhibiting Ca²⁺ influx, thereby preventing nuclear translocation of nuclear factor of activated T cells 2 (NFAT2) and reducing its transcriptional activity. This mechanism not only contributes to its anticancer effects but also enhances antitumor immunity by downregulating PD-1 and PD-L1 expression¹⁷. Similarly, Mibefradil, a blocker of T and L type Ca²⁺ channels, has demonstrated the ability to reduce tumor growth and prolong survival in preclinical models of melanoma, glioma, ovarian, breast, and pancreatic cancers, supporting its potential for repurposing in cancer therapy^18,19.

Other CCBs, including Lercanidipine and Nicardipine, have been reported to downregulate PD-L1 expression by inhibiting phosphorylation of signal transducer and activator of transcription 1 (STAT1), a key regulator of PD-L1 transcription. Lercanidipine has also been shown to suppress interferon-γ (IFN-γ)–induced PD-L1 expression, thereby enhancing CD8⁺ T-cell cytotoxic activity^9,19. Likewise, Amlodipine reduces PD-L1 expression by disrupting Ca²⁺-dependent stabilization of the PD-L1 protein, resulting in enhanced CD8⁺ T-cell–mediated immune responses²⁰. Collectively, these findings highlight Ca²⁺-dependent regulation of PD-L1 and support CCBs as a promising strategy for combination immunotherapy²¹.

In this study, we employed a comprehensive computational framework to investigate the potential repurposing of DHP-CCBs as SMIs of the PD-1/PD-L1 immune checkpoint. Molecular docking analyses were performed to evaluate the binding interactions between PD-L1 and selected DHP-CCBs. Promising candidates were identified based on predicted binding affinity, binding pose, and interaction patterns at the PD-1 binding interface of PD-L1. Finally, molecular dynamics (MD) simulations were conducted to assess the stability of the selected PD-L1/DHP-CCBs complexes under dynamic conditions. To the best of our knowledge, this is the first comprehensive in silico study evaluating DHP-CCBs as potential PD-L1 binders. Our in silico results suggest that selected DHP-CCBs exhibit favorable predicted binding to PD-L1, supporting their potential repurposing as candidate SMIs of the PD-1/PD-L1 immune checkpoint.

Materials and methods

Selection and preparation of receptor

The X-ray crystallographic structure of the PD-1/PD-L1 complex (PDB ID: 4ZQK)²² was retrieved from the Protein Data Bank (http://www.rcsb.org) for molecular docking simulations. The initial water molecules were excluded to avoid unwanted interactions; then AutoDock atom types and bond orders were assigned. Also, hydrogen atoms and Gasteiger–Marsili charges were added using AutoDock Tools 4.2^23,24. Finally, receptor structure was saved in PDBQT format.

Selection of binding site and grid box

For molecular docking, the binding site of PD-L1 was selected according to the identified binding site in the crystallographic structure of the PD-1/PD-L1 complex (PDB ID: 4ZQK)²². The PD-L1 monomer was extracted from this complex and used for docking to preserve the native conformation of the PD-1 binding interface, consistent with previous computational studies employing the same structural model^25–27. The PDBsum database (https://www.ebi.ac.uk/thornton-srv/databases/pdbsum/) and LigPlot⁺ software²⁸ were utilized to analyze this complex and precisely identify the key residues of PD-L1 directly involved in binding to PD-1. Based on this crystallographic structure, the key residues including Phe19, Val23, Asp26, Ile54, Tyr56, Glu58, Gln66, Arg113, Met115, Ser117, Gly119, Gly120, Ala121, Asp122, Tyr123, Lys124, and Arg125 were selected as the reference amino acids. Subsequently, a grid box was predicted with dimensions of X = 26 Å, Y = 26 Å, Z = 26 Å and a grid spacing of 1 Å using AutoDock Tools 4.2. Before molecular docking simulation this grid box verified by AutoDock Tools 4.2 and Pymol 3.1²⁹ that selected dimensions completely enclosed the entire binding site.

Selection and preparation of ligands

In this study, DHP-CCBs were selected to study their potential inhibitory effects on PD-1/PD-L1 interaction. The two-dimensional (2D) structures of 20 DHP-CCBs (Fig. 1), as ligand, were downloaded from the PubChem database (https://pubchem.ncbi.nlm.nih.gov/) in SDF format and then subjected to energy minimization and molecular dynamics optimization using Chem3D software version 17.1 by the MM2 force field. Subsequently, the minimized structures were converted into PDBQT format using Open Babel software version 2.3.2³⁰ for molecular docking simulation. Based on literature, the CA-170 compound exhibits potential tumor-suppressive effects by interrupting the PD-1/PD-L1 interaction; therefore, in this study, it was selected as a reference ligand^31–33.

Fig. 1.

2D representation of some DHP-CCBs and CA-170 compounds.

Virtual screening and molecular docking studies

In this step, virtual screening was performed by AutoDock Vina 1.2.0^34,35 to do molecular docking and predict the most favorable binding conformation and orientation of the a set of selected 20 DHP-CCBs within the PD-L1 binding site. The docking results were ranked based on binding affinity. The molecular interactions-such as hydrogen bonds and hydrophobic interactions between the best ranked DHP-CCBs and PD-L1 were studied and visualized using PyMOL3.1 and Discovery Studio software V24.1.0334^29,36. Consequently, the ligands exhibiting the most favorable biding affinity, binding poses, and molecular interactions were selected as lead compounds for future analysis.

