Abstract This study developed and optimized an RBD–ACE2 binding immunoassay as a preliminary screening platform for evaluating phytochemical-mediated interference with the interaction between the receptor-binding domain (RBD) of SARS-CoV-2 and the human angiotensin-converting enzyme 2 (ACE2) receptor. Although the selected compounds are well-reported bioactive phytochemicals, the novelty of this work lies in integrating an optimized immunoassay-based screening approach with D-optimal mixture design and molecular docking to evaluate both individual and combined effects on RBD–ACE2 binding. The immunoassay was applied to screen phytochemicals, including flavonoids, polyphenols, and terpenoids, for their RBD–ACE2 binding inhibitory activity. A D-optimal design was employed to evaluate the effects of selected candidate compounds, namely baicalein, scutellarein, and gallic acid, on the RBD–ACE2 binding inhibition model. Molecular docking analysis was further used to support possible interactions between these compounds and key residues at the RBD–ACE2 interface. The findings indicate that the optimized immunoassay, combined with mixture design and docking analysis, may provide a useful preliminary approach for screening phytochemicals that interfere with SARS-CoV-2 RBD–ACE2 binding. However, further validation using reference controls, cell-based antiviral assays, mechanistic studies, and in vivo evaluation is required before antiviral or therapeutic relevance can be established.

Keywords: COVID-19, Receptor-binding domain, Human angiotensin-converting enzyme 2, Immunoassay, Molecular docking

Funding: This study was granted by Faculty of Medicine, Khon Kaen University, Thailand (Grant Number: IN67083).

Citation:  Butdapheng, K., Sillapapibool, P., Sae-Foo, W., Putalun, W., Phoolcharoen, W., and Choonong, R. 2026. In vitro binding inhibition and molecular docking analysis of baicalein, scutellarein, and gallic acid against SARS-CoV-2 RBD–ACE2 interaction. Natural and Life Sciences Communications. 25(4): e2026092.

Graphical Abstract:

INTRODUCTION

Although the acute phase of the COVID-19 pandemic has largely transitioned into a post-pandemic period, SARS-CoV-2 continues to circulate under changing population immunity and immune-waning dynamics (Imrie et al., 2026). Reinfections remain an ongoing concern, particularly because emerging variants can partially escape pre-existing immunity. Recent evidence indicates that viral evolution, immune escape, and waning immunity continue to shape SARS-CoV-2 transmission and reinfection patterns (Wei et al., 2024; Chemaitelly et al., 2025). Therefore, approaches that target key molecular events involved in SARS-CoV-2 infection remain relevant.

The interaction between the receptor-binding domain (RBD) of the SARS-CoV-2 spike protein and the human angiotensin-converting enzyme 2 (ACE2) receptor is a critical step for the entry of virus into human cells. This molecular mechanism is important because the RBD of the spike protein precisely binds to the ACE2 receptor, facilitating the viral entry and initiating infection. The structure of ACE2 receptor complements the RBD, with recent studies providing detailed crystallographic insights showing how key residue substitutions in the SARS-CoV-2 spike protein increase its binding affinity compared to similar viruses, thereby enhancing its transmissibility (Wang et al., 2020; Yang et al., 2020). Because mutations in the spike protein, particularly within or near the RBD, may influence receptor binding and immune recognition, the RBD–ACE2 interface remains an important target for screening compounds that interfere with viral entry-related processes (Mahdi et al., 2025).

Phytochemicals, including flavonoids, polyphenols, and terpenoids, are plant-derived compounds known for diverse biological activities, including antioxidant, anti-inflammatory, and antiviral-related properties (Zhang and Tsao, 2016; Parida et al., 2020; Al-Hakeim et al., 2023). Recent reviews further support the investigation of natural products as potential modulators of coronavirus entry-related mechanisms, including spike protein binding and RBD–ACE2 interaction (Szabó et al., 2024). However, despite increasing interest in phytochemicals as potential modulators of coronavirus entry, current evidence is still largely based on computational prediction, broad antiviral assays, or evaluation of individual compounds. Moreover, studies that systematically evaluate both individual phytochemicals and predefined phytochemical mixtures using a mixture-design approach remain limited. This limitation constrains direct comparison of candidate compounds and of mixture effects using binding-based experimental evidence.

