低光降解性氟喹诺酮类抗生素的分子设计及光解路径推断

doi:10.7503/cjcu20180475

摘要/Abstract

摘要：

以已知光降解半衰期(t_1/2)的12种氟喹诺酮(FQs)同系物按摩尔比4:1选择训练集和测试集(随机组合), 釆用Sybyl-X 2.0软件QSAR模块中结合比较分子力场分析(CoMFA)方法和比较分子相似性指数分析(CoMSIA)方法进行三维定量构效关系(3D-QSAR)研究, 建立以FQs分子结构参数为自变量, lgt_1/2为因变量的3D-QSAR模型, 并预测剩余14种FQs分子的t_1/2. 结果表明, 任意组合所构建的CoMFA和CoMSIA模型交叉验证相关系数q²在0.564~0.655区间内(>0.5), 非交叉验证相关系数r²在0.996~1.000区间内(>0.9), 表明所构建模型稳定性较强且预测能力良好; 对剩余14种FQs分子的t_1/2预测发现, 第四类FQs的t_1/2普遍高于第三类FQs; 选取环丙沙星(CIP)为目标分子, QSAR三维等势图分析其C-13位置引入体积较小的基团有利于降低目标分子的t_1/2值, 据此设计出9种t_1/2值显著降低的新型CIP分子, 其中引入·OH基团的新型分子t_1/2下降幅度最为显著(27.2%); 对CIP分子以及新型CIP分子可能发生的光降解路径进行推断发现, CIP光降解过程中发生芳香环羟基化的路径所需能垒最小, 推断为FQs类抗生素降解过程中的主要反应类型, 而氧化脱羧反应能垒最大, 发生哌嗪环断裂的路径最多, 而新型CIP分子光解过程中发生芳香环羟基化、氧化脱羧及羟基化脱氟3种反应的能垒较原CIP分子变化不大, 仅哌嗪环断裂所需要能垒明显小于CIP分子需要的能垒; 新型CIP分子及其光解产物的生物降解性评价发现, 新型CIP分子不仅光解活性提高, 其生物降解性同时提高0.26%~59.71%, 且其光解产物的生物降解性也显著上升(12.03%~34.38%), 即CIP分子以及新型CIP分子可通过光降解最终生成生物降解性显著提高的物质, 从而达到进一步控制FQs环境行为的目的.

关键词: 氟喹诺酮, 半衰期, 分子修饰, 光降解路径, 生物降解

Abstract:

Dividing the obtained data of photodegradation half-life(t_1/2) for 12 fluoroquinolones(FQs) into the training set and test set in a radio of 4:1(random combination), comparative molecular field analysis(CoMFA) and comparative molecular similarity indices analysis(CoMSIA) were used to establish 3D-QSAR models to predict the lgt_1/2 values of the remaining 14 FQs, with the structural parameters as independent variables and the lgt_1/2 values as the dependent variables through the Sybyl-x software. The results showed that the cross-validation correlation coefficients(q²) for CoMFA and CoMSIA models constructed by random combination were 0.564—0.655(>0.5) and the non-cross-validated correlation coefficients(r²) were 0.996—1.000(>0.9), indicating that the models were robust and predictive. The prediction of the photolysis t_1/2 found that the fourth type of FQs generally had a higher t_1/2 value than the third type of FQs. Taking ciprofloxacin(CIP) as the target molecule, introducing the smaller groups at the selected C13 position of CIP helped to design 9 modified CIP molecules with t_1/2 values significantly decreased, and the t_1/2 value of the modified molecule with the introduction of OH groups reduced most(27.2%). The photodegradation paths inference of CIP molecules and modified molecules indicated that the minimum energy barrier was required for the path of aromatic ring hydroxylation during CIP photodegradation, and the oxidative decarboxylation reaction had the largest energy barrier and the most paths of piperazine ring fracture. For the modified CIP molecules, the energy barriers of three reactions, aromatic ring hydroxylation, oxidative decarboxylation, and hydroxylation defluorination remained basically unchanged but the energy barrier required for the piperazine ring fracture significantly reduced. Evaluation of the biodegradability of modified CIP molecules and their photolysis products revealed that not only the photolytic activity of modified CIP molecules increased, but also the biodegradability increased by 0.26% to 59.71%, and the biodegradability of the photolysis products also increased significantly(12.03%—34.38%). It indicated that CIP and modified CIP molecules can eventually generate products with significantly improved biodegradability through photodegradation in favor to further control the environmental behaviors of FQs.

