Alzheimer’s disease (AD) is a progressive neurodegenerative disorder that is growing rapidly among the elderly population around the world. Studies show that a lack of acetylcholine and butyrylcholine due to the overexpression of enzymes Acetylcholinesterase (AChE) and Butyrylcholinesterase (BChE) may lead to reduced communication between neuron cells. As a result, seeking novel inhibitors targeting these enzymes might be vital for future treatment of AD. Ondansetron is used to prevent nausea and vomiting caused by chemotherapy or radiation treatments, and is herein shown to be a potent inhibitor of cholinesterase. Comparison is made between Ondansetron and FDA-approved cholinesterase inhibitors Rivastigmine and Tacrine. Molecular docking demonstrates that interactions between the studied ligand and aromatic residues in the peripheral region of the active site are important in binding. Molecular dynamics simulations and binding pose metadynamics show that Ondansetron is highly potent against both enzymes, and far better than Rivastigmine. Inhibitor activities evaluated by in vitro studies confirm that the drug inhibits AChE and BChE by non-competitive and mixed inhibition, respectively, with IC50 values 33 µM (AChE) and 2.5 µM (BChE). Based on the findings, we propose that Ondansetron may have therapeutic applications in inhibiting cholinesterase, especially for BChE.
Article
In silico and in vitro Studies Confirm Ondansetron as a Novel Acetylcholinesterase and Butyrylcholinesterase Inhibitor
https://doi.org/10.21203/rs.3.rs-2105715/v1
This work is licensed under a CC BY 4.0 License
Journal Publication
published 12 Jan, 2023
You are reading this latest preprint version
Alzheimer’s disease
Acetylcholinesterase
Butyrylcholinesterase
Ondansetron
Drug
Molecular docking
Molecular dynamics simulations
Alzheimer’s disease (AD) is one of the most prevalent and irreversible neurodegenerative disorders affecting elderly, and is characterized by different types of gradual symptoms, from fluctuations in behavioral and social skills to memory loss. Eventually, severe aspects of disability to perform daily routine activities will appear in the patient [1]. More than 20 million individuals worldwide suffer from this dementia, a number that is expected to increase with the growth of the elderly population in the future [2, 3].
In spite of the fact that there is no clear understanding of AD pathogenesis, both genetic and environmental factors play an important role in AD development [4]. Multiple hypotheses, such as the amyloid-beta oligomer hypothesis, the cholinergic hypothesis, and the tau hypothesis have been presented, aiming to shed light AD progression and to identify new therapeutic approaches against AD [5].
According to the cholinergic hypothesis, the levels of Acetylcholine (ACh) and Butyrylcholine (BCh) which have neurotransmitter functions, are decreased in the brain regions of AD patients [6]. The absence of these neurotransmitters disrupt the conduction of electrical impulses through the nerve cells by reducing the cholinergic signaling and neurotransmission in the brain. As a result, severe cell damage and memory loss will occur due to the improper function of the brain [2, 7].
As seen in Fig. 1, the neurotransmitters are packed into vesicles and released into the synaptic cleft. The receptors accept the neurotransmitters in the postsynaptic neuron and rapidly cleave them into choline and acetate by two different types of enzymes [8]: Acetylcholinesterase (AChE) and Butyrylcholinesterase (BChE) which are widely distributed in the central nervous system [4, 9]. Choline will subsequently be recycled into a new neurotransmitter for the next message [8].
It has been observed that in the brains of patients with AD, overexpression of these enzymes and lack of ACh/BCh occurs and leads to reduced communication between neuron cells. As a result, approaches that inhibit AChE and/or BChE may hinder AD progression [6].
Many attempts have been made to discover more effective drugs for reducing the symptoms and slowing down the development of AD. The main FDA-approved cholinesterase inhibitors are Donepezil, Galantamine, Huperzine, Rivastigmine, and Tacrine [6, 10], summarized in Table 1 [11, 12].
The mechanism of action of Tacrine and Rivastigmine is to block the catalytic site of cholinesterase; of these, Rivastigmine furthermore induces a structural change in the binding pocket of the enzyme. Both are inhibitors of AChE as well as BChE, and easily cross the blood brain barrier [6, 13]. Donepezil blocks the catalytic site of AChE by forming a hydrogen bond and an aromatic interaction in the active site, but does not inhibit BChE [13].
|
Compound |
Mechanism of Inhibition |
Penetration through Blood-Brain Barrier |
2D structure |
|---|---|---|---|
|
Rivastigmine |
non-competitive |
Good |
|
|
Tacrine |
non-competitive |
Good |
|
|
Galantamine |
competitive |
Good |
|
|
Donepezil |
non-competitive |
Good |
|
|
Huperzine |
non-competitive |
Good |
|
Galantamine is an alkaloid compound isolated from the bulbs and flowers of Galanthus woronowii, belonging to the Amaryllidaceae family. It has lower toxicity and potency, and releases ACh by allosteric modulation of nicotinic acetylcholine receptors and inhibition of AChE. Huperzine is another alkaloid derived from the Chinese herb Huperzia serrata, and is a potent, reversible, selective inhibitor of AChE [2, 13].
However, the main controversial issue in stopping the development of AD is that the efficacy and performance of these agents are still not perfect [13]. Moreover, significant adverse effects, such as gastrointestinal disturbance, hepatotoxicity, syncope, sleep disturbances, and hypotension have also been documented. Therefore, searching for more potent and stronger agents with less adverse effects is still highly relevant for improved treatment of this disease [12].
