IDENTIFICATION OF QUINOLONE-TRAIZOLE HYBRIDS AS POTENTIAL INHIBITORS USING IN SILICO APPROACH

Abstract

Alzheimer’s disease (AD) is a most common form of dementia and results in memory loss, disorientation, impaired thinking and changes in personality as well as mood. The dementia cases mostly turn into the Alzheimer’s disease (AD). Another disease, the Parkinson’s disease (PD), mainly affects dopaminergic neurons in substantia nigra. A series of previously synthesized quinolone-triazole hybrids were investigated and evaluated for their anti-acetyl cholinesterase and against monoamine oxidase reactive properties through in silico characterization. The ground state electronic properties were determined by electron density of the compounds through DFTs (density functional theory) calculation. The geometries of all compounds were optimized using Becke-3-parameter-Lee-Yang-Parr (B3LYP) method and 6-311g basis set. Docking studies were conducted using the AutoDock and Molecular Operating Environment (MOE) software and the results of the potent derivatives found to be encouraging. The data confirmed that all the compounds have drug-like properties and was further confirmed by ADMET (absorption, distribution, metabolism, excretion and toxicity) properties.

Keywords:
Alzheimer’s disease; DFT studies; ADMET

INTRODUCTION

Alzheimer’s disease (AD) is a neurodegenerative brain disorder of unknown cause.¹ It mostly starts in late middle age or in late age resulting in memory loss, disorientation, impaired thinking and changes in personality as well as mood.² According to the 2015 World Alzheimer’s report, there are close to 46 million AD patients worldwide whereas this number will increase to 130 million by the midst of this century.³ The cost of AD is a sizeable financial burden all over the world which costs are expected to reach up to 1 trillion dollars in 2018. It is considered that the inhibition of acetylcholinesterase is a dominant strategy for the cure of AD symptoms as there are four accepted treatments, i.e., donepezil, rivastigmine, tacrine and galantamine.⁴-⁶ The literature implied that the cognitive insufficiencies related with Alzheimer’s disease results in the decreased levels of acetylcholine in the brain due to the non-functioning cholinergic neurons. In AD pathogenesis, the role of non-cholinergic neurotransmitter system has earned lower attention whereas Alzheimer’s pathology has been associated with the levels of non-cholinergic neurotransmitters in the brain and their role in interceding the onset of symptoms is less understood.⁷ Parkinson’s disease (PD) is a neurodegenerative disorder and affects dopaminergic neurons in substantia nigra.⁸ Roughly 10 million people are affected with Parkinson’s disease worldwide and 1 million in United States diagnosed with PD every year.⁹ After Alzheimer disease, it is the most common neurodegenerative disorder.¹⁰ The progression is generally different from one to another person and symptoms usually develop slowly with passing years. PD is the biggest factor as most people are diagnosed in their 60s. Some people in rare cases can establish before the age of 50 and is known as young-onset PD.¹¹ Recent studies suggest that men are more likely to have PD than women. Parkinson’s disease usually happens when a bunch of the cells in substantia nigra begin to die. Dopamine is produced by these cells which is a neurotransmitter and send signals to parts of brain that control coordination and movement.¹² The dopamine bearing cells begin to die and the amount of dopamine decreases substantially when a person has Parkinsonism.¹³ The brain receives the necessary information abnormally and thus resulting in abnormal movements whereas in most people, this process leads to tremor, rigidity, postural instability and slowness.¹⁴ Cholinesterases are a family of esterases which lyses choline-bases esters that serve as neurotransmitters. Acetylcholinesterase (AChE) and butyrylcholinesterase (BChE) are two types of cholinesterase and they have their specificities differ with respect to their types.¹⁵ AChE is an enzyme which presents α/β hydrolase fold in the structure of protein and covered by fourteen α-helices. It is present in the synaptic cleft and cleave Ach to cause the development of acetate and choline.¹⁶ The butyrylcholinesterase is a cholinesterase enzyme which is nonspecific and hydrolyzes choline-based esters. It is formed in the liver of humans and chiefly in plasma.¹⁷ AChE and BChE are shown in Figure 1. The location of monoamine oxidase in most cell types is on the outer membrane of the mitochondria.¹⁸ In central nervous system (CNS) and peripheral tissues its function is the deamination of biogenic and xenobiotic monoamines. The enzyme MAO has two types, monoamine oxidase A (MAO-A) and monoamine oxidase B (MAO-B), both having the same ancestral gene.¹⁹,²⁰

Figure 1
(a) Acetylcholinesterase in complex with huprine W and fasciculin; (b) butyrylcholinesterase in complex with tacrine; (c) crystal structure of human MAO-A with harmine; (d) the structure of human MAO-B in complex with the inhibitor safinamide

AD and PD can be controlled by many drugs approved by FDA (Food and Drug Administration) which can slow the progression of symptoms. The cholinesterase inhibitors and monoamine oxidase inhibitors are shown in Figure 2.