Therefore, this study aimed to develop and optimize an RBD–ACE2 binding immunoassay for evaluating phytochemical-mediated inhibition of SARS-CoV-2 RBD–ACE2 interaction. By combining immunoassay-based screening with D-optimal mixture design and molecular docking, this study evaluated the individual and combined effects of selected phytochemicals, including baicalein, scutellarein, and gallic acid, on RBD–ACE2 binding. This integrated approach provides updated experimental evidence for preliminary screening of natural compounds that interfere with RBD–ACE2 binding, while further validation using reference controls, cell-based antiviral assays, and in vivo studies is required before antiviral or therapeutic relevance can be established.

MATERIALS AND METHODS

Chemicals and reagents

Recombinant C-terminal His-tagged SARS-CoV-2 receptor binding domain (RBD) was produced from transient expression in Nicotiana benthamiana (Rattanapisit et al., 2020; Rattanapisit et al., 2021; Siriwattananon et al., 2021). Recombinant human angiotensin-converting enzyme 2 (ACE2) protein (Fc chimera) and goat anti-human IgG Fc (HRP) were purchased from Abcam (UK). Bovine serum albumin (BSA) was purchased from Nacalia tesque (Japan). Skim milk was purchased from Himedia (India). 2,2-azino-bis-3-ethylbenzothiazoline-6-sulphonic acid (ABTS) was purchased from Sigma-Aldrich (Germany). All chemical reagents were analytical grade.

ACE2 and RBD binding immunoassay

The development of recombinant human ACE2 protein and recombinant C-terminal His-tagged SARS-CoV-2 RBD binding immunoassay for screening candidate inhibitory compounds is shown in Figure 1. Initially, a 96-well immunoplate was coated with varying concentrations of RBD (0.5 to 1 µg/mL, 100 µL/well) in carbonate buffer pH 9.6 and incubated at 37°C for 1 h, followed by triple washes with phosphate-buffer saline containing 0.05% Tween 20 (PBS-T). The next step involved selecting a suitable blocking agent (2% BSA or 5% skim milk in PBS-T, 300 µL/well) and incubating under the same conditions, with subsequent washes. ACE2 was added at a volume of 50 μL/well in concentrations ranging from 0.1 to 20 μg/mL. Subsequently, 50 μL of the sample solutions—maintaining a final ethanol concentration of 10% or less—were added to each well; 10% ethanol was used as the blank control. After mixing and incubation for 1 h, the plate was washed, and goat anti-human IgG Fc antibody was added (1:1,000, 100 µL/well) and incubated for 1 h. Finally, the plate was washed three times with PBS-T, and ABTS substrate (100 µL/well) was added. The plate was incubated at 37°C for 20 min. The greenish color was then measured at a wavelength of 405 nm with a reference of 492 nm. The percentage of inhibition of RBD-ACE2 binding was calculated using the following equation:

Where ODcontrol represents the absorbance of wells containing ACE2 and 10% ethanol without test compounds, and ODsample represents the absorbance of wells treated with the tested compounds. All assays were performed in triplicate. Then, the half-maximal inhibitory concentration (IC₅₀) values of the selected compounds were determined from concentration–response curves using nonlinear regression analysis with a four-parameter logistic model in GraphPad Prism version 8 (GraphPad Software, CA, USA). IC₅₀ values are expressed as mean ± SD from triplicate experiments.

Figure 1. Schematic diagram of the SARS-CoV-2 RBD–ACE2 binding inhibition immunoassay. The assay consisted of five main steps: (I) RBD coating, (II) blocking of non-specific binding, (III) addition of ACE2 and sample solutions, (IV) detection with peroxidase-conjugated anti-human IgG-Fc antibody, and (V) color development using ABTS substrate. RBD–ACE2 binding inhibition was assessed based on the reduction in absorbance compared with the control.

Experimental design for optimization

The optimization of component ratios and experimental design was conducted using Design-Expert software, version 13 (MN, USA). A D-optimal design was employed to optimize the inhibitory effects of candidate compounds that demonstrated more than 50% inhibition in RBD–ACE2 binding immunoassay screenings. The objective of the D-optimal design strategy is to maximize the determinant of the design's information matrix (X'X). The volume ratios of the selected candidate compounds, expressed in micromolars (µM), were varied as independent variables.