For validation of the docking protocol, a re-docking study was performed. Since the PD-1/PD-L1 complex (PDB ID: 4ZQK) does not contain SMIs, the structure of PD-L1 homodimer complexed with the inhibitor BMS-200 (PDB ID: 5N2F) was used instead. Subsequently, BMS-200 was extracted from the complex and re-docked on the PD-L1. The root means square deviation (RMSD) between the docked and the crystallographic structure of the BMS-200/PD-L1 complex was calculated to validate the docking procedure (Supplementary Fig. S1). The obtained RMSD of 0.485 Å confirms the reliability of this docking model.

Studies of molecular dynamics simulation

To investigate the conformational changes of the selected lead compounds in complex with PD-L1, molecular dynamics (MD) simulations were conducted using the Gromacs software (version 2025.0)^37–39. The CHARMM36 force field was employed for preparing protein for MD simulations. The topology and parameter files for ligands were generated using the CHARMM-GUI Ligand Reader & Modeler module, which assigns atom types and generates force field parameters consistent with the CHARMM36 all-atom force field. These processed topology files were then incorporated into the GROMACS simulation workflow, enabling accurate representation of both protein and ligand molecules during molecular dynamics simulations. Each receptor-ligand complex was placed in a water box with TIP3P water molecules, subsequently, the system neutralized with Na⁺ and Cl^- ions. Energy minimization and equilibration were performed, as described previously^40–43. Finally, MD simulations were carried out for 200 nano seconds (ns). The obtained MD trajectories were analyzed to evaluate the stability, conformational changes, and dynamic behavior of the complexes by using root mean square deviation (RMSD), radius of gyration (Rg), hydrogen bonds (H-bonds), and protein root means square fluctuation (RMSF).

Principal component analysis (PCA)

Correlative dynamics in biomolecules play a crucial role in their biological activity. To elucidate these significant motions in proteins, PCA is widely employed⁴⁴. In this study, PCA was conducted on equilibrated portion of the MD trajectory using Gromacs molecular dynamics package. Therefore, a covariance matrix of atomic fluctuations was constructed using the gmx covar module. Then, this matrix was diagonalized via the gmx anaeig tool to derive the corresponding eigenvectors and eigenvalues, which describe the essential motions of the protein⁴³.

Binding free energy calculation

The molecular mechanics Poisson–Boltzmann surface area (MM/PBSA) was employed to evaluate the binding free energy (ΔG_bind) between PD-L1 and selected lead compounds using the gmx_MMPBSA tool⁴⁵. For each system, snapshots were extracted at regular intervals of 2 ps from the 200 ns MD simulation trajectories. The individual energy components, including van der Waals (DE_vdw), electrostatic (DE_ele), polar solvation (ΔE_GB), non-polar solvation (ΔE_SURF), gas-phase (ΔG_GAS), total solvation (ΔG_SOLV), and total binding free energy (ΔG_bind) were subsequently calculated⁴⁵.

Results

Structural and molecular assessment of the PD-1/PD-L1 immune checkpoint interaction

PD-1 is a type I transmembrane protein encoded by the pdcd1(pd1) gene, consisting of 288 amino acid residues with an approximate molecular weight of 31,647 Da. PD-1 belongs to the cytotoxic T lymphocyte antigen (CD28/CTLA-4) family of co-receptors. According to the UniProtKB (Q15116), human PD-1 comprises a signal peptide (1–24 residues) followed by the mature PD-1 chain (25–288). This protein is glycosylated at positions 49, 58, 74, and 116 during the post-translational modification. Also, PD-1 contains three domains; an extracellular domain (25–171 residues), a transmembrane domain (171–191 residues), and a cytoplasmic domain (192–288 residues). The extracellular domain of PD-1 consists a single IgV (immunoglobulin variable)-like domain (35–145 residues) composed of ten β-strands (βA’, βA, βB, βD, βE, βC, βC’, βF, βG’, and βG). This protein has two loops; FG loop (between βF and βG), which directly mediated interaction with PD-L1 and is critical for binding, and BC loop (between βB and βC), which contributes to structural stability. Additionally, two disulfide bonds (Cys54-Cys123) and (Cys76-Cys96) play essential roles in maintaining the stability of protein structure. Transmembrane domain of PD-1 includes; a hydrophobic α-helix that anchors PD-1 within the T cell membrane. The cytoplasmic domain of PD-1 contains; two motifs ITIM (immunoreceptor tyrosine-based inhibitory motif) and ITSM (immunoreceptor tyrosine-based switch motif). These motifs contain two phosphorylation sites which transmit inhibitory signals upon PD-L1 binding⁴⁶.

There are two ligands for PD-1; PD-L1 (CD274) and PD-L2 (CD273). PD-1 interacts with these ligands through its IgV-like domain and lead to do signaling transduction to intracellular domain⁴⁷. It is well documented that PD-L1 plays a more important role in cancer immune evasion, although PD-L2 may contribute in certain cases⁴⁸. However, in this study focuses on PD-L1.

PD-L1 is encoded by the cd274 gene and contains 290 amino acid residues with 33,275 Da molecular weight. The amino acid sequence of human PD-L1 obtained from UniProtKB (Q9NZQ7) showed that this protein includes a signal peptide (1–18 residues) and a PD-L1 chain (19–290) which is glycosylated at positions 35, 192, 200, and 219 during the post-translational modification. Similar to PD-1, PD-L1 contains three domains; an extracellular domain (19–238 residues), a transmembrane domain (239–259 residues), and a cytoplasmic domain (260–290 residues). The extracellular domain includes two Ig-like domains (IgV and IgC)- which mediate PD-1 interaction. In fact, IgV domain (19–127 residues) serves as the primary binding with PD-1. It consists 4 α-helices and 10 β-sheets (βA’, βA, βB, βD, βE, βC, βC’, βF, βG’, and βG). Among these, the βC, βC’, βF, and βG play an important role in binding to PD-1. These β-sheets formed two loops-CC’ loop (between βC and βC’) and FG loop (between βF and ΒG)-which play critical roles in ligand binding. This domain also has a hydrophobic core that facilitates stable binding with PD-1 and a single disulfide bond (Cys40-Cys114) that stabilizes the IgV fold. While IgC domain (133–225 residues) contributes more to the structural stability of PD-L1 than direction interaction with PD-1⁴⁶. The cytoplasmic domain includes of three motifs; RMLDVEKC and DTSSK which are RNA pol-like motifs and a QFEET motif. These motifs are responsible for the phosphorylation of signal transducer and activator of transcription in tumor cells⁴⁹.

