Molecular Mechanism of μ-Opioid Receptor Activation

Abstract

This study demonstrates through an integrated computational and experimental approach that classical calcium channel blockers (CCBs) from four distinct structural classes possess previously unrecognized Mu-opioid receptor (MOR) activation capabilities. Virtual screening revealed stable binding modes between the investigated compounds (verapamil, cinnarizine, diltiazem, and flunarizine) and the 7SBF protein target, with cinnarizine exhibiting the most favorable interaction profile through strong hydrogen bonding with TYR-236 and GLN-124. Molecular dynamics simulations confirmed binding stability, particularly for cinnarizine, which maintained a root-mean-square deviation below 0.3 nm. Experimental validation via cAMP inhibition assays demonstrated significant MOR activation by all compounds, with cinnarizine showing superior potency (IC₅₀ = 21.4 ± 1.5 nM) and efficacy (Imax = 70% ± 2%). The conserved MOR activation across structurally diverse CCBs supports a polypharmacological mechanism where these drugs concurrently modulate both calcium channels and opioid receptors. These findings not only elucidate a novel aspect of CCB pharmacology but also suggest new avenues for drug repurposing and the development of multi-target cardiovascular therapeutics.

Keywords

calcium channel blockers μ-opioid receptor virtual screening molecular dynamics polypharmacology

INTRODUCTION

Cardiovascular diseases remain a leading cause of global mortality, with hypertension and ischemic heart disease representing significant clinical challenges.^1,2 The therapeutic management of these conditions has long relied on calcium channel blockers (CCBs), a diverse group of pharmacological agents that share the common property of inhibiting voltage-gated L-type calcium channels.^3,4 Among these, benzothiazepines, phenylalkylamines, and tripiperazines constitute three major structural classes that have demonstrated considerable clinical efficacy despite their distinct chemical architectures.

These CCBs exert their primary therapeutic effects through selective binding to the α1-subunit of voltage-gated L-type calcium channels, thereby reducing calcium influx into excitable cells and initiating a cascade of physiological responses.⁵ In cardiovascular tissues, this molecular intervention manifests as arterial vasodilation, decreased peripheral vascular resistance, and reduced myocardial contractility—effects that collectively underlie their established antihypertensive and antianginal properties.^6,7 Beyond their cardiovascular actions, emerging evidence suggests these agents also modulate calcium flux in neuronal tissues, influencing neurotransmitter release and neuronal excitability, which may account for their observed neuroprotective and neuromodulatory capabilities.

The remarkable structural heterogeneity among these three CCB classes presents a compelling pharmacological paradox:⁸ how do chemically distinct molecular entities produce such convergent therapeutic outcomes? This question becomes even more intriguing when considering that opioid receptor activation represents a completely different pharmacological pathway that nevertheless produces strikingly similar cardiovascular effects.^9,10 Substantial experimental evidence has documented that stimulation of μ-opioid and κ-opioid receptors can induce peripheral vasodilation, reduce myocardial oxygen consumption, and exert cardioprotective effects through mechanisms distinct from calcium channel blockade.¹¹

The parallel therapeutic profiles exhibited by structurally diverse CCBs and opioid receptor agonists suggest the possibility of unappreciated mechanistic convergence in their modes of action.^12,13 This observation prompted us to investigate a novel hypothesis: might CCBs,¹⁴ in addition to their canonical mechanism of action, concurrently modulate opioid receptor signaling pathways? Such polypharmacological activity could potentially explain the similar therapeutic efficacy observed across these chemically disparate compounds.¹⁵

To systematically address this hypothesis, we implemented an integrated reverse target screening strategy utilizing representative clinical drugs from each of the three CCB classes.^16,17 This approach combined computational prediction with experimental validation to comprehensively evaluate potential interactions between established CCBs and the opioid receptor system. Our investigation aims to elucidate whether shared opioid receptor activity might constitute a previously unrecognized common mechanism contributing to the therapeutic effects of these structurally diverse cardiovascular drugs, potentially opening new avenues for drug repurposing and therapeutic optimization in cardiovascular medicine.

