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. 2022 Oct 18;7(43):38254–38268. doi: 10.1021/acsomega.2c05518

Chalcone Derivatives with a High Potential as Multifunctional Antioxidant Neuroprotectors

Adriana Pérez-González †, Romina Castañeda-Arriaga ‡, Eduardo Gabriel Guzmán-López ‡, Luis Felipe Hernández-Ayala ‡, Annia Galano ‡,*
PMCID: PMC9631883  PMID: 36340167

Abstract

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A systematic, rational search for chalcone derivatives with multifunctional behavior has been carried out, with the support of a computer-assisted protocol (CADMA-Chem). A total of 568 derivatives were constructed by incorporating functional groups into the chalcone structure. Selection scores were calculated from ADME properties, toxicity, and manufacturability descriptors. They were used to select a subset of molecules (23) with the best drug-like behavior. Reactivity indices were calculated for this subset. They were chosen to account for electron and hydrogen atom donating capabilities, which are key processes for antioxidant activity. The indexes showed that four chalcone derivatives (dCHA-279, dCHA-568, dCHA-553, and dCHA-283) are better electron and H donors than the parent molecule and some reference antioxidants (Trolox, ascorbic acid, and α-tocopherol). In addition, based on molecular docking, they are predicted to act as catechol-O-methyltransferase (COMT), acetylcholinesterase (AChE), and monoamine oxidase B (MAO-B) inhibitors. Therefore, these four molecules are proposed as promising candidates to act as multifunctional antioxidants with neuroprotective effects.

1. Introduction

Oxidative damage to DNA, lipids, and proteins, i.e., molecules of high biological relevance, is an important cause of many chronic diseases including cancer,1−4 arthritis, and cardiovascular5−9 and neurodegenerative diseases.10−17 However, these diseases are of multifactorial nature,18−39 which means that, albeit beneficial in this context, antioxidant capacity alone is not enough for fighting them. Moreover, such a nature imposes difficult challenges for developing efficient treatments. Thus, a new paradigm in the drug design has emerged: Finding single chemical entities with multiple biological activities.40−44

In recent years, the computational design of new molecules, in particular, those with potential use as medical drugs, has significantly increased. Although antioxidants are straightforward protectors against oxidative stress (OS), they may also exhibit other properties. Thus, the rational design of multifunctional antioxidants aimed at treating multifactorial diseases seems to be a logical pursuit. Since neuronal tissue is particularly sensitive to OS,45 the present work focuses in antioxidants with potential neuroprotective effects.

Neurodegeneration-related diseases are among the most severe pathologies currently afflicting humankind, and the trend is not encouraging. As the global population age increases, so does the number of individuals developing neurodegenerative conditions. Thus, the search for effective cures or preventive treatments is becoming progressively more urgent. Despite numerous studies devoted to these kinds of health disorders, their etiologies still remain ambiguous, and no effective cures have been found yet. However, important facts are known. Neurotransmitter deficiency, oxidative stress, metal dyshomeostasis, mitochondrial dysfunction, as well as protein aggregation, misfolding, and phosphorylation play key roles in neurodegeneration and lead to neuronal death.45 On the other hand, monoamine oxidase (MAO), acetylcholinesterase (AChE), and catechol-O-methyltransferase (COMT) inhibitors have beneficial effects on neurodegenerative disorders including Parkinson’s and Alzheimer’s diseases.44,46−52

Molecular docking tools are generally applied as a first approach in computational drug discovery. This strategy can be used to afford atomistic insights into the molecular recognition between a therapeutic target and promising molecules. However, a simplified computational approach can lead to questionable results and finding inactive drugs.53 To overcome these disadvantages, molecular docking must be accomplished by computational protocols that can take in account the physicochemical (log P, pKa, among others) and ADME properties to enhance the accuracy of the predictions.

Chalcone itself ((E)-1,3-diphenyl-2-propene-1-one, CHA, Scheme 1) can be considered as a multifunctional molecule. Some of its already known properties are anticancer,54−65 anti-inflammatory,66−69 anti-HIV,70 antidiabetic,71−73 antiproliferative,74 antimicrobial,75−78 antimalarial, and antioxidant.69,75−86 Therefore, it is not surprising that synthetic and naturally occurring chalcones have been studied and developed as pharmaceutically molecules (Table 1).

Scheme 1. Chalcone ((E)-1,3-diphenyl-2-propene-1-one, CHA).

Scheme 1

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Table 1. Some Versatile Chalcone Derivatives That Have Been Previously Synthesized.

1.