To date, several three-dimensional (3D) structure of the extracellular domain of human PD-1 and PD-L1 proteins either in their apo forms or in complex with each other, mAbs, and other inhibitors have been deposited in protein data bank (PDB: www.rcsb.org)^46,50.

Krzysztof M. Zak et al. evaluated crystal structures of the human PD-1/PD-L1 complex and reported that their interaction is mediated through their extracellular domains, which involve a large hydrophobic surface (1970 A²), indicating a strong and stable interaction²². Furthermore, PD-L1 contains three key binding sites that contribute to its interaction potential and are of particular interest for inhibitor design. The first binding site features a hydrophobic pocket containing the residues Tyr56, Glu58, Arg113, Met115, and Tyr123, which can accommodate aromatic six-membered ring structures. The second binding site, composed of Met115, Ala121, and Tyr123 residues, can bind branched aliphatic groups, with possible hydrogen bonding to the carbonyl oxygen of Ala121. These two binding sites have been identified as potential drug binding pockets. The third binding site comprises an extended groove formed by residues Asp122 to Arg125, adjacent to Asp26, which offers multiple hydrogen bonding opportunities but its shallow structure makes it a less favorable target for SMIs³¹. According to the crystal structure of the PD-1/PD-L1 complex (PDB ID: 4ZQK), a hydrophobic core is generated, which includes Val64, Ile126, Leu128, Ala132, Ile134 residues from PD-1 and Ile54, Tyr56, Met115, Ala121, Tyr123 residues from PD-L1. This core is surrounded by a peripheral network of polar residues which form hydrogen bonds, further reinforcing the complex stability. Based on the PD-1/PD-L1 complex structure, the following PD-L1 residues—Phe19, Val23, Asp26, Ile54, Tyr56, Glu58, Gln66, Arg113, Met115, Ser117, Gly119, Gly120, Ala121, Asp122, Tyr123, Lys124, and Arg125—were selected as reference amino acids for flexible molecular docking studies (Fig. 2).

Fig. 2.

Sequence and 3D structure of PD-1/PD-L1 complex. (A) Sequence of PD-L1 and (B) PD-1 proteins in the PDB ID (4ZQK). (C) 3D Structural of PD-L1 (purple) and PD-1 (firebrick) complex. Binding residues are colored in yellow and green in PD-L1 and PD-1, respectively. Close-up view of interacting residues (labeled and shown in stick representation) of PD-L1 (D) and PD-1 (E).

Identification of potential inhibitors of PD-1/PD-L1 interaction

In this study, a structure-based virtual screening was carried out to identify DHP-CCBs with potential inhibitory activity against the PD-1/PD-L1 interaction. Figure 3 represented an overall workflow of the study’s method. 20 DHP-CCBs as ligands were docked into the binding site of PD-L1 by using AutoDock Vina 1.2.0 software. The results of molecular docking were ranked by predicted binding affinities. In order to comparison, the known inhibitor CA-170 was also docked. The results of docking showed that Pranidipine (PubChem CID: 6436048, formula: C₂₅H₂₄N₂O₆) had the highest binding affinity (-7.8 kcal/mol), following Isradipine (PubChem CID: 3784, formula: C₁₉H₂₁N₃O₅) and Efonidipine (PubChem CID: 119171, formula: C₃₄H₃₈N₃O₇P) also showed high affinities of -7.5 kcal/mol. In contrast, CA-170 (PubChem CID: 126843231, formula: C₁₂H₂₀N6O₇) showed binding affinity of -5.9 kcal/mol. Interestingly, Nitrendipine (PubChem CID: 4507, formula: C₁₈H₂₀N₂O₆) exhibited the lowest binding affinity (-5.4 kcal/mol) (Table 1).

Fig. 3.

Workflow for in silico screening, repurposing, of potential inhibitor against PD-1/PD-L1 axis.

Table 1.

Evaluation of the binding affinity (kcal/mol) and interacting residues of PD-L1 in the complexes with DHP-CCBs and CA-170.

