Evaluating Pharmacokinetics and Toxicity of Potential Therapeutics Against Enterococcus faecalis in Endodontic Pathologies

Introduction

Endodontic infections are principally polymicrobial; however, in cases of posttreatment infection, Enterococcus faecalis has been identified as a dominant and persistent pathogen [1]. It is a gram-positive, facultative anaerobic bacterium capable of surviving in alkaline environments, such as the root canal system after endodontic treatment [1]. E. faecalis, in particular, can survive harsh conditions, such as high pH, elevated salt concentrations, and prolonged nutrient deprivation, making it a significant pathogen of secondary or persistent endodontic infections [2]. The organism has a complex pathogenicity profile that includes biofilm formation, resistance to calcium hydroxide, and expression of surface adhesins, such as aggregation substance and enterococcal surface proteins. Moreover, the capability to form resistant biofilms within the complex anatomy of root canals is a major challenge for antibacterial efficacy. Endodontic treatment aims to eradicate pathogenic microorganisms from the root canal system. However, due to the anatomical complexity of lateral canals, apical deltas, and dentinal tubules, complete sterilization is almost impossible [3,4]. These limitations limit the physical and chemical scope of irrigation and drugs [5]. E. faecalis can survive in a dormant state and resume growth after the return of favorable conditions. The efficacy of antibacterial active ingredients in root canal therapy depends not only on their antibacterial spectrum but also on their pharmacokinetics, predominantly the ability to penetrate the dentinal tubules, maintain therapeutic concentrations for a suitable period of time, and resist enzymatic disorders [6].

Theoretical drug profiling is a novel tool in pharmaceutical science that uses computational models and simulations to predict the pharmacological and toxicological behavior of compounds [7]. It is mainly applied in early drug discovery, allowing scientists to evaluate antibacterial potential, biofilm inhibition, tissue permeability, and cytotoxicity [8]. These profiles include parameters such as molecular weight, lipophilicity, topological polar surface area, and binding affinity to bacterial targets involved in peptidoglycan synthesis, such as enzymes and DNA gyrase [9].

In the case of E. faecalis, a rational drug design approach can target specific pathogenic mechanisms such as quorum sensing [9]. Drugs with selective affinity and the ability to disrupt cell communication in biofilm-forming bacteria may provide new strategies for endodontic disinfection [10]. It is important that such compounds have favorable pharmacokinetics in the dentin-pulp complex and minimal toxicity to periodontal ligament cells and periapical tissues [11].

Toxicity remains an important barrier when using novel or repurposed antibacterial agents for endodontic use. Although the efficacy observed in in vitro antibacterial studies may seem promising, translation into clinical use is limited by cytotoxic effects on host tissues, particularly when root canal materials come into contact through apical or lateral canal pathways [12]. The active substance must be biocompatible and should ideally induce minimal inflammation or necrosis. In silico models, such as the Ames mutagenicity test, hepatotoxicity prediction, and blood-brain barrier (BBB) penetration simulations, are valuable tools for preliminary screening [13].

Endodontic applications are primarily local; however, systemic exposure to compounds can occur, especially in patients with immune or other medical disorders [14]. Consequently, full profiling of absorption, distribution, metabolism, excretion (ADME), and toxicity (ADMET) is crucial for the theoretical assessment of candidate drugs. Theoretical drug profiling forms the basis for identifying promising lead compounds by selecting candidates for in vitro verification. The combination of high-throughput screening and biofilm modeling can accelerate the development of targeted, biocompatible therapies for persistent endodontic infections [15,16].

This highlights the importance of balancing antibacterial efficacy with host tissue safety to ensure long-term success in endodontic treatment. Here, our main objective is to understand the electronic structures and biological interactions between bioactive compounds and the target protein, along with ADMET and quantitative structure–activity relationship (QSAR) activity.

