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Article

In Silico SwissADME Analysis of Antibacterial NHC–Silver Acetates and Halides Complexes

by
Jarosław Sączewski
1,*,
Łukasz Popenda
2 and
Joanna Fedorowicz
3
1
Department of Organic Chemistry, Faculty of Pharmacy, Medical University of Gdansk, 107 Gen. J. Hallera Av., 80-416 Gdansk, Poland
2
NanoBioMedical Centre, Adam Mickiewicz University, Wszechnicy Piastowskiej 3, 61-614 Poznan, Poland
3
Department of Chemical Technology of Drugs, Faculty of Pharmacy, Medical University of Gdansk, 107 Gen. J. Hallera Av., 80-416 Gdansk, Poland
*
Author to whom correspondence should be addressed.
Appl. Sci. 2024, 14(19), 8865; https://doi.org/10.3390/app14198865
Submission received: 3 September 2024 / Revised: 25 September 2024 / Accepted: 27 September 2024 / Published: 2 October 2024
(This article belongs to the Special Issue Recent Advances in Medicinal and Synthetic Organic Chemistry)

Abstract

:
This study investigates the antibacterial N-heterocyclic carbene (NHC)–silver complexes using the SwissADME platform, a web-based tool developed by the Swiss Institute of Bioinformatics (SIB). NHCs, particularly their silver complexes, have gained significant interest in medicinal chemistry for their potential as antibacterial and anticancer agents. The effectiveness of these complexes is closely linked to their structure, including factors like lipophilicity, which enhance their ability to penetrate bacterial cells and sustain the release of active silver ions. SwissADME provides computational estimates of pharmacokinetic properties, including absorption, distribution, metabolism, and excretion (ADME) characteristics, as well as drug-likeness and toxicity assessments. By evaluating parameters like molecular weight, topological polar surface area, lipophilicity (LogP), and water solubility, SwissADME offers insights into the drug-like potential of compounds. This study is inspired by a comprehensive review of antibacterial NHC–silver complexes published from 2006 to 2023, which identified superior structures with notable biological activity. The primary aim is to determine whether these active complexes exhibit distinct SwissADME parameters compared to others, providing a deeper understanding of the factors that influence their biological efficacy and aiding in the identification of promising drug candidates. Finally, experimental stabilities of exemplary complexes were confronted with absolute LUMO values derived from DFT calculations.