No	Name	Binding affinity (kcal/mol)	HB-AAsa	NH-AAsb
1	CA-170	-5.9	Glu58, Asn63, Arg113	Trp57, Asp61, Ile64, Ile65, Gln66, Met115, Tyr123
2	Azelnidipine	-5.7	Glu58	Ile54, Val55, Tyr56, Phe67, Val68, Arg113, Ser117, Ala121, Tyr123
3	Amlodipine	-5.7	Arg113, Tyr123	Trp57, Glu58, Asp61, Asn63, Gln66, Cys114, Met115
4	Aranidipine	-6.6	Asn63, Gln66	Tyr56, Trp57, Glu58, Asp61, Ile65, Arg113, Cys114, Met115, Tyr123
5	Barnidipine	-6.3	-	Ile54, Tyr56, Asn63, Gln66, Ser117, Ala121, Asp122, Tyr123
6	Benidipine	-6.1	-	Ile54, Tyr56, Ser117, Ala121, Asp122, Tyr123
7	Cilnidipine	-6.6	Tyr56	Ile54, Glu58, Asp61, Asn63, Gln66, Arg113, Mer115, Tyr123
8	Dibudipine	-5.6	Tyr56	Ile54, Gln66, Ser117, Ala121, Asp122, Tyr123
9	Efonidipine	-7.5	Arg113	Ile54, Glu58, Asn63, Ala121, Asp122,
10	Felodipine	-6.7	Asn63, Gln66	Trp57, Glu58, Ile65, Arg113, Cys114, Met115, Tyr123
11	Isradipine	-7.5	Arg113	Ile54, Trp57, Glu58, Cys114, Ser117, Tyr123
12	Lacidipine	-5.8	Arg113	Glu58, Asn63, Gln66, Cys114, Met115
13	Lercanidipine	-6.8	Tyr123, Arg113	Ile54, Val55, Tyr56, Glu58, Phe67, Val68, Ser117, Ala121
14	Manidipine	-6.1	-	Glu58, Asn63, Arg113, Cys114, Met115, Ala121, Asp122, Tyr123
15	Mebudipine	-6.6	Arg113	Ile54, Glu58, Asn63, Cys114, Ile116, Ser117, Tyr123
16	Nicardipine	-5.8	-	Trp57, Glu58, Asn63, Gln66, Arg113, Met115, Tyr123
17	Nifedipine	-6.2	Asn63, Arg113	Trp57, Glu58, Gln66, Cys114, Met115, Tyr123
18	Nilvadipine	-6.1	Asn63, Arg113	Ile54, Tyr56, Glu58, Asp61, Gln66, Met115, Tyr123
19	Nimodipine	-6.3	Arg113	Ile54, Val55, Tyr56, Glu58, Asp61 Asn63, Gln66, Cys114, Met115, Ile116, Ser117, Tyr123
20	Nitrendipine	-5.4	Tyr56, Arg113	Ile54, Glu58, Aan63, Cys114, Tyr123
21	Pranidipine	-7.8	Asn63	Trp57, Glu58, Asp61, Val76, Arg113, Cys114, Met115

^a Hydrogen bonds forming Amino Acids, ^b Hydrophobic bonds forming Amino Acids.

The interactions between PD-L1 and the selected lead compounds with the highest binding affinity were further analyzed. Pranidipine, Isradipine, and Efonidipine were chosen as lead compounds for detailed investigation, with CA-170 serving as a reference control. The 2D structures and the 3D interactions of the resulting PD-L1/compound complexes were visualized using Discovery Studio software and PyMOL (Supplementary Fig. S2 and Fig. 4). Molecular docking results demonstrated that CA-170 interacts with PD-L1 through a variety of noncovalent interactions. The CA-170 belongs to the 1,2,4-oxadiazole family. A carbonyl oxygen adjacent to the oxadiazole ring forms a conventional hydrogen bond with Asn63. Another carbonyl oxygen engages in hydrogen bonds with Arg113. Additionally, the terminal hydroxyl group of the ethanolamine moiety forms a hydrogen bond with Glu58. Surrounding residues, including Trp57, Asp61, Ile64, Ile65, Gln66, Met115, and Tyr123, contribute to stabilization of the complex through van der Waals interactions within the binding pocket. (Supplementary Fig. S2A and Fig. 4). Also, molecular docking analysis indicated that Pranidipine interacts with PD-L1 through multiple noncovalent interactions. Specifically, a carbonyl oxygen of Pranidipine forms a hydrogen bond with Asn63. Furthermore, additional van der Waals forces contribute to the stabilization of Pranidipine through methyl and phenyl moieties with residues Trp57, Glu58, Asp61, Val76, Arg113, Cys114, and Met115 (Supplementary Fig. S2B and Fig. 4).

Fig. 4.

Three-dimensional structure illustration of interactions between PD-L1 and CA-170 and selected lead compounds with the highest affinity. The 3D structure of PD-L1 protein in complexes are shown in cartoon representation, the helix, sheet, and loop regions of PD-L1 are colored in lightpink, violetpurple, and gray, respectively. The violetpurple and hotpink stick represent residues involved in interactions non-hydrogen bonds and hydrogen bonds, respectively. Also, the hydrogen bonds in the complexes are shown as block dotted lines. The 3D illustrates CA-170, Pranidipine, Isradipine, and Efnodipin compounds in complex with PD-L1 are represented as green, orange, yellow, and blue, respectively with stick representation in the complexes.

Isradipine includes several nitrogen and oxygen groups, which increases its potential for hydrogen bond formation. The methyl carbonyl ester group of Isradipine forms a conventional hydrogen bond with Arg113. Additionally, the phenyl moiety of the compound—likely part of the dihydropyridine ring—engages in a π-anion interaction with Glu58. Furthermore, several van der Waals interactions mediate stabilization of the complex, involving residues, including Trp57, Cys114, Ser117, and Tyr123, while an additional alkyl interaction is observed with Ile54 (Supplementary Fig. S2C and Fig. 4). Also, Efnodipine/PD-L1 complex revealed that the 3-nitrophenyl group of Efnodipine forms a conventional hydrogen bond with Arg113 residue. The negatively charged substituent of Efnodipine participates in an attractive charge interaction with Asp122. Additionally, the 2-(N-benzylanilino) ethyl ester moiety engages a π-alkyl bond with Ile54, while an additional alkyl interaction is observed with Ala121. Also, van der Waals interactions form with Glu58 and Asn63 residues. These interactions collectively stabilize the complex (Supplementary Fig. S2D and Fig. 4).