Key words: Fluoroquinolone, Half-life, Molecular modification, Photolysis path, Biodegradation

中图分类号:

O641
O623

TrendMD:

赵晓辉, 褚振华, 李鱼. 低光降解性氟喹诺酮类抗生素的分子设计及光解路径推断. 高等学校化学学报, 2018, 39(12): 2707.

ZHAO Xiaohui,CHU Zhenhua,LI Yu. Molecular Design of Lower Photodegradation Fluoroquinolone Antibiotics and Their Photolysis Paths Inference^†. Chem. J. Chinese Universities, 2018, 39(12): 2707.

图/表 10

Scheme 1 Molecular structures of the FQs(A),DIF and SAR(B)

Table 1 Statistical parameters and contributions of the molecular fields for CoMFA and CoMSIA models*

Model	n	q²	r²	SEE	F	SEP	$r pred 2$	$Q ext 2$	SDEP_ext	S	E	H	D	A
CoMFA	6	0.592	0.998	0.002	14968.907	0.001	0.989	0.788	0.293	71%	29%	－	－	－
CoMSIA	7	0.633	1.000	0.004	33088.010	0.002	1.000	0.753	0.312	48.10%	5.70%	31.30%	10.80%	4.10%

Table 1 Statistical parameters and contributions of the molecular fields for CoMFA and CoMSIA models*

Model	n	q²	r²	SEE	F	SEP	$r pred 2$	$Q ext 2$	SDEP_ext	S	E	H	D	A
CoMFA	6	0.592	0.998	0.002	14968.907	0.001	0.989	0.788	0.293	71%	29%	－	－	－
CoMSIA	7	0.633	1.000	0.004	33088.010	0.002	1.000	0.753	0.312	48.10%	5.70%	31.30%	10.80%	4.10%

Table 2 lgt1/2 values of FQs predicted using the CoMFA and CoMSIA models

FQ	No.	Compound	Exp.	CoMFA		CoMSIA
FQ	No.	Compound	Exp.	Pred.	Relative error(%)	Pred.	Relative error(%)
Third	1	CIP^a	1.36	1.378	1.3	1.362	0.14
	2	DAN^b	1.06	1.061	0.09	1.06	0
	3	DIF^b	1.86	1.812	-2.6	1.858	-0.11
	4	ENR^b	1.52	1.521	0	1.521	0
	5	FLE		0.635		0.752
	6	GRE		1.147		1.312
	7	LEV^b	2.08	2.081	0	2.081	0
	8	LOM^a	0.28	0.275	-1.8	0.281	0.36
	9	NAD		1.076		1.049
	10	NOR^b	1.46	1.459	-0.07	1.462	0.13
	11	OFL^b	2.47	2.459	-0.45	2.461	-0.36
	12	PEF		1.659		1.622
	13	SAR^b	1.49	1.490	0	1.489	-0.06
	14	SPA		0.517		0.515
	15	MAR^b	0.79	0.762	-3.5	0.785	-0.63
Fourth	16	PAZ		2.413		2.433
	17	MOX		1.937		1.975
	18	PRU		1.849		1.813
	19	RUF		1.564		1.568
	20	BAL^b	2.73	2.730	0	2.731	0
	21	SIT		1.221		1.248
	22	CLI		1.255		1.064
	23	OLA		2.141		2.173
	24	BES		1.246		1.251
	25	ANT		2.162		2.148
	26	GAT^b	2.15	2.144	0.47	2.132	0.93

Fig.1 Contour maps for BAL of the CoMFA(A, B) and CoMSIA(C, D) models(A, C) Steric fields; (B) electrostatic fields; (D) hydrophobic fields.

Fig.2 Contour maps of steric fields for DIF(A, B), SAR(C, D) and CIP(E, F) of the CoMFA(A, C, E) and CoMSIA(B, D, F) models

Table 3 lgt1/2 predicted values, frequency and pLOEC values of the modified CIP molecules*

Compound	CoMFA		CoMSIA		Frequency/ cm^-1	pLOEC
Compound	Predicted values of lgt_1/2	Change rate(%)	Predicted values of lgt_1/2	Change rate(%)	Frequency/ cm^-1	pLOEC
CIP	1.36		1.36			7.58
	1.14	-16.2	1.08	-20.6	65.24	7.69
	1.11	-18.4	1.14	-16.2	72.37	7.96
	0.99	-27.2	1.02	-25	68.54	8.34
	1.03	-24.3	1.04	-23.5	77.39	8.07
	1.08	-20.6	1.03	-24.3	61.02	8.04
	1.13	-16.9	1.06	-22.1	63.46	7.78
	1.29	-5.1	1.22	-10.3	75.21	8.01
	1.27	-6.6	1.24	-8.8	70.23	8.19
	1.25	-8.1	1.14	-16.2	65.30	8.08
	1.68	+23.5	1.63	+19.9	－	8.32

Fig.3 Photolysis paths inference of CIPThe ΔE and ΔH are in kJ/mol.

