Computationally Assisted Lead Optimization of Novel Potent and Selective MAO-B Inhibitors
Abstract
:1. Introduction
2. Materials and Methods
2.1. General
2.2. Synthesis of Derivatives
2.2.1. Procedure for Preparation of Compounds 1–4
Synthesis of (E) 4-Propyloxy-4′,6′-Dimethoxy-2′-Hydroxychalcone (1a)
Synthesis of 5,7-Dimethoxy-4′-Propyloxyflavone (1b)
Synthesis of 7-Methoxy-4′-Propyloxy-5-Hydroxyflavone (1c)
Synthesis of (E) 4-Isopropyloxy-4′,6′-Dimethoxy-2′-Hydroxychalcone (2a)
Synthesis of 5,7-Dimethoxy-4′-Isopropyloxyflavone (2b)
Synthesis of 7-Methoxy-4′-Isopropyloxy-5-Hydroxyflavone (2c)
Synthesis of (E) 4-Isobutyloxy-4′,6′-Dimethoxy-2′-Hydroxychalcone (3a)
Synthesis of 5,7-Dimethoxy-4′-Isobutyloxyflavone (3b)
Synthesis of 7-Methoxy-4′-Isobutyloxy-5-Hydroxyflavone (3c)
Synthesis of (E) 4-Propargyloxy-4′,6′-Dimethoxy-2′-Hydroxychalcone (4a)
Synthesis of 5,7-Dimethoxy-4′-Propargyloxyflavone (4b)
Synthesis of 7-Methoxy, 4′-Propargyloxy-5-Hydroxyflavone (4c)
2.2.2. Procedure for Preparation of Compounds 5
Synthesis of 4-(Cyclopropyl Methoxy)Benzaldehyde (Compound IV)
Synthesis of (E) 4-(Cyclopropyl Methoxy)-4′,6′-Dimethoxy-2′-Hydroxychalcone (5a)
Synthesis of 5,7-Dimethoxy-4′(Cyclopropyl Methoxy)Flavone (5b)
2.2.3. Procedure for Preparation of Compounds 6–8
Synthesis of (E) 4-Bromo-4′,6′-Dimethoxy-2′-Hydroxychalcone (6a)
Synthesis of 5,7-Dimethoxy-4′-Bromoflavone (6b)
Synthesis of 5,7-Dimethoxy-4′-Aminopropylflavone (6c)
Synthesis of 7-Methoxy-4′-Aminopropyl-5-Hydroxyflavone (6d)
Synthesis of 5,7-Dimethoxy-4′-Aminoisopropylflavone (7c)
Synthesis of 7-Methoxy-4′-Aminoisopropyl-5-Hydroxyflavone (7d)
Synthesis of 5,7-Dimethoxy-4′-(Cyclopropyl Methylamino)Flavone (8c)
Synthesis of 7-Methoxy-4′-(Cyclopropyl Methylamino)-5-Hydroxyflavone (8d)
2.3. Evaluation of Pan Assay Interference Compounds (PAINS)
2.4. Monoamine Oxidase Inhibition Assay and Determination of IC50 Values for Synthesized Compounds
2.5. Enzyme Kinetics, Mechanism Studies, Analysis of Reversibility, and Binding Assays of Acacetin 7-O-Methyl Ether Analogs
2.6. Molecular Modeling Studies
3. Results and Discussion
3.1. Comparison of Acacetin and Acacetin 7-O-Methyl Ether with Known Flavonoids
3.2. In Silico Optimization and Design
3.2.1. Molecular Dynamics (MD) Simulations of the Binding Modes of Acacetin and Acacetin 7-O-Methyl Ether
3.2.2. Active Site Hydration of MAO-B: MD Simulations, Thermodynamics, and Ligand Designs
3.3. Chemistry
3.3.1. Synthesis of Modified Flavonoids 1–5
3.3.2. Synthesis of Modified Flavonoids 6–8
3.4. Biological Assays
3.4.1. Determination of Inhibitory Effects of Modified Flavonoids and Intermediates on MAO-A and -B
3.4.2. Evaluation of MAO-B Inhibition Kinetics and Analysis of Binding and Time-Dependent Inhibition of Modified Flavonoids (1–4) c
3.5. Computational Analysis of Enzyme–Inhibitor Interactions for Modified Flavonoids (1–4) c
4. Conclusions
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Flavonoids | MAO-A (IC50, μM) * | MAO-B (IC50, μM) * | Selectivity Index MAO-A/B |
---|---|---|---|
Naringenin [38] | 955 ± 129 | 288 ± 18 | 3.32 |
Chrysin [39] | 0.25 ± 0.04 | 1.04 ± 0.17 | 0.24 |
Rhamnocitrin [40] | 0.051 ± 0.001 | 2.97 ± 2.97 | 0.02 |
4′-O-Methyl kaempferol [38] | 1.350 ± 0.198 | >100 | 0.01 |
3,4′-Di-O-methyl kaempferol [41] | 0.033 ± 0.042 | 9.667 ± 2.39 | 0.03 |
Quercetin [39] | 1.52 ± 0.09 | 28.39 ± 5.41 | 0.05 |
Isorhamnetin [42] | 6.42 ± 7.69 | 21.2 ± 4.99 | 0.30 |
Luteolin [43] | 8.57 ± 0.47 | >100 | 0.08 |
Vetulin [11] | 18.79 ± 0.29 | 0.44 ± 0.01 | 42.70 |