The primary outcome of interest (dependent variable) was the percentage of inhibition between RBD and ACE2 binding (Y1), with different volume combinations tested according to model analysis. Repeated design points were intentionally included in the D-optimal design to estimate pure error and assess experimental reproducibility for model fitting and lack-of-fit evaluation. The software was employed to derive a mathematical model for the outcome of interest, framing each response (Y) within a quadratic equation:

             Y= b₁X₁+ b₂X₂+ b₃X₃+ b₁₂X₁X₂+ b₁₃X₁X₃ + b₂₃X₂X₃  

Where Y is the measured response; X₁, X₂, and X₃ are the levels of the independent variable from selected candidate compounds and b₁, b₂, b₃, …, and b₂₃ are the regression coefficients of independent variables (X₁, X₂, X₃, …, and X₂X₃), respectively.

An appropriate model was selected based on predefined statistical criteria, including a significant model p-value (<0.05), a non-significant lack-of-fit p-value (>0.05), and a coefficient of determination (R²) greater than 0.8. Model performance was further evaluated by comparing the predicted and actual responses. In addition, individual and overall desirability scores were calculated for each outcome to support the optimization process. The optimal volume combination was selected based on the highest desirability score, indicating the most favorable response. To evaluate consistency, the selected combination was replicated five times. Statistical analysis was then performed to compare the predicted and observed outcomes and to confirm the predictive reliability of the model.

Molecular docking

The RBD of the SARS-CoV-2 spike protein was sourced from the Protein Data Bank (PDB ID: 6M0J). For docking preparations, all water molecules and heteroatoms were removed using BIOVIA Discovery Studio Visualizer, version 2024 (Biovia), and hydrogen atoms were added to ensure accurate geometry and charge distribution. The modified protein structure was then saved in PDB format for subsequent docking simulations. Candidate compound ligands, identified through ACE2-RBD binding immunoassays, were retrieved from the PubChem database. These ligands underwent energy minimization using the Chem3D force field to achieve stable, low-energy conformations and were saved in MOL2 format for docking. Docking simulations were conducted with GOLD software version 5.3.0 (USA), targeting the concave surface of the receptor-binding motif (RBM) of the RBD, which spans amino acids 437 to 507 and interfaces with the ACE2 receptor. The simulations produced several predicted binding poses and GOLD fitness scores, which were recorded for each ligand. The GOLD fitness scores were used to rank predicted docking poses and to support qualitative comparison of ligand–RBD interactions. Docking poses and interactions were visualized using Discovery Studio Visualizer. Because the selected RBD–ACE2 complex structure (PDB ID: 6M0J) does not contain a co-crystallized small-molecule ligand suitable for direct redocking, redocking validation was not performed. Therefore, the docking analysis was used as supportive in silico evidence to explore possible ligand–RBD interactions at the RBD–ACE2 interface.

RESULTS

Optimization of the RBD–ACE2 binding immunoassay

 The optimization of the RBD–ACE2 binding immunoassay was performed to determine the appropriate concentration of RBD as the coating antigen (0.5 or 1.0 µg/mL) and to identify the optimal absorbance response across a range of ACE2 concentrations (0.1–20 µg/mL). Figure 2 shows the ACE2 reactivity curves obtained using different RBD coating concentrations and blocking agents (2% BSA and 5% skim milk). The results indicated that blocking with 2% BSA produced a higher and more consistent absorbance signal than 5% skim milk. Regarding the RBD coating concentration, no substantial difference in absorbance response was observed between 0.5 and 1.0 µg/mL RBD. Therefore, 0.5 µg/mL RBD was selected for subsequent assay development because it provided an adequate signal response while allowing more efficient use of the recombinant protein.

Furthermore, an ACE2 concentration of 3 µg/mL was chosen for reliable detection of competitive inhibition. This concentration represented an appropriate point on the ACE2 reactivity curve, providing sufficient signal intensity while avoiding potential receptor oversaturation. Vehicle and blank controls were included to account for background signals and solvent effects during assay optimization. However, validated positive and negative reference samples were not included in this study; therefore, the developed RBD–ACE2 binding inhibition immunoassay should be regarded as a preliminary screening platform. Further validation using appropriate reference samples is warranted to confirm assay performance and reliability for broader phytochemical screening applications.