1. Introduction

N-Heterocyclic carbenes (NHCs) are organic compounds characterized by a carbene carbon atom located within a nitrogen-containing heterocyclic ring [1]. Although these compounds are reactive and difficult to isolate, the first successful isolation of a free NHC in 1991 paved the way for rapid advancements. Since then, NHCs have become widely used in organometallic chemistry, where they now rival traditional ligands like tertiary phosphines. NHCs are particularly well-known for their strong σ-donating properties, allowing them to form robust bonds with metals—often stronger than those formed by phosphines. This electron-rich nature influences various reactions, such as cross-coupling reactions, making NHCs highly versatile in catalytic applications. The bonding of NHCs with metals involves not just σ-donation but also π-backdonation, which is influenced by factors such as the saturation, aromaticity, and steric bulk of the NHC ligands. As a result, NHCs have been widely adopted in catalytic processes, including C-H activation and bond formation. Their ability to stabilize and activate metal centers has made them valuable in the development of novel materials and catalytic systems [2].
Silver is increasingly recognized for its ability to generate reactive oxygen species (ROS), which play a crucial role in their antimicrobial properties. The interaction of silver with biological systems leads to the production of ROS, including hydroxyl radicals and superoxide, which can induce oxidative stress in microbial cells, ultimately resulting in cell death [3,4]. This oxidative stress is particularly pronounced in aerobic conditions, where bacteria exhibit heightened susceptibility to silver due to increased ROS generation [3,5]. The mechanism by which silver induces ROS involves the release of silver ions (Ag+), which can interact with cellular components and disrupt metabolic processes, including the respiratory chain, leading to mitochondrial dysfunction and apoptosis [6,7]. To enhance silver’s proven antibiotic properties, N-heterocyclic carbenes (NHCs) can be incorporated as supplementary biomolecules. The transport of NHC–silver(I) complexes into bacterial cells is a crucial factor in boosting antibacterial activity. NHC–silver(I) complexes have attracted considerable interest in medicinal chemistry, especially for their potential as antibacterial [8,9,10,11] and anticancer agents [8,9,10,11,12,13,14,15,16,17]. The effectiveness of these complexes is closely linked to the structure of the NHC core and the nature of the substituents on the nitrogen atoms, which can be tailored to optimize properties like lipophilicity and the controlled release of silver ions within the intracellular environment. The antibacterial activity of NHC–silver complexes is primarily attributed to their ability to slowly release silver ions, which disrupt essential bacterial cell functions [18]. It is assumed that this antimicrobial activity is further enhanced by the lipophilicity of the complexes, which facilitates better penetration into bacterial cells and ensures a sustained release of active agents. Moreover, NHC–silver complexes have demonstrated efficacy against a broad spectrum of pathogens, including Gram-positive and Gram-negative bacteria, as well as fungi and yeast. This broad-spectrum activity underscores their potential as promising candidates for the development of new antimicrobial agents [11,19]. The role of lipophilicity in enhancing the biological activity of NHC–silver complexes is especially critical, making it a focal point in the continued research and development of these compounds as potent antimicrobial and anticancer agents [20,21,22].
SwissADME is a web-based tool developed by the Swiss Institute of Bioinformatics (SIB) for predicting the drug-likeness, pharmacokinetics, and medicinal chemistry suitability of small molecules [23,24]. It is widely used by researchers to evaluate various properties essential for drug discovery, including absorption, distribution, metabolism, and excretion (ADME) characteristics, as well as potential toxicity and overall drug-likeness. The tool calculates several physicochemical properties, such as topological polar surface area (TPSA), molecular weight, water solubility, and lipophilicity (LogP). It also assesses whether a compound adheres to Lipinski’s rule of five, which predicts good oral bioavailability. SwissADME provides a bioavailability radar, a graphical representation of six key physicochemical properties, allowing quick visualization of a molecule’s drug-likeness. Additionally, SwissADME predicts pharmacokinetic properties such as gastrointestinal absorption, blood–brain barrier penetration, cytochrome P450 interactions, and other metabolic and excretion characteristics. It evaluates the synthetic accessibility of compounds and their potential for promiscuous binding to various biological targets. This comprehensive set of computational predictions, based on the chemical structure of compounds, makes SwissADME an invaluable tool for medicinal chemists, pharmacologists, and researchers involved in the early stages of drug discovery and development, aiding in the identification of promising drug candidates. SwissADME is offered free of charge with the expectation that it will be useful. Although the said in silico platform is commonly used for the evaluation of medicinal chemistry projects, the Swiss Institute of Bioinformatics (SIB) makes no guarantees or warranties concerning the results obtained from using any information provided through their server. The SIB is not liable for any incidental, consequential, direct, or indirect damages arising from the use of results, data, or information provided through their server.
The main aim of this work is to characterize antibacterial NHC–silver acetate and halide complexes using the SwissADME platform to elucidate the key factors that influence the biological activity of the investigated structures. Our study is primarily based on a comprehensive review by Stephen R. Isbel, Siddappa A. Patil, and Alejandro Bugarin [25], which surveys the literature on antibacterial N-heterocyclic carbene (NHC)-silver complexes published from 2006 to 2023. Their review provides a thorough examination of the development and antibacterial efficacy of these complexes over this period. Among the various complex structures, the superior ones were identified based on the biological activity results [25]. Hence, the question arises as to whether the more active complexes differ in any way from the others in terms of the parameters provided by the SwissADME tool.
Since the computational methods utilized in the SwissADME platform are based on molecular mechanics, as well as various topological and atom-based models [23,24], we have explored the electronic structures of representative NHC–silver complexes using quantum chemical calculations (DFT) and compared the results with the reported experimental stabilities. These parameters are crucial in the context of biological activity [26].