Diosmetin [42] | 5.74 ± 0.57 | 1.58 ± 0.88 | 3.63 |
Apigenin [44] | 0.64 ± 0.11 | 1.12 ± 0.27 | 0.30 |
Genkwanin [40] | 0.14 ± 0.01 | 0.35 ± 0.03 | 0.40 |
Acacetin [9] | 0.121 ± 0.001 | 0.049 ± 0.0007 | 4.28 |
Acacetin 7-O-methyl ether [11] | >100 | 0.198 ± 0.001 | >505.05 |
Synthesized Analogs | MAO-A (IC50, μM) * | MAO-B (IC50, μM) * | Selectivity Index MAO-A/B |
---|---|---|---|
1a | 0.515 ± 0.024 | 0.40 ± 0.024 | 1.3 |
2a | 0.117 ± 0.05 | 0.049 ± 0.0035 | 2.4 |
3a | 0.42 ± 0.002 | 0.22 ± 0.0211 | 1.9 |
4a | 9.42 ± 1.78 | 8.33 ± 0.7871 | 1.1 |
5a | 1.30 ± 0.09 | 0.90 ± 0.035 | 1.4 |
1b | >100 | 1.70 ± 0.076 | >58.8 |
2b | >100 | 2.64 ± 0.0880 | >37.9 |
3b | >100 | 7.23 ± 2.1438 | >13.8 |
4b | >100 | >100 | >1 |
4a | 9.42 ± 1.78 | 8.33 ± 0.7871 | 1.1 |
4b | >100 | >100 | >1 |
5b | >100 | 22.82 ± 2.098 | >4.4 |
1c | 48.78 ± 2.01 | 0.033 ± 0.0042 | 1478.2 |
2c | 30.74 ± 0.023 | 0.016 ± 0.0070 | 1921.3 |
3c | >100 | 0.031 ± 0.0070 | >3225.8 |
4c | 62.70 ± 5.21 | 0.049 ± 0.0014 | 1279.6 |
6a | 12.349 ± 0.249 | >100 | >0.1 |
6b | 87.830 ± 5.449 | 28.407 ± 2.639 | 3.1 |
6c | >100 | 22.097 ± 2.479 | >4.5 |
7c | 37.492 ± 0.476 | 9.447 ± 0.113 | 4.0 |
8c | 39.095 ± 5.144 | 12.727 ± 0.290 | 3.1 |
6d | 85.484 ± 1.585 | 1.554 ± 0.137 | 55.0 |
7d | 40.500 ± 2.374 | 0.417 ± 0.012 | 97.1 |
8d | 39.114 ± 0.555 | 2.185 ± 0.088 | 17.9 |
Acacetin | 0.105 ± 0.0014 | 0.042 ± 0.0021 | 2.5 |
Acacetin 7-O-methyl ether [11] | >100 | 0.198 ± 0.001 | >505.05 |
Clorgyline | 0.0039 ± 0.0002 | 2.15 ± 0.212 | 0.002 |
Deprenyl | 33.00 ± 1.411 | 0.046 ± 0.0014 | 717.4 |
Safinamide [46] | 90.00 ± 2.470 | 0.060 ± 0.005 | 1500.0 |
Harmine | 0.0031 ± 0.0003 | 39.000 ± 1.412 | 0.00008 |
Compound | Monoamine Oxidase A | Monoamine Oxidase B | ||
---|---|---|---|---|
Ki (nM) * | Type of Inhibition | Ki (nM) * | Type of Inhibition | |
1c | - | - | 43 ± 3.8 | mixed/partially reversible |
2c | - | - | 52 ± 3.1 | mixed/reversible |
3c | - | - | 37 ± 9.5 | mixed/irreversible |
4c | - | - | 68 ± 7.1 | mixed/irreversible |
Acacetin | 30 ± 1.8 | competitive/reversible | 21 ± 1.8 | competitive/reversible |
Acacetin 7-O-methyl ether [11] | -- | 45 ± 3.0 | competitive/partially reversible | |
Deprenyl | - | - | 43 ± 4.0 | mixed/irreversible |
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Gogineni, V.; Nael, M.A.; Chaurasiya, N.D.; Elokely, K.M.; McCurdy, C.R.; Rimoldi, J.M.; Cutler, S.J.; Tekwani, B.L.; León, F. Computationally Assisted Lead Optimization of Novel Potent and Selective MAO-B Inhibitors. Biomedicines 2021, 9, 1304. https://doi.org/10.3390/biomedicines9101304
Gogineni V, Nael MA, Chaurasiya ND, Elokely KM, McCurdy CR, Rimoldi JM, Cutler SJ, Tekwani BL, León F. Computationally Assisted Lead Optimization of Novel Potent and Selective MAO-B Inhibitors. Biomedicines. 2021; 9(10):1304. https://doi.org/10.3390/biomedicines9101304
Chicago/Turabian StyleGogineni, Vedanjali, Manal A. Nael, Narayan D. Chaurasiya, Khaled M. Elokely, Christopher R. McCurdy, John M. Rimoldi, Stephen J. Cutler, Babu L. Tekwani, and Francisco León. 2021. "Computationally Assisted Lead Optimization of Novel Potent and Selective MAO-B Inhibitors" Biomedicines 9, no. 10: 1304. https://doi.org/10.3390/biomedicines9101304
APA StyleGogineni, V., Nael, M. A., Chaurasiya, N. D., Elokely, K. M., McCurdy, C. R., Rimoldi, J. M., Cutler, S. J., Tekwani, B. L., & León, F. (2021). Computationally Assisted Lead Optimization of Novel Potent and Selective MAO-B Inhibitors. Biomedicines, 9(10), 1304. https://doi.org/10.3390/biomedicines9101304