Investigation of an Immunoassay with Broad Specificity to Quinolone

Subscriber access provided by UCL Library Services

Article

Investigation on an immunoassay broad-specificity to quinolone drugs using GALAHAD and advanced QSAR Jiahong Chen, Ning Lu, Xing Shen, Qiushi Tang, Chijian Zhang, Jun Xu, Yuanming Sun, Xinan Huang, Zhenlin Xu, and Hongtao Lei J. Agric. Food Chem., Just Accepted Manuscript • DOI: 10.1021/acs.jafc.6b00039 • Publication Date (Web): 16 Mar 2016 Downloaded from http://pubs.acs.org on March 17, 2016

Just Accepted “Just Accepted” manuscripts have been peer-reviewed and accepted for publication. They are posted online prior to technical editing, formatting for publication and author proofing. The American Chemical Society provides “Just Accepted” as a free service to the research community to expedite the dissemination of scientific material as soon as possible after acceptance. “Just Accepted” manuscripts appear in full in PDF format accompanied by an HTML abstract. “Just Accepted” manuscripts have been fully peer reviewed, but should not be considered the official version of record. They are accessible to all readers and citable by the Digital Object Identifier (DOI®). “Just Accepted” is an optional service offered to authors. Therefore, the “Just Accepted” Web site may not include all articles that will be published in the journal. After a manuscript is technically edited and formatted, it will be removed from the “Just Accepted” Web site and published as an ASAP article. Note that technical editing may introduce minor changes to the manuscript text and/or graphics which could affect content, and all legal disclaimers and ethical guidelines that apply to the journal pertain. ACS cannot be held responsible for errors or consequences arising from the use of information contained in these “Just Accepted” manuscripts.

Journal of Agricultural and Food Chemistry is published by the American Chemical Society. 1155 Sixteenth Street N.W., Washington, DC 20036 Published by American Chemical Society. Copyright © American Chemical Society. However, no copyright claim is made to original U.S. Government works, or works produced by employees of any Commonwealth realm Crown government in the course of their duties.

Page 1 of 38

Journal of Agricultural and Food Chemistry

1

Investigation on an immunoassay broad-specificity to quinolone

2

drugs using GALAHAD and advanced QSAR

3 4

Jiahong Chena, Ning Lua, Xing Shena, Qiushi Tanga, Chijian Zhanga, Jun Xub, Yuanming Suna,

5

Xin-an Huangc *, Zhenlin Xua *, Hongtao Leia *

6 7

a

8

Engineering & Technique Research Centre of Food Safety Detection and Risk

9

Assessment,South China Agricultural University, Guangzhou 510642, China

Guangdong Provincial Key Laboratory of Food Quality and Safety / Guangdong Provincial

10

b

11

East Circle at University City, Guangzhou 510006, China

12

c

13

Guangzhou University of Chinese Medicine, Guangzhou 510405, China

School of Pharmaceutical Sciences & Institute of Human Virology, Sun Yat-sen University, 132

Tropical Medicine Institute & South China Chinese Medicine Collaborative Innovation Center,

14 15

* Corresponding authors. † Phone: 8620-8528-3448. Fax: 8620-8528-0270. E-mail:

16

[email protected] (Hongtao Lei), [email protected] (Zhenlin Xu). ‡ Phone: 8620-3658-5475.

17

Fax: 8620-8637-3516. E-mail: [email protected] (Xinan Huang).

18 19 20

ACS Paragon Plus Environment


21

ABSTRACT

22

Polyclonal antibody against quinolone drug pazufloxacin (PAZ) but with a

23

surprising broad-specificity was raised to simultaneously detect 24 quinolones (QNs).

24

The developed competitive indirect enzyme-linked immunosorbent assay (ciELISA)

25

exhibited that the limits of detection (LODs) for 24 QNs, ranging from 0.45 ng/mL to

26

15.16 ng/mL, were below the maximum residue levels (MRLs). For better

27

understanding the obtained broad-specificity, the genetic algorithm with linear

28

assignment of hypermolecular alignment of datasets (GALAHAD) was used to

29

generate the desired pharmacophore model and superimpose the QNs, and then the

30

advanced comparative molecular field analysis (CoMFA) and advanced comparative

31

molecular similarity indices analysis (CoMSIA) models were employed to study the

32

three-dimensional quantitative structure-activity relationship (3D QSAR) between

33

QNs and the antibody. It was found that the QNs could interact with the antibody with

34

different binding poses, and the cross-reactivity was mainly positively correlated with

35

the bulky substructure containing electronegative atom at the 7-position, while

36

negatively associated with the large bulky substructure at the 1-position of QNs.

37

KEYWORDS pazufloxacin, immunoassay, specificity, quinolone, QSAR

38


Page 2 of 38

Page 3 of 38


39 40

INTRODUCTION Quinolone drugs were a class of widely used antibacterials for human use in the

41

middle 1980s, and some were approved for animals treatment in the middle 1990s

42

since they became the most commonly prescribed antibiotics1. However, the extensive

43

use in animal industry and aquaculture may bring health risk of human through the

44

food chain. As a result, in order to protect consumers, the maximum residue limits

45

(MRLs) have been set for several quinolones (QNs) by European Union (EU)2, Japan3

46

and China4, etc.