Figure 2. Reactivity curve of the ACE2 with optimization of coating concentration (μg/ml) and blocking agent. BSA = 2% bovine serum albumin; Sk = 5% skimmed milk.

Screening activity of plant-derived compounds against ACE2 and RBD binding immunoassays

 The developed in vitro assay was performed as a screening method to evaluate the inhibitory effects of various plant-derived compounds on the interaction between the RBD and the ACE2 receptor, a critical step for SARS-CoV-2 viral entry. The compounds screened were characterized as phytochemical structures. As detailed in Table S1, the percentage of binding inhibition is reported. Baicalein, scutellarein, and gallic acid were identified as candidates that achieved greater than 50% inhibition at a screening concentration of 10 µM. This single-concentration screening was used only as a preliminary selection step to identify candidate compounds for further evaluation and was not intended to fully characterize compound potency. Subsequent dose-response analyses were conducted for each compound, and the IC₅₀ values were calculated. Baicalein exhibited the most potent inhibition, with an IC₅₀ of 4.50 ± 0.21 µM, followed by gallic acid at 9.07 ± 0.54 µM, and scutellarein at 12.58 ± 1.38 µM.

Model analysis

According to the screening immunoassay data on the RBD-ACE2 interaction, baicalein, scutellarein, and gallic acid demonstrated more than 50% inhibition. Consequently, these compounds were selected for further model analysis. The independent variables were the volume ratios of the selected compounds: baicalein at 20 µM (X₁), scutellarein at 10 µM (X₂), and gallic acid at 20 µM (X₃). These concentrations were selected based on preliminary screening responses to provide measurable and distinguishable inhibitory effects suitable for model construction, rather than based solely on IC₅₀ values. Although baicalein showed higher potency, 20 µM was used to ensure a consistent response within the working range of the assay. Preliminary studies established the experimental range for these volume ratios, as detailed in Table S2. The primary outcome measured was the percentage of inhibition of RBD to ACE2 binding (Y1), with 16 different volume combinations evaluated, as listed in Table 1.

Table 1. Experimental volume ratios and observed values of response, specifically the percentage of inhibition across multiple trials (n=3).

Note: Repeated experimental conditions were intentionally included in the D-optimal design to estimate pure error and assess experimental reproducibility. Values are expressed as mean ± SD (n = 3).

The quadratic model was determined to be the most suitable for evaluating the inhibition percentage, as evidenced by a statistically significant p-value of 0.0016. This level of significance suggests that the model parameters collectively exert a strong influence on the response variable. Additionally, the model's lack of fit p-value of 0.0758 indicates its adequacy in fitting the data, further supported by an R² value of 0.8181. The analysis revealed that the three primary variables—20 µM baicalein, 10 µM scutellarein, and 20 µM gallic acid— and their interactions influenced the observed responses.

Utilizing statistical software, the analysis revealed a correlation between the three variables and the inhibition response, as illustrated by a trace plot. This trace plot, along with a contour map, demonstrated the effects of the variables, extending from the reference blend at the overall centroid to each vertex. The slopes of the trace plot indicated an observable influence of the variables on the inhibition percentage. The three factors—20 µM baicalein, 10 µM scutellarein, and 20 µM gallic acid—were specifically selected for contour mapping to further elucidate their interactions and individual impacts on response levels. The mathematical representation of the response inhibition is expressed in the equation below, where the asterisk (*) denotes statistically significant model terms (P < 0.05).

         Y= 65.33X₁^* + 56.22X₂^*+ 57.38X₃^*- 38.48X₁X₂^*+ 53.90X₁X₃^* - 4.24X₂X₃^*

The contour plot outlined the experiment's constrained regions and the effects of each candidate compound on the responses. The equation demonstrated relationships by correlating the coefficients b₁, b₂, b₃, ..., b₂b₃ with the influence of the factors X₁, X₂, X₃, ..., X₂X₃ on the inhibition percentage. The presence of baicalein, scutellarein, and gallic acid influenced the inhibition percentage, as detailed in the trace plot (Figure 3a). Increases in the proportions of baicalein and gallic acid were associated with higher inhibition percentages, as depicted in both the quadratic model and the contour plot (Figure 3b).