2. Materials and Methods

The studied complexes [26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68], represented in the simplified molecular-input line-entry system (SMILES) format, were subjected to SwissADME analysis. The analyzed structures were divided into two groups: active compounds A (136 entries) and superior complexes S (61 entries) (Figure 1 and Figure 2), and compared to the entire dataset A + S. The results are presented in the corresponding box and whisker plots (Figure 3, Figure 4, Figure 5, Figure 6, Figure 7, Figure 8, Figure 9, Figure 10, Figure 11, Figure 12 and Figure 13) and BOILED-Egg charts (Figure 14). The complete results are available in the Supplementary Materials.
All the calculations in this paper were performed using Spartan software package from Wavefunction, Inc. [69]. The geometries were fully optimized in vacuo using the DFT EDF2 method with a 6-31+G* basis set. Frequency calculations were conducted for all structures to confirm they correspond to energy minima [70]. The LUMO absolute values presented in Figure 15 [square root of e/au3] were mapped on the isodensity surface (0.002 e/au3).
Figure 1. NHCs–silver acetate complexes. NHCs–silver acetate complexes. Active complexes (A) are shown in black, superior complexes (S) are shown in red.
Figure 1. NHCs–silver acetate complexes. NHCs–silver acetate complexes. Active complexes (A) are shown in black, superior complexes (S) are shown in red.
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Figure 2. NHCs–silver halide complexes. Active complexes (A) are shown in black, superior complexes (S) are shown in red.
Figure 2. NHCs–silver halide complexes. Active complexes (A) are shown in black, superior complexes (S) are shown in red.
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3. Results and Discussion

3.1. Molecular Weight

The median molecular weights for the A + S, A, and S groups are very similar and amount to 499.31, 500.13, and 493.35, respectively (Figure 3). As a consequence, although the group of superior complexes exhibits slightly lower molecular weights and all three groups nearly align with Lipinski’s rule of five, molecular weight alone cannot definitively determine the strong biological activity of a given complex.
Figure 3. Molecular weight distribution within groups A + S, A, and S.
Figure 3. Molecular weight distribution within groups A + S, A, and S.
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3.2. Heavy Atoms

As in the case of molecular weight, the median number of heavy atoms for all three groups of complexes is identical and amounts to 29 (Figure 4). Despite the fact that the group of superior compounds S contains, on average, fewer heavy atoms than the other groups, this parameter cannot be directly linked to the increased biological activity of the complexes.
Figure 4. Distribution of the number of heavy atoms within the groups A + S, A, and S.
Figure 4. Distribution of the number of heavy atoms within the groups A + S, A, and S.
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3.3. Aromatic Heavy Atoms

In the case of the number of aromatic heavy atoms, a slight preference for lower values can be observed in the most active complexes S (Figure 5). Hence, the median/average values for the A + S, A, and S groups amounts to 17/17.3, 17/17.85, and 15/16.15, respectively.
Figure 5. Distribution of the number of aromatic heavy atoms within the groups A + S, A, and S.
Figure 5. Distribution of the number of aromatic heavy atoms within the groups A + S, A, and S.
Applsci 14 08865 g005

3.4. Fraction of sp3 Hybridized Carbon Atoms

The results presented above are complementary to the values of the parameter describing the fraction of sp3 hybridized carbon atoms. Thus, the S group is characterized by a higher percentage of aliphatic carbon atoms compared to the A + S and A groups with median values of 0.29 vs. 0.25 and 0.24, respectively (Figure 6). It is worth noting that the less biologically active cohort A includes outliers with C sp3 fraction values exceeding 0.6, which strongly affect the corresponding average. This observation indicates that the high lipophilicity of a complex does not always translate into increased biological activity. In the discussed case, the complex with morpholino substituents 123 (0.6) is more active than piperazine analogues 122 (0.65) and 124 (0.62).
Figure 6. Distribution of C sp3 fraction within groups A + S, A, and S.
Figure 6. Distribution of C sp3 fraction within groups A + S, A, and S.
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3.5. Rotable Bonds