47

Several analytical methods5-6 for QNs residues have been carried out with

48

instrumental techniques. Though instrumental methods are accurate and sensitive,

49

they are time-consuming, laborious, low throughput and expensive. Immunoassays,

50

which are on the basis of antigen-antibody interaction, can avoid the weakness of

51

instrumental techniques. Up to now, several enzyme-linked immunosorbent assays

52

(ELISAs) have been developed for detection of QNs residues due to its high

53

sensitivity and easy operation7-9. However, most of the developed immunoassays had

54

just shown a limited specificity, and they can detect only a single compound or just a

55

10-12 few of ToQNs date, a .broad specificity immunoassay can be established by employing a

56

broad specificity antibody that originated from a multi-hapten antigen13 or a generic

57

hapten14. However, due to the lack in understanding of the specific interactions

58

between antibodies and target analytes or haptens, the hapten design and the

59

production of antibody with broad specificity are still based on trial and error test15.



60

In this study, pazufloxacin (PAZ) (Table 2), a fluoquinolone drug containing a

61

1-amino-cyclopropyl groups at the 7-position but not a popularly common piperazinyl

62

in many QNs, was used as immunizing hapten to generate polyclonal antibody. It is

63

interesting that the resultant antibody against PAZ demonstrated extremely broad

64

recognition spectrum up to 24 QNs, and a highly sensitive enzyme linked

65

immunosorbent assay was then successfully developed. To better understanding the

66

broad specific recognition mechanism between the obtained antibody and QNs, 23

67

QNs were superimposed by the genetic algorithm with linear assignment of

68

hypermolecular alignment of datasets (GALAHAD) method, then subjected to the

69

study of three-dimensional quantitative structure-activity relationship (3D QSAR)

70

using the comparative molecular field analysis (CoMFA) and comparative molecular

71

similarity indices analysis (CoMSIA) approaches.

72

MATERIALS AND METHODS

73

Reagents and Instrumentation

74

Reagents and Animals

75

PAZ, racemic ofloxacin (OFL), prulifloxacin (PRU), ciprofloxacin (CIP),

76

rufloxacin (RUF), lomefloxacin (LOM), pefloxacin (PEF), enrofloxacin (ENR),

77

norfloxacin (NOR), garenoxacin (GAR), gatifloxacin (GAT),danofloxacin(DAN),

78

nalidixic acid (NAL), difloxacin (DIF), clinafloxacin (CLI), oxolinic acid (OXO),

79

pipemidic acid (PIP), sparfloxacin (SPA), moxifloxacin (MOX), sarafloxacin (SAR),

80

marbofloxacin (MAR) and tosufloxacin (TOS) were purchased from Veterinary

81

Medicine Supervisory Institute of China (Beijing, China). S-(-)-Ofloxacin (SOF) and


Page 4 of 38

Page 5 of 38


82

R-(+)-Ofloxacin (ROF) were obtained from Daicel Chiral Technologies Company

83

(Shanghai, China). The structures of the QNs are shown in Table 2. Bovine serum

84

albumin (BSA), ovalbumin (OVA), 1-(3-(dimethylamino)propyl)-3-ethyl

85

(EDC), glutaraldehyde (25%), complete and incomplete Freund’s adjuvant,

86

peroxidase−immunoglobulin G (HRP−IgG), 3,3′,5,5′-tetramethyl benzidine (TMB)

87

were purchased from Sigma-Aldrich (St. Louis, MO). All of the chemicals and

88

solvents, which were analytical grade or better, were obtained from a local chemical

89

supplier (Yunhui Trade Co., Ltd., Guangzhou, China). BABL/c female mice, 6-8

90

old, were raised at the Laboratory Animal Center of South China Agricultural

91

University (Guangzhou, China). All of the experiments were carried out following

92

ethical guidelines of the Animal Care and Use Committee of South China Agricultural

93

University (Institute certificate number SYXK(Yue)2014-0136; internal Protocol

94

2014-12).

95

Instrumentation

96

Ultraviolet-visible (UV-vis) Spectroscopy was recorded on a UV-4000

97

spectrophotometer (Hitachi, Japan). ELISA plates were washed in a Wellwash MK2

98

microplate washer (Thermo Scientific, USA). ELISA absorbance values were

99

measured at a wavelength of 450 nm with a Multiskan MK3 microplate reader

100

(Thermo Scientific).

101

Preparation of Immunogens and Coating Antigens

102

Carbodiimide (EDC) Coupling Method



103

PAZ (9.14 mg) and BSA (15 mg) were dissolved in 1 mL 0.9% sodium chloride

104

solution, respectively. Then PAZ solution was added into the BSA solution drop by

105

drop. After the reaction mixture was stirred thoroughly, 9 mg EDC was added and

106

stirred for 15 min. Finally, the reaction product was dialyzed against 0.9% sodium

107

chloride solution for 3 days at 4 °C and stored at -20 °C until use16. The immunogen

108

produced by this method was designated as PAZ-D-BSA. The coating antigen

109

PAZ-D-OVA was prepared using the same procedure above.

110

Glutaraldehyde (GDA) Coupling Method

111

PAZ (9.14 mg) and BSA (15 mg) were dissolved in 1 mL 0.9% sodium chloride

112

solution, respectively. Then 6 µL 25% GDA solution was added into PAZ solution.