Assessment of the stability of the selected complexes

To validate the molecular docking results and evaluate the dynamic stability of the selected complexes, MD simulation were carried out during 200 ns of simulations. To assess the stability of the PD-L1 complexes with the selected lead compounds, the RMSD of the protein backbone relative to the initial conformation was calculated (Fig. 5). The PD-L1/CA-170 complex exhibited RMSD fluctuations ranging from 0.06 to 0.22 nm. This complex demonstrated structural stability during the first 65 ns (0.06–0.14 nm), followed by substantial fluctuations between 66 and 165 ns (0.09–0.22 nm). Notably, the system re-equilibrated in the final 35 ns of the simulation, achieving a stable binding pose over the protein’s active site.

Fig. 5.

Analysis of MD simulations results. RMSD plots of the selected protein/compound complexes during 200 ns simulations. Rg plots of the selected protein/compound complexes during 150–200 ns simulations. Number of Hydrogen Bonds of protein/compound interactions during 150–200 ns simulations. RMSF plot of the backbone Ca atoms of the complexes versus residue number in the sequence. In all plots represent the (A) PD-L1/CA-170, (B) PD-L1/Pranidipine, (C) PD-L1/Isradipine, and (D) PD-L1/Efnodipin complexes are shown.

The RMSD value for the PD-L1/Pranidipine complex varied from 0.06 to 0.15 nm during simulations. This complex showed substantial fluctuations during the first 40 ns (0.06–0.15 nm), Then exhibited structural stability between 40 and 80 ns (0.08–0.13 nm), followed by fluctuation between 80 and 100 ns (0.09–0.15 nm). The system subsequently re-established stability for the remainder of the simulation, indicating a well-defined binding mode and limited conformational flexibility within the PD-L1 binding site.

The PD-L1/Isradipine complex displayed significant dynamics behavior in the first 117 ns, with backbone RMSD value fluctuating between 0.08 and 0.14 nm. This was followed by a shift to a slightly higher range (0.08–0.16 nm) from 117 to 144 ns, fluctuations continued, and the RMSD value increased (0.07 to 0.2 nm) from 144 to 170 ns. Subsequently, the system converged and maintained a highly stable RMSD of approximately 0.13 nm for the final 30 ns of the simulation. The PD-L1/Efonidipine complex demonstrated a backbone RMSD value between 0.05 and 0.18 nm during the simulation. This complex demonstrated substantial fluctuations between 0.05 and 0.16 nm in the first 30 ns. Then it displayed fluctuations between 0.07 and 0.15 nm from 30 to 117 ns, followed by a noticeable fluctuation (teaching 0.18 nm) between 60 and 65 ns. This was followed by a shift to a slightly higher range (0.08–0.14 nm) from 117 to 120 ns, after which the RMSD value plateaued and remained approximately stable until the end the simulation. The RMSD values of the protein backbone indicated that the systems reached a stable state with minimal fluctuations after approximately 150 ns of simulation. Consequently, subsequent analyses—including the Rg, hydrogen bond, and RMSF profiles—were performed over the final 50 ns of the trajectory.

The Rg was analyzed to assess the structural compactness and overall stability of the PD-L1 complexes during the last 50 ns simulations. As illustrated in Fig. 5, the Rg values for the PD-L1/CA-170 complex varied from 1.35 to 1.40 nm between 150 and 180 ns, while it exhibited from 1.37 to 1.47 nm in the last 20 ns. The RMSD value for the complexes of PD-L1 with Pranidipine, Isradipine, and Efonidipine were within a narrow range of 1.37–1.41 nm. Notably, the PD-L1/Pranidipine complex consistently exhibited slightly greater compactness than other complexes, suggesting a more stable and tightly packed protein structure.

H-bond formation between PD-L1 and the selected lead compounds was analyzed to further evaluate the stability of complexes during the last 50 ns simulations (Fig. 5). The results indicate that the complexes of CA-170, Pranidipine, Isradipine and Efonidipine with PD-L1 possessed 0–8, 0–5, 0–3, and 0–6 H-bonds, respectively. However, all complexes exhibited 3 H-bonds highly stable, suggesting strong and stable interaction, while the Isradipine complexes maintained one H-bonds during last 50 ns simulations. Despite a consistent average of H-bond, the CA-170 complex exhibited the highest degree of fluctuation in its H-bond compared with other complexes.

For obtaining information about average fluctuation of each residue during the simulation, the RMSF were evaluated (Fig. 5). The results of RMSF value uncovered that all residues in the Pranidipine and Isradipine complexes were 0.03–0.22 nm. While those in the CA-170 and Efonidipine complexes fluctuated between 0.03 and 0.19 nm and 0.03–0.20 nm, respectively. Generally, the fluctuation of residues in the complexes was below 0.20 nm, except for residues 46 which fluctuated up to approximately 0.22 nm in the Pranidipine and Isradipine complexes. Notably, the overall RMSF patterns of all complexes were highly similar.

Assessment of dynamic behavior of selected complexes via PCA analysis

To extract crucial insights into protein dynamics, we performed PCA on the molecular dynamic’s trajectories. This technique discriminates slow, collective global motions from faster, localized fluctuations, thereby reducing the complexity of high-dimensional data to reveal dominant conformational changes. To visualize these motions and assess protein stability, we projected the trajectories onto the first two principal components (PC1/PC2). As shown in Fig. 6, the PCA revealed distinct dynamic behaviors among the complexes. The CA-170 complex showed a wider distribution in PCA space, indicating greater structural flexibility and higher global motions. In contrast, Pranidipine, Isradipine, and especially Efonidipine exhibited more compact distributions, suggesting decreased flexibility and enhanced structural stability of PD-L1. Notably, Efonidipine and Isradipine complexes showed lower eigenvalues compared with the other complexes.

Fig. 6.

Principal Component Analysis (PCA) of PD-L1 complexes. Projection onto first two principal components at 200 ns MD simulations and eigenvalue distribution for the first 20 Eigenvectors.