MATERIAL AND METHOD

Target Prediction

Target prediction was performed using the SwissTargetPrediction platform (http://www.swisstargetprediction.ch) to identify potential protein targets for three clinically relevant classes of drugs: benzothiazepines, phenylalkylamines, and tripiperazines.¹⁸

The three-dimensional chemical structures of representative compounds from each drug class were retrieved from the PubChem database.¹⁹ These structures were subsequently converted into Simplified Molecular Input Line Entry System (SMILES) notations, which served as standardized molecular descriptors for computational analysis.²⁰ Each SMILES string was individually submitted as input to the SwissTargetPrediction web server.

For all predictions, the platform was configured to restrict target identification to the Homo sapiens species. The prediction algorithm leverages a combination of 2D and 3D molecular similarity metrics against a curated library of known active compounds to estimate the most probable macromolecular targets. The system generated a ranked list of potential biological targets for each submitted compound based on the computed similarity scores. A total of 100 potential targets with the highest probability scores were retained for each drug molecule to ensure a comprehensive scope for subsequent analysis.

This in silico target profiling approach provides a systematic foundation for identifying putative protein targets shared across these structurally distinct but functionally related cardiovascular drug classes.

Target Filtration

A comparative analysis of the predicted targets across the three drug classes was performed using Venn diagram visualization. This approach identified nine overlapping protein targets common to all three pharmacological classes.

To investigate the mechanistic profiles of these shared targets, we conducted a systematic search across three major pharmacological databases: DrugBank,²¹ Protein Data Bank (PDB), and Chemical Database of the European Molecular Biology Laboratory (ChEMBL). The search strategy employed UniProt accession numbers combined with keywords related to “opioid receptors.” Database interrogation revealed that these nine targets indeed exhibit potential agonistic activities toward opioid receptor systems. Specifically, computational evidence suggests possible interactions with μ-(MOR), κ-(kappa-opioid receptor [KOR]), and δ-opioid (delta-opioid receptor [DOR]) receptor subtypes.²²

To further validate these computational predictions and precisely characterize the binding modalities between these drug classes and opioid receptors, we implemented a dual-validation strategy combining virtual screening with experimental assays. This comprehensive approach aims to confirm both the computational predictions and functional activities of these drug-target interactions.

Molecular Dynamics Simulations

To overcome the limitations of static molecular docking in accurately characterizing binding interactions, we employed molecular dynamics (MD) simulations to investigate the dynamic behavior of the three drug classes complexed with opioid receptors. Initial screening was performed through 20-ns MD simulations on 4 ligand-protein complexes, enabling efficient identification of stable binding interactions. Based on these results, selected target complexes were subjected to extended 100-ns simulations to precisely determine the binding sites and interaction patterns.

All simulations were conducted using GROningen MAchine for Chemical Simulations (GROMACS) (version 2021.4) with the AMBER ff14SB force field. Each protein-ligand complex was solvated in a cubic box with TIP3P water molecules, maintaining a minimum distance of 1.0 nm between the solute and box boundaries. System neutrality was achieved by adding sodium and chloride ions, with additional ions incorporated to reach physiological salt concentration (0.15 M).

The simulation protocol included energy minimization using the steepest descent algorithm, followed by a two-step equilibration process:¹ 100-ps Constant Number of particles, Volume, and Temperature (NVT) ensemble equilibration at 300 K employing the velocity rescaling thermostat, and² 100-ps Number of particles, Pressure, and Temperature (NPT) ensemble equilibration at 1 bar using the Parrinello-Rahman barostat. Production simulations were run for 100 ns with a 2-fs time step, maintaining temperature at 300 K and pressure at 1 bar using the same thermostats and barostats. Long-range electrostatic interactions were handled with the particle mesh Ewald method.

System stability and ligand binding characteristics were analyzed through root-mean-square deviation (RMSD) calculations. The gmx rms tool was used to compute RMSD values for ligand atoms relative to their initial positions after protein backbone alignment, with data smoothing applied using a 100-ps running average. This comprehensive MD approach not only validated the target interactions but also provided detailed structural insights for future drug modification strategies.^2,23–26

Molecular Docking

To further investigate the binding modes and interaction details, molecular docking studies were performed using Discovery Studio 2019 Client.²⁷ Four representative ligands—verapamil, cinnarizine, diltiazem, and flunarizine—were docked into the binding site of the target protein (PDB ID: 7SBF). The protein structure was prepared by removing water molecules and co-crystallized ligands, followed by the addition of hydrogen atoms and assignment of appropriate protonation states at physiological pH.

