3D-QSAR and Interaction Mechanism of Flavonoids s P-glycoprotein Inhibitors†

doi:10.7503/cjcu20160643

Abstract

Abstract:

A 3D-QSAR model of 30 flavonoids as P-glycoprotein inhibitors was constructed using Topomer CoMFA. Nine flavonoids were used to validate the external predictive power of the obtained model. The multiple correlation coefficients of fitting, cross validation and external validation were 0.971, 0.728 and 0.816, respectively. The molecular docking modes of chrysin and its prenylated derivatives were established by the Surflex-dock method. The results showed the lipophilicity of flavonoids could be improved by the prenyl modification, which resulted to the stronger interaction with the hydrophobic pocket of P-glycoprotein.

Key words: Flavonoids, P-glycoprotein, 3D-Quantitative structure-activity relationship, Topomer CoMFA, Surflex-dock

CLC Number:

O621
R914

TrendMD:

LIU Benguo, LIU Jiangwei, LI Jiaqi, GENG Sheng, MO Haizhen, LIANG Guizhao. 3D-QSAR and Interaction Mechanism of Flavonoids s P-glycoprotein Inhibitors^†[J]. Chem. J. Chinese Universities, 2017, 38(1): 41.

Figures/Tables 8

Fig.1 Chemical structures of flavonoids

Table 1 Bioactivity data of 3a flavonoids

Compd.	Type	3	4	5	6	7	8	2'	3'	4'	5'	6'	K_D/ (μmol·L^-1)	Group
1	Ⅰ	—H		—H	—H	—OH	—H	—H	—H	—H	—H	—H	34.900	Training
2	Ⅰ	—H		—OH	—H	—OH	—H	—H	—H	—OH	—H	—H	10.100	Training
3	Ⅰ	—OH		—H	—H	—H	—H	—H	—H	—H	—H	—H	10.100	Training
4	Ⅰ	—H		—OH	—H	—OH	—H	—H	—H	—H	—H	—H	8.900	Test
5	Ⅰ	—CH₃		—OH	—H	—OH	—H	—H	—H	—H	—H	—H	8.900	Training
6	Ⅰ	—OH		—OH	—H	—OH	—H	—H	—OH	—OH	—H	—H	7.000	Training
7	Ⅰ	—OH		—OH	—H	—OH	—H	—H	—H	—F	—H	—H	6.800	Training
8	Ⅰ	—OH		—OH	—H	—OH	—H	—H	—H	—OH	—H	—H	6.700	Test
9	Ⅰ	—H		—OH	—H	—OH	—H	—H	—F	—F	—H	—H	6.300	Training
10	Ⅰ	—H		—OH	—H	—OCH₃	—H	—H	—H	—H	—H	—H	6.300	Training
11	Ⅰ	—OH		—OH	—H	—OH	—H	—H	—H	—H	—H	—H	5.300	Training
12	Ⅱ	—H	—OH	—H	—H			—OH	—H	—OH	—H	—OH	4.800	Test
13	Ⅱ	—H	—H	—H	—H			—OH	—H	—OH	—H	—OH	4.600	Training
14	Ⅰ	—OH		—OH	—H	—OH	—H	—H	—H	—OCH₃	—H	—H	4.500	Training
15	Ⅰ	—OH		—OH	—H	—OH	—H	—Cl	—H	—Cl	—H	—H	4.000	Training
16	Ⅱ	—H	—F	—H	—H			—OH	—H	—OH	—H	—OH	3.600	Test
17	Ⅰ	—H		—OH	—CH₃	—OH	—H	—H	—H	—H	—H	—H	3.100	Training
18	Ⅱ	—H	—OCH₃	—H	—H			—OH	—H	—OH	—H	—OH	2.300	Training
19	Ⅰ	—H		—OH	—H	—OH	—H	—H	—H	—I	—H	—H	2.200	Training
20	Ⅰ	—H		—OH	—CH₃	—CH₃	—H	—H	—H	—H	—H	—H	1.300	Test
21	Ⅱ	—H	—Cl	—H	—H			—OH	—H	—OH	—H	—OH	1.300	Training
22	Ⅰ	—H		—OH	—H	Isopropyl	—H	—H	—H	—H	—H	—H	1.300	Training
23	Ⅰ	—OH		—OH	—H	—OH	—H	—H	—H	—I	—H	—H	1.100	Training
24	Ⅰ	—H		—OH	—H	—OH	Benzyl	—H	—H	—H	—H	—H	0.990	Test
25	Ⅰ	—H		—OH	—H	—OH	Dimethylallyl	—H	—H	—OH	—H	—H	0.700	Training
26	Ⅱ	—H	—Br	—H	—H			—OH	—H	—OH	—H	—OH	0.570	Training
27	Ⅰ	—OH		—OH	—H	—OH	Dimethylallyl	—H	—H	—H	—H	—H	0.450	Training
28	Ⅰ	—H		—OH	Benzyl	—OH	—H	—H	—H	—H	—H	—H	0.340	Test
29	Ⅰ	—H		—OH	Prenyl	—OH	—H	—H	—H	—H	—H	—H	0.300	Training
30	Ⅰ	—H		—OH	—H	—OH	Prenyl	—H	—H	—H	—H	—H	0.280	Training
31	Ⅱ	—H	—I	—H	—H			—OH	—H	—OH	—H	—OH	0.250	Training
32	Ⅰ	—OH		—OH	—H	—OH	Prenyl	—H	—H	—H	—H	—H	0.220	Test
33	Ⅰ	—OH		—OH	Prenyl	—OH	—H	—H	—H	—H	—H	—H	0.210	Training
34	Ⅰ	—OH		—OH	—H	—OH	Dimethylallyl	—H	—H	—OCH₃	—H	—H	0.200	Training
35	Ⅰ	—H		—OH	—H	—OH	Dimethylallyl	—H	—H	—H	—H	—H	0.200	Training
36	Ⅰ	—CH₃		—OH	—H	—CH₃	Dimethylallyl	—H	—H	—H	—H	—H	0.150	Test
37	Ⅰ	—OH		—OH	—H	—OH	—H	—H	—H	n-C₈H₁₇	—H	—H	0.060	Training
38	Ⅰ	—H		—OH	Geranyl	—OH	—H	—H	—H	—H	—H	—H	0.045	Training
39	Ⅰ	—H		—OH	—H	—OH	Geranyl	—H	—H	—H	—H	—H	0.025	Training