Binding free energy calculations

To study the interactions between PD-L1 and the lead compounds (Pranidipine, Isradipidine, Efonidipine), as well as CA-170, the binding free energies (ΔGbind) were calculated using the MM/PBSA method. These calculations were carried out during 200 ns MD trajectories. The evaluated energy components comprised DE_vdw, DE_ele, ΔE_GB, ΔE_SURF, ΔG_GAS, ΔG_SOLV, and ΔG_bind, as summarized in Table 2. The results exhibited that all selected lead compounds and CA-170 had negative binding energies, indicating a spontaneous binding to PD-L1. Among the lead compounds, Pranidipine showed the most favorable binding free energy with a ΔG_bind value of -9.52 kcal/mol. This was followed by Isradipidine (-8.13 kcal/mol), and Efonidipine (-7.81 kcal/mol). While as CA-170 demonstrated the weakest binding energy ( -1.41 kcal/mol).

Table 2.

Binding free energies (kcal/mol) of PD-L1/lead compound complexes.

No	Name compound	Delta energy (kcal/mol)
		DEvdw	DEele	ΔEGB	ΔESURF	ΔGGAS	ΔGSOLV	ΔGbind
1	CA-170	-5.13 ± 6.27	-6.44 ± 11.71	11.10 ± 13.15	-0.94 ± 1.15	-11.57 ± 15.85	10.16 ± 12.19	-1.41 ± 4.51
2	Pranidipine	-18.10 ± 8.30	-6.81 ± 6.81	18.13 ± 8.58	-2.74 ± 1.24	-24.91 ± 12.40	15.39 ± 7.58	-9.52 ± 5.74
3	Isradipidine	-18.89 ± 6.37	-5.85 ± 5.39	19.36 ± 7.89	-2.75 ± 0.95	-24.74 ± 9.69	16.61 ± 7.08	-8.13 ± 3.69
4	Efonidipine	-15.90 ± 4.46	-7.95 ± 5.88	18.48 ± 5.67	-2.44 ± 0.64	-23.85 ± 7.93	16.04 ± 5.29	-7.81 ± 3.75

DE_vdw: van der Waals interaction energy; DE_ele: Electrostatic interaction energy; ΔE_GB: Electrostatic solvation free energy; ΔE_SURF: Non-polar solvation free energy (surface area contributions); ΔG_GAS: Gas-phase interaction energy (DE_vdw + DE_ele); and ΔG_SOLV: Solvation free energy (ΔE_GB + ΔE_SURF).

In addition, per-residue free-energy decomposition analysis was performed to determine the key residues contributing to stabilizing the selected lead compounds within the PD-L1 binding site (Fig. 7). The PD-L1/CA-170 complex exhibited uniformly weak contributions from residues Tyr56, Glu58, Glu60, Asp61, Lys62, Asn63, Gln66, Val76, Arg113, Cys114, Met115, Asp122, and Tyr123, which is consistent with its overall lower binding free energy.

Fig. 7.

Energetic decomposition of key residues in PD-L1/lead compound complexes.

In the PD-L1/Pranidipine complex, residues Met115 (-1.17 kcal/mol), and Tyr56 (-0.70 kcal/mol) showed the most favorable ΔG contributions, van der Waals interactions play major role in ligand stabilization. Moderate contributions were showed for Tyr123 (-0.43 kcal/mol), Arg113 (-0.41 kcal/mol), Ala121 (-0.29 kcal/mol), Gln66 (-0.25 kcal/mol), and Asn63 (-0.19 kcal/mol) residues. In contrast, Glu58, Glu60, Asp61, Lys62, Ile64, Ile65, Asp73, Lys75, Val76, Gln77, Cys114, Asp122, Tyr123, Lys124, Arg125 residues exhibited weak per-residue ΔG contributions.

Per-residue decomposition of analysis of the PD-L1/Isradipidine complex revealed several residues with strong stabilizing effects, including Arg113 (-1.19 kcal/mol), Met115 (-1.81 kcal/mol), Tyr56 (-1.30 kcal/mol). These residues primarily contributed through favorable van der Waals interactions. Tyr123 (-0.56 kcal/mol), Ile54 (-0.49 kcal/mol), and Cys114 (-0.23 kcal/mol) residues showed moderate contributions, whereas Val55, Try56, Glu58, Asp61, Lys62, Asn63, Ile64, Gln66, Val68, Cys114, Ile116, Ser117, Ala121, and Asp122, Lys124 residues exhibited weak per-residue ΔG contributions.

For the PD-L1/Efonidipine complex, the most favorable per-residue ΔG contributions were observed for residues Tyr56 (-1.47 kcal/mol), Met115 (-1.42 kcal/mol), Ile54 (-0.81 kcal/mol), and Gln66 (-0.74 kcal/mol). Moderate contributions were detected for Tyr123 (-0.26 kcal/mol), Val68 (-0.18 kcal/mol), Ser116 (-0.16 kcal/mol), Asn63 (-0.13 kcal/mol), Cys114 (-0.13 kcal/mol) residues. The residues Trp57, Glu58, Met59, Glu60, Asp61, Lys62, Arg113, Ala121, and Lys124 displayed generally weak per-residue ΔG contributions.