113

After the mixed thoroughly, PAZ solution was added dropwise to BSA solution and

114

the reaction mixture was stirred for 3 h gently. Finally, the reaction product was

115

dialyzed (over 3 days at 4 °C) against 0.9% sodium chloride solution and stored at

116

-20 °C until use17. The produced immunogen was named as PAZ-P-BSA. The coating

117

antigen PAZ-P-OVA was prepared as the same procedure above. The artificial

118

antigens, proteins and PAZ were dissolved in 0.9% sodium chloride solution and

119

characterized by UV-vis spectra18.

120

Production of Polyclonal Antibodies

121

Three BABL/c female mice aged 6-8 weeks were immunized subcutaneously

122

75 µg of PAZ-D-BSA in the mixture of 75 µL PBS and 75 µL Freund’s complete

123

adjuvant. Booster injections were given with the same amount of immunogen

124

emulsified with incomplete Freund’s adjuvant at intervals of 2 week after the initial


Page 6 of 38

Page 7 of 38


125

injection. After 1 week from each booster injection, mice were tail-bled and the

126

were used for the determination of antibody titers by ciELISA using a homologous

127

coating antigen. The polyclonal antibody obtained was divided into aliquots, labeled,

128

and stored at -20 °C until use. The polyclonal antibody using immunogen PAZ-P-BSA

129

were produced as the same procedure above.

130

ELISA Procedure

131

The ELISA was established for QNs on the basis of the common procedure of

132

ciELISA19. The 96-well plates were coated with coating antigens (100 µL/well) in

133

carbonate buffer at 37 °C overnight. Next, the wells were washed twice with 300 µL

134

PBST (0.1% Tween-20) and blocked with 120 µL 5% skimmed milk in PBST at 37 °C

135

for 3 h, and the plates were dried at 37 °C for 1 h. The wells were then incubated with

136

50 µL of diluted PAZ standard solution and 50 µL of diluted antibody in PBST. After

137

incubated in 37 °C water bath for 40 min, the wells were washed five times with

138

PBST. Then 100 µL/well HRP-IgG (diluted 1:5000 in PBST) was added and

139

incubated at 37 °C for 30 min. After five washes, 100 µL TMB solution (400 µL of

140

0.6% TMB−dimethyl sulfoxide and 100 µL of 1% H2O2 diluted with 25 mL of

141

citrate−acetate buffer, pH 5.5) was added to the wells and incubated for 10 min.

142

Finally, the reaction was stopped by the addition of 50 µL of 2 mol/L H2SO4. The

143

absorbance of the reaction solution at 450 nm (A450) was recorded.

144

Statistical Analysis

145

The ciELISA standard curves were obtained by plotting absorbance against

146

analyte concentration. And a four-parameter equation was used to generate the



147

sigmoidal curve using Origin 8.5 software (Origin Lab Corp., Northampton, MA,

148

USA): Y=(A-D)/[1+(x/C)B]+D. Where A and D correspond to maximal and minimal

149

absorbance, respectively, B is the slope of the sigmoidal curve, and C is the PAZ

150

concentration that inhibited 50% of PAZ standard antibody binding.

151

In this study, the limit of detection (LOD) and the limit of quantification (LOQ)

152

were set as the standard concentration that inhibited 10% and 20% of PAZ standard

153

antibody binding, respectively20, 21. The working range was set as the standard

154

concentration that inhibited 20%-80% of PAZ standard antibody binding22.

155 156

Standard deviation and determination coefficient (R2) were determined by standard curves using Origin 8.5 software.

157

Cross-reactivity

158

The specificity of the ELISA was determined using 24 QNs under optimized

159

ELISA conditions. The cross-reactivities (CRs) were calculated according to the

160

following equation, where IC50 value refers to the concentration at which 50% of the

161

anti-PAZ is bound to the analyte.

162

CR%= [IC50 (PAZ)/IC50 (structurally related compounds)] ×100%

163

Molecular Modeling and 3D QSAR Analysis

164

Data Set

165

In order to investigate the chiral recognition of the antibody, MAR, ROF and

166

RUF, each possessing a similar rigid fused substructure as PAZ, were selected as the

167

test set. On consideration of the integrity of the substructures in training and test sets,

168

NOR and DIF, containing the same substructures as in the training set, were chose as


Page 8 of 38

Page 9 of 38


169

test molecules. OXO has an exclusive dioxolone ring, so it was put into test set.

170

Therefore, 17 molecules (PAZ, SOF, PRU, CIP, LOM, PEF, ENR, GAR, GAT, DAN,

171

NAL, CLI, PIP, SPA, MOX, SAR and TOS) were used to form the training set, and

172

DIF, MAR, NOR, OXO, ROF and RUF comprised the test set. The observed IC50

173

values of these molecules were converted into corresponding pIC50 (-log IC50) values.