Figure 3. Trace plot (piepel) of the response between inhibition percentage of RBD and ACE-2 binding and deviation from reference blend (a); Contour plot of the response between inhibition percentage of RBD and ACE-2 binding and volume ratio (b). (A: baicalein, B: scutellarein and C: gallic acid)

Best-predicted blend within the tested design space

The selected optimized blend of candidate compounds, determined through model analysis, aimed to identify the most favorable ratio within the tested design space of baicalein, scutellarein, and gallic acid for inhibiting the interaction between the RBD and the ACE2 receptor. The best predicted combination, which achieved the highest desirability score among the tested conditions of 0.563, consisted of baicalein at a final concentration of 10.8 µM (27 µL) and gallic acid at 9.2 µM (23 µL), with no inclusion of scutellarein (0 µL). Although this desirability score indicates a moderate level of optimization, this combination was selected as the best predicted blend under the present experimental constraints. This specific mixture was predicted to achieve an inhibition rate of 76.10%.

To verify the predictive accuracy of the model, the selected blend, as dictated by the desirability function, was experimentally evaluated in five replicates. This selected blend, predicted to achieve an inhibition rate of 76.10%, showed an observed inhibition rate of 75.56% ± 1.03. The comparison between the predicted and observed values showed no statistically significant difference (P = 0.3043), supporting the predictive consistency of the model under the tested conditions.

Molecular docking of selected compounds with RBD of SARS-CoV-2

The docking study was conducted to investigate the predicted molecular interactions of candidate compounds (baicalein, scutellarein, and gallic acid) with the RBD of SARS-CoV-2. The docking calculations were performed using GOLD 5.3.0 software with the CHEMPLP scoring function. The GOLD fitness scores for baicalein, scutellarein, and gallic acid were 43.97, 43.79, and 42.26, respectively. These scores were used for relative ranking of predicted docking poses and qualitative comparison of ligand–RBD interactions, rather than as direct measures of binding affinity. The 3D and 2D binding site analysis showed the predicted interactions of baicalein (Figure 4), scutellarein (Figure 5) and gallic acid (Figure 6) within the RBD interface region of SARS-CoV-2.

Figure 4. The molecular interactions between baicalein and the RBD of SARS-CoV-2. 3D representation of the RBD with baicalein docked within its binding site (a). Baicalein is depicted in a stick model (yellow carbon backbone). 2D interaction diagram between the baicalein and specific amino acid residues within the RBD (b).

Figure 5. The molecular interactions between scutellarein and the RBD of SARS-CoV-2. 3D representation of the RBD with scutellarein docked within its binding site (a). Scutellarein is displayed in a stick model (yellow carbon backbone). 2D interaction diagram between the scutellarein and specific amino acid residues of the RBD (b).

Figure 6. The molecular interactions between gallic acid and the RBD of SARS-CoV-2. 3D molecular surface model of the RBD with gallic acid docked within its binding site (a). Gallic acid is shown in a stick model (yellow carbon backbone). 2D interaction diagram between the gallic acid and specific amino acid residues of the RBD (b).

Baicalein, scutellarein, and gallic acid showed comparable GOLD fitness scores and predicted interactions within the RBD interface region of SARS-CoV-2. Baicalein (Figure 4b) was predicted to interact with several residues within this region, including TYR449, GLN498, GLY496, SER494, GLN493, TYR453, ARG403, TYR505, ASN501, and GLY502. Conventional hydrogen bonds with SER494, GLN493, TYR453, and ARG403, together with surrounding van der Waals interactions, may contribute to stabilization of the predicted docking pose.

Scutellarein (Figure 5b) was predicted to interact with residues TYR449, GLN498, GLY496, GLN493, SER494, TYR453, TYR495, ARG403, TYR505, ASN501, and GLY502 within the RBD interface region. Conventional hydrogen bonds with GLN493, SER494, TYR453, and TYR495, together with surrounding van der Waals interactions, may contribute to stabilization of the predicted docking pose. Although baicalein and scutellarein shared several interacting residues, their predicted hydrogen-bonding patterns were not identical.