Ondansetron (trade name Zofran) is a type 3 serotonin (5-hydroxytryptamine) receptor (5-HT3) antagonist (Fig. 2A), which is prescribed against vomiting and nausea caused by chemotherapy or radiation treatment [14, 15]. In comparison with other antiemetic drugs, the side effects are moderate, with high safety and efficacy. It is also used as an antiemetic drug for the first trimester of pregnancy, as well as in treatment against opioids, pruritus alcoholism, anxiety disorders, withdrawal syndrome, and gastrointestinal motility disorders [16, 17].
Some studies have explored the effect of Ondansetron on cholinesterase inhibition. For example, the combination of Ondansetron with Pyridostigmine as an organophosphorus pretreatment compound showed significant decrease in AChE activity in red blood cells of guinea pigs [18].
Another study demonstrated that the 5-HT3 receptor antagonist Ondansetron together with the FDA approved drugs Donepezil, potentiates the effects of AChE inhibition on the neuronal network oscillations in the rat dorsal hippocampus [19].
Another usage of Ondansetron as a therapeutic agent is in the treatment of psychiatric disorders which involve abnormalities of interoception and associated neural circuitry centered on the insula. Different doses of Ondansetron were applied for patients whereby it was finally suggested that 24 mg of this drug modulates the hyperactivity in these regions [20].
Regarding BChE inhibition, recent in silico and in vitro studies of a new series of Tacrine hybrids (spiro[chromeno[4,3-b] thieno[3,2-e] pyridine]-7-amines) showed that these to exerted BChE inhibitory activity, thereby proving to be promising candidates for evaluation in synthetic AD models [21].
Based on the above findings, it is clear that Ondansetron can affect the nerve system. As a result, this study aims to explore Ondansetron as an inhibitor of cholinesterases. As a first step, different in silico approaches were applied to investigate the interaction of Ondansetron with AChE and BChE. Comparison was made between this drug in both systems with the two FDA-approved cholinesterase inhibitors Rivastigmine and Tacrine (Table 1) to evaluate its efficacy. In vitro and kinetics studies of the inhibition strongly confirm the computational model. The calculation of Ki and IC50 values for these enzymes along with Arrhenius plots verify the drug binding and modes of action, and indicates that Ondansetron has stronger affinity towards BChE compared to AChE. Moreover, our study shows that Ondansetron is more potent than Rivastigmine in both enzymes but less so compared to Tacrine. For the latter, however, severe adverse effects including hepatotoxicity has led to its withdrawal in many countries [11]; thus new and safer drugs targeting cholinesterases is of significant importance.
2.1. In silico studies
2.1.1. Protein preparation
The three-dimensional crystal structures of the target enzymes AChE (PDB ID:4M0E) [22] and BChE (PDB ID:5DYW) [23] were downloaded from the Protein Data Bank (PDB) (http://www.rcsb.org). The proteins were prepared and refined using the Protein Preparation Wizard in Maestro (Schrödinger 2021–4, www.schrodinger.com). Bond orders were assigned during the preprocessing stage of the crystal structures, and after retrieving missing loops or side chains, all water molecules beyond 3.0 Å were deleted from the system. Protein hydrogen bond assignments were optimized and protonation states at pH 7 determined using PROPKA [24]. Finally, a restrained minimization with the OPLS4 force field [25] was performed with an RMSD convergence of heavy atoms of 3.0 Å.
2.1.2. Ligand preparation and docking
All ligand structures were downloaded from the PubChem database (www.pubchem.ncbi.nlm.nih.gov) and transferred into Maestro Schrödinger using LigPrep. The Epik module [26] was used for possible ionization states at physiological pH 7.0 +/- 2.0, and the OPLS4 force field was selected for the optimization. In order to perform the molecular docking and predict the interactions of the protein-ligand complexes, the Schrödinger Induced Fit Docking (IFD) methodology was used [27, 28]. All three inhibitors were docked towards the active sites of both AChE and BChE. The grid box for the docking was defined at the centroid of the binding site. During the initial docking procedure, the van der Waals scaling factor was set at 0.5 for both receptor and ligand. The Prime refinement step was set on side chains of residues within 5 Å of the ligand. No constraints were applied, and all remaining parameters were set to default.
2.1.3. Molecular dynamics (MD) simulations
The stabilities of the ligand-protein complexes were investigated through MD simulations for 200 ns, using the Desmond engine [29], in Schrödinger (Schrödinger 2021–4, www.schrodinger.com). The TIP3P force field was used to model water molecules [30]. Periodic boundary conditions were applied with a 10 Å water buffer around the protein in a cubic simulation box. Na+ and Cl− ions were added to neutralize the system and to give a final NaCl concentration of 150 mM. The OPLS4 force field was used for the proteins and ligands. The isothermal–isobaric (NPT) ensemble was used, and for adjusting the temperature and pressure of the systems, Nose Hoover thermostat [31], and the Martyna–Tobias–Klein barostat [32] were employed at 300 K and 1.01325 bar, respectively. All data analyses such as calculation of root mean square deviations (RMSD), root mean square fluctuation (RMSF), and protein-ligand contacts were obtained from the simulation interaction diagram (SID) program in Schrödinger 2021–4 (www.schrodinger.com).
2.1.4. Clustering
To obtain representative structures for the Binding Pose Metadynamics simulations (BPMD), Desmond Trajectory clustering in Maestro (Schrödinger 2021–4) was used, based on the obtained MD trajectories. The lowest energy structures from the most populated clusters were selected for the BPMD calculations.