Table 1 Bioactivity data of 3a flavonoids

Compd.	Type	3	4	5	6	7	8	2'	3'	4'	5'	6'	K_D/ (μmol·L^-1)	Group
1	Ⅰ	—H		—H	—H	—OH	—H	—H	—H	—H	—H	—H	34.900	Training
2	Ⅰ	—H		—OH	—H	—OH	—H	—H	—H	—OH	—H	—H	10.100	Training
3	Ⅰ	—OH		—H	—H	—H	—H	—H	—H	—H	—H	—H	10.100	Training
4	Ⅰ	—H		—OH	—H	—OH	—H	—H	—H	—H	—H	—H	8.900	Test
5	Ⅰ	—CH₃		—OH	—H	—OH	—H	—H	—H	—H	—H	—H	8.900	Training
6	Ⅰ	—OH		—OH	—H	—OH	—H	—H	—OH	—OH	—H	—H	7.000	Training
7	Ⅰ	—OH		—OH	—H	—OH	—H	—H	—H	—F	—H	—H	6.800	Training
8	Ⅰ	—OH		—OH	—H	—OH	—H	—H	—H	—OH	—H	—H	6.700	Test
9	Ⅰ	—H		—OH	—H	—OH	—H	—H	—F	—F	—H	—H	6.300	Training
10	Ⅰ	—H		—OH	—H	—OCH₃	—H	—H	—H	—H	—H	—H	6.300	Training
11	Ⅰ	—OH		—OH	—H	—OH	—H	—H	—H	—H	—H	—H	5.300	Training
12	Ⅱ	—H	—OH	—H	—H			—OH	—H	—OH	—H	—OH	4.800	Test
13	Ⅱ	—H	—H	—H	—H			—OH	—H	—OH	—H	—OH	4.600	Training
14	Ⅰ	—OH		—OH	—H	—OH	—H	—H	—H	—OCH₃	—H	—H	4.500	Training
15	Ⅰ	—OH		—OH	—H	—OH	—H	—Cl	—H	—Cl	—H	—H	4.000	Training
16	Ⅱ	—H	—F	—H	—H			—OH	—H	—OH	—H	—OH	3.600	Test
17	Ⅰ	—H		—OH	—CH₃	—OH	—H	—H	—H	—H	—H	—H	3.100	Training
18	Ⅱ	—H	—OCH₃	—H	—H			—OH	—H	—OH	—H	—OH	2.300	Training
19	Ⅰ	—H		—OH	—H	—OH	—H	—H	—H	—I	—H	—H	2.200	Training
20	Ⅰ	—H		—OH	—CH₃	—CH₃	—H	—H	—H	—H	—H	—H	1.300	Test
21	Ⅱ	—H	—Cl	—H	—H			—OH	—H	—OH	—H	—OH	1.300	Training
22	Ⅰ	—H		—OH	—H	Isopropyl	—H	—H	—H	—H	—H	—H	1.300	Training
23	Ⅰ	—OH		—OH	—H	—OH	—H	—H	—H	—I	—H	—H	1.100	Training
24	Ⅰ	—H		—OH	—H	—OH	Benzyl	—H	—H	—H	—H	—H	0.990	Test
25	Ⅰ	—H		—OH	—H	—OH	Dimethylallyl	—H	—H	—OH	—H	—H	0.700	Training
26	Ⅱ	—H	—Br	—H	—H			—OH	—H	—OH	—H	—OH	0.570	Training
27	Ⅰ	—OH		—OH	—H	—OH	Dimethylallyl	—H	—H	—H	—H	—H	0.450	Training
28	Ⅰ	—H		—OH	Benzyl	—OH	—H	—H	—H	—H	—H	—H	0.340	Test
29	Ⅰ	—H		—OH	Prenyl	—OH	—H	—H	—H	—H	—H	—H	0.300	Training
30	Ⅰ	—H		—OH	—H	—OH	Prenyl	—H	—H	—H	—H	—H	0.280	Training
31	Ⅱ	—H	—I	—H	—H			—OH	—H	—OH	—H	—OH	0.250	Training
32	Ⅰ	—OH		—OH	—H	—OH	Prenyl	—H	—H	—H	—H	—H	0.220	Test
33	Ⅰ	—OH		—OH	Prenyl	—OH	—H	—H	—H	—H	—H	—H	0.210	Training
34	Ⅰ	—OH		—OH	—H	—OH	Dimethylallyl	—H	—H	—OCH₃	—H	—H	0.200	Training
35	Ⅰ	—H		—OH	—H	—OH	Dimethylallyl	—H	—H	—H	—H	—H	0.200	Training
36	Ⅰ	—CH₃		—OH	—H	—CH₃	Dimethylallyl	—H	—H	—H	—H	—H	0.150	Test
37	Ⅰ	—OH		—OH	—H	—OH	—H	—H	—H	n-C₈H₁₇	—H	—H	0.060	Training
38	Ⅰ	—H		—OH	Geranyl	—OH	—H	—H	—H	—H	—H	—H	0.045	Training
39	Ⅰ	—H		—OH	—H	—OH	Geranyl	—H	—H	—H	—H	—H	0.025	Training