Figure 2
Acetylcholinesterase and monoamine oxidase inhibitors

Monoamine oxidase and cholinesterase enzymes are very important enzymes and they control the level of neurotransmitters such as acetylcholine and monoamines. There are several compounds which have shown inhibitory activities against these enzymes but are known to be more toxic and less potent. Thus, there is urgent need of new class of inhibitors for the treatment of these neurological disorders. There is extensive literature on different inhibitors of cholinesterase and monoamine oxidases but there is plenty room for the development of new targeted drugs which must have less toxic profile and with greater potency. Our study was focused on the identification of potential inhibitors of cholinesterase and monoamine oxidases which can lead to targeted therapy and to develop new leads and selective inhibitors with low toxic profile.

EXPERIMENTAL

The compound library was collected from the previously reported article²¹ where the quinolone-triazole hybrids were analyzed as potent inhibitors of acetylcholinesterase potential and are shown in Figure 3. Here in this study, we are focused on the in silico findings of these molecules. This may lead to more potent compounds to be synthesized with respect to selective acetylcholinesterase, butyrlcholinesterase and monoamine oxidase A and B inhibitors in future.

Figure 3
Quinolone-triazole hybrids

Density functional theory (DFT)

For the execution of the DFT, we used Gaussian 09 program with the basic set B3LYP/6-311g.²² For determining the electronic structure of atoms and molecules, DFT is a compelling theory. To obtain the following information, the present study focused on, i.e., HOMO (highest occupied molecular orbital) and LUMO (lowest unoccupied molecular orbital), optimized structures and the global as well as local reactivity descriptors. For the inspection of fchk files, the GaussView 6 program was used.²³

Molecular docking studies

For the anticipation of inhibitory mechanism of the previously synthesized coumarin-based dual acetylcholinesterase and monoamine oxidase inhibitors, the docking studies were conducted. MOE (Molecular Operating Environment 2016.01) software²⁴ was used to accomplish docking studies. For the determination of the most active amino acids, the MOE’s site finder function was used. The default GBVI/WSA ∆G and triangle matcher placement method were used as docking functions.²⁵ Afterwards when the docking was completed, the poses were generated of ligands with best binding energies. The poses of the derivatives were generated with the best pose taken in 2D and 3D.

Preparation of ligand

With the help of Chem3D Pro, the derivatives were taken and converted in the convenient format. By using this software, the energy of the derivative was minimized and the conversion took place into the suitable formats.²⁶

Preparation of protein

The protein targets MAO-A and MAO-B were taken from the protein data bank (https://www.rcsb.org/) with the PDB ID: 4BDT,²⁷ 4BDS,²⁷ 2Z5Y,²⁸ and 2V5Z,²⁹ respectively. AutoDock³⁰ and MOE³¹ software were used for the preparation of targeted proteins.

Visualization

For the determination of docked poses, Discovery Studio Visualizer, version 2020 (Visualizer 2005)³² was used.

Filtration for drug-likeness and virtual screening (ADMET properties)

For the determination of derivatives which exhibit drug-likeness properties, the ADMET (absorption, distribution, metabolism, excretion and toxicity) is an excellent in silico technique. With the help of online server ADMET lab 2.0, we calculated physicochemical properties, absorption and distribution properties, metabolism, excretion and medicinal properties.³³

RESULTS AND DISCUSSION

Density functional theory (DFT) calculations

In the present study, we have reported the detailed quantum chemistry of quinolone-triazole hybrids as acetylcholinesterase and monoamine oxidase inhibitors including optimization, global reactivity descriptors as well as HOMO-LUMO analysis of the quinolone-triazole hybrids using DFT/B3LYP/6-311G calculation. The optimization energy, dipole moment, polarizability and HOMO LUMO energies and their gap values are given in Table 1.

Table 1
Optimization energies, HOMO and LUMO energies and their gap calculated in gas phase

Compound	Optimization energy / eV	Dipole moment	Polarizability / α	EHOMO / eV	ELUMO / eV	EHOMO - ELUMO / eV
9b	-2100.688	8.567	279.158	-0.211	-0.085	-0.297
9c	-1908.945	10.775	234.429	-0.204	-0.094	-0.298
9e	-1738.405	8.899	244.027	-0.246	-0.087	-0.333
9f	-1777.718	7.828	261.939	-0.246	-0.085	-0.331
10a	-1541.849	10.364	270.800	-0.203	-0.071	-0.274
10c	-1389.420	10.657	234.396	-0.196	-0.072	-0.269
10d	-1641.099	8.691	267.370	-0.209	-0.072	-0.282
10e	-1218.879	8.844	248.559	-0.230	-0.067	-0.297
11a	-1984.891	5.738	267.986	-0.228	-0.083	-0.311
11b	-2024.209	5.939	286.025	-0.228	-0.088	-0.316
11c	-1832.470	5.517	233.167	-0.228	-0.086	-0.315
11d	-2084.138	6.088	268.354	-0.232	-0.086	-0.319
11e	-1661.959	8.073	249.759	-0.224	-0.085	-0.309
11f	-1701.285	8.526	250.085	-0.225	-0.087	-0.312
12a	-1465.364	7.779	266.872	-0.214	-0.060	-0.275
12d	-1564.612	7.460	267.050	-0.219	-0.064	-0.283
12e	-1142.398	6.777	250.408	-0.213	-0.058	-0.272

HOMO: highest occupied molecular orbital; LUMO: lowest unoccupied molecular orbital.