2.1.5. Binding Pose Metadynamics (BPMD)
Binding Pose Metadynamics was performed using a set biasing force, to explore how stable ligands are in the binding pocket of the receptor. Weaker ligands will experience higher fluctuations with larger RMSD values in comparison with the more stably bound ones [33].
MD clustering based on the previous stage was done to remove any bad contacts from the initial structure. BPMD was implemented in Schrodinger Maestro version 2021-4. 10 independent metadynamics simulations of 10 ns each were performed for each system using the Root-Mean-Square Deviation (RMSD) of the ligand heavy atoms relative to their starting position as the Collective Variable (CV).
2.2. Experimental studies
2.2.1. Sample collection
A blood sample was obtained from a healthy volunteer. The protocol was approved by the Human Ethics Committee of Shahid Beheshti University (SBU) of Iran with the approval number of IR.SBU.REC.1401.050. All research was performed in accordance with relevant guidelines/regulations, and informed consent was obtained from all participants. The volunteer had no significant medical disorder, was not taking any previous medication for at least 30 days, had no history of alcohol, drug or cigarette abuse, and no recurrent or a past history of psychiatric illness. From the volunteer, 5 ml of blood was collected in vacutainer tubes.
2.2.2. Acetylcholinesterase preparation
AChE of the erythrocytes is bound to its plasma membrane. The blood sample was obtained using EDTA (1.5 mg/ ml) as anticoagulant agent. The sample was centrifuged at 3000 g for 5 minutes to precipitate the erythrocytes. The plasma and buffy coats were removed and discarded. Erythrocytes were washed twice with isotonic solution (NaCl 0.15 M), each time centrifuged to precipitate the intact cells. Erythrocytes were lysed by adding a hypotonic solution (NaCl 0.01 M). The solution was centrifuged at 15000 g for 20 minutes and the supernatant, which contained hemoglobin and other cell contents, was removed. The precipitate was washed with phosphate buffer 0.1 M, pH 7, and centrifuged at 15000 g for 20 minutes. The final precipitate (cell membrane) was collected for the AChE assay.
2.2.3. Butyrylcholinesterase preparation
BChE is found in blood serum. The blood sample was taken without adding any anticoagulants and was allowed to clot and then centrifuged at 3000 g for 5 minutes. The serum as a source of BChE was separated and stored at − 20 ℃ for further use.
2.2.4. Enzyme assay
Both AChE and BChE were assayed according to the Ellman colorimetric method [34]. In brief, the assay tube contained 1500 µl 0.1 M phosphate buffer at pH 7, 50 µl 50 mM acetylthiocholine, 50 µl 10 mM DTNB (5,5'-dithiobis-2-nitrobenzoic acid), and 20 µl of either blood serum as a source of BChE, or plasma membrane solution as a source of AChE. The assay was performed either in the absence or presence of Ondansetron in the concentration range 8–84 µM. The yellow reaction product TNB (5-thio-2-nitrobenzoic acid) was monitored at λ = 410 nm using a UV-Visible 1240 Shimadzu spectrophotometer and the activities of the enzymes calculated using the TNB extinction coefficient 13600 M− 1 cm− 1.
The effect of pH and different temperatures on the enzyme activity was also measured in the absence or presence of the drug with the final concentration of 21 µM. Lineweaver- Burk plots were used to define the kinetic parameters and inhibition types. Arrhenius plots were used to compare the activation energies of the enzymes in the presence or absence of the drug by calculating Vmax of the enzymes at different temperatures [35, 36].
The Lowry method [37] was used to measure the protein content of the samples, and casein was used for the standard curves.
3.1. In silico studies
3.1.1. Molecular docking study
The structure of AChE contains several subunits such as catalytic the triad located in the active site of a 20 Å deep narrow gorge containing several conserved amino acids. The most important part, called also the esteratic site, contains the three essential amino acids Ser203, His447, and Glu334 (Fig. 2B). A second part is the peripheral anionic site (PAS) which extends beyond Tyr337 at the catalytic/peripheral site interface to the entrance of the gorge, and contains several aromatic side chains (e.g., Tyr72, Trp86, Tyr124, Phe295, Tyr337 and Phe338). Kinetic and thermodynamic studies have shown that inhibitors can interact with either or both of the two binding regions [22]. In BChE, the catalytic triad located at the bottom of the 20 Å gorge is made up by Ser198, His438, and Glu325 (Fig. 2C) [38]. Four aromatic residues in the peripheral site, Trp82, Trp231, Tyr 332, and Phe329, plus Asp70 have been found to be are important for ligand binding[39, 40].
Alignment was made between the amino acid sequences (Figure S1), aiming to compare the active site residues in both enzymes. The structures were taken from the PDB database, AChE (PDB ID: 4M0E) and BChE (PDB ID:5DYW), and BChE was set as reference. The sequence identity and similarity between the two proteins are 55% and 70%, respectively, and the identity of the active site residues (Ser-Glu-His) is conserved (Figure S1).
| AChE | Docking score (kcal/mol) | MMGBSA (kcal/mol) |
|---|---|---|
| Ondansetron | -6.364 | -50.88 |
| Tacrine | -7.689 | -58.15 |
| Rivastigmine | -5.616 | -47.43 |
| BChE | ||
| Ondansetron | -6.954 | -54.14 |
| Tacrine | -7.171 | -58.44 |
| Rivastigmine | -5.282 | -22.96 |
Knowledge of the structures of the two enzymes is essential for compound selection and determination of binding modes of the cholinesterase inhibitors. The active sites of holoenzymes of AChE and BChE were identified (Figs. 2B and C) [41] followed by Induced Fit Docking (IFD) of the ligand set containing Ondansetron and the two FDA approved cholinesterase inhibitors Tacrine and Rivastigmine (Table 1). Table 2 shows the docking results and free energies of binding calculated using MMGBSA for both enzymes. The trends between the docking scores and MMGBSA energies are highly consistent, and indicate that Tacrine is the strongest binder in both systems, and Rivastigmine the weakest. Ondansetron seems to bind better to BChE, whereas Rivastigmine is more potent towards AChE.