Material and Methods

COMPUTATIONAL DETAILS:

We modeled 5 compounds, with the compounds identified as follows: compound 1: 2-hydroxyethyl methacrylate; compound 2: dimethyl adipate; compound 3: dimethyl glutarate; compound 4: dimethyl succinate; and compound 5: ethylene glycol dimethyl acrylate. We performed modeling with GaussView 6.1 software [17] and performed geometry optimizations with Gaussian 16 [18]. In this study, all optimizations were conducted using the B3LYP functional [19,20] and 6-311G(d,p) basis set [21].

MOLECULAR DOCKING:

Before performing molecular docking, the amino acid sequence of the enterococcal surface protein (UniProt ID: B1PCJ4) was retrieved from UniProt (https://www.uniprot.org/uniprotkb/B1PCJ4/entry). The docking grid was defined with the central coordinates set at x=−5.851, y=4.351, z=4.585. Molecular docking was conducted using AutoDock Vina 1.2.0, [22,23] and the resulting ligand-protein interactions were visualized by using the Discovery Studio Visualizer 4.0 [24].

ADMET ANALYSIS:

In silico ADME screening uses computational methods to predict a drug candidate’s pharmacokinetic properties [25,26]. These properties determine how a drug is processed in the body, influencing its efficacy and safety. To perform this analysis, we use the ADMET 2.0 online server (https://admetmesh.scbdd.com) [27], which provides a complete assessment of a drug’s behavior, aiding in informed decision-making for further development. Similarly, in silico toxicity screening predicts toxicity using computational tools such as molecular docking, QSAR models, and toxicity databases [28]. This approach helps assess risks in vital organs, including the brain, kidneys, heart, and liver, by analyzing interactions with key proteins and enzymes involved in metabolism [29]. Integrating computational screening with experimental validation can identify safety concerns early, prioritize promising compounds, reduce development costs, and accelerate drug discovery.

Results

OPTIMIZATION AND FRONTIER MOLECULAR ORBITAL ANALYSIS:

First, we optimized all 5 compounds, as shown in Figure 1, and determined the energy gap by using frontier molecular orbital theory to effectively examine molecular properties, with electronic structure, charge transfer, and optical characteristics. This is represented by the transition of an electron from the highest occupied molecular orbital (HOMO) to the lowest unoccupied molecular orbital (LUMO), leading to electronic absorption within the system [30,31].

In molecular systems, the HOMO functions as an electron donor, while the LUMO acts as an electron acceptor. These orbitals play a crucial role in defining key electronic properties, including orbital energy levels, electron density distribution, and atomic bonding interactions. Understanding these factors provides a valuable understanding of a molecule’s electronic behavior and its interactions with other species. Additionally, the LUMO corresponds to electron affinity, whereas the HOMO represents ionization potential. The energy gap between these is a critical parameter influencing chemical reactivity and stability. A smaller HOMO-LUMO gap typically correlates with higher polarizability, increased chemical reactivity, and lower kinetic stability. The computed HOMO values for compounds 1 to 5 were −7.46 eV, −7.58 eV, −7.54 eV, −7.70 eV, and −7.56 eV, respectively (Figure 2). The computed LUMO values for compounds 1 to 5 were −1.27 eV, 0.18 eV, 0.07 eV, −0.05 eV, and −1.42 eV, respectively. The computed energy gaps for compounds 1 to 5 were 6.19 eV, 7.76 eV, 7.61 eV, 7.65 eV, and 6.14 eV, respectively (Figure 2). The computed energy gaps for the compounds showed a distinct trend, reflecting variations in their electronic structures and reactivity. Compound 5 and compound 1 showed the lowest energy gaps, at 6.14 eV and 6.19 eV, respectively, indicating that these molecules require less energy for electronic transitions and are likely to be more chemically reactive. In contrast, compounds 2, 3, and 4 showed relatively higher energy gaps of 7.76 eV, 7.61 eV, and 7.65 eV, respectively, signifying greater kinetic stability and lower reactivity. The smaller the energy gap signifies that less energy is required for electronic transitions, enabling easier excitation of electrons from the HOMO to the LUMO. This typically resembles higher chemical reactivity, as molecules with a smaller energy gap can readily interact with other species and making them more reactive toward nucleophilic or electrophilic attack.