The investigation of the HOMO-LUMO band gap guide us in understanding various molecular properties of a compound like chemical reactivity, stability of the molecule, and also electrical and optical properties of the molecule. Optimized structures of all compounds are shown in Figure 4.

Figure 4
Optimized structure of 9a-12e

Molecular structures include HOMO and LUMO, these orbitals have energy gaps which predicts the reactivity and stability of a compound. The orbitals which possess high energy gap shows high stability, low chemical reactivity and considered as hard molecule. The orbitals which show less energy gap have high chemical reactivity, low kinetic stability, more polarized and known as soft molecule.

HOMO is the highest occupied molecular orbital which donates electrons whereas LUMO is the lowest occupied molecular orbital which tends to accept electrons. The derivative 10c shows highest energy gap, -0.26946 eV, which indicates high kinetic stability, low chemical reactivity and will be hard molecule while the derivative 9e shows -0.333 eV, lowest energy gap and low stability with high chemical stability and is a soft molecule. The highest HOMO energy of the derivative 10c, -0.196 eV, will be with highest stability and low chemical reactivity. The derivative 12a, -0.060 eV, which has highest LUMO energy with low chemical reactivity and high kinetic stability. The derivative 11b showed high polarizability, 286.02, and the derivative 11c showed lowest polarizability is 233.16. In defining the electrostatic potential, the red part which is more electronegative and is highly involved in the interaction, blue part show that the derivative is slightly involved in the interaction whereas the green part indicates that the derivative is not involved in the interaction. Figures 5-8 show HOMO-LUMO orbitals.

Figure 5
HOMO-LUMO orbitals (9b-9f)

Figure 6
HOMO-LUMO orbitals (10a-10e)

Figure 7
HOMO-LUMO orbitals (11a-11f)

Figure 8
HOMO-LUMO orbitals (12a-12e)

Global reactivity descriptors

Chemical reactivity can be determined by the following parameters: chemical hardness (η), chemical softness (S), electronegativity (X), electrophilicity index (ω) and electronic chemical potential (μ). Hardness (η = (E_HOMO - E_LUMO)/2) is expressed by reactivity and stability of a chemical system. Electronegativity (X = -(E_HOMO + E_LUMO)/2) is the power to attract electrons towards it. The electrophilicity index (ω = μ2/2η) is the power of a molecule to accept electrons with the help of chemical potential and chemical hardness The chemical reactivity of the selected compounds is shown in Table 2. The compound 9e (-0.167) eV has the highest chemical potential among the other compounds. The electronegativity properties of the derivatives were also comparable whereas the derivative 9e (0.167 eV) has highest electronegativity among the other derivatives. The derivative 10e (0.082 eV) is the harder among all the other compounds while the derivative 9e (0.080 eV) and 9f (0.080 eV) have the same hardness in the series. Similarly, the derivative 9c (9.078 eV) is a softer molecule among other derivatives in the series and has the highest electrophilicity index (0.202 eV) among the other compounds.

Table 2
Global reactivity descriptors for the compounds 9b-12e

Compound	Chemical potential (µ) / eV	Electronegativity (X) / eV	Hardness (η) / eV	Softness (S) / eV	Electrophilicity index (ω) / eV
9b	-0.149	0.149	0.063	7.943	0.176
9c	-0.149	0.149	0.055	9.078	0.202
9e	-0.167	0.167	0.080	6.277	0.174
9f	-0.166	0.166	0.080	6.225	0.171
10a	-0.137	0.137	0.066	7.583	0.143
10c	-0.135	0.135	0.062	8.087	0.147
10d	-0.141	0.141	0.068	7.340	0.146
10e	-0.149	0.149	0.082	6.130	0.135
11a	-0.156	0.156	0.073	6.882	0.167
11b	-0.158	0.158	0.070	7.110	0.178
11c	-0.158	0.158	0.071	7.032	0.175
11d	-0.160	0.160	0.073	6.839	0.175
11e	-0.155	0.155	0.069	7.206	0.173
11f	-0.156	0.156	0.069	7.253	0.177
12a	-0.138	0.138	0.077	6.496	0.124
12d	-0.142	0.142	0.077	6.476	0.130
12e	-0.136	0.136	0.077	6.473	0.120

Molecular docking

The molecular docking was done using the MOE software and the results were visualized by the Discovery Studio Visualizer. The docking score is shown in Table 3 and the detailed ligand protein interaction are given in Figure 9.