Figure 3A shows the interactions of Ondansetron docked in the active site of AChE. Ondansetron forms π–π stacking interactions with the aromatic rings of Phe295, Phe338, and Trp86. In addition, Trp86 displays one π-cation interaction. Tyr124 and Tyr337 forms hydrogen bonding and π-cation interactions, respectively. There are no direct interactions between the ligand and residues of the catalytic site; however, as outlined above, some aromatic residues in the peripheral site located at the entry to the active gorge are responsible for binding many inhibitors and contribute to catalytic efficiency and activity. Among these, Trp86 and Tyr337 are the two major aromatic residues that are involved in binding to the ligands [22, 40]. Our docking results suggest a non-competitive inhibition for Ondansetron through allosteric site binding, resulting in decreasing efficacy of enzyme.
Non-competitive binding modes were also observed for Tacrine and Rivastigmine. Tacrine forms the same hydrogen bond with Tyr124 and π–π stacking interaction with Phe338. Two additional π–π stacking interactions with Phe338 and Phe295, were also observed for Rivastigmine.
In BChE (Fig. 3B), one hydrogen bond and one π-cation interaction are predicted to be formed between Ondanestron and one of the main catalytic residues, His438. The most significant residues to have interactions with ligands in the peripheral site of BChE are Trp82, Trp231, and Phe329 [40]. This is in accord with the current study, Trp82 forms π-cation interaction with Ondansetron, and this amino acid plus Trp231 and Phe329 form π–π stacking interactions with the aromatic rings of the inhibitor. The interaction with His438 in the binding pocket and the residues at the peripheral site would suggest that Ondansetron binds to BChE through mixed inhibition, i.e., binding the active site of the enzyme (competitive inhibition) or the enzyme-substrate complex (noncompetitive inhibition) with different affinity. Rivastigmine forms one π–π stacking interaction with Trp82, and three hydrogen bonds were observed with Asp70, Gly116 and Gly117. For Tacrine, there is one π–π stacking interaction with Trp82, and one ionic bond with Asp70.
Based on free energy binding values for Ondansetron and the two FDA-approved drugs in AChE and BChE (Table 2), the higher free energies of binding noted for Ondansetron and Tacrine indicate that these ligands bind most strongly to the holo form of the enzyme. To gain better insight into the stability of the formed complexes, MD and BPMD simulations were conducted as follows.
3.1.2. Molecular dynamics simulations
The stability and accuracy of the different protein-ligand complexes obtained from the docking studies were explored through 200 ns MD simulations.
The RMSD data for the ligands binding to AChE (Fig. 4A) shows a continuous increase throughout the simulation for Rivastigmine, indicating that this ligand may be more prone to dissociate. We find that despite the ligand remaining largely in the binding cavity of the protein and still has interactions with some crucial amino acids such as Trp86 in the last snapshot of the MD simulation, the changes are significant with part of the aromatic ring and the alkylamine substituent (Table 1) exposed to the solvent and undergo large fluctuations. This is in agreement with the lower docking score and MMGBSA values (Table 2) for Rivastigmine. Our results suggest that there are no considerable changes in RMSD values for Ondansetron or Tacrine, and that both form stable complexes with AChE.
The RMSD graphs for BChE (Fig. 4B) shows that all three inhibitors have preserved their binding affinity and are still tightly coupled to their respective binding sites. Ondansetron lies below ∼2Å and no significant fluctuations were observed throughout the simulation period, implying that the binding of the ligand to the active site of BChE is very stable and strong. As in AChE, Tacrine in BChE displays slightly lower RMSD values compared to our target ligand; however, analyses of the MD trajectory shows that part of the ligand is exposed to the solvent which accounts for the minor fluctuations seen in the graph during the simulation. Analysis of the MD trajectory for Rivastigmine shows that the ligand remained stable in the binding pocket after an initial large movement/adjustment as noted by the large jump in RMSD at t = 10 ns. The MMGBSA binding energy calculations (Table 2), show higher binding affinities for Ondansetron than Rivastigmine in BChE.
We also analyzed the protein root-mean-square-fluctuations (RMSF), showing which residues or parts of a protein that fluctuate the most during the MD simulation (Fig. 4C and D). The RMSF values of AChE with any of the ligands bound remain relatively stable (Fig. 4C). The highest variation is related to residues 482–486, when the protein had Ondansetron as ligand. These are part of a loop close to the C-terminal and are not involved with the active site of the protein. The red arrows in the graph indicate the location of the key amino acids in the peripheral site (1, 2, 3, 4, 5 for Trp86, Tyr124, Phe295, Tyr337 and Phe338, respectively). For AChE with Rivastigmine bound, some fluctuations are observed for residues 281–284, and very close to Phe295 (arrow number 3), indicating that the interaction of the ligand with these parts creates changes to the protein. Except for these parts, the protein was highly stable throughout the simulations, irrespective of ligand. We also note that the catalytic residues (black arrows 6, 7, and 8) display very low RMSF values during all the simulations.