MOLECULAR ELECTROSTATIC POTENTIAL:

Molecular electrostatic potential maps offer a 3-dimensional representation of a molecule’s polarity, showing variations in electron density across its structure. These maps enable the identification of regions with different electrostatic potential, where areas of high electron density correspond to negative electrostatic potential, related to nucleophilic sites, while regions of low electron density correspond to positive electrostatic potential, indicating electrophilic sites [32–34]. By visualizing these charge distributions, molecular electrostatic potential map analysis provides valuable insights into molecular reactivity, aiding in the prediction of interaction sites for electrophilic and nucleophilic attack. Figure 3 shows the molecular electrostatic potential maps of compounds 1 to 5, represented as solid and transparent surfaces (isovalue=0.02). The electrostatic potential is visually distinguished using a color gradient, where red specifies the most electronegative regions, blue specifies the most electropositive areas, and green indicates neutral electrostatic potential. The electrostatic potential follows a gradient in the order red < orange < yellow < green < blue, reflecting variations in electron density across the molecular surface. The potential values of compounds 1 to 5 were computed as −5.821 e−2, −4.909 e−2, −5.351 e−2, −5.053 e−2, and −4.658 e−2, respectively. Compound 1 shows the most negative potential value, representing a higher concentration of electron density in certain regions, which may enhance its interaction with electrophiles. Compounds 3 and 4 display slightly less negative potential values, suggesting a moderate electron-donating ability. In contrast, compounds 2 and 5 have the least negative electrostatic potential, indicating a lower electron density and possibly reduced nucleophilicity. This decreasing trend suggests that compounds with less negative potential values have weaker electrostatic interactions and may exhibit lower reactivity in electrophilic reactions.

MOLECULAR DOCKING:

Molecular docking was conducted to inspect the binding interactions and stability of ligands (compounds 1–5) within the active site of the enterococcal (B1PCJ4) protein. This helps in understanding how each ligand fits within the protein’s binding pocket, the nature of intermolecular interactions, and the overall binding affinity. By assessing docking scores, we can predict the strength and stability of ligand-protein complexes, which is crucial for evaluating potential biological activity [35]. The binding energy values for all compounds were calculated and are presented in Table 1, providing insight into their relative affinities and potential effectiveness as inhibitors or therapeutic agents.

Compound 1 had a binding affinity of −4.0 Kcal/mol and showed van der Waals interaction with GLU A: 163 (4.06Å), conventional hydrogen bonding with LYS A: 139 (5.51Å), and unfavorable acceptor-acceptor interaction with ILE A: 161 (2.90Å) (Figure 4).

Compound 2 had a binding affinity of −3.9 Kcal/mol and showed van der Waals interaction with THR A: 60 (1.85Å), conventional hydrogen bonding with ASP A: 59 (2.05Å), and carbon hydrogen bonding with GLU A: 55 (3.75Å) (Figure 4).

Compound 3 had a binding affinity of −3.8 Kcal/mol and showed van der Waals interaction with THR A: 60 (2.41Å), conventional hydrogen bonding with ASP A: 59 (2.16Å), and unfavorable acceptor-acceptor interaction with ASP A: 48 (2.94Å) (Figure 5).

Compound 4 had a binding affinity of −3.4 Kcal/mol and showed van der Waals interaction with THR A: 60 (2.09Å), conventional hydrogen bonding with ASP A: 59 (2.74Å), and carbon hydrogen bonding with ASP A: 59 (3.62Å) (Figure 5).

Compound 5 had a binding affinity of −4.1 Kcal/mol and showed van der Waals interaction with ASN A: 190 (2.09Å), conventional hydrogen bonding with GLU A: 163 (1.98Å), and carbon hydrogen bonding with LYS A: 139 (3.76Å) (Figure 6).