Table 4 Energy barriers and enthalpies of possible reaction paths for CIP molecules and the modified molecules

Path	Reactant	Reaction product	Energy barrier/ (kJ·mol^-1)	Enthalpies/ (kJ·mol^-1)	Path	Reactant	Reaction product	Energy barrier/ (kJ·mol^-1)	Enthalpies/ (kJ·mol^-1)
1	CIP	IM1a	325.56	-282.46		IM5b	IM5c	143.39	-565.22
	IM1a	IM1b	286.69	-528.61		IM5b	IM5c'	161.92	-504.21
	IM1b	P1	363.55	-560.78		IM5c	IM5d	227.44	-691.24
2	CIP	P2	38.83	-423.46		IM5d	IM5e	372.50	-327.23
	CIP	P3	4.69	-508.65		IM5e	P1	301.83	-469.86
	CIP	P4	50.88	-374.64	6	OH-CIP	IM6a	90.29	-385.05
	P4	IM2a	261.54	-148.36		IM6a	IM6b	23.51	-432.04
	IM2a	IM2b	315.81	-171.59		IM6b	P7	148.95	-373.80
	IM2b	P5	146.94	-391.25	7	OH-CIP	IM7a	31.97	-461.45
3	CIP	IM3a	437.69	-288.86		IM7a	IM7b	148.20	-328.69
	IM3a	IM3b	372.63	-272.42		IM7a	IM7c	117.19	-432.21
	IM3b	P5	146.94	-391.25		IM7c	IM7d	189.83	-510.49
4	CIP	P6	127.07	-355.93		IM7d	IM7f	166.23	-331.62
5	CIP	IM5a	52.84	-372.54		IM7f	IM7g	66.44	-403.13
	IM5a	IM5b	196.77	-471.16		IM7g	P8	277.69	-302.67

Fig.4 Photolysis paths inference of the modified CIP molecules

Table 5 Scoring functions for docking of FQs, the modified CIP molecules and their photolysis products

Compound	Total score	Compound	Total score	Change rate of total score(%)	Compound	Total score	Change rate of total score(%)
BAL	4.46	CIP	3.49		CIP	3.49
CIP	3.49	1	4.36	24.93	P1	4.48	28.37
DAN	4.52	2	4.06	16.33	P2	4.35	24.64
DIF	3.59	3	4.10	13.99	P3	3.91	12.03
ENR	4.16	4	5.34	45.57	P4	4.31	23.50
GAT	4.83	5	3.69	4.88	P5	4.41	26.36
LEV	3.69	6	3.79	5.62	P6	3.91	12.03
LOM	2.81	7	4.67	31.98	N1	4.28	22.64
NOR	2.66	8	3.50	0.26	N2	4.69	34.38
OFL	3.04	9	2.94	-11.78
SAR	3.70	10	5.58	59.71
MAR	3.67

