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Estimating flavonoid oxidation potentials: mechanisms and charge-related regression models Cover

Estimating flavonoid oxidation potentials: mechanisms and charge-related regression models

Open Access
|Jun 2023

Figures & Tables

Figure 1

The dependence of experimental Ep1 (pH = 3) on ∑s(C)AOSPRad for the set of 35 flavonoids. Quadratic regression yielded R2 = 0.920, S.E. = 0.071, and S.E.cv = 0.080 (Model 1 in Table 2)
The dependence of experimental Ep1 (pH = 3) on ∑s(C)AOSPRad for the set of 35 flavonoids. Quadratic regression yielded R2 = 0.920, S.E. = 0.071, and S.E.cv = 0.080 (Model 1 in Table 2)

Scheme 1

As an example, the structure of 3,3’,4’-tryhydroxyflavone (3,3’,4’THF) is given with the numbering of atoms in the skeleton
As an example, the structure of 3,3’,4’-tryhydroxyflavone (3,3’,4’THF) is given with the numbering of atoms in the skeleton

Figure 2

The dependence of experimental Ep1 (pH = 3) on ∑s(C)ΔNACCat-Neut for the set of 35 flavonoids. Quadratic regression yielded R2 = 0.943, S.E. = 0.060, and S.E.cv = 0.065 (Model 2 in Table 2)
The dependence of experimental Ep1 (pH = 3) on ∑s(C)ΔNACCat-Neut for the set of 35 flavonoids. Quadratic regression yielded R2 = 0.943, S.E. = 0.060, and S.E.cv = 0.065 (Model 2 in Table 2)

Figure 3

The dependence of experimental Ep1 (pH 3) on ∑s(C)ΔNACRad-Anion for the set of 35 flavonoids. Quadratic regression yielded R2 = 0.942, S.E. = 0.061, and S.E.cv = 0.066 (Model 3 in Table 2)
The dependence of experimental Ep1 (pH 3) on ∑s(C)ΔNACRad-Anion for the set of 35 flavonoids. Quadratic regression yielded R2 = 0.942, S.E. = 0.061, and S.E.cv = 0.066 (Model 3 in Table 2)

Figure 4

The dependence of experimental Ep1 (pH 3) on ∑s(C)ΔNACRad-Neut for the set of 35 flavonoids. Quadratic regression yielded R2 = 0.844, S.E. = 0.100, and S.E.cv = 0.108 (Model 4 in Table 2)
The dependence of experimental Ep1 (pH 3) on ∑s(C)ΔNACRad-Neut for the set of 35 flavonoids. Quadratic regression yielded R2 = 0.844, S.E. = 0.100, and S.E.cv = 0.108 (Model 4 in Table 2)

Figure 5

The dependence of experimental Ep1 (pH 3) on the mean values of ∑s(C)ΔNACCat-Neut, ∑s(C)ΔNACRad-Anion and ∑s(C)ΔNACRad-Neut (variables 1, 2, and 3, Table 1) for the set of 35 flavonoids. Quadratic regression yielded R2 = 0.970, S.E. = 0.043, and S.E.cv = 0.046 (Model 5 in Table 2)
The dependence of experimental Ep1 (pH 3) on the mean values of ∑s(C)ΔNACCat-Neut, ∑s(C)ΔNACRad-Anion and ∑s(C)ΔNACRad-Neut (variables 1, 2, and 3, Table 1) for the set of 35 flavonoids. Quadratic regression yielded R2 = 0.970, S.E. = 0.043, and S.E.cv = 0.046 (Model 5 in Table 2)

Figure 6

Correlation of experimental vs. theoretical Ep1 values for the set of 35 flavonoids at pH 3 and 7 (N = 70). Theoretical values were calculated by the model: Ep1 = a1 (mean of variables 1, 2, and 3) + a2 (mean of variables 1, 2, and 3)2 + a3 NOH + a4 pH + b; r = 0.991, S.E. = 0.039, and S.E.cv = 0.042
Correlation of experimental vs. theoretical Ep1 values for the set of 35 flavonoids at pH 3 and 7 (N = 70). Theoretical values were calculated by the model: Ep1 = a1 (mean of variables 1, 2, and 3) + a2 (mean of variables 1, 2, and 3)2 + a3 NOH + a4 pH + b; r = 0.991, S.E. = 0.039, and S.E.cv = 0.042

The values for the first oxidation potential, Ep1, for 35 flavonoids at pH 3 and 7, active site (A site), the sum of atomic orbital spin populations over the carbon atoms in the skeleton of a flavonoid radical molecule, ∑s(C)AOSPRad, the sum of differences in the net atomic charges between cation and neutral flavonoid (∑s(C)ΔNACCat-Neut), radical and anion (∑s(C)ΔNACRad-Anion) and radical and neutral flavonoid (∑s(C)ΔNACRad-Neut) calculated using the PM6 in water method and their mean values, and the number of OH groups in a flavonoid_