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In this work, a systematic, rational search for chalcone derivatives with multifunctional behavior has been carried out using the CADMA-Chem computational protocol. The multifunctionality looked for here consists of antioxidant activity through more than one way of action as well as neuroprotection. Drug-like behavior, toxicity, synthetic accessibility, electron and hydrogen atom donating capabilities, and the potential for inhibiting COMT, AChE, and monoamine oxidase B (MAO-B) enzymes were explored. Based on the obtained results, the most promising candidates are identified and proposed for further investigations.

2. Results and Discussion

2.1. Sampling the Search Space

The 568 chalcone derivatives investigated in this study are reported in Table S1, Supporting Information. They include 32 monosubstituted, 520 disubstituted, 15 trisubstituted, and 1 tetrasubstituted candidates. The latter is dCHA-568 (butein) and was included in the investigation because it is a naturally occurring molecule widely spread in the plant kingdom that has several health benefits (Table 2). It can be found in various genera including Dahlia, Butea, Searsia, and Coreopsis.87,88 The molecular design, performed in a systematic and rational way, led to 17 candidates that have been previously synthesized. Some of their properties have been previously evaluated. Their structures and already proven activities are reported in Table 2.

Table 2. Chalcone Derivatives Emerging in the Computational Design That Have Been Previously Synthesized and Investigated to Some Extent−.

2.1.

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The index used for sampling the search space was the selection score (SS). It accounts for several desirable aspects, including ADME properties that are in line with drug-like behavior, low toxicity, and easy manufacturability. Tables S2 and S3, Supporting Information show the individual values of all the properties estimated for the designed chalcone derivatives.

Based on the SS index, 23 molecules were selected as the most promising ones (Scheme 2) and were carried out to the following step of the investigation. The parent molecule was included for comparison purposes. Among the 568 investigated derivatives, 65 were found to have negative SS values, and for 21 it was not possible to calculate the selection score because the software used failed to predict their mutagenicity. These 86 candidates were eliminated from posterior searches. Most of the designed molecules (472 of them, 83% of the total) have SS values higher than the average of the reference set (Figure 1), which indicates that they all are expected to exhibit drug-like behavior; i.e., they are not expected to present bioavailability, absorption, or permeation issues. In addition, for three of them (dCHA-48, dCHA-148, and dCHA-333), the SS is higher than that of the unmodified chalcone. The high selection score of the parent molecule is a consequence of its very low value of synthetic availability; i.e. the unmodified chalcone is a molecule very easy to synthesize.

Scheme 2. Structure and SS Values of Chalcone and the Derivatives Selected for the Next Stage of the Investigation.

Scheme 2

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Figure 1.

Figure 1

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Selection score (SS) for the chalcone derivatives designed in this work. Horizontal lines mark the arithmetic mean of the reference set (red) and the score for the parent molecule (chalcone, green).

The values of SE,ADME2, SE,ADME8, SE,ADMET, and SE,ADMETSA were calculated for the 23 dCHA shown in Scheme 2, and they are reported in Table S4 and plotted in Figure 2. The average of SE,ADME2 = 0.83, while its minimum value is 0.23 (dCHA-568), and the maximum is 1.58 (unmodified chalcone). This elimination score includes only log P and molecular weight, and has the smallest contribution to the total deviation from the reference set. It has been previously used by other authors,95,96 who reported average values of SE,ADME2 equal to 1.5 and 1.2, for 152 and 1791 oral drugs, respectively.

Figure 2.

Figure 2

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Elimination score (SE) for the most promising chalcone derivatives, according to SS. Columns are divided to show the influence of the new contributions included in each score, with respect to the previous one.

The other six ADME properties, considered as a whole, deviates from the references to a larger extent, except for derivative dCHA-333 (SE,ADME8, Figure 2). In general, the toxicity contributions (SE,ADMET) are the largest ones, with the highest values corresponding to dCHA-39 > dCHA-148 > chalcone. On the other hand, the term associated with synthetic accessibility is of a similar magnitude, and smaller than the toxicity term, for all the investigated compounds.

To gain a better understanding of the deviation of every property, with respect to the reference set of molecules, a more detailed plot was built (Figure 3). Oral rat 50% lethal dose (LD50) is the descriptor deviating the most with respect to the average value of the reference set of molecules. The largest values correspond to dCHA-39, dCHA-148, dCHA-566, and unmodified chalcone, in that order. However, such deviations arise from molecules that are significantly less toxic to rats than the average value. In fact, almost all the designed derivatives were found to be less toxic than such an average. The only exceptions are dCHA-290, dCHA-293, and dCHA-540. This shows the importance of carefully analyzing the individual terms contained in the elimination scores. In this case, deviations in LD50 actually correspond to a desired behavior. Thus, all of the derivatives deviating the most were kept as potential candidates and carried to the next stage of the investigation.

Figure 3.

Figure 3

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Individual contributions to the elimination score (SE), for the most promising chalcone derivatives.

