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

Ante Miličević

doi:10.2478/aiht-2023-74-3721

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

Archives of Industrial Hygiene and Toxicology

Volume 74 (2023): Issue 2 (June 2023)

By: Ante Miličević

Open Access

|Jun 2023

Figures & Tables

Table 1

The values for the first oxidation potential, E_p1, 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, $\sum_{s (C)} {AOSP}_{Rad}$ , the sum of differences in the net atomic charges between cation and neutral flavonoid ( $\sum_{s (C)} Δ {NAC}_{Cat-Neut}$ ), radical and anion ( $\sum_{s (C)} Δ {NAC}_{Rad-Anion}$ ) and radical and neutral flavonoid ( $\sum_{s (C)} Δ {NAC}_{Rad-Neut}$ ) calculated using the PM6 in water method and their mean values, and the number of OH groups in a flavonoid.

No.	Flavonoid	A site	E_p1/V (pH=3)	E_p1/V (pH=7)	$\sum_{s (C)} {AOSP}_{Rad}$	$\sum_{s (C)} Δ {NAC}_{Cat-Neut}$ (var. 1)	$\sum_{s (C)} Δ {NAC}_{Rad-Anion}$ (var. 2)	$\sum_{s (C)} Δ {NAC}_{Rad-Neut}$ (var. 3)	Mean var. 1-3	N_OH
1	3,3′,4′THF	4′	0.456^b	0.197^b	0.527	0.353	0.333	0.249	0.312	3
2	3′,4′DHF	4′	0.513^b	0.283^b	0.631	0.373	0.387	0.272	0.344	2
3	3HF	3	0.751^b	0.566^b	0.697	0.428	0.44	0.239	0.369	1
4	5HF	5	1.164^b	0.909^b	0.845	0.516	0.493	0.358	0.456	1
5	7,8DHF	8	0.456^b	0.225^b	0.538	0.339	0.361	0.217	0.306	2
6	Apigenin	4′	0.928^c	0.696^g	0.792	0.467	0.46	0.335	0.421	3
7	Chrysin	5	1.162^c	0.956^g	0.923	0.508	0.493	0.375	0.459	2
8	Galangin	3	0.655^c	0.430^b	0.733	0.437	0.444	0.244	0.375	3
9	Luteolin	4′	0.513^b	0.288^g	0.631	0.366	0.38	0.266	0.337	4
10	Quercetin	4′	0.435^c	0.180^g	0.519	0.350	0.325	0.248	0.308	5
11	Myricetin	4′	0.351^c	0.089^d	0.364	0.281	0.253	0.229	0.254	6
12	EGC	4′	0.307^e	0.028^e	0.471	0.283	0.293	0.248	0.275	6
13	EC	4′	0.390^f	0.150^f	0.621	0.372	0.374	0.28	0.342	5
14	Morin	3	0.458^c	0.227^g	0.591	0.380	0.335	0.239	0.318	5
15	EGCG	4′	0.367^c	0.051^e	0.472	0.298	0.294	0.248	0.28	5
16	ECG	4′	0.477^c	0.162^f	0.622	0.362	0.374	0.276	0.337	4
17	Naringenin	4′	0.929^c	0.704^h	0.790	0.480	0.462	0.356	0.433	3
18	Kaempferid	3	0.584^c	0.369^h	0.654	0.414	0.407	0.233	0.351	3
19	Dyhidromyricetin	4′	0.354^d	0.098^d	0.470	0.305	0.302	0.245	0.284	6
20	Rutin	4′	0.504^c	0.267^h	0.610	0.361	0.367	0.271	0.333	4
21	Hesperetin	3′	0.737ⁱ	0.510ⁱ	0.751	0.423	0.429	0.322	0.391	3
22	Daidzein	4′	0.795ⁱ	0.592ⁱ	0.772	0.451	0.432	0.328	0.404	2
23	Kaempferol	3	0.498ⁱ	0.235ⁱ	0.659	0.419	0.409	0.234	0.354	4
24	Acacetin	5	1.174ⁱ	0.952ⁱ	0.925	0.509	0.491	0.374	0.458	2
25	Naringin	4′	0.959ⁱ	0.732ⁱ	0.791	0.466	0.463	0.348	0.426	2
26	Neohesperidin	3′	0.766ⁱ	0.549ⁱ	0.750	0.424	0.424	0.322	0.39	2
27	Hesperidin	3′	0.739ⁱ	0.542ⁱ	0.750	0.424	0.424	0.322	0.39	2
28	Quercitrin	4′	0.500ⁱ	0.270ⁱ	0.610	0.361	0.367	0.271	0.333	4
29	Gossypin	4′	0.416ⁱ	0.132ⁱ	0.515	0.349	0.328	0.244	0.307	5
30	567THF	6	0.411^a	0.162^a	0.409	0.304	0.293	0.233	0.277	3
31	Fisetin	4′	0.435^a	0.183^a	0.524	0.355	0.331	0.252	0.313	4
32	37DHF	3	0.643^a	0.474^a	0.726	0.436	0.448	0.246	0.377	2
33	4′7DHF	4′	0.948^a	0.692^a	0.793	0.474	0.466	0.339	0.426	2
34	Genistein	4′	0.809^a	0.613^a	0.773	0.450	0.433	0.328	0.404	3
35	6HF	6	0.975^a	0.751^a	0.742	0.449	0.467	0.322	0.413	1