174

Molecular Conformation and Alignment

175

The molecular modeling was conducted using SYBYL program package23. PAZ

176

was used as the template. Its conformation was searched, and identified by energy

177

minimization using the Tripos force-field with the Powell conjugate gradient

178

minimization algorithm and a convergence criterion of 0.005 kcal/(mol×Å). The other

179

data set molecules were constructed from the template molecule by using the

180

“SKETCH” option function in SYBYL. The Gasteiger-Hückel charge was used to

181

calculate the partial atomic charges. Concerning the most of QNs’ structures, the large

182

conformational difference of the substituents at the 7-position may be not conducive

183

to the molecular alignment. Thus whether the nitrogen atoms at the 4-position of

184

piperazinyl rings were well aligned was considered as a benchmark to select

185

molecules for generating a desired pharmacophore model. With the exclusion of the

186

template PAZ, the concerned nitrogen atoms of SOF, PRU, CIP, and MAR met the

187

above criterion. So these five molecules were used as the subset to generate the

188

pharmacophore models with GALAHAD method. Then the desired pharmacophore

189

model was generated and used as the template to superimpose the data set molecules.

190

The parameters were set as defaults.



191

CoMFA and CoMSIA Descriptors

192

CoMFA steric and electrostatic interaction fields of each molecule were

193

calculated on a 3D cubic lattice. A sp3 carbon probe atom with Van der Waal radius of

194

1.52 Å and +1 charge was used to generate the steric and electrostatic filed energies.

195

The energy values were truncated at 30.0 kcal/mol. The CoMFA standard

196

(CoMFA-STD) and region focusing (CoMFA-RF) methods were used to scale the

197

steric and electrostatic fields. The cross-validated correlation coefficient R2 (q2) and

198

the optimum number of components (ONC) were obtained using the partial least

199

square (PLS) method with leave one out (LOO) option. Using the obtained ONC, the

200

final non-cross-validated model was developed.

201

Besides the steric and electrostatic fields, CoMSIA evaluated hydrophobic,

202

hydrogen-bond donor and hydrogen-bondacceptor fields. CoMSIA descriptors were

203

calculated with the same probe atom as that used in the CoMFA-STD. The attenuation

204

factor was the default.

205 206

Moreover, the progressive scrambling method was used to examine the stabilities of established models. The parameters were defaults.

207

RESULTS AND DISCUSSION

208

Immunnoreagent Preparation

209

UV-vis spectra of artificial antigens, BSA, OVA and PAZ are shown in Fig.S1a.

210

The absorbance for PAZ-D-BSA and PAZ-D-OVA by carbodiimide coupling method

211

gave a significant shifted peak at 320 nm compared with the 330 nm peak for PAZ,

212

while the maximum absorbance of BSA and OVA were at 278 nm, which indicated


Page 10 of 38

Page 11 of 38


213

that PAZ was successfully conjugated with BSA and OVA, respectively. The artificial

214

antigens PAZ-P-BSA and PAZ-P-OVA by GDA coupling method was verified as in

215

the procedure above and the results indicated artificial antigens were successfully

216

conjugated (Fig. S1b).

217

AntiseraTiter

218

Antisera of immunized mice from different groups, which injected with

219

PAZ-D-BSA and PAZ-P-BSA, respectively, were collected 1 week after the 3rd

220

booster injection and detected for the presence of antibody recognizing the

221

immunizing hapten by a chequerboard titration (Table 1). Antiserum PAZ-D-BSA

222

group showed a higher titration than antiserum PAZ-P-BSA did. The titer values after

223

the 3rd injection were about 1.0 at 1/32,000 dilution for antiserum PAZ-D-BSA, while

224

at dilutions of up to 1:8,000 for antiserum PAZ-P-BSA. As is known to all, the

225

appropriate immunogen has an important effect on animals' immunity effect19. In this

226

study, the carboxyl group of PAZ can react with the amino group of carrier protein

227

and expose the amino group of PAZ for carbodiimide coupling; alternatively, the

228

amino group of PAZ can link with the amino group of carrier protein and expose the

229

carboxyl group of PAZ for glutaraldehyde coupling. The different groups of PAZ

230

would be exposed to the greatest extent by these two coupling methods, respectively.

231

Previous research24 showed that the more complex hapten structure could lead to the

232

better CR, such as those containing a benzene ring, heterocyclic ring or branched

233

chain. For PAZ, the structure of amino group, adjacent to the cyclopropyl group, is

234

more complicated than that carboxyl group. This may lead to the better animals'



235

immunity effect of PAZ-D-BSA group. Thus, the antiserum of immunized mice from

236

carbodiimide coupling method with higher affinity was selected for further

237

development25.

238

ELISA Optimization

239

To develop a specific and sensitive ELISA, dilutions of the coating antigen and

240

antibody were optimized. Accordingly, three dilutions of coating antigen PAZ-D-OVA,

241

which were 1:10000,1:20000 and 1:25000, respectively, were detected in combination

242

with dilutions of antibody PAZ-D-BSA from 1:1000 to 1:64000 using a checkerboard

243

procedure26 (Fig. S2). Also, the lowest possible coating concentration that allows

244

reliable detection of the label which does not affect the competition is desired for

245

highest sensitivity27. Thus, the optimal combination of the immunoreagents was a

246

coating antigen PAZ-D-OVA dilution at 1:25000 with dilution of 1:8000 for the

247

antibody, producing a maximum absorbance of around 1 in the absence of an

248

analyte28.