2.2. Evaluating Potential As Antioxidants

The pKa values estimated for the 23 chalcones derivatives selected in the sampling search are reported in Table 3. The molar fractions corresponding to each acid–base species, at physiological pH, are also reported in this table. The deprotonation routes proposed as the most likely ones are reported in Scheme S1, Supporting Information. The distribution diagrams, as a function of the pH, are shown in Scheme S2.

Table 3. Estimated pKa Values and Molar Fractions of the Protonated (Mfprot), Neutral (Mfneutral), Anionic (Mfanion), Dianionic (Mfdian), and Trianionic (Mftrian) Species of Chalcone Derivatives at pH = 7.4.

  pKa1 pKa2 pKa3 pKa4 Mfprot Mfneutral Mfanion Mfdian Mftrian
dCHA-3 8.44         0.916 0.084    
dCHA-39 7.15 11.74       0.360 0.640 <10–4  
dCHA-48 8.15 8.59       0.841 0.150 0.010  
dCHA-53 8.97 10.76       0.974 0.026 <10–4  
dCHA-148 3.58 3.77         <10–3 1.000  
dCHA-180 1.84 8.99       0.975 0.025    
dCHA-183 1.90 8.45       0.918 0.082    
dCHA-255 4.06 11.90       <10–3 1.000 <10–4  
dCHA-261 3.75 13.13       <10–3 1.000    
dCHA-279 5.83 11.07       0.026 0.974 <10–3  
dCHA-283 5.23 9.61       0.007 0.987 0.006  
dCHA-288 5.61 10.01       0.016 0.982 0.002  
dCHA-290 6.26 8.60       0.064 0.881 0.056  
dCHA-293 6.06 8.77       0.042 0.919 0.039  
dCHA-333 3.72 8.98       <10–3 0.974 0.026  
dCHA-432 2.46 3.82       <10–3 1.000    
dCHA-464 4.15         0.001 0.999    
dCHA-540 2.63 6.57         0.129 0.871  
dCHA-549 2.93 6.28         0.071 0.930  
dCHA-553 7.24 8.26 12.44     0.378 0.547 0.075 <10–6
dCHA-566 7.16 10.51 12.36     0.365 0.634 <10–3 <10–8
dCHA-567 7.27 8.54 12.12     0.409 0.551 0.040 <10–6
dCHA-568 7.34 8.69 11.59 12.81   0.453 0.520 0.027  
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At pH = 7.4, monoanions are the most abundant species for almost half of the studied compounds (dCHA-255, dCHA-261, dCHA-279, dCHA-283, dCHA-288, dCHA-290, dCHA-293, dCHA-333, dCHA-432, and dCHA-464), with populations ranging from 88% to almost 100%. The neutral species is the main one for dCHA-3, dCHA-48, dCHA-53, dCHA-180, and dCHA-183, with abundances from 84% to 98%. The dianions are the dominant species only for dCHA-148, dCHA-540, and dCHA-549 (87% to ∼100%). On the other hand, five of the chalcone derivatives are predicted to have significant populations of both neutral and monoanionic species. They are dCHA-39 (36%/64%), dCHA-553 (38%/55%), dCHA-566 (37%/63%), dCHA-567 (41%/55%), and dCHA-568 (45%/52%). This feature is particularly appealing because while the neutral species is likely to passively cross biological membranes, phenolate anions usually account for most of the antioxidant activity of phenols.

In addition, chalcone derivatives dCHA-283, dCHA-288, dCHA-290, dCHA-293, and dCHA-333 are enol compounds. Thus, they present keto–enol equilibria, both as neutral and monoanions. These equilibria, as well as the relative abundance of keto and enol species, according to their Maxwell–Boltzmann distribution, are provided in Scheme S3, Supporting Information.

Reactivity indexes: first ionization energy (IE), electronic affinity (EA), electrophilicity (ω), electrodonating and electroaccepting powers (ω– and ω+), chemical potential (μ), hardness (η), and the lowest bond dissociation energy (l-BDE) of the acid–base species with Mf > 10–2 are reported in Table S5, Supporting Information. The whole set of BDEs is gathered in Table S6, where the chemical nature of the H donating sites is clarified for each chalcone derivative.

The electron and hydrogen donating ability map for antioxidants (eH-DAMA) for the investigated compounds is shown in Figure 4. Only acid–base species with Mf > 10–2 have been included in the map. For comparison purposes, the eH-DAMA also includes Trolox, ascorbic acid, α-tocopherol, and the oxidant H2O2/•OOH pair. The latter is used to represent peroxyl radicals in general which are among the ROS that can be efficiently scavenged by antioxidants to retard OS.97,98 The parent molecule, i.e., unmodified chalcone, has not been included in this map because this molecule does not have H-donor groups. The map comprises two descriptors representing the most common reaction mechanisms of antioxidants: formal H atom transfers (f-HAT) and single electron transfer mechanism (SET). BDE accounts for the former, while for the latter any reactivity index directed related with electron donor capabilities can be used. Ionization energies have been chosen here. This choice was made searching for simplicity and considering the chemical nature of the SET mechanism as well as free radical targets in biological systems that are not highly reactive (peroxyl radicals, for example). Therefore, the associated kinetics is not expected to be located in the inverted region of the Marcus parabola.