The RMSF values for BChE with Ondansetron bound (Fig. 4D) indicate that this complex is quite stable during the whole simulation time, while for Rivastigmine and Tacrine some fluctuations are observed. There are two regions with large peaks close to amino acids in the binding site: residues 70–77 corresponding to a loop including Asp70 in peripheral site (arrow 4) and Trp82 (arrow 5); and residues 334–341 corresponding to a loop very close to Phe329 (arrow 7). For the BChE – Rivastigmine complex, residues 101–105 shows large fluctuations; this is a region that this part are not close to the binding pocket. For the system with Tacrine bound, two large peaks are seen near the C- and N-termini, far from the active site areas, as is the region with residues 281–285. The location of the three main residues in the catalytic pocket, Ser198, Glu325 and His438, respectively, are shown by black arrows. These residues as well as Trp231 (arrow 6) were found to remain highly stable during the MD simulations of BChE.
3.1.3. Binding Pose Metadynamics (BPMD)
Next, we performed binding pose metadynamics (BPMD) simulations for all six systems. The pose stability was evaluated based on the PoseScore, i.e., the RMSD of the ligand with respect to the initial ligand heavy atoms coordinates. The threshold value for ligand stability in the binding pocket is a PoseScore ≤ 2 Å [33].
Figure 5 shows that when Ondansetron is located in AChE the calculated values of PoseScore is 1.10 Å, indicative of stable binding. For Tacrine in AChE, the obtained PoseScore is 1.03 Å, and for Rivastigmine 2.15 Å with a large increase in value, indicating lower ligand stability in comparison with the other two ligands. When Ondansetron is bound to BChE, the PoseScore value is 1.29 Å and for Tacrine and Rivastigmine it is 1.13 Å and 2.39 Å, respectively. Based on these results and the RMSD data (Figs. 4A and B) and the energetics reported in Table 2, we conclude that Ondansetron can be a more potent candidate against cholinesterase compared to Rivastigmine; however, less so than Tacrine in both proteins.
3.2. Experimental studies
3.2.1. Binding of Ondansetron to the enzymes
The effect of Ondansetron on AChE and BChE activity was investigated through different in vitro assays, and the type of inhibition and the kinetic factors of the binding calculated. The enzymatic activities of AChE and BChE were evaluated spectrophotometrically at room temperature using Ellman’s method as described in the method section, with the rate of increase in absorbance at 410 nm followed for 5 minutes. The experiments were done in triplicate. The tested concentrations of AChE and BChE ranged from 0–84 µM and 0–42 µM respectively. Lineweaver-Burk plots were used to determine the type of inhibition. It was found that the drug inhibits AChE by non-competitive inhibition (Fig. 6A), while for BChE Ondansetron exhibits mixed inhibition (Fig. 6B). The data thus confirm the analysis from the docking regarding the type of inhibition (Fig. 3). The Km of AChE was constant in the presence and absence of the drug and was calculated to ∼ 0.27 mM, while the Km of BChE was variable depending on concentrations of the drug.
3.2.2. Affinity of Ondansetron in the enzymes
Since the Km of AChE in the presence of the drug was constant, Vmax was used to calculate IC50 in this case (Fig. 7A). For BChE, Km of mixed inhibition was instead used to determine the IC50 value (Fig. 7B). The IC50 values thus obtained are 33 µM and 2.5 µM for AChE and BChE, respectively. This can be compared to the IC50 values of the reference inhibitors: Rivastigmine was was reported to have an IC50 value in AChE of 71µM [42] or 501 µM [43], whereas for Tacrine the values range between 0.2–0.45 µM [44, 45]. Ondansetron is hence a more potent binder to AChE than Rivastigmine, but less so than Tacrine, which agrees perfectly with the MMGBSA data of Table 2. The IC50 values for Rivastigmine and Tacrine in BChE were reported to be 7.72–19.95 µM and 0.15 µM, respectively [43, 46, 47], indicating that Ondansetron is at least 3–8 times more potent than Rivastigmine but again less effective than Tacrine. Again, this trend is fully supported by the computed binding energies (Table 2). We note however that the most important adverse effect of Tacrine is liver toxicity, gastrointestinal problem, nausea, and vomiting and that it due to this has been withdrawn in some countries [11]. We thus propose that Ondansetron may be a more potent alternative drug targeting cholinesterase than Rivastigmine, and with relatively mild side effects as compared to Tacrine.
Calculation of the inhibition constants (Ki) were carried out through LineweaverBurk and their secondary plots (Fig. 7C and D). The x-intercept indicates the Ki value, giving Ki = 35 µM and 6.1 µM for Ondansetron in AChE and BChE, respectively, which also confirms the higher binding affinity of Ondansetron in BChE noted computationally.
3.2.3. Effect of pH on Ondansetron binding
Both cholinesterases are pH dependent and perform optimally at pH above 7 [48]. In general, the rate of enzymatic hydrolysis of acetylcholine and butyrylcholine increases from pH 6 to 9 and shows maximum activity at pH 8. However, the activity decreases markedly at pH 11 [49]. Several physiological and pathophysiological conditions contribute to the pH in the neurons. Both anionic sites and positive groups on the surface of ACh receptors recognize this protein for the attachment (Fig. 1) as a result both groups are involved in modulating the pH value in neuron cells. Moreover, some selective ions in the membranes of nerve cells contribute to fixing alkaline charges [50].