Gallic acid (Figure 6b) was predicted to occupy the RBD interface region and interact with residues ASN501, TYR505, ARG403, GLY496, TYR453, SER494, and GLN493. Conventional hydrogen bonds with ASN501, TYR505, TYR453, SER494, and GLN493, together with a pi-donor hydrogen bond involving GLN493 and weaker interactions with ARG403 and GLY496, may contribute to stabilization of the predicted docking pose.

Overall, the predicted ligand–RBD interactions were mainly supported by hydrogen bonds and van der Waals interactions. These docking results provide supportive in silico evidence for possible interactions of the selected compounds at the RBD–ACE2 interface. However, they should be interpreted qualitatively and should not be considered direct evidence of binding affinity, competitive inhibition, viral entry prevention, or antiviral activity.

DISCUSSION

The entry of SARS-CoV-2 into host cells employs a specific mechanism in which the virus uses the receptor-binding domain (RBD) located on its spike (S) protein to bind to the ACE2 receptor on the host cell. Following the successful attachment of the S protein to the ACE2 receptor, the S protein undergoes a conformational change, enabling the fusion of the viral and host cell membranes. This process requires the participation of protease enzymes such as TMPRSS2 and cathepsin L (Shang et al., 2020; Jackson et al., 2022). Consequently, one strategy for identifying potential entry-related inhibitors involves evaluating compounds that interfere with the interaction between RBD and ACE2. Research efforts are thus focused on developing methods to identify agents capable of reducing RBD–ACE2 binding.

In this study, a screening method was developed in which the RBD was coated onto an immunoplate, allowing test substances to compete with the ACE2 receptor for binding to the immobilized RBD. This contrasts with other approaches where the RBD and ACE2 proteins are bound to secondary antibodies labeled with luminescent tags; in these methods, luminescence is observed in the absence of an inhibitor, providing results within approximately two hours (Alves et al., 2021). Another study employed a screening method by coating ACE2 onto a surface, then testing substances along with RBD to compete for binding activity (David et al., 2021). However, the method developed in this study offers an alternative for performing a screening inhibitory immunoassay based on the same RBD-ACE2 interaction concept. In the present assay, blank and vehicle controls were included to account for background signals and solvent effects. However, a well-established positive reference inhibitor was not included, which limits the ability to fully validate the assay performance and benchmark the inhibitory activity of the tested compounds. Therefore, this assay should be considered an optimized preliminary screening platform rather than a fully validated method. While it allowed the preliminary identification of compounds capable of reducing RBD–ACE2 binding, this developed method has limitations related to in vitro testing and is not suitable for some substances that may precipitate proteins. Future studies should include appropriate positive reference controls, together with cell-based antiviral assays, to further confirm assay validity, biological relevance, and benchmarking performance.

Baicalein and scutellarein, both flavonoids, are known for their anti-inflammatory and antioxidant properties (Liao et al., 2021). Baicalein is commonly found in medicinal plants such as Scutellaria baicalensis and Oroxylum indicum, whereas scutellarein and its glycosides have been reported in several Scutellaria species, including Scutellaria barbata, as well as Erigeron breviscapus (Dinda et al., 2017; Wang and Ma, 2018). Gallic acid, a triphenolic compound, is recognized for its broad spectrum of health benefits including anti-inflammatory and antioxidant effects (Kahkeshani et al., 2019). Baicalein, derived from the roots of Scutellaria baicalensis, has demonstrated antiviral activity against a variety of viruses (Liu et al., 2021), including influenza (Nayak et al., 2014) and hepatitis B (Pollicino et al., 2018), attributed to its ability to disrupt viral replication and assembly. Further studies have highlighted its antiviral properties against coronaviruses, showcasing mechanisms such as the inhibition of viral replication and modulation of host immune responses (Zandi et al., 2021), thereby supporting its biological relevance for further investigation.