Figure 4.

Figure 4

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Electron and hydrogen donating ability map for antioxidants (eH-DAMA).

Based on how the eH-DAMA map is constructed, molecules located to the left of the target oxidant (the H2O2/•OOH) can deactivate it by H donation, while those located below it can do the same by electron donation. Accordingly, molecules located at the bottom-left are expected to be the best antioxidants since they can exert their protection through both chemical routes.

According to the eH-DAMA map, the most promising candidates, among the investigated compounds, are dCHA-279, dCHA-568, dCHA-553, and dCHA-283. They are all expected to surpass Trolox, ascorbic acid, and α-tocopherol as free radical scavengers. dCHA-279 is expected to be particularly efficient as a H donor, dCHA-553 as an electron donor, and dCHA-568 as both. In addition, based on their structures, dCHA-283, dCHA-553, and dCHA-568 should also be efficient as metal chelators. Further investigations are still needed in this regard as well as the potential risk of dCHA-279 and dCHA-283, which have a thiol group. Thus, it would be relevant to study if they might promote disulfide bridges.

2.3. Evaluating Potential As Neuroprotectors

Docking experiments were performed to investigate the potential neuroprotective activity of chalcone analogs. For this purpose, in the docking protocol, ΔGU has been used as a scoring function to rank the affinity of chalcone derivatives with the enzymes. Fortunately, and as discussed below, this methodology was able to reproduce some experimental findings. Although this approach yielded reliable simulations, it is important to note that other scoring functions are available99 to construct training/test sets appropriate for predicting protein–ligand binding modes. The studied proteins have an important role in the development of Parkinson’s and Alzheimer’s diseases. Consequently, inhibition by chalcones might indicate an important therapeutic effect against those conditions.

2.3.1. Cathecol O-Methyltransferase (COMT)

COMT protein is responsible of a dopamine degradation. For that purpose, it uses a Mg2+ cofactor. The residues Asp141, Lys144, Asp169, Asn179, Glu199, and the S-adenosylmethionine (SAM) fragment complete the catalytic site of the enzyme.100 Docking simulations revealed that the chalcone derivatives can bind to Mg2+ through metal–donor interactions. Additionally, the chalcone–COMT complexes are stabilized by several noncovalent bonds. For instance, Figure 5 shows the dCHA-553-COMT complex, in which the derivative forms two Mg–O bonds. The Pearson hardness of both atoms can explain this high affinity. Several conventional H-bonds with key residues (Lys144, Asn169, and Asp170) also were found as well as some alkyl and π-bonds (S−π and π–π). All of them contribute to adduct stabilization.

Figure 5.

Figure 5

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Complex dCHA-553-COMT (right) and close-up of the binding pocket (left). Several covalent and noncovalent interactions stabilize the complex. Dashed lines represent the interactions (gray = covalent bonds, green = H-bonds, violet = S−π and π–alkyl, and purple = π–π stacked)

The large number of interactions in dCHA-553 is reflected in its ΔGU value (Table 4), which is higher than that estimated for tolcapone (a well-known COMT inhibitor). The results in Table 4 suggest that dCHA-553 and dCHA-568 derivatives have better inhibition effects than the reference drug. In addition, the binding of all derivatives with COMT is predicted to be stronger than that of the unmodified chalcone. It should be noted that the calculated Ki for tolcapone correlates well with the experimental half-maximal inhibitory concentration (IC50).100

Table 4. Binding Energy (ΔGU), Estimated Inhibition Constant (Ki), and the Residues That Participate in the Best Docked Pose in the Chalcone–COMT Complexes.
Compound ΔGU (kcal/mol) Ki (μM) Interacting residues
chalcone –6.06 35.73 Trp38, Trp143, Pro174, Leu198
dCHA-279 –7.12 5.96 Mg2+(A), Trp38, Met40, Asn170, Pro174, Gln199, and Met201
dCHA-283 –6.25 25.92 Trp138, Trp143, Lys144, Pro74, and Leu198
dCHA-553 –8.64 0.46 Mg2+(B), Met40, Trp143, Lys144, Asn170, Pro174, and Gln199.
dCHA-568 –8.23 0.91 Mg2+(B), Trp38, Met40, Asp141, Lys144, Pro174, and Glu199
dCHA-568_N –7.19 5.29 Mg2+(A), Trp38, Met40, Lys144, Asn170, Pro174, and Gln199
tolcaponea –8.07 1.35 Mg2+(B), Met40, Trp143, Lys144, Asn170, Pro174, and Leu198
dopamine –6.11 32.84 Mg2+(A), Met40, Asp141, Lys144, Asp169, Asn170, and Pro174
levodopa –7.09 6.27 Mg2+(A), Met40, Asp141, Trp143 Lys144, Asn170, Pro174, and Gln199
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a