Table 3
Docking score of the compounds against AChE, BChE, MAO-A and MAO-B

Compound	Docking score / (kJ mol-1)
	AChE	BChE	MAO-A	MAO-B
9b	-40.16	-25.08	-39.92	-42.16
9c	-32.96	-26.44	-7.16	-35.44
9e	-42.48	-24.36	-42.12	-45.28
9f	-39.32	-23.28	-42.04	-41.4
10a	-36.68	-26.76	-40.92	-43.84
10c	-38.12	-27.68	-34.08	-35.2
10d	-35.32	-26.52	-40.36	-42.28
10e	-37.4	-26.52	-40.4	-42.6
11a	-42.76	-29.12	-44.83	-37.24
11b	-41.72	-28.04	-42.56	-36.24
11c	-38.84	-28.4	-34.72	-36.04
11d	-32.96	-28.76	-40.12	-42.48
11e	-43.72	-30.56	-40.48	-42.72
11f	-42.92	-30.16	-39.92	-41.52
12a	-43.64	-30	-40.16	-41.84
12d	-37.52	-29.6	-42.04	-23.64
12e	-36.68	-32.48	-42.32	-42.76
Donepezil	-30.652	-32.508	-	-
Huprine	-29.508	-22.293	-	-
Clorgyline	-	-	-29.868	-
Deprenyl	-	-	-	-30.871

AChE: acetylcholinesterase; BChE: butyrylcholinesterase; MAO-A: monoamine oxidase A; MAO-B: monoamine oxidase B.

Figure 9
2D and 3D interaction of most potent compounds against AChE, BChE, MAO-A and MAO-B

11e with AChE

The detailed 3D and 2D binding interactions of derivative 11e within active site of AChE is shown in Figure 9. The amino acid residues which were involved in bonding and non-bonding interactions with 11e included LEU289, TRP286, TYR341, LEU76 and TYR72 which played a very significant role in ligand-protein binding. Briefly, compound 11e formed two π-π-T shaped interactions that were formed with the substituted benzene ring and with pyridine ring by amino acid TYR341. One π-π stacked interaction was formed with the triazole ring by amino acid LEU289. π-σ interaction was formed with hydrogen attached with nitrogen in the pyridine ring involving amino acid TRP286. The amino acid residue LEU76 and TYR72 which formed π-alkyl interaction with the benzene ring and with chlorine attached to substituted benzene and with hydrogen attached to nitrogen in the pyridine ring. The amino acids which formed van der Waals interactions were PHE295, PHE297, TYR124, TYR337, PHE338 and ASP74.

12e with BChE

The detailed 3D and 2D binding interactions of derivative 12e within active site of BChE is shown in Figure 9. The amino acid residues which were involved in bonding and non-bonding interactions with 12e included ALA328 and TYR332 which played a very significant role in ligand-protein binding. Briefly, compound 12e formed two π-π stacked interactions that were formed with the benzene by amino acid TYR332. The amino acid residue ALA328 which formed π-alkyl interaction with the benzene ring triazole ring. The amino acids which formed van der Waals interactions were ASP70, SER79, TRP430, GLY115, GLY121, THR120, PHE329 and PRO285.

11a with MOA-A

The detailed 2D binding interactions of derivative 11a within active site of MOA-A is shown in Figure 9. The amino acid residues which were involved in bonding and non-bonding interactions with 11a included TYR444, TYR407, CYS406, TRP397, LYS305, GLY66, ALA68, TYR69, GLN74, MET445, and GLY214 which played a significant role in ligand-protein binding. Briefly, amino acid residue formed five conventional hydrogen bond with oxygen of methypyridine and 4-phenyl-4H-1,2,4-triazole-3 thiol. GLY66 formed halogen bond and LYS305 formed hydrogen bond with 7-chloro-1 ethyl-6-flouroquinilin-4(1H)-one. MET445 formed π-σ bond and π-sulfur bond with 4-phenyl-4H-1,2,4 triazole-3-thiol. TYR407 formed alkyl and π-alkyl interaction with methypyridine and benzene ring. TRP397 formed π-σ bond with chlorine end of benzene ring. GLY443, GLN215, GLY71, VAL70, GLU446, GLY67 formed the van der Waals interactions with compound 11a.

9e with MOA-B

The detailed 3D binding interactions of derivative 9e within active site of MOA-B is shown in Figure 9. The amino acid residues which were involved in bonding and non-bonding interactions with 9e included TYR435, TYR398, PHE343, TYR326, GLY434, GLN206 and played significant role in ligand-protein binding. GLN206 formed halogen bond, TYR398 and PHE343 formed stacked and π-π-T shaped bond with substituted benzene itself. TYR326 formed π-σ bond with the chlorine end of benzene. MET436, THR43, GLY58, SER59, TYR60, LEU328, CYS172, ILE198, TYR188 formed van der Waals interaction with compound 9e.

ADMET

Physicochemical properties

Among series 9b-9f, 10a-10e, 11a-11f, 12a-12e, the most potent compounds were 9b, 10e, 11e, 12e, respectively. The controls used in these series were: clorgyline, deprenyl, donepezil and adriamycin. LogP is used to show a hydrophilicity of the compound; if the logP value is negative, the compound is hydrophilic. All of the compounds in Table 4 are lipophilic. LogS value shows solubility: the lower the logS value, the greater the solubility, which enhances absorption. For drugs with central nervous system (CNS) activity, the optimal lipophilicity for blood-brain barrier (BBB) penetration is a logD ≤ 2. A logD more than 4 is considered inappropriate for a CNS medication. The topological polar surface area (TPSA) is used for forecasting the oral absorption of drug-like compounds. A greater TPSA value indicates decreased membrane permeability. Thus, a lower TPSA level was acceptable for drug-likeness. The TPSA value should be low for optimal CNS diffusion. If a derivative has a TPSA 70, it is considered to be sufficiently bioavailable. Our findings showed slightly greater TPSA values; however, this can be changed in the future by removing polar atoms from the produced molecules. The number of hydrogen bond donors (nHD) is equal to the sum of all OHs and NHs, whereas the number of hydrogen bond acceptors (nHA) is equal to the sum of all nitrogen and oxygen atoms with no positive charge; nHA 0-12 and nHD 0-7 are the optimal ranges.