Discussion

Recently, immunotherapy has revolutionized cancer treatment, with inhibition of the PD-1/PD-L1 axis being among the most extensively studied strategies. The PD-1/PD-L1 interaction serves as a critical immune checkpoint, suppressing T-cell proliferation, cytokine secretion, and cytotoxic activity^{25,32,51–53}. Currently, designed SMIs against PD-L1 include; (1) compounds that structured based on the biphenyl scaffold, such as BMS (Bristol-Myers Squibb) derivatives, which are known to promote PD-L1 dimerization and sterically blocks its interaction with PD-1^54,55; (2) amino acids-based molecules that mimic the receptor-ligand interface, such as macrocyclic peptide which capable of maintaining stable binding to monomeric PD-L1⁵⁶. But none of the SMIs targeting the PD-1/PD-L1 pathway have received approval from the FDA⁵⁷. However, CA-170 is an orally administrable inhibitor to PD-L1 that underwent clinical trials in 2016 and is now progressing through phase-II clinical trials. It disrupts PD-1/PD-L1 interaction without inducing PD-L1 dimerization^33,58,59.

Consequently, exploring alternative strategies, such as SMIs, remains a major research challenge. Therefore, a key step in the rational design of effective SMIs is understanding the molecular mechanisms of cellular signaling pathways⁶⁰. In this context, computational approaches provide a useful framework for characterizing ligand–target interactions at the atomic level. Accordingly, the present study aimd to elucidate the interaction mechanisms between selected DHP-CCBs and PD-L1 using molecular docking and MD simulations. These methods were employed to explore the binding modes, stability, and dynamic behavior of PD-L1/DHP-CCBs complexes, providing preliminary mechanistic insights into the potential inhibitory effects of DHP-CCBs on the PD-1/PD-L1 immune checkpoint. Therefore, a set of DHP-CCBs was screened to identify potential PD-L1 inhibitor candidates in silico. Molecular docking suggested Pranidipine, Isradipine, and Efonidipine as lead compounds with the highest predicted binding affinities. The docking results revealed that the selected lead compounds, along with CA-170 as a reference, interacted with key residues within the primary hydrophobic pocket (Glu58, Arg113, and Met115) and the secondary pocket (Ala121 and Tyr123). The presence of carbonyl- and hydroxyl-mediated hydrogen bonds, aromatic π-interactions, and van der Waals contacts indicates that these compounds can be accommodated within the binding pockets, contributing to complex stabilization and demonstrating the ability of PD-L1 to accommodate diverse chemical scaffolds.

MD simulations are extensively applied to investigate protein–ligand interactions, evaluate the stability of molecular complexes, and characterize their dynamic behavior at the atomic level. In this study, the results of MD simulations showed clear differences in the dynamic stability of PD-L1/selected lead compound complexes with CA-170.

Furthermore, per-residue free-energy decomposition analysis provided additional insight into the binding mechanism. The analysis showed that Tyr56, Arg113, Met115 and Tyr123 were the dominant contributors to ligand stabilization, exhibiting the most favorable per-residue ΔG values across all lead compounds. These findings highlight the potential importance of these residues in ligand interactions within the PD-L1 binding site and imply that the selected lead compounds may interact with key residues involved in PD-L1 inhibition.

The obtained results from MD simulations showed that Pranidipine, Isradipine and Efonidipine in complex with PD-L1 revelated relatively stability behavior over time, as reflected by their RMSD and Rg values. While PD-L1/CA-170 complex showed the larger fluctuations in RMSD and Rg, indicating comparatively lower structural stability during the simulation period.

Overall, the results demonstrated that the PD-L1/Pranidipine complex displayed the most favorable predicted binding affinity (-7.8 kcal/mol), and total binding free energy (-9.52 kcal/mol), along with comparatively higher stability. This enhanced stability was supported by lower RMSD fluctuations, higher structural compactness (Rg), and reduced residual fluctuations (RMSF) compared with the other selected lead compounds and reference inhibitor CA-170, highlighting that may represent a promising lead candidate.

Pranidipine, a third-generation DHP-CCBs, exhibits anti-inflammatory and anti-fibrotic properties in cardiovascular disease in addition to its long-acting antihypertensive effects. Preclinical evidence shows that it reduces TGF-β1 and collagen-III expression in autoimmune myocarditis models and increases superoxide dismutase (SOD) activity in endothelial cells, thereby mitigating oxidative stress and vascular inflammation. It also likely inhibits the NF-κB pathway, decreasing pro-inflammatory cytokines and adhesion molecule expression. These pleiotropic effects suggest a cardioprotective role in cardiac remodeling and vascular inflammation⁶¹. Therefore, the anti-inflammatory, anti-oxidant, and anti-fibrotic mechanisms of Pranidipine observed in cardiovascular disease suggest a potential anti-cancer effect. Although direct evidence of Pranidipine’s anti-cancer activity is currently limited, further in vitro and in vivo studies on cancer cells and tumor models are warranted to validate this potential. While Pranidipine itself has not been previously investigated in cancer, related DHPs—such as Nifedipine, Lercanidipine, Nicardipine, and Amlodipine—have shown anti-cancer potential through inhibition the mechanisms of NFAT2 or STAT1 signaling and PD-L1 downregulation, suggesting that Pranidipine may exert similar immunomodulatory effects¹⁷, Lercanidipine, Nicardipine^9,62, and Amlodipine²⁰. The present study suggests that Pranidipine, through stable binding to PD-L1 and engagement of critical residues within both primary and secondary binding pockets, may similarly exert immunomodulatory effects by inhibiting the PD-1/PD-L1 interaction. These computational findings provide a structural rationale supporting the potential repurposing of DHP-CCBs, especially Pranidipine, as accessible and orally bioavailable candidates warranting future investigation in cancer immunotherapy.