IC50 = 0.930 μM.100

The studied chalcone derivatives can bind to Mg2+ with one (A) or two (B) coordination bonds. This significantly affects the binding energy, the (B) conformation being the most stabilized mode of union. Consequently, the B complexes have higher ΔGU values. On the other hand, all the investigated derivatives were found to have a stronger interaction with COMT than dopamine and levodopa. Therefore, these compounds can potentially protect them both from COMT-induced degradation and prevent their depletion. In addition, if these compounds are capable of blocking COMT function, they would preserve levodopa (and its biological functions) for extended times. This would allow lower dosing and, consequently, its undesirable side effects.

2.3.2. Monoamino Oxidase B (MAO-B)

MAO-B enzyme catalyzes the oxidative deamination of alkylamines. Malfunction of MAO-B has been associated with depression conditions and attention deficit hyperactivity disorder (ADHD).101 In the catalytic site, the enzyme contains several residues including Lys296, Trp388, Tyr398, and Tyr435. A FAD cofactor covalently bonded completes the active MAO-B region.

Among the studied chalcone derivatives, dCHA-553 is the one that presents the strongest interaction mode with the enzyme. The best docked pose reveals that the compound is bonded to the protein pocket mainly through π-forces. Even though the derivative has several donors and acceptor H-bonds, only one dipole interaction was observed. The carbonyl group acts as an acceptor in the O···H bond with Tyr435. Two π-bonds with aromatic rings of the FAD fragment were detected in this adduct. Figure 6 shows a complete scheme of the stabilizing interactions in the dCHA-553–MAOB complex.

Figure 6.

Figure 6

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Stabilizing interactions in the dCHA-553–MAOB complex. Dotted lines represent the intermolecular forces that stabilize the adduct. Green: H-bonds, violet: π-alkyl, pink: π–π T shaped, purple: σ–π, and yellow: S−π.

Although the chalcone derivatives present a lower interaction energy than the MAO-B inhibitor Safinamide, its estimated inhibition constant values are of a nanomolar order (Table 5). The docking protocol can reproduce the experimental results obtained with the enzyme and the inhibitor (RMSD = 1.9 Å). Surprisingly, the chalcones with major stabilization promote FAD hydrophobic interactions. For instance, in the complex that has the lowest value of ΔGU (dCHA-279-MAOB), the interaction with the cofactor is missing. All derivatives are predicted as better MAOB inhibitors than the parent molecule. All of them, including the unmodified chalcone, are predicted to interact with MAO-B in a stronger way than the natural substrates of this enzyme, i.e., than dopamine and phenylethylamine. Although the role of the MAO-B in the Parkinson’s disease it is not clear, it is well-known that its inhibitors help nerve cells make better use of the neurotransmitters. In this sense, the docking results suggest that the studied compounds can inhibit the action of this enzyme, and hence, they may exhibit important neuroprotective effects.

Table 5. Binding Energy (ΔGU), Estimated Inhibition Constant (Ki), and the Residues That Participate in the Best Docked Pose in the Chalcone–MAOB Complexes.
Compound ΔGU (kcal/mol) Ki (μM) Interacting residues
chalcone –6.88 8.94 Leu171, Cys172, Ile199, Tyr326, Tyr398, Tyr435
dCHA-279 –8.03 1.28 Pro102, Leu171, Cys172, Gln206, ile316, Tyr326
dCHA-283 –8.94 0.28 Leu171, Cys172, Ile198, Ile199, Gln206, Tyr398, Tyr435, FAD
dCHA-553 –9.12 0.20 Leu171, Cys172, Ile199, Tyr336, Phe343, Tyr398, Tyr435, FAD
dCHA-568 –8.82 0.34 Leu171, Cys172, Ile199, Tyr326, Tyr435, FAD
dCHA-568_N –8.68 0.43 Leu171, Ile199, Tyr398, Tyr326, Tyr435, FAD
safinamide –9.75 0.07a Leu171, Cys172, Ile199, Gln206, Ile316, Tyr326, Tyr398, FAD
dopamine –5.54 86.02 Leu171, Cys172, Leu199, Gln206, FAD
phenylethylamine –5.81 54.51 Leu171, Phe343, Tyr398, FAD
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a

IC50 = 0.45 μM,102 RMSD = 1.9 Å.