The box plots with whiskers showing the number of bonds capable of rotation in all studied groups look identical (Figure 7), except for the outliers that feature two methyl carboxylate groups (46, 51) or four methoxy groups (50). This may mean that the number of rotating bonds of active complexes may range from 2 to 10.
Figure 7. Distribution of the number of rotable bonds within the groups A + S, A, and S.
Figure 7. Distribution of the number of rotable bonds within the groups A + S, A, and S.
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3.6. Molecular Refractivity

The similarity in the average values of calculated molecular refraction for the three groups A + S, A, and S, which are 111.66, 113.20, and 108.21, respectively, suggests that the overall polarizability and molecular volume characteristics of these groups are quite comparable (Figure 8). It should be emphasized, however, that the right-skewed distribution in cohort S, evidenced by relatively higher mean value when compared to A + S and A (113.1 vs. 111.6 and 111.46) may suggest a bimodal distribution of molecular refraction in the context of superior complexes.
Figure 8. Molecular refractivity distribution within groups A + S, A, and S.
Figure 8. Molecular refractivity distribution within groups A + S, A, and S.
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3.7. Topological Polar Surface Area (TPSA)

The medians of TPSA parameters for all groups are nearly identical and equal to 37.55, 36.16, and 39.40 (Figure 9). However, careful analysis of the data indicates that the group S contains more complexes characterized by the presence of polar groups. This observation is in contradiction with the theory indicating the existence of a correlation between the high lipophilicity of a complex and its augmented biological activity.
Figure 9. Topological polar surface area distribution within groups A + S, A, and S.
Figure 9. Topological polar surface area distribution within groups A + S, A, and S.
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3.8. Lipophilicity

The latter conclusion can be further supported by theoretical results from lipophilicity assessments (Figure 10). All models, except for MLOGP, show that the cohort of the superior complexes (S) have lower median values and first quartile (Q1) assessments compared to the groups A + S and A. The results obtained from the XLOGP, WLOGP, MLOGP, and Consensus LogP models are highly correlated, with correlation coefficients R exceeding 0.95. Silicos-IT LogP shows a weaker correlation with the other models, with R coefficients ranging from 0.88 to 0.90. Although the models used are consistent with each other in terms of relative lipophilicity parameters for individual groups of complexes, it should be noted that the XLOGP3 and WLOGP models yield results that are almost two orders of magnitude higher than those from the MLOGP, Silicos-IT LogP, and Consensus LogP models. To shed light on this issue, the theoretical lipophilicity parameters for complex 195 were compared with the experimental logP value reported by Guarra et al. [71]. Unfortunately, the logP value of 1.2, established using the shake-flask method, does not correspond to the values provided by the XLOGP3 (8.47), WLOGP (7.68), MLOGP (5.72), Silicos-IT LogP (4.57), and Consensus LogP (5.29) methodologies (Figure 11).
Figure 10. Distribution of theoretical lipophilicity parameters, i.e., XLOGP3, WLOGP, MLOGP, Silicos-IT LogP, and Consensus LogP, within groups A + S, A, and S.
Figure 10. Distribution of theoretical lipophilicity parameters, i.e., XLOGP3, WLOGP, MLOGP, Silicos-IT LogP, and Consensus LogP, within groups A + S, A, and S.
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Figure 11. Experimental and theoretical lipophilicity parameters for complex 195.
Figure 11. Experimental and theoretical lipophilicity parameters for complex 195.
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3.9. Solubility