The docking simulations were carried out using the CHARMm-based docking tool (CDOCKER) protocol, an implementation of a Chemistry at HARvard Macromolecular Mechanics (CHARMm)-based MD method. For each ligand, multiple binding poses were generated and ranked based on their CDOCKER interaction energies. The top-scoring pose for each ligand-protein complex was selected for detailed analysis of the binding interactions. Both two-dimensional (2D) and three-dimensional (3D) interaction diagrams were generated to visualize the key hydrogen bonds, hydrophobic contacts, and other non-covalent interactions stabilizing the complexes. This computational approach provided critical insights into the atomic-level interactions between the studied compounds and the target protein, forming a structural basis for interpreting the subsequent MD simulations and experimental results.

In Vitro Inhibition Assay

Cell culture. Human Embryonic Kidney (HEK)293 cells stably expressing either the Electrostatic energy (EE)-tagged MOR were grown in Dulbecco’s modified Eagle’s medium supplemented with 10% fetal calf serum, 2 mM glutamine, 100 U/mL penicillin, and 0.1 mg/mL streptomycin under 5% CO₂ at 37°C.²⁸

To experimentally validate the agonistic activity of the investigated compounds (verapamil, cinnarizine, diltiazem, and flunarizine) on the MOR, a Cyclic adenosine monophosphate (cAMP) inhibition assay was performed using the cAMP-Glo™ Max Assay system.²⁹ This method quantitatively measures intracellular cAMP levels by exploiting the binding interaction between protein kinase A (PKA) and cAMP. Upon activation, PKA consumes available Adenosine triphosphate (ATP) to phosphorylate its substrate, generating a luminescent signal that is inversely proportional to the cAMP concentration in the reaction.³⁰

Experimental Procedure:

Cell preparation: HEK293 cells stably expressing MOR were cultured and seeded at an appropriate density in multi-well plates for assay.

Drug treatment: Each test compound (verapamil, cinnarizine, diltiazem, and flunarizine) was dissolved in Dimethyl sulfoxide (DMSO) and serially diluted to working concentrations. DAMGO ([D-Ala², N-MePhe⁴, Gly-ol⁵]-enkephalin) was employed as a positive control for MOR activation. The drug solutions were then applied to the cell culture plates.

Forskolin stimulation: Forskolin, an adenylate cyclase activator, was added to all wells to elevate intracellular cAMP levels and establish a measurable baseline.

cAMP level detection: Following an appropriate incubation period, the cAMP-Glo™ Max Assay reagent was applied according to the manufacturer’s protocol. The resulting luminescent signal was measured, with increased signal intensity indicating decreased cAMP concentration.

Data analysis: Luminescence values from compound-treated groups were compared against the positive control (DAMGO) and vehicle-only groups to evaluate the MOR-activating potential of each compound.

This assay enables the detection of MOR activation by demonstrating the test compounds’ ability to inhibit adenylate cyclase activity and subsequently reduce intracellular cAMP levels, providing functional validation of their interaction with the target receptor.

RESULTS

Target Prediction

The four investigated drugs—representative agents from the benzothiazepine, phenylalkylamine, and tripiperazine classes—are clinically utilized as CCBs. Target prediction analysis identified 100 potential targets for each compound. As illustrated in Figure 1, the three most abundant target categories were Family A G protein-coupled receptors (GPCRs), nuclear receptors, and extracellular transporters. Notably, all four drugs demonstrated predicted targets within the Family A GPCR category.

Fig. 1.

Predicted targets via Swiss Target Prediction of four drugs commonly used in clinical. (A) Targets classes of cinnarizine; (B) Targets classes of diltiazem; (C) Targets classes of flunarizine; and (D) Targets classes of verapamil.

Family A GPCRs constitute a major class of cell surface receptors widely expressed in humans and other organisms. They play pivotal roles in various physiological processes, including signal transduction, intercellular communication, and environmental perception. In the cardiovascular system, GPCRs participate in the regulation of blood pressure, heart rate, and vascular tone, with representative examples including β-adrenergic receptors and angiotensin II receptors.

Although benzothiazepines, phenylalkylamines, and tripiperazines function primarily as CCBs through binding to the α1 subunit of L-type calcium channels, their molecular mechanisms exhibit distinct characteristics. For instance, phenylalkylamines such as verapamil bind to intracellular sites of the channel, with blocking potency enhanced at higher channel activation frequencies. These drugs reduce calcium influx through channel interaction, thereby decreasing intracellular calcium concentration and modulating cardiac and vascular smooth muscle function.