2.3.3. Acetylcholinoesterase (AChE)

AChE is the enzyme responsible for the hydrolysis of acetylcholine and other choline neurotransmitters.103 In Alzheimer’s disease, it is associated with the loss of cholinergic neurons in some parts of brain. For this reason, AChE inhibitors slow down the degradation of acetylcholine in the synaptic clefts and, consequently, enhance cholinergic neurotransmission.103 The active site in AChE involves two principal regions: the peripheral site and the catalytic site. The peripheral site contains two residues Asp74 and Trp386. The catalytic region comprises the amino acids Trp86, Ser203, Glu334 and Hys447.104

The docking of chalcone derivatives indicate that these molecules can form stable adducts with this protein. Several of the residues that participate in this stabilization are key amino acids for esterase activity. Figure 7 shows the intermolecular forces involved in the formation of the dCHA-568–AChE complex. In its best docked pose, hydrogen interactions predominate. The oxygen atoms present in this derivative form H-bonds with Asp74, Tyr124, Tyr133, Tyr337, and Tyr341. Nonconventional C···H bonds are formed with Trp86, Ser125, Ser203, and Glu202. Two of them are fragments of the catalytic enzyme site. Finally, two π-stacking interactions of the aromatic moiety in the dCHA-568 with Trp86 and Tyr341 were detected. Theis interaction path resembles the one presented in donopezil cocrystallized with AChE.104 This compound presents a potent inhibition activity (Ki,obs = 2.9 nM), which was simulated with our computational protocol with remarkable accuracy (Ki,calc = 2.9 nM, RMSD = 0.4 Å).

Figure 7.

Figure 7

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Stabilizing interactions in the dCHA-568–AChE complex. Green dotted lines: conventional O···H bonds, gray: nonconventional C···H bonds, and violet: π–π stacking interactions.

According to the results (Table 6), all of the chalcone derivatives present high stabilization energy, i.e., high inhibition potential of this enzyme. All of them have an estimated inhibition constant in the low nanomolar range. Nevertheless, donopezil shows the best estimated inhibition effect. The chalcone derivatives are capable for interacting with the main amino acids in the peripheral and catalytic sites. Additionally, they show a higher binding energy than the natural substrate (acetylcholine) and the parent molecule. Thus, the obtained results strongly suggest that the chalcone derivatives investigated here might function as acetylcholine protectors, inhibiting the natural action of AChE.

Table 6. Binding Energy (ΔGU), Estimated Inhibition Constant (Ki), and the Residues That Participate in the Best Docked Pose in the Chalcone–AChE Complexes.
Compound ΔGU (kcal/mol) Ki (μM) Interacting residues
chalcone –5.80 55.44 Tyr124, Glu202, Tyr341, His447
dCHA-279 –9.20 0.18 Trp86, Ser125, Glu202, Tyr337, Tyr341
dCHA-283 –9.56 0.10 Asp74, Trp86, Tyr124, Tyr337, Try341
dCHA-553 –9.64 0.08 Arp86, Tyr124, Trp286, Phe295, Tyr337, Try341
dCHA-568 –10.22 0.03 Asp74, Trp86, Tyr124, Ser125, Tyr133, Glu202, Ser203, Tyr337, Tyr341
dCHA-568_N –9.65 0.08 Tyr124, Val294, Tyr337, Phe338, Tyr341, Hys447
donopezila –11.72 2.5 × 10–3 Tyr72, Trp86, Tyr124, Trp286, Leu289, Phe338, Tyr337, Tyr341
acetylcholine –4.99 217.89 Trp86, Gly121, Tyr124, Tyr133, Ser203
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a

IC50 = 2.9 nM.104 RMSD = 0.4 Å.

3. Conclusions

Neurodegenerative diseases are among the most severe pathologies currently afflicting humankind. Therefore, searching for medical drugs for these health disorders is of high importance. Their multifactorial nature is one of the reasons why finding a cure for them is a tremendous challenge. On the other hand, oxidative stress has been identified as a key factor in the onset and development of these diseases.

In this work, a systematic rational search for chalcone derivatives with multifunctional behavior has been carried out, with the support of a computer-assisted protocol (CADMA-Chem). The searched multifunctionally consists of diverse antioxidant ways of action and neuroprotection, mainly related to Alzheimer’s and Parkinson’s diseases.

The incorporation of up to three functional groups in the chalcone structure (−OH, −NH2, −SH, and −COOH) led to the construction of 567 chalcones derivatives. The 568 candidate is butein, which has four hydroxyl groups in its structure. It was included in the study because it is a naturally occurring molecule widely spread in the plant kingdom that has several health benefits.

Selection and elimination scores were calculated from ADME properties, toxicity, and manufacturability descriptors. They were used to select a subset of molecules (23) with the best drug-like behavior. Some of them have already been synthesized.