Limitations

Although this study provides preliminary insights, several limitations should be acknowledged. This study represents the initial phase of screening small-molecule PD-1/PD-L1 inhibitors within a drug-repurposing strategy focusing on DHP-CCBs. While the in silico analyses including molecular docking, molecular dynamics simulations, and binding free-energy calculations provide useful information regarding the interactions between PD-L1 and selected DHP-CCBs, these findings are based on computational predictions and cannot fully reflect the complexity of biological systems. In addition, the cellular activity and immunological effects of the identified compounds were not evaluated in the present study. Therefore, the predicted binding affinities and interaction patterns should be interpreted with caution and require experimental validation. Future studies will evaluate the top-performing PD-L1/DHP-CCB complexes in vitro, using co-culture systems with PD-L1–expressing tumor cells and activated T cells to assess restoration of T cell activity and IFN-γ release.

Conclusion

This study employed molecular docking and molecular dynamics simulations to explore the potential of DHP-CCBs as inhibitors of the PD-1/PD-L1 immune checkpoint. The results suggest that several DHP-CCBs, including Pranidipine, Isradipine, and Efonidipine, may serve as potential lead compounds. Among them, Pranidipine exhibited the highest predicted binding affinity compared with the reference inhibitor CA-170. While Isradipine and Efonidipine had comparable predicted affinities with Pranidipine. The predicted binding affinity of these lead compounds may be associated with their multivalent interaction profiles involving key residues in the PD-L1 binding site through conventional hydrogen bonds, van der Waals forces, and specific π-interactions. Molecular dynamics simulations further suggested stable binding of the selected lead compounds with PD-L1, particularly Pranidipine, during the 200 ns simulation. Overall, these computational findings provide preliminary structural insights that supporting the potential repurposing of DHP-CCBs as PD-1/PD-L1 inhibitors; however, experimental validation will be required to confirm their biological activity.

Supplementary Information

Below is the link to the electronic supplementary material.

Author contributions

Samira Khani : Writing—review & editing, Methodology, Data curation, Formal analysis, Conceptualization.Reyhaneh Kalhor: Conceptualization, Data curation, Investigation, Methodology, Visualization, Maryam Ghasemi: Methodology, Data curation, Funding acquisition, Conceptualization. Hourieh Kalhor: Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Project administration, Resources, Software, Supervision, Validation, Visualization, Writing—original draft, Writing—review and editing .

Funding

This study was supported by the Cellular and Molecular Research Center of Qom University of Medical Sciences, Qom, Iran (grant number: 3965).

Data availability

The datasets used and analyzed during the current study are available from the following public repositories: Protein and Sequence Data: The amino acid sequence of human PD-1 is available from UniProtKB under accession code Q15116. Direct URL: https://www.uniprot.org/uniprotkb/Q15116.The amino acid sequence of human PD-L1 is available from UniProtKB under accession code Q9NZQ7. Direct URL: https://www.uniprot.org/uniprotkb/Q9NZQ7. The crystal structure of the PD-1/PD-L1 complex is available from the Protein Data Bank under accession code 4ZQK. Direct URL: https://www.rcsb.org/structure/4ZQK. Chemical Compound Data (Screened Ligands): The chemical structures and data for the 20 dihydropyridine calcium channel blockers screened and CA-170 in this study are publicly available in the PubChem database under the following Compound Identifiers (CIDs): 1. CA-170:pubchem CID 126843231 (https://pubchem.ncbi.nlm.nih.gov/compound/117941887). 2. Azelnidipine: pubchem CID 65948 (https://pubchem.ncbi.nlm.nih.gov/compound/65948). 3. Amlodipine: pubchem CID 2162 (https://pubchem.ncbi.nlm.nih.gov/compound/2162). 4. Aranidipine: pubchem CID 2225 (https://pubchem.ncbi.nlm.nih.gov/compound/2225). 5. Barnidipine: pubchem CID 443869 (https://pubchem.ncbi.nlm.nih.gov/compound/443869). 6. Benidipine: pubchem CID 656668 (https://pubchem.ncbi.nlm.nih.gov/compound/656668). 7. Cilnidipine: pubchem CID 5282138 (https://pubchem.ncbi.nlm.nih.gov/compound/5282138). 8. Dibudipine: pubchem CID 10252115 (https://pubchem.ncbi.nlm.nih.gov/compound/10252115). 9. Efonidipine: pubchem CID 119171 (https://pubchem.ncbi.nlm.nih.gov/compound/119171). 10. Felodipine: pubchem CID 3333 (https://pubchem.ncbi.nlm.nih.gov/compound/3333). 11. Isradipine: pubchem CID 3784 (https://pubchem.ncbi.nlm.nih.gov/compound/3784). 12. Lacidipine: pubchem CID 5311217 (https://pubchem.ncbi.nlm.nih.gov/compound/5311217). 13. Lercanidipine: pubchem CID 65866 (https://pubchem.ncbi.nlm.nih.gov/compound/65866). 14. Manidipine: pubchem CID 4008 (https://pubchem.ncbi.nlm.nih.gov/compound/4008). 15. Mebudipine: pubchem CID 10407950 (https://pubchem.ncbi.nlm.nih.gov/compound/10407950). 16. Nicardipine: pubchem CID 4474 (https://pubchem.ncbi.nlm.nih.gov/compound/4474). 17. Nifedipine: pubchem CID 4485 (https://pubchem.ncbi.nlm.nih.gov/compound/4485). 18. Nilvadipine: pubchem CID 4494 (https://pubchem.ncbi.nlm.nih.gov/compound/4494). 19. Nimodipine: pubchem CID 4497 (https://pubchem.ncbi.nlm.nih.gov/compound/4497). 20. Nitrendipine: pubchem CID 4507 (https://pubchem.ncbi.nlm.nih.gov/compound/4507). 21. Pranidipine: pubchem CID 6436048 (https://pubchem.ncbi.nlm.nih.gov/compound/6436048). Other Data: All other data generated and/or analyzed during the current study are available from the corresponding author on reasonable request.

Declarations

Competing interests

The authors declare no competing interests.