Table 4
Physicochemical properties of the derivatives 9b-12e

Compound	Molecular weight	Density	nHA	nHD	TPSA	logS	logP	logD
9b	432.08	1.07	6	3	75.16	-5.394	3.41	2.931
9c	370.07	1.109	6	3	75.16	-4.974	2.534	1.942
9e	402.09	1.067	7	3	92.23	-5.865	3.489	2.838
9f	416.11	1.056	7	3	92.23	-5.522	2.977	2.697
10a	380.13	0.994	6	3	75.16	-5.23	3.31	2.913
10c	332.13	1.007	6	3	75.16	-4.448	1.775	1.676
10d	398.12	1.025	6	3	75.16	-5.531	3.525	3.014
10e	364.15	0.977	7	3	92.23	-5.277	3.015	2.769
11a	338.04	1.13	5	1	55.61	-3.974	2.481	1.886
11b	414.07	1.071	5	1	55.61	-4.596	3.933	2.947
11c	352.06	1.113	5	1	55.61	-4.153	3.319	2.111
11d	418.05	1.114	5	1	55.61	-4.794	3.847	2.764
11e	384.08	1.069	6	1	72.68	-5.543	3.536	2.549
11f	398.09	1.057	6	1	72.68	-5.818	3.831	2.856
12a	362.12	0.992	5	1	55.61	-4.046	3.239	2.747
12d	380.11	1.024	5	1	55.61	-4.338	3.396	2.797
12e	346.14	0.974	6	1	72.68	-4.836	3.086	2.477
Clorgyline	271.050	1.035	2	0	12.470	-4.011	3.712	3.608
Deprenyl	187.149	0.840	1	0	3.240	-2.207	2.662	2.925
Donepezil	379.210	0.929	4	0	38.770	-4.307	4.191	3.631
ADR	543.17	1.051	12	9	212.39	-2.69	1.76	0.689

nHA: hydrogen bond acceptors; nHD: hydrogen bond donors; TPSA: topological polar surface area; logS: logarithm of solubility; logP: logarithm of hydrophilicity; logD: logarithm of lipophilicity; ADR: adriamycin.

According to absorption and distribution properties, volume of distribution (VD) has an optimal range of 0.04-20 L kg^-1. VD is indicated by drug concentration in plasma divided by total concentration in body. According to our findings shown in Table 5, all of our compounds showed optimal VD values. CaCO₂ permeability values show in vivo intestinal drug absorption. Optimal values should be higher than -5.15 log unit. Most of our compounds showed normal values of CaCO₂ permeability. HIA is the human intestinal absorption. The greater its value, greater will be intestinal absorption. All our compounds indicated good HIA value. Blood brain barriers (BBB) indicate that all our compounds can cross BBB due to their lipophilic nature. Plasma protein binding (PPB) optimal value should be less than 90%. A greater PPB shows that the compound has low therapeutic index. Based on our findings, our compounds had optimal PPB values except compounds 9a, 9b, 10d, 11b, 11d which showed PPB greater than 90%.

Table 5
Absorption and distribution properties of the derivatives 9b-12e

Compound	VD / (L kg-1)	HIA	CaCO2 permeability / (log unit)	BBB and BPB	PPB / %
9b	1.059	0.008	-5.273	0.611	92.64
9c	1.373	0.023	-5.019	0.95	78.23
9e	0.843	0.005	-5.065	0.895	89.91
9f	0.901	0.005	-5.003	0.749	87.86
10a	0.854	0.035	-5.191	0.887	87.61
10c	1.096	0.012	-5.06	0.947	62.59
10d	0.829	0.011	-5.229	0.568	91.48
10e	0.681	0.023	-5.016	0.911	73.79
11a	1.392	0.007	-4.678	0.908	87.81
11b	0.737	0.004	-4.739	0.844	95.51
11c	1.378	0.007	-4.659	0.949	88.72
11d	0.866	0.004	-4.808	0.968	94.55
11e	1.092	0.016	-4.768	0.933	87.48
11f	1.042	0.016	-4.74	0.637	94.58
12a	0.916	0.004	-4.658	0.97	84.61
12d	0.851	0.004	-4.777	0.974	88.79
12e	1.069	0.015	-4.745	0.887	77.96
Clorgyline	3.760	0.001	-4.251	0.992	95.30
Deprenyl	5.842	0.005	-4.915	0.996	91.39
Donepezil	0.14	0.413	0.097	0.986	10.635
ADR	1.01	0.751	-6.006	0.011	91.28

VD: volume of distribution; HIA: human intestinal absorption; BBB: blood brain barrier; BPB: blood-placenta barrier; PPB: plasma protein binding; ADR: adriamycin.