249

On the basis of optimal results, the standard curve for PAZ ciELISA was

250

obtained as seen in Fig.1. The assay exhibited an IC50 of 10.3 ng/mL, LOD and LOQ

251

of 1.4 and 2.9 ng/mL for PAZ, respectively. Whereas the working range was 2.9 - 36.8

252

ng/mL (y = – 0.542x+1.124).

253

Immunoassay Specificity

254

In order to investigate the impact of the molecular properties on the assay

255

specificity, the CR was evaluated to estimate the affinity of the antibody to the

256

structure related compounds. In this study, the molar CRs of 24 QNs were detected


Page 12 of 38

Page 13 of 38


257

using PAZ as the reference compound (CR = 100.0%) (Table 2), it was found that the

258

antibody showed a strong recognition to SOF (CR = 69.6%), this could be due to the

259

similar structures of SOF and PAZ, which were only different at the 7-position.

260

Comparing the structures of OFL and MAR, it revealed that, if the carbon atom,

261

which was connected to N-1, was replaced by nitrogen atom, the CR dramatically

262

dropped from 66.7 % for OFL to 4.4 % for MAR. This suggests that the oxygen

263

nitrogen hetero atomic ring at the 1-position was likely a key structural factor for

264

antibody recognition. In addition, the structure of ROF is similar to SOF in all

265

respects except for the configuration of the chiral carbon, and this difference almost

266

resulted in a 3-fold decrease in the CR value for ROF (CR = 25.8%) compared to SOF

267

(CR = 69.6%), implying that the chiral structure also played an important role in

268

affecting the affinity of the antibody. Interestingly, compared with OFL, PRU, not

269

only lacked of oxygen nitrogen hetero atomic ring at the 1-position, but also changed

270

methyl group into dioxole group at the 7-position, showed a similar CR of 64.0%. As

271

some research have revealed, as well as spatial structure, hydrophobic and electronic

272

properties both made significant contributions to antibody recognition29, 30.

273

Generally, the immunizing hapten structure is more similar to the analyte, the

274

resultant antibody will be more possible to demonstrate high CR to its

275

structure-similar analytes27,31. Compared with other previously reported generic

276

haptens such as ciprofloxacin, norfloxacin and ofloxacin27, 31, 32, PAZ did not possess

277

the commonly shared piperazinyl in many QNs, Thus, this structure feature possibly

278

decreased its similarity to many QNs containing piperazinyl. However, it is interesting



279

in this study to found that the raised antibody showed an extremely broad specificity

280

to 24 QNs and even all LODs was below their MRLs. Due to lack of information

281

about the antibody physicochemical properties, the possible mechanisms of antibody

282

recognition was still unclear. For better explaining the high CR the GALAHAD and

283

advanced 3D QSAR methods were used for the further investigation.

284

Molecular Superimposition

285

The GALAHAD generated the best twenty pharmacophore models. Each model

286

contained 9 features, namely, one positive nitrogen atom, one negative center, two

287

hydrophobic centers, two hydrogen bond donor atoms and three hydrogen bond

288

acceptor atoms. All these models each had a Pareto rank of 0, which indicated that none

289

of the models was superior to any other33. In this study, the model with the maximal

290

steric overlap at AA3, AA4, HY6, HY7 and NC8 features was chosen as the template

291

for the subsequent molecular superimposition. The model and superimposed molecules

292

were displayed in Fig. 2.

293

3D QSAR Statistical Results

294

No matter in the CoMFA-STD or CoMFA-RF models, there was a sharp rise in

295

q2 and a minimal standard error of prediction (SEP) when the number of components

296

(NC) was 3, which led to the determination that 3 was the preferred ONC due to the

297

suggestion that each additional component ideally increases q2 by 5-10%34, 35. The

298

progressive scrambling test also supported the afore mentioned conclusion due to the

299

comprehensive result of the maximal cross-validated correlation coefficient (Q2),

300

almost the minimal calculated cross-validated standard error (cSDEP) and the slope


Page 14 of 38

Page 15 of 38


301

of q2 with respect to the correlation of the original dependent variables versus the

302

perturbed dependent variables (dq2/dr2yy') closer to 1.0 in the CoMFA-RF models

303

(Table 3)36. Therefore, the 3-component and region focused CoMFA model was

304

generated.

305

In CoMSIA, the five fields, namely, steric (S), electrostatic (E), hydrophobic (H),

306

hydrogen bond donor (D) and hydrogen bond acceptor (A) field, are not totally

307

independent from each other. These fields were systemically combined, and the

308

analyses of PLS with automatic option were conducted. The combinations with higher

309

q2 were chosen for the subsequent analysis to determine the optimal combination.

310

Table S1 showed that the combination of SA gave the higher q2 and smallest SEP.

311

Since there was a sharp increase in q2, the CoMSIA model with the ONC of 7 was

312

created based on S and A force-fields.