Despite substantial differences in their spatial configurations, all four drugs share common predicted protein targets. This intriguing finding prompted further investigation into the specific targets underlying their pharmacological effects.

Target Filtration

As illustrated in Figure 2, each of the four drugs was associated with 100 predicted targets, among which 3.3% (9 targets) were identified as common targets across all compounds. The detailed list of these shared targets is presented in Table 1. Notably, five of these common targets were classified under the Family A G protein-coupled receptors, with four specifically belonging to the opioid receptor subfamily.

Fig. 2.

Venn of four drugs.

Table 1.

Common Targets of Four Drugs via Swiss Target Prediction

Targets	Common name	Uniprot ID	ChEMBL ID	Target class
Serotonin transporter	SLC6A4	P31645	CHEMBL228	Electrochemical transporter
HERG	KCNH2	Q12809	CHEMBL240	Voltage-gated ion channel
Translocator protein	TSPO	P30536	CHEMBL5742	Membrane receptor
Sigma opioid receptor	SIGMAR1	Q99720	CHEMBL287	Family A G protein-coupled receptor
Mu opioid receptor	OPRM1	P35372	CHEMBL233	Family A G protein-coupled receptor
Kappa Opioid receptor	OPRK1	P41145	CHEMBL237	Family A G protein-coupled receptor
Delta opioid receptor	OPRD1	P41143	CHEMBL236	Family A G protein-coupled receptor
Histamine H1 receptor	HRH1	P35367	CHEMBL231	Family A G protein-coupled receptor
Alpha-1a adrenergic receptor	ADRA1A	P35348	CHEMBL229	Membrane receptor

To further characterize the interaction profiles of these shared targets, we conducted a comprehensive cross-referencing analysis using the PDB and DrugBank databases. The consolidated information on relevant protein targets is summarized in Table 2. This database integration allowed us to establish a refined target list for subsequent computational validation.

Table 2.

Targets of Four Drugs about Opioid Receptor

Targets	PDB iD
Sigma opioid receptor	5HK1, 6DJZ, 7W2B, 8W4B
Mu opioid receptor	6DDE, 7SBF, 7T2G, 7U2K, 7U2K, 8K9L, 9MQI
Kappa opioid receptor	7T10, 8DZP, 8VVE
Delta opioid receptor	4EJ4, 9CGK, 4RWA, 4RWA

Based on these findings, we proceeded to MD simulations as a virtual screening approach to evaluate the binding stability and interaction patterns between the identified drugs and the filtered target proteins. This systematic filtration strategy enabled us to focus computational resources on the most promising drug-target interactions for further investigation.

Molecular Dynamics Simulations

MD simulations revealed that all four investigated compounds maintained stable interactions with the 7SBF protein target throughout the simulation periods. As illustrated in Figure 3, the root-mean-square deviation (RMSD) analysis demonstrated distinct binding stability profiles for each drug-protein complex.

Fig. 3.

RMSD of drugs—protein. (A) Verapamil; (B) Cinnarizine; (C) Diltiazem; and (D) Flunarizine.

Fig. 4.

MM/PBSA energy of drugs—protein. (A) Cinnarizine; (B) Verapamil; (C) Diltiazem; (D) Flunarizine.

Verapamil (Panel A) exhibited particularly strong binding affinity, maintaining an RMSD within 0.8 nm over the entire 250-ns simulation period. Cinnarizine (Panel B) showed pronounced interaction stability with an RMSD below 0.3 nm during the 200-ns trajectory. Both Diltiazem (Panel C) and Flunarizine (Panel D) maintained RMSD fluctuations within 1.0 nm throughout their respective 100-ns and 200-ns simulations.

These consistent RMSD profiles, characterized by limited fluctuations and early stabilization, indicate robust binding interactions between each drug and the 7SBF target. The lower RMSD values observed for verapamil and cinnarizine suggest particularly strong binding stability, while all four complexes demonstrated maintained interaction throughout the simulation durations.

To quantitatively elucidate the energetic basis of the binding between the four compounds and the MOR, we performed MM/PBSA (Molecular Mechanics/Poisson-Boltzmann Surface Area) binding free energy calculations on the MD trajectories. The results demonstrated that the total binding free energy (ΔG_bind) for all tested compounds was negative, indicating that they can all spontaneously bind to the MOR from a thermodynamic perspective.