The metabolism and excretion is indicated in Table 6. All of our compounds were inhibitors of CYP1A2, CYP2C19, CYP2C9 and CYP3A4 and CYP2D6. The clearance rate of a drug from the body is classified as high (> 15), moderate (5-15), or low (less than 5). All our compounds had low clearance rate.

Table 6
Metabolism and excretion properties 9b-12e

Compound	Metabolism					Excretion
	CYP1A2 inhibitor	CYP2C19 inhibitor	CYP2C9 inhibitor	CYP2D6 inhibitor		CL	T1/2
9b	0.37	0.711	0.89	0.021		3.079	0.165
9c	0.354	0.48	0.54	0.009		4.001	0.163
9e	0.152	0.429	0.428	0.007		1.78	0.083
9f	0.155	0.428	0.353	0.003		2.087	0.102
10a	0.316	0.611	0.887	0.019		2.503	0.369
10c	0.3	0.37	0.47	0.006		4.064	0.436
10d	0.232	0.588	0.848	0.053		2.603	0.152
10e	0.131	0.353	0.004	0.085		1.426	0.174
11a	0.902	0.866	0.344	0.186		5.247	0.175
11b	0.779	0.959	0.94	0.601		4.683	0.074
11c	0.916	0.934	0.618	0.246		4.516	0.143
11d	0.564	0.923	0.676	0.249		3.03	0.082
11e	0.576	0.739	0.573	0.094		1.417	0.18
11f	0.726	0.937	0.916	0.158		3.038	0.081
12a	0.687	0.923	0.85	0.275		2.807	0.527
12d	0.626	0.919	0.804	0.282		2.655	0.187
12e	0.551	0.697	0.711	0.172		1.093	0.478
Clorgyline	0.944	0.679	0.043	0.955		11.241	0.229
Deprenyl	0.384	0.133	0.016	0.925		10.465	0.808
Donepezil	0.14	0.413	0.097	0.986		10.635	0.164
ADR	0.489	0.013	0.016	0.005		9.556	0.847

CYP1A2: cytochrome P450 family 1 subfamily A polipeptide 2 inhibitor; CYP2C19: cytochrome P450 2C19 inhibitor; CYP2C9: cytochrome P450 family 2 subfamily C member 9 inhibitor; CYP2D6: cytochrome P450 family 2 subfamily D member 6 inhibitor; CL: clearance; T1/2: half-life time; ADR: adriamycin.

The toxicity profile of the compounds was tested and is shown in Table 7. The synthetic accessibility score (SA score) is used to calculate the ease of synthesis of drug like compounds. The substances with SA score less than 6 are easy to synthesize. Our findings showed that our compounds fall within the optimal range of SA score which means they were easy to produce. The AMES toxicity test is done to check if the compound is mutagen or not. All derivatives showed severe carcinogenicity. None of our compounds showed eye corrosion and eye irritation. All our compounds had a lower respiratory toxicity profile. Therefore, it can be suggested that all potent derivatives have good ADMET profile compared to the controls. Further investigation is required for improving the toxicity profile of compounds.

Table 7
Medicinal and toxicity properties of the derivatives 9b-12e

Compound	Medicinal					Toxicity
	Synthetic accessibility score	Lipinski rule	AMES toxicity	Carcinogenicity		Eye corrosion	Eye irritation	Respiratory toxicity
9b	2.363	accepted	0.936	0.845		0.003	0.013	0.336
9c	2.504	accepted	0.843	0.931		0.003	0.017	0.646
9e	2.28	accepted	0.81	0.8		0.003	0.017	0.041
9f	2.315	accepted	0.716	0.434		0.003	0.009	0.017
10a	2.163	accepted	0.913	0.915		0.003	0.064	0.61
10c	2.325	accepted	0.833	0.947		0.003	0.026	0.444
10d	2.224	accepted	0.954	0.926		0.003	0.04	0.746
10e	2.117	accepted	0.633	0.921		0.003	0.027	0.027
11a	2.767	accepted	0.93	0.96		0.003	0.022	0.962
11b	2.55	accepted	0.908	0.892		0.003	0.02	0.874
11c	2.736	accepted	0.942	0.952		0.003	0.02	0.953
11d	2.629	accepted	0.953	0.942		0.003	0.018	0.923
11e	2.538	accepted	0.939	0.846		0.003	0.03	0.858
11f	2.515	accepted	0.785	0.772		0.003	0.029	0.395
12a	2.413	accepted	0.935	0.929		0.003	0.035	0.941
12d	2.475	accepted	0.957	0.946		0.003	0.022	0.927
12e	2.379	accepted	0.929	0.903		0.003	0.049	0.846
Clorgyline	2.262	accepted	0.017	0.357		0.004	0.021	0.969
Deprenyl	2.621	accepted	0.035	0.435		0.004	0.033	0.951
Donepezil	2.677	accepted	0.026	0.079		0.003	0.013	0.965
ADR	3.951	accepted	0.378	0.923		0.003	0.01	0.894

AMES toxicity: Salmonella/microsome mutagenicity assay; ADR: adriamycin.