1a ref(17),

1b ref 20),

1c ref(21),

1d ref(25)

1e ref(26),

1f ref(27),

1g (22),

1h (23),

1i (18)

Table 2

Quadratic regression models (E_p1 = ax² + bx + c) for the estimation of E_p1 based on $\sum_{s (C)} {AOSP}_{Rad}$ , $\sum_{s (C)} Δ {NAC}_{Cat-Neut}$ (var. 1), $\sum_{s (C)} Δ {NAC}_{Rad-Anion}$ (var. 2), $\sum_{s (C)} Δ {NAC}_{Rad-Neut}$ (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.)	R²	S.E.	S.E._cv
1	$\sum_{s (C)} {AOSP}_{Rad}$	3.04(58)	−2.27(76)	0.76(24)	0.920	0.071	0.080
2	$\sum_{s (C)} Δ {NAC}_{Cat-Neut}$ (1)	14.3(24)	−7.7(19)	1.39(38)	0.943	0.060	0.065
3	$\sum_{s (C)} Δ {NAC}_{Rad-Anion}$ (2)	20.1(26)	−11.9(20)	2.14(38)	0.942	0.061	0.066
4	$\sum_{s (C)} Δ {NAC}_{Rad-Neut}$ (3)	37(11)	−17.3(63)	2.47(90)	0.844	0.100	0.108
5	Mean (var. 1, 2 and 3)	17.2(24)	−8.2(18)	1.30(31)	0.970	0.043	0.046

The dependence of experimental E_p1 (pH = 3) on $\sum_{s (C)} {AOSP}_{Rad}$ 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)

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

The dependence of experimental E_p1 (pH = 3) on $\sum_{s (C)} Δ {NAC}_{Cat-Neut}$ 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 E_p1 (pH 3) on $\sum_{s (C)} Δ {NAC}_{Rad-Anion}$ 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 E_p1 (pH 3) on $\sum_{s (C)} Δ {NAC}_{Rad-Neut}$ 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 E_p1 (pH 3) on the mean values of $\sum_{s (C)} Δ {NAC}_{Cat-Neut}$ , $\sum_{s (C)} Δ {NAC}_{Rad-Anion}$ and $\sum_{s (C)} Δ {NAC}_{Rad-Neut}$ (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)

Correlation of experimental vs. theoretical E_p1 values for the set of 35 flavonoids at pH 3 and 7 (N = 70). Theoretical values were calculated by the model: E_p1 = a₁ (mean of variables 1, 2, and 3) + a₂ (mean of variables 1, 2, and 3)² + a₃ N_OH + a₄ pH + *b; r* = 0.991, S.E. = 0.039, and S.E._cv = 0.042

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DOI: https://doi.org/10.2478/aiht-2023-74-3721 | Journal eISSN: 1848-6312 (formerly 0004-1254) | Journal ISSN: 0004-1254

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Language: English, Croatian

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

Keywords:

atomic charges,

electrochemical oxidation,

PM6,

polyphenols,

QSPR

Related subjects:

Medicine,

Basic medical science,

Basic medical science, other

© 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.

Volume 74 (2023): Issue 2 (June 2023)

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

Figures & Tables

Table 1

Table 2

Figure 1

Scheme 1

Figure 2

Figure 3

Figure 4

Figure 5

Figure 6

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