Gallic acid, a phenolic acid widely distributed in various fruits and vegetables, also showed inhibitory activity in the present RBD–ACE2 binding assay. Known for its antioxidant and anti-inflammatory properties, gallic acid has been widely investigated for its biological activities. Its ability to reduce RBD–ACE2 binding in this assay suggests a possible entry-related mechanism that warrants further investigation. (Baraskar et al., 2023). Scutellarein, previously reported to possess antiviral properties, including as an inhibitor of HIV-1 (Zhang et al., 2005), may also be relevant for further investigation in the context of SARS-CoV-2 entry-related mechanisms. To place the present IC₅₀ values in context, baicalein showed an IC₅₀ of 4.50 µM in the RBD–ACE2 binding inhibition assay, which is within the micromolar range reported for baicalein in other SARS-CoV-2-related experimental systems. For example, baicalein has been reported to inhibit SARS-CoV-2 3CLpro activity with an IC₅₀ of 0.39 µM, although this assay targets viral protease activity rather than RBD–ACE2 binding (Liu et al., 2021). Gallic acid and scutellarein also showed micromolar IC₅₀ values in the present assay; however, directly comparable IC₅₀ data for these compounds against RBD–ACE2 binding remain limited. Therefore, the present findings provide additional binding-based evidence for these phytochemicals, but comparisons with previous studies should be interpreted cautiously because of differences in assay format, molecular target, endpoint, and experimental conditions.

These coefficients reflect the relative contribution of individual compounds and their interaction effects within the tested mixture design (Cornell, 2002). The data showed that individual factors and selected interaction terms influenced the RBD–ACE2 binding inhibition response. Rather than indicating a direct biological mechanism, these model terms suggest that the inhibitory response may depend on how the compounds interact in combination under the present assay conditions. Thus, the response surface methodology (RSM) analysis was used as a statistical tool to identify mixture patterns associated with increased binding inhibition, while further mechanistic studies are required to confirm the biological basis of these interactions. The baicalein–gallic acid blend showed the most favorable predicted RBD–ACE2 binding inhibition response within the tested design space, suggesting that this pair may contribute more effectively to the observed binding inhibition than mixtures containing scutellarein. However, because the desirability score was moderate, this combination should be interpreted as the best predicted blend within the tested design space rather than a fully optimized formulation.

The absence of scutellarein from the selected blend does not indicate a lack of intrinsic activity, since scutellarein showed inhibitory activity when tested individually. Instead, its absence may reflect compound interaction effects, where scutellarein did not further enhance the response under the present assay conditions. However, this interpretation remains model-based and requires further mechanistic validation.

This study evaluated the inhibitory activity of three phytochemical compounds—baicalein, scutellarein, and gallic acid—against SARS-CoV-2 RBD–ACE2 binding. Previous in silico studies have suggested that baicalein and scutellarein may interact with key amino acid residues on the SARS-CoV-2 spike protein (Shah et al., 2021; Gokhale et al., 2022). However, these docking results should be interpreted as supportive computational evidence rather than direct evidence of binding affinity or antiviral activity. Previous in vitro studies have also reported that baicalein inhibited SARS-CoV-2 entry into Vero E6 cells (Liu et al., 2022), while gallic acid reduced SARS-CoV-2 spike protein-induced ACE2 upregulation in endothelial cells (Youn et al., 2022). Importantly, viral entry inhibition was not directly evaluated in the present study; therefore, these previous reports are discussed only as supporting background for the biological relevance of the selected compounds.

Previous studies have reported that baicalein, scutellarein, and gallic acid may interact with viral replication-related proteins or host-response pathways, including RNA-dependent RNA polymerase (RdRp), Mpro/3CLpro, and oxidative stress-related mechanisms (Yu et al., 2012; Liu et al., 2021; Zandi et al., 2021). These mechanisms were not investigated in the present study and are discussed only to provide biological context for the selected compounds. Therefore, the interpretation of the present results is limited to RBD–ACE2 binding inhibition under the assay conditions used.

Beyond the individual compounds investigated in this study, other plant-derived phytochemicals and extracts have also been evaluated in SARS-CoV-2 spike/RBD–ACE2-related assays. For example, epigallocatechin gallate has been reported to inhibit SARS-CoV-2 spike RBD binding to ACE2 in an ELISA-based assay, with an IC₅₀ of 33.9 µM (Ohishi et al., 2022). In addition, Goc et al. (2021) screened 56 polyphenols and plant extracts for their ability to interfere with SARS-CoV-2 RBD binding to immobilized ACE2 and spike-mediated pseudo-virion entry. More recently, Curcuma longa extract and related phytochemical constituents were evaluated using competitive ELISA targeting the ACE2–RBD interaction, with L-tartaric acid showing the strongest inhibition (IC₅₀ = 0.009 mg/mL) (Najimi et al., 2025). In addition, extracts from Terminalia bellirica have demonstrated inhibitory effects on viral infection and immune modulation, supporting the pharmacological relevance of phytochemical-rich plant materials (Jantakee et al., 2023; Winidmanokul et al., 2024). Macadamia integrifolia leaves and husks have also been reported to contain substantial amounts of phenolics and flavonoids, with selected fractions showing biological activity in vitro (Srisawang et al., 2024). These examples provide contextual support for the relevance of plant-derived compounds in entry-related binding assays; however, direct comparison with the present findings should be made cautiously because of differences in assay format, tested materials, endpoints, and experimental conditions.