313

The statistical data were listed in Table 4. Since the predictive r2 of CoMFA was

314

higher than that of CoMSIA, it seemed that the CoMFA was more rational. The

315

residuals ranged from -0.876 to 0.228 and -0.888 to 0.184 in the CoMFA and

316

CoMSIA models (Table S2), respectively. The distributions of residuals reflected the

317

random/systematic errors of the both models. The scatter plots of the predicted versus

318

experimental activities indicated that the errors mainly resulted from the random

319

errors (Fig. 3). Since there were five molecules with the residual less than 0.5 log unit

320

in the CoMFA model, whereas only four in the CoMSIA model, the CoMFA model

321

showed higher prediction ability than CoMSIA, which was consistent with the

322

previous conclusion. However, the CoMSIA model provided an alternative way to



323

understand the recognition mechanism. In the CoMFA model, the major deviation was

324

derived from MAR. MAR has an exclusive electronegative nitrogen atom in the

325

oxadiazine ring. Because no model has been established for this nitrogen atom, this

326

over-predicted the values of MAR. It implies that an electronegative atom in this

327

region is undesirable for activity. It implied that an electronegative atom in this region

328

is undesirable for the binding affinity.

329

CoMFA Contour Analysis

330

In Fig.4a-4c, the CoMFA steric contours were around the oxazine ring (Reg. 1)

331

and the 1-amino-cyclopropyl group (Reg. 2) of PAZ. In Reg. 1, the green contours

332

separately interacted with the methyl and methylene groups at the oxazine ring of

333

PAZ (Fig. 4a) and SOF (Fig.4b); however, these groups of ROF were brought near to

334

the yellow contours due to their opposite conformations (Fig. 4c). That explained that

335

why SOF (exp. pIC50 = 7.337 and so forth) had higher binding activity than ROF

336

(6.907). There was a large block of yellow contours under the nitrogen of the

337

quinoline ring in Reg. 1. As far as the molecular pairs of CIP (7.237) versus SAR

338

(6.251), and PEF (7.155) versus DIF (6.703) were concerned, the former in each pair

339

had higher binding activity than the latter, which was agreement with the large yellow

340

contours overlapping the substituted phenyl group in the latter.

341

In Reg. 2, the two bottom blocks of green contours covered the amino and

342

cyclopropyl groups of PAZ (Fig. 4a); while all the three blocks of green contours

343

interacted with the 4-methyl-piperazinyl group (Fig. 4b, 4c). 4-methyl-piperazinyl

344

was more favorable than piperazinyl in Reg. 2, which gave the reason why PEF had


Page 16 of 38

Page 17 of 38


345

higher activity (7.155) than NOR (7.004). The yellow contours near the

346

3-methyl-3,6-diazabicyclo[2.2.1]heptanyl group of DAN and the ethyl group of ENR

347

in Reg.2 indicated the bulky group in this areas decreased the binding activity, which

348

made the activities of DAN(6.870) and ENR(7.114) lower than CIP(7.237).

349

In Fig. 4e-4g, two blocks of red electrostatic contours suggested that high

350

electrondensity near these areas would increase the cross-reactivity. Fig. 4e illustrated

351

that the amino group of PAZ was near to the small block of red contours, while Fig. 4f

352

showed the large red contours were neighboured to the nitrogen at the 4-position of

353

piperazinyl of SOF. Two blue polyhedrons implied that electropositive groups were

354

desirable for these regions, however DIF, SAR and TOS had electronegative fluorine

355

atoms near to these regions, which additionally led to the lower activities of DIF, SAR

356

and TOS (5.203).

357

CoMSIA Contour Analysis

358

CoMSIA contour maps could provide an alternative way to investigate the CRs.

359

The color schemes in the Reg. 1 of the CoMSIA steric contours (Fig. 5a) were similar

360

to that of CoMFA. The yellow contours were associated with the cross-reactivity

361

decreasing when bulky groups located in this area. The green contours demonstrated

362

the area that distinguished the S-form form R-form of OFL. In Reg. 2, the rational

363

bulky group could increase the cross-reactivity. For instance, the 4-methyl-piperazinyl

364

group could lead to higher binding activity than the piperazinyl group.

365 366

Each of the magenta blocks was separately close to the ketone group of the quinoline ring and the the 4-position nitrogen of the piperazinyl ring (Fig. 5b). That



367

indicated the acceptor atoms in these two areas could increase the binding activity.

368

The red contours near the oxygen of oxazine ring meant that acceptor atom in this

369

area was undesirable for cross-reactivity.

370

Since the amino and cyclopropyl groups of PAZ were perpendicular to the

371

quinolone ring (Figure 4a and 4e), respectively, it made the antibody form two

372

mutually almost orthogonally cavities, one for the amino and cyclopropyl, and the

373

other for the quinoline ring. Although the distance from the shared carbon to the

374

nitrogen in amino or to distal end carbon in cyclopropy was not longer than that the

375

distance from the carbon at 1-position to the nitrogen at 4-position of piperazinyl the

376

contour maps of both CoMFA and CoMSIA demonstrated that the formed cavity

377

could accommodate the bulky piperazinyl. The both models also implied that the

378

antibody might possess electropositive atom, which can bind the nitrogen of amino

379

group and 4-position nitrogen of piperazinyl. These may contribute to the main reason

380

that the resultant antibody against PAZ exhibited a broad specificity.