Compound 5 showed the highest binding affinity (−4.1 Kcal/mol), indicating a stronger interaction with the protein, followed in order by compounds 1 to 4. Compound 4 had the lowest binding affinity (−3.4 Kcal/mol), showing the weakest interaction with the protein.

ADMET ANALYSIS:

ADMET analysis evaluates a drug’s absorption, distribution, metabolism, excretion, and toxicity properties, crucial for assessing its pharmacokinetics. Absorption parameters include Caco-2 and MDCK permeability, P-glycoprotein (P-gp) inhibition, and human intestinal absorption (HIA), which determine how well a drug crosses biological membranes. A Caco-2 permeability value above −5.15 log cm/s and MDCK permeability over 2×10⁻⁶ cm/s indicate good absorption. P-gp inhibition and substrate probability scores (0–1) help predict interactions with efflux transporters affecting drug bioavailability. Distribution is analyzed through plasma protein binding (PPB), volume of distribution (Vd), BBB penetration, and fraction unbound (Fu). Drugs with PPB <90% have better availability, while Vd values between 0.04 and 20 L/kg suggest effective distribution. BBB penetration (0–1 scale) is crucial for central nervous system drugs, with Fu >5% indicating high unbound drug levels in plasma. Metabolism involves inhibition of cytochrome enzymes (CYP1A2, CYP2C19, CYP2C9, CYP2D6, and CYP3A4) that metabolize most drugs. The probability of enzyme inhibition (0–1 scale) determines the likelihood of metabolic interactions.

Excretion is assessed through clearance and half-life (T1/2). Drugs with clearance ≥5 mL/min/kg exhibit efficient elimination, while a T1/2 score of 0 to 0.3 indicates a favorable elimination rate.

Compound 1 has 3 H-bond acceptors, 1 H-bond donor, and 4 rotatable bonds. The Caco-2 permeability value is −4.289, indicating excellent absorption. However, the P-gp inhibitor and P-gp substrate values fall within the excellent range (0–0.1). The HIA and human oral bioavailability 20% (F20%) values also indicate excellent absorption and bioavailability. The PPB value is good, while the Vd is excellent, at 0.713. The BBB penetration value is high (++), and the Fu is good, at 80.315%, suggesting a significant portion of the drug remains unbound in plasma. Metabolism analysis revealed that compound 1 effectively inhibits CYP1A2, CYP2C19, CYP2C9, CYP2D6, and CYP3A4 within a good range. The clearance value is 8.722, indicating good elimination, while the T1/2 is 0.895, suggesting a moderate duration in the body. Compound 2 has 4 H-bond acceptors, no H-bond donors, and 7 rotatable bonds. Its Caco-2 permeability (−4.488) indicates excellent absorption, with P-gp inhibitor and P-gp substrate values also in the optimal range (0–0.1). The HIA and F20% values confirm high absorption and bioavailability (Table 2). The PPB value is good, while the Vd (0.542) is excellent. It shows high BBB penetration (+++), and Fu of 75.725% suggests a significant unbound drug fraction. Compound 2 effectively inhibits CYP1A2, CYP2C19, CYP2C9, CYP2D6, and CYP3A4, ensuring good metabolic stability. With a clearance of 9.382, it demonstrates efficient elimination, and its T1/2 of 0.929 indicates moderate retention in the body. Compound 3 has 4 H-bond acceptors, no H-bond donors, and 6 rotatable bonds. Its Caco-2 permeability (−4.477) suggests excellent absorption, with optimal P-gp inhibitor and P-gp substrate values. HIA and F20% confirm high bioavailability. It has good PPB, excellent Vd (0.512), high BBB penetration (+++), and Fu of 77.748%, indicating a significant unbound fraction. Compound 3 effectively inhibits CYP1A2, CYP2C19, CYP2C9, CYP2D6, and CYP3A4, ensuring good metabolic stability. With a clearance of 8.573, it shows efficient elimination, and its T1/2 of 0.927 suggests moderate retention. Compound 4 has 4 H-bond acceptors, no H-bond donors, and 4 rotatable bonds. Its Caco-2 permeability (−4.619) suggests excellent absorption, with optimal P-gp inhibitor and P-gp substrate values. HIA and F20% confirm high bioavailability. It has good PPB, excellent Vd (0.420), high BBB penetration (+++), and Fu of 77.667%, indicating a significant unbound fraction. Compound 3 effectively inhibits CYP1A2, CYP2C19, CYP2C9, CYP2D6, and CYP3A4, ensuring good metabolic stability. With a clearance of 8.236, it shows efficient elimination, and its T1/2 of 0.919 suggests moderate retention. Compound 5 has 4 H-bond acceptors, no H-bond donors, and 7 rotatable bonds. Its Caco-2 permeability (−4.460) indicates excellent absorption, with optimal P-gp inhibitor and P-gp substrate values. HIA and F20% confirm high bioavailability. It has good PPB, excellent Vd (1.007), high BBB penetration (+), and Fu of 56.734%, suggesting a significant unbound fraction. Compound 5 effectively inhibits CYP1A2, CYP2C19, CYP2C9, CYP2D6, and CYP3A4, ensuring good metabolic stability. With clearance of 12.388, it shows efficient elimination, and its T1/2 of 0.796 indicates moderate retention.