参考文献 34

[1]	Dorival-García N., Zafra-Gómez A., Navalón A., Sci. Total Environ., 2013, 442(1), 317—328
[2]	Koga H., Itoh A., Murayama S., Med. Chem., 1980, 23(12), 1358—1363
[3]	Domagala J.M., Hann L.D., Heifetz C.L., Med. Chem., 1986, 29, 394—404
[4]	Xu Y., Liu Y., Li H., Sci. China Chem., 2015, 58(3), 508—513
[5]	Zhang Q.Q., Journal of Yangtze University, 2009, 6(4), 201—202
	(张茜倩. 长江大学学报, 2009, 6#(4), 201—202)
[6]	Debnath A., Mogha N.K., Masram D.T., Appl. Biochem.Biotech., 2015, 175(5), 2659—2667
[7]	Godoy A.A., Kummrow F., Pamplin P.A., Chemosphere,2015, 138, 281—291
[8]	Golet E.M., Strehler A., Alder A.C., Anal. Chem., 2002, 74(21), 5455—5462
[9]	Cheng D., Xie Y., Yu Y., Wetlands,2016, 36(1), 167—179
[10]	Liao X., Li B., Zou R., Environ. Sci. Pollut.Res., 2016, 23(8), 7911—7918
[11]	Ge L.K., Effects of Aqueous Dissolved Matter on Photodegradation of Phenicol and Fluoroquinolone Antibiotics, Dalian University of Technology, Dalian, 2009
	(葛林科. 水中溶解性物质对氯霉素类和氟喹诺酮类抗生素光降解的影响, 大连: 大连理工大学, 2009)
[12]	Burhenne J., Ludwig M., Spiteller M., Environ. Sci. Pollut.Res., 1997, 4(2), 61—67
[13]	Ahmad I., Bano R., Musharraf S.G., Aaps Pharmscitech,2014, 15(6), 1588—1597
[14]	Martinez L.J., Sik R.H., Chignell C.F., Photochem. Photobiol., 1998, 67(4), 399—403
[15]	Agrawal N., Ray R.S., Farooq M., Photochem. Photobiol., 2010, 83(5), 1226—1236
[16]	Boreen A.L., Arnold W.A., Mcneill K., Aquat. Sci.,2003, 65(4), 320—341
[17]	Albini A., Monti S., Chem. Soc.Rev., 2003, 32(4), 238—250
[18]	Ahmad I., Ahmed S., Anwar Z., Int. J. Photoenergy,2016, 58(7), 1—19
[19]	Lv Y.Y., Yin C.S., Liu H.Y., Yi Z.S., Wang Y., J. Environ.Sci., 2008, 20(12), 1433—1438
[20]	Frisch M.J., Trucks G. W., Schlegel H. B., Scuseria G. E., Robb M. A., Cheeseman J. R., Scalmani G., Barone V., Mennucci B., Petersson G. A., Nakatsuji H., Caricato M., Li X., Hratchian H. P., Izmaylov A. F., Bloino J., Zheng G., Sonnenberg J. L., Hada M., Ehara M., Toyota K., Fukuda R., Hasegawa J., Ishida M., Nakajima T., Honda Y., Kitao O., Nakai H., Vreven T., Montgomery J. A. Jr., Peralta J. J., Ogliaro F., Bearpark M., Heyd J. J., Brothers E., Kudin K. N., Staroverov V. N., Kobayashi R., Normand J., Raghavachari K., Rendell A., Burant J. C., Iyengar S. S., Tomasi J., Cossi M., Rega N., Millam J. M., Klene M., Knox J. E., Cross J. B., Bakken V., Adamo C., Jaramillo J., Gomperts R., Stratmann R. E., Yazyev O., Austin A. J., Cammi R., Pomelli C., Ochterski J. W., Martin R. L., Morokuma K., Zakrzewski V. G., Voth G. A., Salvador P., Dannenberg J. J., Dapprich S., Daniels A. D., Farkas Ö., Foresman J. B., Ortiz J. V., Cioslowski J. F. D., Gaussian 09, Revision E.01, Gaussian Inc., Wallingford CT, 2009
[21]	Chen Y., Cai X.Y., Jiang L., Ecotox. Environ. Safe.,2016, 124, 202—212
[22]	Golbraikh A., Tropsha A., Mol. Graph.Modell., 2002, 20, 269—276
[23]	Wang X.L., Gu W.W., Guo E.M., Cui C.Y., Li Y., Environ. Sci. Pollut.Res., 2017, 24(17), 14802—14818
[24]	Zhang P., Li H.X., Liu Y.C., Acta Biophysica Sinica,2010, 26(11), 1015—1024
	(张鹏, 李海霞, 刘艳成. 生物物理学报, 2010, 26(11), 1015—1024)
[25]	Gu W.W., Chen Y., Zhang L., Li Y., Hum. Ecol. Risk Assess., 2016, 23(1), 40—55
[26]	He Z.W., Study on the Photodegradation of Ciprofloxacin in Water, Henan Normal University, Xinxiang, 2011
	(何占伟. 环丙沙星在水溶液中的光化学降解研究, 新乡: 河南师范大学, 2011)
[27]	Tong L., Guo L., Lv X., J. Mol. Graph.Model., 2017, 71, 1—12
[28]	Li M., Wei D.B., Zhao H.M., Du Y.G., Chemosphere,2014, 95(1), 220—226
[29]	Xin M.L., Chu Z.H., Li Y., Chem. J. Chinese Universities,2018, 39(2), 299—309
	(辛美玲, 褚振华, 李鱼. 高等学校化学学报, 2018, 39(2), 299—309)
[30]	El Najjar N.H., Deborde M., Journel R., Water Res., 2013, 47(1), 121—129
[31]	Wetzsteiz H.G., Stadler M., Tichy H.V., Appl. Environ.Microb., 1999, 65(4), 1556—1563
[32]	Witte B.D., Langenhove H.V., Hemelsoet K., Chemosphere,2009, 76(5), 683—689
[33]	Wetzstein H.G., Schmeer N., Karl W., Appl. Environ.Microb., 1997, 64(3), 4272—4281
[34]	Wang X.L., Chu Z.H., Yang J.W., Li Y., Environ. Sci. Pollut.Res., 2017, 24(32), 25114—25125