CONCLUSIONS

In the present study, previously synthesized quinolone-triazole hybrids were subjected to density functional theory (DFT), molecular docking and ADMET (absorption, distribution, metabolism, excretion and toxicity) properties. DFT results of the compounds after being optimized in the gas phase have supported our studies. Most of the compounds were evaluated using density functional theories and found to be stable. Molecular docking was done from both of the softwares Molecular Operating Environment and AutoDock and the results showed good binding scores. In docking studies, the derivatives have good docking scores against all the targets. It is indicated that with high binding energies and significant inhibitory potential, these analogs must be synthesized and therefore the findings of current study suggest that these analogs are potent inhibitors of both cholinesterase and monoamine oxidases and can be lead compounds in finding new treatments against Alzheimer’s and Parkinson’s disease.

SUPPLEMENTARY MATERIAL

Complementary material for this work is available at http://quimicanova.sbq.org.br/, as a PDF file, with free access.

ACKNOWLEDGMENTS

This research was funded by Princess Nourah bint Abdulrahman, University Researchers supporting project number (PNURSP2024R116), Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia.

REFERENCES

• ¹
  Breijyeh, Z.; Karaman, R.; Molecules 2020, 25, 5789. [Crossref]
  » Crossref
• ²
  Budson, A. E.; Solomon, P. R.; Practical Neurology 2012, 12, 88. [Crossref]
  » Crossref
• ³
  George, D. R.; Qualls, S. H.; Camp, C. J.; Whitehouse, P. J.; The Gerontologist 2013, 53, 378. [Crossref]
  » Crossref
• ⁴
  Bortolami, M.; Rocco, D.; Messore, A.; Di Santo, R.; Costi, R.; Madia, V. N.; Scipione, L.; Pandolfi, F.; Expert Opin. Ther. Pat. 2021, 31, 399. [Crossref]
  » Crossref
• ⁵
  Walczak-Nowicka, Ł. J.; Herbet, M.; Int. J. Mol. Sci. 2021, 22, 9290. [Crossref]
  » Crossref
• ⁶
  Sahoo, A. K.; Dandapat, J.; Dash, U. C.; Kanhar, S.; J. Ethnopharmacol. 2018, 215, 42. [Crossref]
  » Crossref
• ⁷
  Adhami, H. R.; Farsam, H.; Krenn, L.; Phytother. Res. 2011, 8, 1148. [Crossref]
  » Crossref
• ⁸
  Latif, S.; Jahangeer, M.; Razia, D. M.; Ashiq, M.; Ghaffar, A.; Akram, M.; El Allam, A.; Bouyahya, A.; Garipova, L.; Shariati, M. A.; Thiruvengadam, M.; Clin. Chim. Acta 2021, 522, 114. [Crossref]
  » Crossref
• ⁹
  Jamali, Y. A.; Rahu, H. N.; Kumar, A.; Khuhro, A. B.; Shaikh, A. S.; Soomro, S.; Pakistan BioMedical Journal 2024, 7, 60. [Crossref]
  » Crossref
• ¹⁰
  Compagnoni, G. M.; Di Fonzo, A.; Corti, S.; Comi, G. P.; Bresolin, N.; Masliah, E.; Mol. Neurobiol. 2020, 57, 2959. [Crossref]
  » Crossref
• ¹¹
  Mehanna, R.; Jankovic, J.; Parkinsonism Relat. Disord. 2019, 65, 39. [Crossref]
  » Crossref
• ¹²
  Speranza, L.; Di Porzio, U.; Viggiano, D.; de Donato, A.; Volpicelli, F.; Cells 2021, 10, 735. [Crossref]
  » Crossref
• ¹³
  Bloem, B. R.; Okun, M. S.; Klein, C.; The Lancet 2021, 397, 2284. [Crossref]
  » Crossref
• ¹⁴
  Donkelaar, H. J.; Dunnen, W. F.; Lammens, M.; Wesseling, P.; Willemsen, M.; Hori, A. In Clinical Neuroembryology; Donkelaar, H. J.; Lammens, M.; Hori, A., eds.; Springer: Berlin, 2014. [Crossref]
  » Crossref
• ¹⁵
  Abubakar, M. U.; Abubakar, D.; Journal of Environmental Bioremediation and Toxicology 2021, 4, 24. [Crossref]
  » Crossref
• ¹⁶
  To, T. A.; Gagné-Thivierge, C.; Couture, M.; Lagüe, P.; Yao, D.; Picard, M. E.; Lortie, L. A.; Attéré, S. A.; Zhu, X.; Levesque, R. C.; Charette, S. J.; J. Biol. Chem. 2020, 295, 8708. [Crossref]