Reactivity indices that account for electron and hydrogen atom donating capabilities were calculated for this subset. These indexes account for f-HAT and SET mechanisms, which are key processes for antioxidant activity. They showed that four chalcone derivatives (dCHA-279, dCHA-568, dCHA-553, and dCHA-283) are better electron and H donors than the parent molecule and the reference antioxidants Trolox, ascorbic acid, and α-tocopherol.

In addition, based on molecular docking, these four molecules are predicted to act as COMT, AChE, and MAOB inhibitors. Therefore, they are proposed as promising candidates to act as multifunctional antioxidants with neuroprotective effects.

4. Methodology

The methodology that was used in the present work is in line with the CADMA-Chem (Computer-Assisted Design of Multifunctional Antioxidants, based on chemical properties) general protocol. It has been successfully used for the design of other multifunctional antioxidants.105,106 The main aspects of such a strategy are described next.

4.1. Building the Candidates

The candidates were built by mild modification of the chalcone framework, specifically, by incorporating functional groups in sites R1 to R12 (Scheme 3). The functionalization included up to three of the following groups: −OH, −NH2, −SH, and −COOH. They are expected to influence solubility, acid base behavior, free radical scavenging activity, and metal chelating capability. Thus, this strategy can be considered as fragment-based, with meaningful reduction, since the used fragments commonly occur in drug molecules.107

Scheme 3. Sites Considered to Obtained Chalcone Derivatives.

Scheme 3

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4.2. Drug-like Behavior

Absorption, distribution, metabolism, and excretion (ADME) properties were explored for all of the designed chalcone derivatives (dCHA). The physicochemical descriptors relevant to evaluate these properties are

  • Number of donors in H-bond interactions (HBD),

  • Number of acceptors in H-bond interactions (HBA),

  • Molecular weight (MW),

  • Octanol/water partition coefficient (log P),

  • Molar refractivity (MR),

  • Number of non-hydrogen atoms (AtX),

  • Number of rotatable bonds (RB) and

  • Polar surface area (PSA).

They were estimated using Molinspiration Property Calculation Service108 and DruLiTo software.109 Potential bioavailability, absorption, and permeation issues were then evaluated following the Lipinski’s rule of five,110 the Ghose’s rule,111 and Veber’s criteria.112

4.3. Toxicity

It is obvious that medical drugs should not be toxic for humans.113 The potential toxicity of the built chalcone derivatives was evaluated using Ames mutagenicity (M) and the oral rat 50% lethal dose (LD50) descriptor. They were calculated with the consensus method as implemented in the Toxicity Estimation Software Tool (T.E.S.T.), version 4.1, which is based on quantitative structure–activity relationships (QSAR).114

4.4. Synthetic Accessibility

Although several chalcone derivatives have been previously synthesized, it is always important to consider manufacturability when designing new chemical species. Synthetic accessibility (SA) was estimated with the SYLVIA-XT 1.4 program (Molecular Networks, Erlangen, Germany).115,116 The program provides a value between 1 and 10. Higher values mean more difficult synthesis.

4.5. Selection Score (SS)

Reformulating multiobjective problems into a single-objective one is an important step in the design of medical drugs.107 The scoring function is usually constructed as a sum of terms, i.e., one per each individual objective. The selection score used in this work, SS, includes eight terms related to ADME properties, two for toxicity and one for manufacturability (Appendix S1, Supporting Information). It was formulated in such a way that higher SS values indicate better drug-like behavior, lower toxicity, and easier manufacturability.

4.6. Elimination Score (SE)

Because of the way in which SS is constructed, it might mask particular failures for any of the investigated properties. Thus, an elimination score was used to check such a possibility. It includes a term for each property, expressed in such a way that allows identifying deviations from a reference set of molecules. The set used here comprises medical drugs with some neuroprotective effects. It is provided in Table S7, Supporting Information. Their properties are reported in Tables S8 and S9, and the formulation of SE is detailed in Appendix S2, Supporting Information. It is important to note that deviations should be carefully analyzed. Larger deviations can be both desirable or undesirable since the SE values may correspond to better or worse performance than the average of the reference drugs.