381

In this study, a novel hapten PAZ was used for generating broad-specificity

382

polyclonal antibody for 24 QNs. The influence of coupling methods of hapten on

383

antibody sensitivity was also studied. In order to investigate the mechanism of the

384

antibody recognition, QNs were superimposed by GALAHAD method, and subjected

385

to 3D QSAR studies using advanced CoMFA and CoMSIA approaches. Both models

386

offered good statistical parameters, e.g. the q2 greater than 0.71. It was found that the

387

steric field played a major role for the recognition between antibody and QNs. The

388

QNs might interact with the antibody with different binding poses, and the large bulky


Page 18 of 38

Page 19 of 38


389

groups containing electronegative atom at 1-position of the quinoline ring would

390

decrease the cross-reactivity, while rational bulky groups attached to the 7-position

391

would increase the cross-reactivity.

392 393

ABBREVIATIONS USED

394

ciELISA, competitive indirect enzyme-linked immunosorbent assay;

395

GALAHAD, genetic algorithm with linear assignment of hypermolecular alignment

396

of datasets; 3D QSAR, three-dimensional quantitative structure-activity relationship;

397

CoMFA, comparative molecular field analysis; CoMSIA, comparative molecular

398

similarity indices analysis; QNs, quinolones; MRLs, maximum residue limits; EU,

399

European Union; PAZ, pazufloxacin; OFL, racemic ofloxacin; PRU, prulifloxacin;

400

CIP, ciprofloxacin; RUF, rufloxacin; LOM, lomefloxacin; PEF, pefloxacin; ENR,

401

enrofloxacin; NOR, norfloxacin; GAR, garenoxacin; GAT, gatifloxacin; DAN,

402

danofloxacin; NAL, nalidixic acid; DIF, difloxacin; CLI, clinafloxacin; OXO,

403

oxolinic acid; PIP, pipemidic acid; SPA, sparfloxacin; MOX, moxifloxacin; SAR,

404

sarafloxacin; MAR, marbofloxacin; TOS, tosufloxacin; SOF, S-(−)-Ofloxacin; ROF,

405

R-(+)-Ofloxacin; BSA, bovine serum albumin; OVA, ovalbumin; HRP−IgG,

406

horseradish peroxidase−immunoglobulin G; TMB,3,3′,5,5′-tetramethyl benzidine;

407

UV-vis, ultraviolet-visible; GDA, glutaraldehyde; LOD, limit of detection; LOQ, limit

408

of quantification; CR, cross-reactivity; ONC, optimum number of components; PLS,

409

partial-least-square; LOO, leave one out; SEP, standard error of prediction; NC,

410

number of components; Q2, maximal cross-validated correlation coefficient; cSDEP,



411

calculated cross-validated standard error; dq2/dr2yy', dependent variables versus the

412

perturbed dependent variables; STD, Standard CoMFA; RF, CoMFA with region

413

focused; S, steric; E, electrostatic ; H, hydrophobic; D, hydrogen bond donor; A,

414

hydrogen bond acceptor.

415 416

ACKNOWLEDGEMENTS

417

This work was supported by Natural Science Foundation of China (U1301214),

418

Guangdong Natural Science Foundation (S2013030013338, 2015A030313366),

419

Guangdong Planed Program in Science and Technology (2014TX01N250，

420

2010A032000001-4, 2013B051000072), the Program for Research Team in South

421

China Chinese Medicine Collaborative Innovation Center (A1-AFD01514A07) and

422

the Science and Technology Project of Fujian Province (2012Y0003). We would also

423

like to thank Dr Kai Wang for her constructive suggestions in the manuscript revision.

424 425

SUPPORTING INFORMATION

426

Two supplement figures and one supplement table. This material is available free

427

of charge via the Internet at http://pubs.acs.org.

428 429

REFERENCES

430

(1) Anderson, S. A.; Woo, R. Y.; Crawford, L. M., Risk assessment of the impact on

431

human health of resistant Campylobacter jejuni from fluoroquinolone use in beef

432

cattle. Food Control 2001, 12, 13-25.


Page 20 of 38

Page 21 of 38


433

(2) Official Journal of the European Union, Commision Regulation (EU) No 37/2010

434

of 22 December 2009 on pharmacologically active substances and their classification

435

regarding maximum residue limits in foodstuffs of animal origin. 2009.

436

(3) Department of Food Safety, Ministry of Health, Labour and Welfare, The Positive

437

List System for Agricultural Chemical Residues in Foods. 2006.

438

(4) Ministry of Agriculture of the People’s Republic of China, Maximum residue

439

limits for veterinary drugs in animal products. 2002.

440

(5) Yorke, J. C.; Froc, P., Quantitation of nine quinolones in chicken tissues by

441

high-performance liquid chromatography with fluorescence detection. J. Chromatog.

442

A 2000, 882, 63-77.

443

(6) Rizk, M.; Belal, F.; Ibrahim, F.; Ahmed, S.; El-Enany, N. M., Voltammetric

444

analysis of certain 4-quinolones in pharmaceuticals and biological fluids. J.

445

Pharmaceut. Biomed. 2000, 24, 211-218.

446

(7) Huet, A. C.; Charlier, C.; Tittlemier, S. A.; Singh, G.; Benrejeb, S.; Delahaut, P.,

447

Simultaneous determination of (fluoro) quinolone antibiotics in kidney, marine

448

products, eggs, and muscle by enzyme-linked immunosorbent assay (ELISA). J. Agr.

449

Food Chem. 2006, 54, 2822-2827.