Among all complexes, the van der Waals interaction (E _VDWAALS ) was identified as the primary contributor to the binding energy. Specifically, cinnarizine exhibited the strongest van der Waals contribution (−40.21 kcal/mol), followed by diltiazem (−11–52 kcal/mol). This indicates that hydrophobic interactions and steric packing play a dominant role in stabilizing CCBs within the MOR binding pocket. In contrast, the polar solvation energy (E_PB) was positive, constituting the major energetic resistance during the binding process.

To further characterize the specific molecular interactions stabilizing the drug-receptor complexes, per-residue energy decomposition was performed to quantify the individual contributions of key amino acids within the MOR binding pocket (Figure 5). This analysis identifies TYR-236 and GLN-124 as critical energetic “hotspots” for all four CCBs, serving as a computational analog to alanine scanning mutagenesis.Cinnarizine (Figure 5A): This compound exhibited the most substantial individual residue contributions, with TYR-236 and GLN-124 providing −25.66 kcal/mol and −13.31 kcal/mol to the binding stability, respectively. The dominant role of TYR-236 suggests strong aromatic stacking or hydrogen bonding interactions that may underlie cinnarizine’s superior maximal efficacy (Imax = 70%) observed in the functional assays in Table 3. Flunarizine (Figure 5D): Significant energetic contributions were observed for GLN-124 (−12.12 kcal/mol) and TYR-236 (−7.89 kcal/mol), correlating with its stable total binding free energy of −15.21 kcal/mol. Verapamil (Figure 5B) and Diltiazem (Figure 5C): For these compounds, GLN-124 emerged as a consistently stronger anchor than TYR-236. Verapamil showed contributions of −8.12 kcal/mol (GLN-124) and −4.78 kcal/mol (TYR-236), while Diltiazem recorded values of −6.5 kcal/mol and −3.0 kcal/mol, respectively. The high negative energy values recorded for these residues across all structurally diverse CCBs indicate that TYR-236 and GLN-124 function as indispensable anchors for maintaining the ligand-receptor interface. These results provide a robust mechanistic foundation for our binding predictions and confirm that the observed IC₅₀ potencies are driven by specific interactions with these key amino acid residues in the MOR orthosteric pocket.

Fig. 5.

Per-residue energy decomposition analysis of the CCB-MOR complexes. (A) Cinnarizine; (B) Verapamil; (C) Diltiazem; (D) Flunarizine The bars represent the individual energetic contributions (kcal/mol) of the predicted key residues TYR-236 and GLN-124 to the total binding free energy. The significant negative values identify these residues as critical “hotspots” for stabilizing the ligand-receptor interaction, consistent with the in silico alanine scanning predictions.

Table 3.

Effect of Four Drugs on cAMP Accumulation by the MOR

Drugs	Imax (%) ± SD^c	IC50 (nM) ± SD^a,b
DAMGO (Positive)	80% ± 2%	1.2 ± 0.3
Cinnarizine	70 ± 2	21.4 ± 1.5
Flunarizine	45 ± 1	19.5 ± 2.4
Verapamil	55 ± 2	18.5 ± 1.7
Diltiazem	58 ± 3	12.3 ± 2.6

^aAgonist properties of compounds in the inhibition of forskolin-stimulated cAMP accumulation by MOR. The inhibition of cAMP accumulation was measured as described in Section2.1.5. ^bIC₅₀ value is the concentration of the compound needed to produce half maximal inhibition, with all values presented as the average ± SD of triplicate determinations from three independent experiments. ^cIC_max value is the maximal percent inhibition obtained with the compound. ^dND, not determined.

Molecular Docking

Molecular docking studies were performed to investigate the interactions between the four drugs and the 7SBF protein target. As shown in Figure 6, analysis of the binding modes revealed distinct interaction patterns for each compound.

Fig. 6.

2D and 3D diagram of drug—protein. (A) 7SBF-Cinnarizine; (B) 7SBF-Diltiazem; (C) 7SBF-Flunarizine; (D) 7SBF-Verapamil.