TOXICITY ANALYSIS:

Toxicity prediction is crucial in drug development to assess toxicity effects on vital organs. In silico toxicity screening evaluates parameters such as hepatotoxicity, cardiotoxicity, nephrotoxicity, and mutagenicity, helping to identify risks early. Hepatotoxicity assesses liver damage potential, while cardiotoxicity predicts interactions affecting heart function. Nephrotoxicity evaluates kidney-related toxicity, and mutagenicity examines the likelihood of genetic mutations. Other important factors include hERG inhibition, which determines cardiac arrhythmia risks, and AMES toxicity, which identifies mutagenic potential. Drugs with high toxicity scores require modifications to improve safety. This analysis is shown in Table 3.

The toxicity profiles of compounds 1 to 5 were assessed based on numerous parameters, revealing notable trends. None of the compounds displayed hERG blockage, AMES toxicity, or acute oral toxicity in rats, signifying a favorable safety profile in these areas. Similarly, no strong evidence of carcinogenicity was observed, except for with compound 4, which showed mild carcinogenic potential (−). Skin sensitization varied across the compounds, with compound 5 displaying the highest potential (+++), followed by compounds 1 and 4 (++), compound 2 (+), and compound 3 showing no sensitization (−). All compounds showed strong eye corrosion potential (+++), while eye irritation was slightly lower for compounds 2 and 3 (++), compared with the others (+++).

Regarding respiratory toxicity, compounds 1 and 4 showed mild toxicity (−− and +, respectively), whereas the others presented no significant concerns. Compound 3 was the only ligand associated with Food and Drug Administration maximum recommended daily dose (FDAMDD) toxicity (−−), suggesting a potential risk for metabolism-related toxicity. Drug-induced liver injury was lowest for compound 4 (−) and relatively higher for the others (−− to −−−). All compounds demonstrated relatively low systemic toxicity; however, special attention should be given to eye irritation/corrosion, skin sensitization (especially for compound 5), and potential respiratory toxicity in compounds 1 and 4.