  » Crossref
• ¹⁷
  Dirak, M.; Chan, J.; Kolemen, S.; J. Mater. Chem. 2024, 12, 1149. [Crossref]
  » Crossref
• ¹⁸
  Graves, S. M.; Xie, Z.; Stout, K. A.; Zampese, E.; Burbulla, L. F.; Shih, J. C.; Kondapalli, J.; Patriarchi, T.; Tian, L.; Brichta, L.; Greengard, P.; Nat. Neurosci. 2020, 23, 15. [Crossref]
  » Crossref
• ¹⁹
  Kumar, B.; Gupta, V. P.; Kumar, V.; Curr. Drug Targets 2017, 18, 87. [Crossref]
  » Crossref
• ²⁰
  Iacovino, L. G.; Magnani, F.; Binda, C.; J. Neural Transm. 2018, 125, 1567. [Crossref]
  » Crossref
• ²¹
  Mermer, A.; Demirbaş, N.; Şirin, Y.; Uslu, H.; Özdemir, Z.; Demirbaş, A.; Bioorg. Chem. 2018, 78, 236. [Crossref]
  » Crossref
• ²²
  Beytur, M.; Avinca, I.; Heterocycl. Commun. 2021, 27, 1. [Crossref]
  » Crossref
• ²³
  Aljohani, F. S.; Abu-Dief, A. M.; El-Khatib, R. M.; Al-Abdulkarim, H. A.; Alharbi, A.; Mahran, A.; Khalifa, M. E.; El-Metwaly, N. M.; J. Mol. Struct. 2021, 1246, 131139. [Crossref]
  » Crossref
• ²⁴
  Ajani, T. A.; Obikeze, K.; Magwebu, Z. E.; Egieyeh, S.; Chauke, C. G.; BMC Pharmacol. Toxicol. 2023, 24, 67. [Crossref]
  » Crossref
• ²⁵
  Kumar, S.; Ayyannan, S. R.; J. Biomol. Struct. Dyn. 2023, 41, 6789. [Crossref]
  » Crossref
• ²⁶
  Mahesar, P. A.; Channar, P. A.; Ejaz, S. A.; Saeed, A.; Alharbi, F. F.; Shamim, T.; Aziz, M.; Ujan, R.; Kandhro, G. A.; Channar, S. A.; Abbas, Q.; Chem. Pap. 2023, 7, 3447. [Link] accessed in June 2024
  » Link
• ²⁷
  Nachon, F.; Carletti, E.; Ronco, C.; Trovaslet, M.; Nicolet, Y.; Jean, L.; Renard, P. Y.; Biochem. J. 2013, 453, 393 [Crossref]; 4BDT, https://www.rcsb.org/structure/4BDT, accessed in June 2024; 4BDS, https://www.rcsb.org/structure/4BDS, accessed in June 2024.
  » Crossref» https://www.rcsb.org/structure/4BDT» https://www.rcsb.org/structure/4BDS
• ²⁸
  Son, S. Y.; Ma, J.; Kondou, Y.; Yoshimura, M.; Yamashita, E.; Tsukihara, T.; Proc. Natl. Acad. Sci. 2008, 105, 5739 [Crossref]; 2Z5Y, https://www.rcsb.org/structure/2Z5Y, accessed in June 2024.
  » Crossref» https://www.rcsb.org/structure/2Z5Y
• ²⁹
  Binda, C.; Wang, J.; Pisani, L.; Caccia, C.; Carotti, A.; Salvati, P.; Edmondson, D. E.; Mattevi, A.; J. Med. Chem. 2007, 50, 5848 [Crossref]; 2V5Z, https://www.rcsb.org/structure/2V5Z, accessed in June 2024.
  » Crossref» https://www.rcsb.org/structure/2V5Z
• ³⁰
  Aziz, M.; Ejaz, S. A.; Tamam, N.; Siddique, F.; Riaz, N.; Qais, F. A.; Chtita, S.; Iqbal, J.; Sci. Rep. 2022, 12, 6404. [Crossref]
  » Crossref
• ³¹
  Ejaz, S. A.; Alsfouk, A. A.; Batiha, G. E.; Aborode, A. T.; Ejaz, S. R.; Umar, H. I.; Aziz, M.; Saeed, A.; Mahmood, H. M.; Fayyaz, A.; Struct. Chem. 2023, 34, 425. [Crossref]
  » Crossref
• ³²
  Ejaz, S. A.; Bilal, M. S.; Aziz, M.; Wani, T. A.; Zargar, S.; Fayyaz, A.; Hassan, S.; Ahmed, A.; Al Kahtani, H. M.; Siddique, F.; Chem. Biodiversity 2023, 12, e202301190. [Crossref]
  » Crossref
• ³³
  de Araújo, A. C.; Freitas, P. R.; Araújo, I. M.; Siqueira, G. M.; Borges, J. A. O.; Alves, D.; Miranda, G. M.; Nascimento, I. J. S.; de Araújo-Júnior, J. X.; da Silva-Júnior, E. F.; de Aquino, T. M.; Mendonça Junior, J. B.; Mariho, E. S.; dos Santos, H. S.; Tintino, S. R.; Coutinho, H. D. M.; Fundam. Clin. Pharmacol. 2024, 38, 84. [Link] accessed in June 2024
  » Link

Edited by

Editor handled this article: Nelson H. Morgon

Publication Dates

• Publication in this collection
  02 Aug 2024
• Date of issue
  2025

History

• Received
  15 Feb 2024
• Accepted
  13 May 2024
• Published
  02 July 2024