No.FlavonoidA siteEp1/V (pH=3)Ep1/V (pH=7)s(C)AOSPRads(C)ΔNACCat-Neut (var. 1)s(C)ΔNACRad-Anion (var. 2)s(C)ΔNACRad-Neut (var. 3)Mean var. 1-3NOH
13,3′,4′THF4′0.456b0.197b0.5270.3530.3330.2490.3123
23′,4′DHF4′0.513b0.283b0.6310.3730.3870.2720.3442
33HF30.751b0.566b0.6970.4280.440.2390.3691
45HF51.164b0.909b0.8450.5160.4930.3580.4561
57,8DHF80.456b0.225b0.5380.3390.3610.2170.3062
6Apigenin4′0.928c0.696g0.7920.4670.460.3350.4213
7Chrysin51.162c0.956g0.9230.5080.4930.3750.4592
8Galangin30.655c0.430b0.7330.4370.4440.2440.3753
9Luteolin4′0.513b0.288g0.6310.3660.380.2660.3374
10Quercetin4′0.435c0.180g0.5190.3500.3250.2480.3085
11Myricetin4′0.351c0.089d0.3640.2810.2530.2290.2546
12EGC4′0.307e0.028e0.4710.2830.2930.2480.2756
13EC4′0.390f0.150f0.6210.3720.3740.280.3425
14Morin30.458c0.227g0.5910.3800.3350.2390.3185
15EGCG4′0.367c0.051e0.4720.2980.2940.2480.285
16ECG4′0.477c0.162f0.6220.3620.3740.2760.3374
17Naringenin4′0.929c0.704h0.7900.4800.4620.3560.4333
18Kaempferid30.584c0.369h0.6540.4140.4070.2330.3513
19Dyhidromyricetin4′0.354d0.098d0.4700.3050.3020.2450.2846
20Rutin4′0.504c0.267h0.6100.3610.3670.2710.3334
21Hesperetin3′0.737i0.510i0.7510.4230.4290.3220.3913
22Daidzein4′0.795i0.592i0.7720.4510.4320.3280.4042
23Kaempferol30.498i0.235i0.6590.4190.4090.2340.3544
24Acacetin51.174i0.952i0.9250.5090.4910.3740.4582
25Naringin4′0.959i0.732i0.7910.4660.4630.3480.4262
26Neohesperidin3′0.766i0.549i0.7500.4240.4240.3220.392
27Hesperidin3′0.739i0.542i0.7500.4240.4240.3220.392
28Quercitrin4′0.500i0.270i0.6100.3610.3670.2710.3334
29Gossypin4′0.416i0.132i0.5150.3490.3280.2440.3075
30567THF60.411a0.162a0.4090.3040.2930.2330.2773
31Fisetin4′0.435a0.183a0.5240.3550.3310.2520.3134
3237DHF30.643a0.474a0.7260.4360.4480.2460.3772
334′7DHF4′0.948a0.692a0.7930.4740.4660.3390.4262
34Genistein4′0.809a0.613a0.7730.4500.4330.3280.4043
356HF60.975a0.751a0.7420.4490.4670.3220.4131

Quadratic regression models (Ep1 = ax2 + bx + c) for the estimation of Ep1 based on ∑s(C)AOSPRad, ∑s(C)ΔNACCat-Neut (var_ 1), ∑s(C)ΔNACRad-Anion (var_ 2), ∑s(C)ΔNACRad-Neut (var_ 3) and the mean of variables 1, 2, and 3_

Model No.Independent variable (x)a (S.E.)b (S.E.)Intercept c (S.E.)R2S.E.S.E.cv
1s(C)AOSPRad3.04(58)−2.27(76)0.76(24)0.9200.0710.080
2s(C)ΔNACCat-Neut (1)14.3(24)−7.7(19)1.39(38)0.9430.0600.065
3s(C)ΔNACRad-Anion (2)20.1(26)−11.9(20)2.14(38)0.9420.0610.066
4s(C)ΔNACRad-Neut (3)37(11)−17.3(63)2.47(90)0.8440.1000.108
5Mean (var. 1, 2 and 3)17.2(24)−8.2(18)1.30(31)0.9700.0430.046
DOI: https://doi.org/10.2478/aiht-2023-74-3721 | Journal eISSN: 1848-6312 | Journal ISSN: 0004-1254
Language: English, Croatian, Slovenian
Page range: 99 - 105
Submitted on: Feb 1, 2023
Accepted on: May 1, 2023
Published on: Jun 26, 2023
Published by: Institute for Medical Research and Occupational Health
In partnership with: Paradigm Publishing Services
Publication frequency: 4 times per year

© 2023 Ante Miličević, published by Institute for Medical Research and Occupational Health
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.