To this end, the activity of AChE and BChE was investigated within the pH range 5–11 at 30°C, using 21 µM Ondansetron. The optimum activity for AChE alone was observed at pH 9, while in the presence of the drug, a shift to pH 10 was observed (Fig. 8A). For BChE the optimum pH is 9, which remained unaltered in the presence of the drug (Fig. 8B). The pH profiles hence suggest that both enzymes are more active in alkaline pH than in acidic pH, and also show a clear impact of drug binding on the enzymatic activity.
3.2.4. Effect of temperature on Ondansetron binding
The effect of different temperatures (0–70°C) on the enzymatic activity was also measured in the presence (21 µM concentration) and absence of Ondansetron. AChE showed maximum activity at 40 oC and 50 oC in the absence and presence of the drug, respectively (Fig. 9A). BChE showed the same pattern as AChE, with optimum activity at 40°C while in the presence of Ondansetron it shifted to 50°C (Fig. 9B). Both enzymes were also inactivated at 70 oC both in presence and absence of the drug.
Arrhenius plots were obtained to calculate the activation energies of the enzymes and the effect of the drugs thereon. It indicated that, in the presence of Ondansetron, more energy was needed for the reaction to proceed, suggesting that binding of the drug to the enzymes will increase the activation energy. As a result, Vmax of the enzymes were calculated at different temperatures in order to generate Arrhenius plots. For AChE, Vmax increased from 25 to 30 kJ mol− 1 in the presence of the drug (Fig. 9C) while for BChE the activation energy was 23 and 32 kJ mol− 1 in the absence and presence of the drug respectively (Fig. 9D). Again, this supports the finding of Ondansetron binding stronger to BChE, as noted in Table 2.
Ondansetron has herein for the first time been investigated as a potent inhibitor for cholinesterase proteins. In the current study, computational modelling and experimental studies were carried out to explore structural properties and binding mechanisms of the compound. Our observations suggest that it can inhibit the enzyme activity by interaction with residues near or in the active site of the enzymes, in non-competitive or mixed inhibition manners for AChE and BChE, respectively. Analyses of MD simulations revealed that Ondansetron is a stable ligand in both enzymes, and binds with higher affinity in comparison with Rivastigmine. We also carried out different experimental studies by calculating the type of inhibition and Ki values from Lineweaver-Burk plots that fully confirm the data from the in silico modeling. We also investigated the effect of binding to AChE and BChE and show that our target has stronger inhibitory activity towards BChE than AChE (IC50: 2.5 µM and 33 µM, respectively). However, in relation to the two FDA approved drugs Rivastigmine and Tacrine, and considering the serious side effects such as hepatotoxicity for Tacrine, we herein propose that Ondansetron may be a potential repurposed candidate drug to reduce or block cholinesterase activity. Further experimental studies (in vitro and in vivo) would be required to verify the pharmacological relevance of the observed cholinesterase inhibition by Ondansetron.
Supporting information
Sequence alignment of AChE and BChE is available as supplementary material.
Data availability
All docked structures, MD simulation trajectories and BPMD data are available as free download at Zenodo.org, DOI: 10.5281/zenodo.7003730.
Author contributions
All authors designed the initial study and contributed with the analysis of data. AG performed all calculations and DMT performed the in vitro experiments. The manuscript was written and revised through the contributions of all authors. All authors have given approval to the final version of the manuscript.
Competing interests
The authors declare no competing interests.
Acknowledgments
The authors acknowledge funding from the Swedish Research Council (VR), grant number 2019-3684, and generous allocation of computing time at the supercomputing centers C3SE and NSC by the Swedish National Infrastructure for Computing (SNIC), in part funded by the Swedish Research Council through grant agreement no 2018-05973.
- K. Sahin, B. Zengin Kurt, F. Sonmez, and S. Durdagi, “Novel AChE and BChE inhibitors using combined virtual screening, text mining and in vitro binding assays,” J. Biomol. Struct. Dyn., vol. 38, no. 11, pp. 3342–3358, 2020.
- P. Anand and B. Singh, “A review on cholinesterase inhibitors for Alzheimer’s disease,” Arch. Pharm. Res., vol. 36, no. 4, pp. 375–399, 2013.
- U. Košak et al., “Development of an in-vivo active reversible butyrylcholinesterase inhibitor,” Sci. Rep., vol. 6, no. 1, pp. 1–16, 2016.
- I. Vecchio, L. Sorrentino, A. Paoletti, R. Marra, and M. Arbitrio, “The state of the art on acetylcholinesterase inhibitors in the treatment of Alzheimer’s disease,” J. Cent. Nerv. Syst. Dis., vol. 13, p. 11795735211029112, 2021.
- Y. Zhou et al., “Discovery of selective butyrylcholinesterase (BChE) inhibitors through a combination of computational studies and biological evaluations,” Molecules, vol. 24, no. 23, p. 4217, 2019.
- H. Cavdar et al., “Inhibition of acetylcholinesterase and butyrylcholinesterase with uracil derivatives: Kinetic and computational studies,” J. Enzyme Inhib. Med. Chem., vol. 34, no. 1, pp. 429–437, 2019.
- B. Sarkar, S. Alam, T. K. Rajib, S. S. Islam, Y. Araf, and M. Ullah, “Identification of the most potent acetylcholinesterase inhibitors from plants for possible treatment of Alzheimer’s disease: a computational approach,” Egypt. J. Med. Hum. Genet., vol. 22, no. 1, pp. 1–20, 2021.
- J. C. Waymire, “Chapter 11: Acetylcholine neurotransmission,” Neurosci. online, 2000.