Fig.2 Linear regression between the observed and predicted pKD of 39 flavonoids

Table 2 Comparison of QSAR modeling of flavonoids as P-glycoprotein inhibitors

No.	Modeling method	Training set size	Test set size	r²	q²	$r pred 2$
1	2D-QSAR^[11]	46	0	0.912	0.800
2	2D-QSAR^[12]	43	14	0.826	0.721	0.679
3	Ligand based CoMFA^[21]	32	9	0.747	0.639	0.802
4	Ligand based CoMSIA^[21]	32	9	0.810	0.676	0.785
5	Receptor guided CoMFA^[21]	32	9	0.712	0.497	0.841
6	Receptor guided CoMSIA^[21]	32	9	0.805	0.589	0.937
7	Topomer CoMFA	30	9	0.971	0.728	0.816

Table 2 Comparison of QSAR modeling of flavonoids as P-glycoprotein inhibitors

No.	Modeling method	Training set size	Test set size	r²	q²	$r pred 2$
1	2D-QSAR^[11]	46	0	0.912	0.800
2	2D-QSAR^[12]	43	14	0.826	0.721	0.679
3	Ligand based CoMFA^[21]	32	9	0.747	0.639	0.802
4	Ligand based CoMSIA^[21]	32	9	0.810	0.676	0.785
5	Receptor guided CoMFA^[21]	32	9	0.712	0.497	0.841
6	Receptor guided CoMSIA^[21]	32	9	0.805	0.589	0.937
7	Topomer CoMFA	30	9	0.971	0.728	0.816

Fig.3 3D contour plots of the Topomer CoMFA modelThe reference molecule used was sample 4(Chrysin). (A) Steric field map of R1-group; (B) electrostatic field map of R1-group;(C) steric field map of R2-group; (D) electrostatic field map of R2-group.

Fig.4 Docked pose corresponding to the minimum energy conformation for compound 4(A, B) and compound 39(C, D) binding to P-glycoprotein

Fig.5 MOLCAD of lipophilicity in binding pocket of 3G60

Table 3 Total score and ClogP values of chrysin and its prenylated derivatives

Molecules	Compd.	Total score	ClogP	H-donor	H-acceptor	M_w
Chrysin	4	2.69	3.56	2	4	254.24
6-Prenyl-chrysin	29	3.87	5.46	2	4	322.36
8-Prenyl-chrysin	30	4.06	5.51	2	4	322.36
6-Geranyl-chrysin	38	6.66	7.49	2	4	390.48
8-Geranyl-chrysin	39	7.47	7.54	2	4	390.48

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