However, this study is based on preliminary binding in vitro assays and in silico analyses. Although the findings suggest that baicalein, scutellarein, and gallic acid can interfere with SARS-CoV-2 RBD–ACE2 binding under the present assay conditions, the absence of a validated positive reference control limits assay benchmarking and full validation. Another limitation is that the initial phytochemical screening was performed at a single concentration, which may limit the robustness of comparative compound evaluation. Although compounds showing greater than 50% inhibition were further assessed by dose-response analysis and IC₅₀ determination, future studies should include multi-concentration screening for all tested compounds to better characterize potency and response patterns.

In addition, the molecular docking protocol was not validated by redocking or benchmarking with known small-molecule ligands, partly because the selected RBD–ACE2 complex structure (PDB ID: 6M0J) does not contain a co-crystallized small-molecule ligand suitable for direct redocking. Thus, the docking results should be interpreted as supportive computational evidence rather than definitive proof of inhibitory activity. Therefore, further studies using appropriate positive controls, cell-based viral entry or infection models, and in vivo evaluation are required before antiviral efficacy or therapeutic relevance can be established.

CONCLUSION

This study demonstrated the development and optimization of an RBD–ACE2 binding immunoassay as a preliminary screening platform for evaluating phytochemical-mediated inhibition of SARS-CoV-2 RBD–ACE2 interaction. Using this assay, baicalein, scutellarein, and gallic acid showed inhibitory activity against RBD–ACE2 binding under the present assay conditions, with baicalein showing the strongest inhibitory response among the tested compounds. The novelty of this study lies in integrating an optimized immunoassay-based screening approach with D-optimal mixture design and molecular docking analysis to evaluate individual and combined phytochemical effects on RBD–ACE2 binding. Using D-optimal mixture design, a selected baicalein–gallic acid blend was identified as the best predicted combination within the tested design space, which showed the greatest binding inhibition within the experimental model. Molecular docking analysis further provided supportive in silico evidence for the possible interactions of these compounds with key residues at the SARS-CoV-2 RBD–ACE2 interface. These findings suggest that the developed immunoassay may serve as a useful preliminary tool for screening natural compounds that interfere with RBD–ACE2 binding. However, further validation using appropriate positive and negative reference samples, cell-based viral entry or infection models, mechanistic studies, and in vivo evaluation is required before any antiviral or therapeutic relevance can be established.

ACKNOWLEDGEMENTS

This study was granted by Faculty of Medicine, Khon Kaen University, Thailand (Grant Number: IN67083).

AUTHOR CONTRIBUTIONS

Khunkhang Butdapheng: Methodology (Equal), Formal Analysis (Lead), Investigation (Lead), Visualization (Lead), Data Curation (Lead), Writing - Original Draft (Lead); Polpan Sillapapibool: Methodology (Equal), Investigation (Supporting); Worapol Sae-Foo: Methodology (Equal), Investigation (Supporting); Waraporn Putalun: Conceptualization (Lead), Resources (Lead), Supervision (Lead); Waranyoo Phoolcharoen: Resources (Lead), Validation (Lead), Data Curation (Supporting); Rattanathorn Choonong: Conceptualization (Lead), Formal Analysis (Supporting), Investigation (Supporting), Project Administration (Lead), Funding Acquisition (Lead), Writing - Reviewing and Editing (Lead).

CONFLICT OF INTEREST

The authors declare that they have no conflicts of interest.

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Supplementary Information

Table S1. Percentage of RBD-ACE2 binding inhibition at single dose screening (10 µM).

Table S2. Volume factor of selected compounds in the model analysis.