- V. Andrisano, M. Naldi, A. De Simone, and M. Bartolini, “A patent review of butyrylcholinesterase inhibitors and reactivators 2010–2017,” Expert Opin. Ther. Pat., vol. 28, no. 6, pp. 455–465, 2018.
- C. Seniya, G. J. Khan, and K. Uchadia, “Identification of potential herbal inhibitor of acetylcholinesterase associated Alzheimer’s disorders using molecular docking and molecular dynamics simulation,” Biochem. Res. Int., vol. 2014, 2014.
- M. Pohanka, “Inhibitors of acetylcholinesterase and butyrylcholinesterase meet immunity,” Int. J. Mol. Sci., vol. 15, no. 6, pp. 9809–9825, 2014.
- A. Haake, K. Nguyen, L. Friedman, B. Chakkamparambil, and G. T. Grossberg, “An update on the utility and safety of cholinesterase inhibitors for the treatment of Alzheimer’s disease,” Expert Opin. Drug Saf., vol. 19, no. 2, pp. 147–157, 2020.
- G. Marucci, M. Buccioni, D. Dal Ben, C. Lambertucci, R. Volpini, and F. Amenta, “Efficacy of acetylcholinesterase inhibitors in Alzheimer’s disease,” Neuropharmacology, vol. 190, p. 108352, 2021.
- N. Allahham et al., “Selective laser sintering 3D printing of orally disintegrating printlets containing ondansetron,” Pharmaceutics, vol. 12, no. 2, p. 110, 2020.
- B. Sandhya, A. H. Hegde, K. C. Ramesh, and J. Seetharamappa, “Exploring the binding mechanism of ondansetron hydrochloride to serum albumins: spectroscopic approach,” Spectrochim. Acta Part A Mol. Biomol. Spectrosc., vol. 86, pp. 410–416, 2012.
- S. E. Parker, C. Van Bennekom, M. Anderka, and A. A. Mitchell, “Ondansetron for treatment of nausea and vomiting of pregnancy and the risk of specific birth defects,” Obstet. Gynecol., vol. 132, no. 2, pp. 385–394, 2018.
- J. Ye, R. Ponnudurai, and R. Schaefer, “Ondansetron: a selective 5-HT3 receptor antagonist and its applications in CNS‐related disorders,” CNS Drug Rev., vol. 7, no. 2, pp. 199–213, 2001.
- B. R. Capacio, C. E. Byers, D. R. Anderson, R. L. Matthews, and D. E. Brown, “The effect of ondansetron on pyridostigmine-induced blood acetylcholinesterase inhibition in the guinea pig,” Drug Chem. Toxicol., vol. 19, no. 1–2, pp. 1–19, 1996.
- K. Skovgård, C. Agerskov, K. A. Kohlmeier, and K. F. Herrik, “The 5-HT3 receptor antagonist ondansetron potentiates the effects of the acetylcholinesterase inhibitor donepezil on neuronal network oscillations in the rat dorsal hippocampus,” Neuropharmacology, vol. 143, pp. 130–142, 2018.
- E. R. Stern et al., “High-dose ondansetron reduces activation of interoceptive and sensorimotor brain regions,” Neuropsychopharmacology, vol. 44, no. 2, pp. 390–398, 2019.
- Y. G. Kappenberg et al., “Design, synthesis, AChE/BChE inhibitory activity, and molecular docking of spiro [chromeno [4, 3-b] thieno [3, 2-e] pyridine]-7-amine tacrine hybrids,” J. Mol. Struct., vol. 1266, p. 133485, 2022.
- J. Cheung, E. N. Gary, K. Shiomi, and T. L. Rosenberry, “Structures of human acetylcholinesterase bound to dihydrotanshinone I and territrem B show peripheral site flexibility,” ACS Med. Chem. Lett., vol. 4, no. 11, pp. 1091–1096, 2013.
- X. Brazzolotto et al., “Human butyrylcholinesterase produced in insect cells: huprine-based affinity purification and crystal structure,” FEBS J., vol. 279, no. 16, pp. 2905–2916, 2012.
- G. Madhavi Sastry, M. Adzhigirey, T. Day, R. Annabhimoju, and W. Sherman, “Protein and ligand preparation: parameters, protocols, and influence on virtual screening enrichments,” J. Comput. Aided. Mol. Des., vol. 27, no. 3, pp. 221–234, 2013.
- K. Roos et al., “OPLS3e: Extending force field coverage for drug-like small molecules,” J. Chem. Theory Comput., vol. 15, no. 3, pp. 1863–1874, 2019.
- J. R. Greenwood, D. Calkins, A. P. Sullivan, and J. C. Shelley, “Towards the comprehensive, rapid, and accurate prediction of the favorable tautomeric states of drug-like molecules in aqueous solution,” J. Comput. Aided. Mol. Des., vol. 24, no. 6, pp. 591–604, 2010.
- W. Sherman, T. Day, M. P. Jacobson, R. A. Friesner, and R. Farid, “Novel procedure for modeling ligand/receptor induced fit effects,” J. Med. Chem., vol. 49, no. 2, pp. 534–553, 2006.
- R. Farid, T. Day, R. A. Friesner, and R. A. Pearlstein, “New insights about HERG blockade obtained from protein modeling, potential energy mapping, and docking studies,” Bioorg. Med. Chem., vol. 14, no. 9, pp. 3160–3173, 2006.
- K. J. Bowers et al., “Scalable algorithms for molecular dynamics simulations on commodity clusters,” in SC’06: Proceedings of the 2006 ACM/IEEE Conference on Supercomputing, 2006, p. 43.