The water solubility models generally predict that the more active complexes (S) are more soluble than their less active counterparts (A), which aligns with the results from the TPSA and lipophilicity calculations presented above (Figure 12). However, the models used show significant differences. After excluding outliers (i.e., compounds 1 and 5), the ESOL model correlates with Ali data, with a coefficient of R = 0.822. In contrast, the Silicos-IT model shows lower correlations with Ali and ESOL, with R values of 0.49 and 0.63, respectively. Additionally, the models differ by an order of magnitude when comparing the predicted values. For instance, the solubility of complex 2 is predicted to be 111, 5.43, and 40.4 mg/mL according to the Ali, ESOL, and Silicos-IT models, respectively. Similarly, the solubility of complex 123 is predicted to be 3.19, 0.133, and 1.23 mg/mL by these models. Experimentally, the solubilities of complexes 1 and 5 have been established as 11 mg/mL and 123 mg/mL, respectively [33]. Theoretical assessments predicted solubilities of 98.5, 4.02, and 89.4 mg/mL for complex 1, and 208, 11.2, and 145 mg/mL for complex 5. These observations suggest that none of the models used can be considered reliable for predicting the solubility of the tested complexes.
Figure 12. Distribution of theoretical solubility parameters, i.e., ESOL, Silicos−IT, and Ali, within groups A + S, A, and S.
Figure 12. Distribution of theoretical solubility parameters, i.e., ESOL, Silicos−IT, and Ali, within groups A + S, A, and S.
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3.10. Skin Permeability Coefficient (Kp)

The average and mean values of the skin permeability coefficient (Kp) calculated for the A + S, A, and S groups are similar (Figure 13). Surprisingly, the results indicate that the enhanced biological activity of the superior S complexes is not linked in silico to increased skin penetration.
Figure 13. Distribution of the skin permeability coefficient (Kp) within the groups A + S, A, and S.
Figure 13. Distribution of the skin permeability coefficient (Kp) within the groups A + S, A, and S.
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3.11. Blood–Brain Barrier (BBB) Permeation

The BOILED-Egg diagrams for complexes A and S are presented in Figure 14. This type of analysis allows for intuitive evaluation of passive gastrointestinal absorption (HIA) and brain penetration (BBB) of molecules in function of lipophilicity (WLOGP) and polar surface area (TPSA). Additionally, the points colored in blue are predicted to be actively effluxed by P-gp (PGP+) and those in red are predicted as a non-substrate of P-gp (PGP−). A first glance at both graphs indicates that the more biologically active complexes are characterized by lower lipophilicity and a larger polar surface. To this group belong the 4-nitrobenzyl-substituted NHC–silver acetate complexes 26, 2831, the methylated caffeine-based silver acetate complexes 1, 5, and 24, dimethyl-imidazolium-derived complex 2, as well as the cyanuric chloride-derived NHC–silver chloride complex 123. The remaining compounds in group S do not differ in this model from the less active complexes in group A. It should be noted that complexes 32 and 165168 from cohort A, as well as compounds 150, 152, 169, 170 and 173 from group S, were not included in the charts because their predicted lipophilicity parameters exceeded 8.
Figure 14. The BOILED−Egg diagram for complexes A (top) and B (bottom).
Figure 14. The BOILED−Egg diagram for complexes A (top) and B (bottom).
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3.12. Stability vs. LUMO Density