Discussion

The computed energy gaps for compounds 1 to 5 range from 6.14 eV to 7.76 eV, with compound 1 exhibiting the lowest gap (6.19 eV) and compound 2, the highest (7.76 eV). The electrostatic potential values of compounds 1 to 5 were computed as −5.821 e-2, −4.909 e-2, −5.351 e-2, −5.053 e-2, and −4.658 e-2, respectively. Compound 1 exhibits the most negative potential, indicating a higher electron density, which enhances its interaction with electrophiles. Conversely, compounds 2 and 5 show the least negative potential values, suggesting lower electron density and reduced nucleophilicity. This trend indicates that compounds with less negative potential values may have weaker electrostatic interactions and lower reactivity in electrophilic reactions [36]. Molecular docking analysis revealed compound 5 exhibits the highest binding affinity (−4.1 kcal/mol), indicating the strongest interaction with the target enterococcal (B1PCJ4) protein, followed by compounds 1, 2, 3, and 4. Compound 4 shows the lowest binding affinity (−3.4 kcal/mol), suggesting the weakest interaction. These findings highlight compound 5 as the most promising candidate for binding stability, which may be attributed to its optimal electrostatic and hydrophobic interactions within the active site. The pharmacokinetic properties of compounds 1 to 5 were analyzed based on absorption, distribution, metabolism, excretion, and toxicity. All compounds exhibited excellent absorption based on Caco-2 permeability values (−4.289 to −4.619) and high HIA and F20% values. The number of rotatable bonds varies from 4 to 7, with compound 5 having the highest flexibility. The PPB values are consistently good, while the Vd varies, with compound 5 showing the highest value (1.007) and compound 4 the lowest (0.420). The BBB penetration is high for all compounds (++ to +++), indicating their potential to cross the blood-brain barrier [37]. All compounds effectively inhibit CYP1A2, CYP2C19, CYP2C9, CYP2D6, and CYP3A4, ensuring metabolic stability. The clearance values range from 8.236 to 12.388, with compound 5 showing the highest clearance (12.388) and compound 4 the lowest (8.236). The T1/2 values range from 0.796 to 0.929, indicating moderate retention in the body. Toxicity screening was performed to assess potential toxicity on vital organs; hERG blockage, AMES toxicity, and rat acute oral toxicity were absent in all compounds, suggesting a low risk of cardiotoxicity and genotoxicity. Carcinogenicity was minimal across the compounds, except for compound 4, which showed mild carcinogenic potential (−). Compound 5 exhibited the highest sensitization potential (+++), followed by compounds 1 and 4 (++), while compound 3 showed no sensitization (−). All compounds demonstrated strong eye corrosion (+++), while compounds 2 and 3 had slightly lower irritation (++). Compounds 1 and 4 exhibited mild toxicity (−− and +, respectively), whereas others showed no significant concerns. Regarding drug-induced liver injury, compound 4 had the lowest risk (−), while others showed a range of risks (−− to −−−). Only compound 3 showed FDAMDD toxicity (−−), suggesting potential risks in metabolism-related toxicity.

Conclusions

Dental resources are specialized biomaterials used for restoring, bonding, and protecting teeth, necessitating biocompatibility, durability, and strength. Compounds such as 2-hydroxyethyl methacrylate enhance adhesion, while dimethyl adipate, dimethyl glutarate, and dimethyl succinate act as plasticizers, improving flexibility. Ethylene glycol dimethyl acrylate serves as a crosslinking agent, reinforcing dental composites for long-lasting stability and performance. In this study, we used computational approaches to investigate the electronic properties, molecular reactivity, binding interactions, and pharmacokinetic profiles of 5 compounds with potential endodontic relevance. The optimized structures were analyzed using frontier molecular orbital theory to determine the energy gaps, which ranged from 6.14 eV to 7.76 eV, indicating variations in chemical reactivity and stability. Molecular electrostatic potential maps revealed charge distributions, highlighting potential interaction sites for electrophilic and nucleophilic attack. Molecular docking studies were conducted to evaluate binding interactions with the E. faecalis (B1PCJ4) protein, a major contributor to endodontic infections, showing binding affinities between −3.4 and −4.1 kcal/mol, with compound 5 exhibiting the strongest interaction. ADMET analysis assessed pharmacokinetic properties, demonstrating excellent absorption, distribution, metabolism, and elimination for all compounds. Toxicity evaluations indicated minimal risk, except for compound 5, which showed higher skin sensitization potential. These findings provide valuable insights into the potential endodontic applications of these compounds in drug discovery and molecular design.