4.7. Electronic Calculations

Gaussian 09 package of programs117 was used geometry optimizations and frequency calculations. They were carried out using the density functional theory (DFT). The used level of theory comprises the M05-2X functional,118 the 6-311+G(d,p) basis set, and the SMD119 solvation model (with water as solvent). M05-2X is a global hybrid exchange-correlation GGA functional with wide applicability118 and has been recommended for calculating reaction energies involving free radicals.120 It has been widely used for estimating pKa values, bonding dissociation energies, and free radical scavenging activity of several antioxidant molecules.121−132

4.8. Reactivity Indexes

Ionization energies (IE), electron affinities (EA), bond dissociation energies (BDE), electrophilicity (ω), electron-donating and electron-accepting powers (ω– and ω+), chemical potential (μ), and chemical hardness (η) were estimated to analyze the chemical behavior of the designed dCHA (Table S10). IE and EA values were calculated in the framework of the electron propagator theory (EPT).133,134 This approach produces values similar to those derived from experimental strategies. The partial third-order quasiparticle theory (P3)135 was used since it has been reported to have lower mean errors, compared to other methods.136 For the EPT approximations (including P3) to be valid, the values of the pole strength (PS) should be larger than 0.80–0.85137,138 (Table S11).

All of the sites that can act as H donors were considered in the BDE calculations. However, the eH-DAMA map is constructed with the lowest value for each molecule.

4.9. Estimation of pKa Values

The acid constants (pKa) were calculated for the designed chalcone derivatives using the fitted parameters approach (FPA):107,130,132pKa = m ΔGBA + C0.

In this equation, ΔGBA represents the difference in Gibbs energy between the conjugated base and the corresponding acid. The m and C0 parameters are currently available, at numerous levels of theory, for phenols, amines, carboxylic acids, and thiols.130,132 The ones used here are reported in Table S12.

It has been demonstrated that the pKa values calculated with the FPA deviate from experiments by less 0.5 pKa units, in terms of mean unsigned errors.130,132

4.10. Molecular Docking

The crystal structures of catechol-O-methyl transferase (COMT, PDB ID: 4PYL(139)), monoamine oxidase B (MAO-B, PDB ID: 2V5Z),102 and acetylcholinesterase (AChE, PDB ID: 4EY7)104 cocrystallized with tolcapone, safinamide and donepezil, respectively, were obtained from the Protein Data Bank. All structures were modeled considering pH = 7.4. Prior to docking experiments, enzymes were prepared as follows. Monomers, water molecules, noninterest species (DMSO, Cl–, ethylene glycol, etc.), and inhibitors were removed. Then, polar hydrogens and Kollman charges were added using Chimera 1.16 software.140 The optimized structures of all ligands were saved to .mol2 files; atomic charges estimated by the DFT protocol (M05-2X/6-311+G(d,p)) were added manually. The docking studies were carried out using AutoDock Vina software.141 A Lamarckian genetic algorithm study was performed inside the protein–ligand complexes centered at x: −10.49, y: 40.60, z: 61.64 and a grid size of 20 × 20 × 20 Å3 for COMT, x: 53.81, y: 156.34, z: 28.14 and a grid size of 13 × 13 × 13 Å3 for MAO-B and x: −14.00, y: −43.64, z: 27.10 and a grid size of 15 × 13 × 13 Å3 for AChE. 150 individual steps in a population with 2.5 × 104 evaluations resulted in 10 docked poses. For the most stable conformation, docking scores (free binding energy (ΔGU)) are reported. Inhibition constant values were calculated as Ki = e – (ΔG/RT). Finally, the best docked conformation was analyzed with the Discovery Studio 2021 graphic interface.142 The RMSD values for the most stable conformation of tolcapone (COMT), safinamide (MAO-B) and donepezil (AChE) were 1.2, 0.9, and 1.4 Å respectively. The estimated docking scores correlated well with experimental observations. These findings confirm that the docking methodology is suitable to reproduce the binding mode of the studied compounds with the target biomolecules.

Acknowledgments

We gratefully acknowledge the Laboratorio de Visualización y Cómputo Paralelo at Universidad Autónoma Metropolitana-Iztapalapa. A.P.-G. acknowledges the Program of Investigadoras e Investigadores por México-CONACYT from CONACyT - UAMI (2015-2025), ID-Investigador 435.

Supporting Information Available

The Supporting Information is available free of charge at https://pubs.acs.org/doi/10.1021/acsomega.2c05518.

  • Deprotonation routes. Distribution diagrams. Enol and keto isomers. Chalcone derivatives designed in this work. Values of the ADME properties: octanol/water partition coefficient, polar surface area, number of non-hydrogen atoms, molecular weight, number of acceptors in H-bridge interactions, number of donors in H-bridge interactions, number of rotatable bonds, molar refractivity. Values of toxicity (LD50 and Ames mutagenicity), synthetic accessibility, selection and elimination scores. First ionization energy, electron affinity, electrophilicity, electrodonating and electroaccepting powers, chemical potential, chemical hardness, and bond dissociation energy. Reference set of molecules. Pole strength values for the EPT approximation (P3) used to calculate ionization energies and electron affinities. Expressions of the selection and elimination scores (PDF)

The authors declare no competing financial interest.

Supplementary Material

ao2c05518_si_001.pdf (1.5MB, pdf)

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