Even at the first glance, the highly active antibacterial NHC-complexes 1, 2 and 5 substantially differ in terms of their lipophilic profiles—the parameters that condition the pharmacokinetics and pharmacodynamics of drugs. The missing puzzle in the structure–activity relationship is the stability of the NHC–silver complexes, which to a greater extent than lipophilicity, conditions antibacterial potency. Hence, the degradation of silver-NHC complexes arises from hydrolysis or interaction with chloride ions present in the bloodstream, resulting in silver chloride precipitation. Wiley J. Youngs and coworkers demonstrated that the caffeine complex, despite being fairly hydrophilic, proves remarkable stability in water, compared to dimethylimidazole counterparts (Figure 15) [26]. To illustrate the problem, we have performed quantum chemical calculations using density functional theory (EDF2/6-31G*) theoretical approach [70]. The obtained results clearly indicate that stabilities of the studied complexes correlate with the corresponding LUMO absolute values mapped on the isodensity surfaces. In other words, the stable complexes feature smaller unoccupied orbitals, which are involved in the electrophilic hydrolysis (by OH- or Cl- ions) of C Ag bonds. Since all four compounds proved significant antibacterial potencies in vitro, the impact of their lipophilicity parameters within this context should be considered unknown. This statement can be further supported by the fact that introduction of hydroxyalkyl groups greatly (5, 53, 55) enhances the water solubilities of the resulting silver carbene complexes and retains single digit μg/mL MICs against CF pathogens tested in vitro [52,53,72,73].
Figure 15. Stability and lipophilicity of NHC–silver complexes (top) and the LUMO absolute values [square root of e/au3] mapped on the electron isodensity surface (0.002 e/au3) (bottom). The numbers at the bottom pertain to the maximum values on the maps; yellow to blue colors represent corresponding values above 0.
Figure 15. Stability and lipophilicity of NHC–silver complexes (top) and the LUMO absolute values [square root of e/au3] mapped on the electron isodensity surface (0.002 e/au3) (bottom). The numbers at the bottom pertain to the maximum values on the maps; yellow to blue colors represent corresponding values above 0.
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4. Conclusions

We have demonstrated that antibacterial NHC–silver acetates and halides complexes represent an extremely chemically diverse group of compounds. However, the easily accessible pharmacokinetic parameters provided by the SwissADME platform cannot be directly used to explain the biological activity of a given silver complex. Since the biological activity of Ag-NHC complexes is primarily determined by the presence of silver ions, the theory proposed by some authors that lipophilicity enhances antibacterial activity is not supported by our findings. In our view, bulky and lipophilic N-substituents within any NHC system influence the strength of the carbon–silver bond, its susceptibility to hydrolysis, and the electronic structure of the carbon atom [74]. Consequently, lipophilicity and solubility parameters for entire complexes provided by theoretical models like SwissADME are secondary factors and should not be used in isolation to compare different NHC systems. Stability and susceptibility to hydrolysis of NHC–silver complexes can be linked to the LUMO absolute values mapped on the electron isodensity surface as evidenced by DFT calculations. This finding may suggest a direction for future research. Finally, the optimal lipophilicity value for antibacterial Ag-NHC complexes should be considered unknown.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/app14198865/s1, Table S1: Full SwissAdme data for complexes 1194.

Author Contributions

Conceptualization, J.S.; methodology, J.S.; investigation, J.S. and Ł.P.; resources, J.S. and Ł.P.; data curation, J.S. and J.F.; writing—original draft preparation, J.S.; writing—review and editing, J.S.; visualization, J.S. and J.F.; supervision, J.S.; project administration, J.S. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.

Acknowledgments

This work was not supported by NCN.

Conflicts of Interest

The authors declare no conflicts of interest.

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Sączewski, J.; Popenda, Ł.; Fedorowicz, J. In Silico SwissADME Analysis of Antibacterial NHC–Silver Acetates and Halides Complexes. Appl. Sci. 2024, 14, 8865. https://doi.org/10.3390/app14198865

AMA Style

Sączewski J, Popenda Ł, Fedorowicz J. In Silico SwissADME Analysis of Antibacterial NHC–Silver Acetates and Halides Complexes. Applied Sciences. 2024; 14(19):8865. https://doi.org/10.3390/app14198865

Chicago/Turabian Style

Sączewski, Jarosław, Łukasz Popenda, and Joanna Fedorowicz. 2024. "In Silico SwissADME Analysis of Antibacterial NHC–Silver Acetates and Halides Complexes" Applied Sciences 14, no. 19: 8865. https://doi.org/10.3390/app14198865

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