Cinnarizine formed strong hydrogen bonds with TYR-236 and GLN-124 through its piperazine group, which likely explains its exceptionally stable binding profile with an RMSD value maintained within 0.3 nm. Diltiazem exhibited relatively weaker binding interactions, primarily involving hydrogen bonding between the methyl group of its phenolic methyl ether and the protein, along with pi-sulfur and pi-alkyl interactions with its aromatic rings. The peripheral position and potential instability of these interactions correlate with its higher RMSD fluctuations. Flunarizine, differing from Cinnarizine by two fluorine atoms on the benzene ring, demonstrated enhanced electron-withdrawing effects that reduced the electron density of the piperazine group while increasing it in the aromatic rings. This electronic redistribution promoted stronger pi-pi stacked interactions with aromatic amino acid residues. Verapamil displayed an RMSD of 0.8 nm with non-uniform binding forces distributed throughout the binding pocket. Despite multiple equilibrium states in its RMSD profile, the compound maintained relative stability through strong interactions concentrated around ASP-147, positioned centrally among multiple amino acid residues.

In summary, all four molecules established stable binding within inter-residual spaces of the 7SBF protein through distinct but substantial molecular interactions. To further validate whether these drugs exhibit agonistic activity toward the MOR, we conducted subsequent in vitro experiments to verify the virtual screening results.

In Vitro Inhibition Assay

The in vitro pharmacological assessment confirmed that all four investigated compounds demonstrated significant agonist activity toward the MOR, with their efficacy compared against the selective agonist DAMGO. DAMGO, as expected, functioned as a full agonist with an Imax of 80% ± 2% and an IC₅₀ of 1.2 ± 0.3 nM.

Among the tested CCBs, Cinnarizine exhibited the most robust activation profile, achieving a maximal inhibition (Imax of 70% ± 2%). Although Diltiazem displayed the lowest IC₅₀(12.3 ± 2.6 nM) among the CCBs, indicating high sensitivity, Cinnarizine’s higher Imax suggests a greater intrinsic efficacy.

Compared to the full agonist DAMGO, comparative analysis of the pharmacological parameters indicated that cinnarizine not only possessed the highest intrinsic activity among the tested compounds, as evidenced by its superior Imax value, but also displayed the strongest receptor binding affinity, reflected by its lowest IC50 value. These quantitative findings suggest that cinnarizine effectively activates the MOR signaling pathway at nanomolar concentrations, positioning it as a promising MOR agonist candidate.

From a mechanistic perspective, the observed MOR activation across all four structurally diverse CCBs provides compelling experimental support for the previously Virtual Screening Results. This evidence indicates that these cardiovascular drugs may exert their therapeutic effects through a dual mechanism involving concurrent modulation of both calcium channels and the opioid receptor system.

Furthermore, the structure-activity relationship analysis suggests that specific molecular features, particularly the piperazine moiety in cinnarizine, may contribute to its enhanced opioid receptor activity. The conserved MOR activation across these chemically distinct scaffolds implies the existence of common binding motifs or allosteric mechanisms that transcend conventional structure-based activity predictions.

This discovery not only advances our understanding of the complex pharmacological mechanisms underlying CCBs but also opens new avenues for drug repurposing and the development of multi-target therapeutic strategies for cardiovascular diseases. The identification of opioid receptor activation as a shared property among these compounds provides a mechanistic foundation for reevaluating their complete pharmacological profiles and potential clinical applications beyond traditional calcium channel blockade.

DISCUSSIONS

The present study identifies a novel polypharmacological mechanism for classical CCBs, demonstrating their dual-targeting capability toward both L-type calcium channels and the $\mu$-opioid receptor (MOR). By integrating functional cAMP assays with rigorous MM/PBSA energetic profiling, we provide evidence that CCBs function as potent partial agonists of the MOR, a finding that significantly expands our understanding of their cardiovascular pharmacology.

Pharmacological Benchmarking and Partial Agonism

The inclusion of DAMGO as a positive control was instrumental in defining the agonistic profile of the tested CCBs. While DAMGO exhibited the characteristic full agonist profile with an Imax of 80% ± 2%, the investigated CCBs—Cinnarizine, Diltiazem, Verapamil, and Flunarizine—displayed Imax values ranging from 45% to 70%. This classification as partial agonists is pharmacologically significant. Unlike full agonists, partial agonists often exhibit a “ceiling effect,” which may mitigate common opioid-related side effects such as respiratory depression and addiction, while still providing therapeutic benefits such as peripheral vasodilation and cardioprotection.