- W. L. Jorgensen, J. Chandrasekhar, J. D. Madura, R. W. Impey, and M. L. Klein, “Comparison of simple potential functions for simulating liquid water,” J. Chem. Phys., vol. 79, no. 2, pp. 926–935, 1983.
- G. J. Martyna, M. L. Klein, and M. Tuckerman, “Nosé–Hoover chains: The canonical ensemble via continuous dynamics,” J. Chem. Phys., vol. 97, no. 4, pp. 2635–2643, 1992.
- R. M. Wentzcovitch, “Invariant molecular-dynamics approach to structural phase transitions,” Phys. Rev. B, vol. 44, no. 5, p. 2358, 1991.
- L. Fusani, D. S. Palmer, D. O. Somers, and I. D. Wall, “Exploring ligand stability in protein crystal structures using binding pose metadynamics,” J. Chem. Inf. Model., vol. 60, no. 3, pp. 1528–1539, 2020.
- G. L. Ellman, K. D. Courtney, V. Andres Jr, and R. M. Featherstone, “A new and rapid colorimetric determination of acetylcholinesterase activity,” Biochem. Pharmacol., vol. 7, no. 2, pp. 88–95, 1961.
- M. Jahangirvand, D. Minai-Tehrani, F. Yazdi, A. Minai-Tehrani, and N. Razmi, “Binding of cimetidine to Balb/C mouse liver catalase; kinetics and conformational studies,” Curr. Clin. Pharmacol., vol. 11, no. 1, pp. 21–27, 2016.
- F. Yazdi et al., “Functional and structural changes of human erythrocyte catalase induced by cimetidine: proposed model of binding,” Mol. Cell. Biochem., vol. 404, no. 1, pp. 97–102, 2015.
- O. Lowry, N. Rosebrough, A. L. Farr, and R. Randall, “Protein measurement with the Folin phenol reagent,” J. Biol. Chem., vol. 193, no. 1, pp. 265–275, 1951.
- M. Bajda, A. Więckowska, M. Hebda, N. Guzior, C. A. Sotriffer, and B. Malawska, “Structure-based search for new inhibitors of cholinesterases,” Int. J. Mol. Sci., vol. 14, no. 3, pp. 5608–5632, 2013.
- X. Gao, C. Zhou, H. Liu, L. Liu, J. Tang, and X. Xia, “Tertiary amine derivatives of chlorochalcone as acetylcholinesterase (AChE) and buthylcholinesterase (BuChE) inhibitors: the influence of chlorine, alkyl amine side chain and α, β-unsaturated ketone group,” J. Enzyme Inhib. Med. Chem., vol. 32, no. 1, pp. 146–152, 2017.
- T. L. Rosenberry et al., “Comparison of the binding of reversible inhibitors to human butyrylcholinesterase and acetylcholinesterase: A crystallographic, kinetic and calorimetric study,” Molecules, vol. 22, no. 12, p. 2098, 2017.
- S. Darvesh et al., “Carbamates with differential mechanism of inhibition toward acetylcholinesterase and butyrylcholinesterase,” J. Med. Chem., vol. 51, no. 14, pp. 4200–4212, 2008.
- B. David, P. Schneider, P. Schäfer, J. Pietruszka, and H. Gohlke, “Discovery of new acetylcholinesterase inhibitors for Alzheimer’s disease: Virtual screening and in vitro characterisation,” J. Enzyme Inhib. Med. Chem., vol. 36, no. 1, pp. 491–496, 2021.
- M. Krátký, Š. Štěpánková, K. Vorčáková, M. Švarcová, and J. Vinšová, “Novel cholinesterase inhibitors based on O-aromatic N, N-disubstituted carbamates and thiocarbamates,” Molecules, vol. 21, no. 2, p. 191, 2016.
- J. G. Fernández-Bolaños and Ó. López, “Butyrylcholinesterase inhibitors as potential anti-Alzheimer’s agents: an updated patent review (2018-present),” Expert Opin. Ther. Pat., no. just-accepted, 2022.
- K. Zawada et al., “New hybrids of tacrine and indomethacin as multifunctional acetylcholinesterase inhibitors,” Chem. Pap., vol. 75, no. 1, pp. 249–264, 2021.
- M. Sadafi Kohnehshahri et al., “Novel tacrine-based acetylcholinesterase inhibitors as potential agents for the treatment of Alzheimer’s disease: Quinolotacrine hybrids,” Mol. Divers., vol. 26, no. 1, pp. 489–503, 2022.
- M. Scheiner et al., “Dual-acting cholinesterase–human cannabinoid receptor 2 ligands show pronounced neuroprotection in vitro and overadditive and disease-modifying neuroprotective effects in vivo,” J. Med. Chem., vol. 62, no. 20, pp. 9078–9102, 2019.
- I. Wessler, R. Michel-Schmidt, and C. J. Kirkpatrick, “pH-dependent hydrolysis of acetylcholine: Consequences for non-neuronal acetylcholine,” Int. Immunopharmacol., vol. 29, no. 1, pp. 27–30, 2015.
- L. Eränkö, “Effect of pH on the activity of nervous cholinesterases of the rat towards different biochemical and histochemical substrates and inhibitors,” Histochemie, vol. 33, no. 1, pp. 1–14, 1972.
- B. B. Dunning and X. Machne, “pH optimum for the rate of acetylcholine action on neurons,” Agents Actions, vol. 2, no. 3, pp. 131–137, 1971.
No competing interests reported.