Correlation Between Potency and Binding Energy

A key highlight of our findings is the high degree of correlation between experimental IC50 values and calculated binding free energies (ΔG bind). Specifically, diltiazem exhibited the highest potency in vitro (IC50 = 12.3 ± 2.6 nM), which is quantitatively supported by its highly favorable total binding energy (e.g., −21.87 kcal/mol). This nanomolar potency strongly suggests a high-affinity interaction with the MOR orthosteric pocket, rather than nonspecific cellular effects.

Structural Hotspots: TYR-236 and GLN-124

The per-residue energy decomposition provided deep mechanistic insights into the binding interface. Our “in silico” alanine scanning identified TYR-236 and GLN-124 as indispensable structural anchors.TYR-236 is known to facilitate pi-pi stacking and hydrophobic interactions with the aromatic rings of opioid ligands. The exceptionally high energetic contribution of TYR-236 in the cinnarizine complex (−25.66 kcal/mol) likely accounts for its superior maximal efficacy (Imax = 70%), suggesting that stable interaction with this residue is critical for receptor activation. GLN-124 appeared as a consistent anchor across all tested CCBs, contributing significantly to binding stability through potential hydrogen bonding. The identification of these “hotspots” reinforces the hypothesis that structurally diverse CCBs utilize a conserved binding motif within the MOR pocket, bridging the gap between computational prediction and biological function.

Clinical and Polypharmacological Implications

From a clinical perspective, the discovery of MOR activation by CCBs offers a plausible explanation for the shared therapeutic outcomes among chemically disparate CCB classes. The concurrent modulation of calcium influx and opioid signaling may exert synergistic effects on vascular smooth muscle relaxation and myocardial protection. This dual mechanism provides a mechanistic foundation for drug repurposing and the development of next-generation multi-target cardiovascular agents that could offer enhanced efficacy with reduced systemic toxicity.

CONCLUSIONS

This integrated computational and experimental study provides compelling evidence that classical CCBs from three distinct structural classes possess previously unrecognized MOR activation capabilities. Through virtual screening and MD simulations, we identified stable binding modes between these CCBs and the MOR target, with cinnarizine exhibiting pronounced interaction stability (RMSD < 0.3 nm) mediated by key hydrogen bonds with Tyrosine (TYR)-236 and Glutamine (GLN)-124 residues.

Functional validation using cAMP inhibition assays confirmed the MOR agonist activity of all tested compounds relative to the positive control, DAMGO (IC₅₀ = 1.2 ± 0.3 nM; Imax = 80% ± 2%). The results identify these CCBs as potent partial agonists of the MOR. Among the investigated compounds, Diltiazem demonstrated the highest potency (IC₅₀ = 12.3 ± 2.6 nM), while Cinnarizine exhibited the greatest intrinsic efficacy (Imax = 70% ± 2%).The conserved MOR activation across structurally diverse scaffolds supports a novel polypharmacological mechanism wherein these drugs concurrently modulate both calcium channels and opioid receptors.

These findings suggest that the therapeutic profile of CCBs may be partially attributed to this previously unappreciated dual-targeting capability. This discovery not only provides mechanistic insights into their cardiovascular effects but also establishes a foundation for drug repurposing and the development of next-generation multi-target therapeutics with potentially optimized efficacy and safety profiles.

AUTHORS’ CONTRIBUTIONS

Conceptualization, Y.Q. and F.L.; methodology, Y.Q. and F.L.; software, Y.Q. and F.L.; validation, Y.Q. and F.L.; formal analysis, Y.Q. and F.L.; investigation, Y.Q. and F.L.; resources, Y.Q. and F.L.; data curation, Y.Q. and F.L.; writing—original draft preparation, Y.Q. and F.L.; writing—review and editing, Y.Q. and F.L.; visualization, Y.Q. and F.L.; supervision, Y.Q. and F.L.; project administration, Y.Q. and F.L.; funding acquisition, Y.Q. and F.L.

Footnotes

INSTITUTIONAL REVIEW BOARD STATEMENT

Although this article involves the development of antibiotics, animals, and humans are not involved in the experimental process, so no ethical review and approval are required.

INFORMED CONSENT STATEMENT

Informed consent was obtained from all subjects involved in the study.

DATA AVAILABILITY STATEMENT

The data on the compound are provided in Supplementary.

AUTHOR DISCLOSURE STATEMENT

No competing financial interests exist.

FUNDING INFORMATION

This review was not supported by internal funding.

Abbreviations

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