450

(8) Lu, S. X.; Zhang, Y. L.; Liu, J. T.; Zhao, C. B.; Liu, W.; Xi, R. M., Preparation of

451

anti-pefloxacin antibody and development of an indirect competitive enzyme-linked

452

immunosorbent assay for detection of pefloxacin residue in chicken liver. J. Agr.

453

Food Chem. 2006, 54, 6995-7000.



454

(9) Sheng, W.; Li, Y. Z.; Xu, X.; Yuan, M.; Wang, S., Enzyme-linked immunosorbent

455

assay and colloidal gold-based immunochromatographic assay for several (fluoro)

456

quinolones in milk. Microchim. Acta 2011, 173, 307-316.

457

(10) Liu, W.; Zhao, C. B.; Zhang, Y. L.; Lu, S. X.; Liu, J. T.; Xi, R. M., Preparation

458

of polyclonal antibodies to a derivative of 1-aminohydantoin (AHD) and development

459

of an indirect competitive ELISA for the detection of nitrofurantoin residue in water.

460

J. Agr. Food Chem. 2007, 55, 6829-6834.

461

(11) Van Coillie, E.; De Block, J.; Reybroeck, W., Development of an indirect

462

competitive ELISA for flumequine residues in raw milk using chicken egg yolk

463

antibodies. J. Agr. Food Chem. 2004, 52, 4975-4978.

464

(12) Zhao, C. B.; Liu, W.; Ling, H. X.; Lu, S.; Zhang, Y. L.; Liu, J. T.; Xi, R. M.,

465

Preparation of anti-gatifloxacin antibody and development of an indirect competitive

466

enzyme-linked immunosorbent assay for the detection of gatifloxacin residue in milk.

467

J. Agr. Food Chem. 2007, 55, 6879-6884.

468

(13) Samdal, I. A.; Ballot, A.; Løvberg, K. E.; Miles, C. O., Multihapten approach

469

leading to a sensitive ELISA with broad cross-reactivity to microcystins and

470

nodularin. Environ. Sci. Technol. 2014, 48, 8035-8043.

471

(14) Alcocer, M. J. C.; Dillon, P. P.; Manning, B. M.; Doyen, C.; Lee, H. A.; Daly, S.

472

J.; O'kennedy, R.; Morgan, M. R. A., Use of phosphonic acid as a generic hapten in

473

the production of broad specificity anti-organophosphate pesticide antibody. J. Agr.

474

Food Chem. 2000, 48, 2228-2233.


Page 22 of 38

Page 23 of 38


475

(15) Xu, Z. L.; Shen, Y. D.; Zheng, W. X.; Beier, R. C.; Xie, G. M.; Dong, J. X.;

476

Yang, J. Y.; Wang, H.; Lei, H. T.; She, Z. G., Broad-specificity immunoassay for O,

477

O-diethyl organophosphorus pesticides: application of molecular modeling to improve

478

assay sensitivity and study antibody recognition. Anal. Chem. 2010, 82, 9314-9321.

479

(16) Devlin, S., Meneely, J. P., Greer, B., Campbell, K., Vasconcelos, V., Elliott, C. T.,

480

Production of a broad specificity antibody for the development and validation of an

481

optical SPR screening method for free and intracellular microcystins and nodularin in

482

cyanobacteria cultures. Talanta 2014, 122, 8-15.

483

(17) Chen, Y. Q.; Shang, Y. H.; Li, X. M.; Wu, X. P.; Xiao, X. L., Development of an

484

enzyme-linked immunoassay for the detection of gentamicin in swine tissues. Food

485

Chem. 2008, 108, 304–309.

486

(18) Oplatowska, M.; Connolly, L.; Stevenson, P.; Stead, S.; Elliott, C. T.,

487

Development and validation of a fast monoclonal based disequilibrium enzyme-linked

488

immunosorbent assay for the detection of triphenylmethane dyes and their metabolites

489

in fish. Anal. Chim. Acta 2011, 698, 51-60.

490

(19) Wu, J.; Shen, Y. D.; Lei, H. T.; Sun, Y. M.; Yang, J. Y.; Xiao, Z. L.; Wang, H.;

491

Xu, Z. L., Hapten Synthesis and Development of a Competitive Indirect

492

Enzyme-Linked Immunosorbent Assay for Acrylamide in Food Samples. J. Agr. Food

493

Chem. 2014, 62, 7078-7084.

494

(20) Xie, H. L.; Ma, W.; Liu, L. Q.; Chen, W.; Peng, C. F.; Xu, C. L.; Wang, L. B.,

495

Development and validation of an immunochromatographic assay for rapid

496

multi-residues detection of cephems in milk. Anal. Chim. Acta 2009, 634, 129-133.



497

(21) Garcia-Fernandez, J.; Trapiella-Alfonso, L.; Costa-Fernandez, J. M.; Pereiro, R.;

498

Sanz-Medel, A., A Quantum Dot-Based Immunoassay for Screening of Tetracyclines

499

in Bovine Muscle. J. Agr. Food Chem. 2014, 62, 1733-1740.

500

(22) Feng, H. Y.; Zhou, L. P.; Shi, L.; Li, W. L.; Yuan, L. J.; Li, D. Q.; Cai, Q. Y.,

501

Development of enzyme-linked immunosorbent assay for determination of

502

polybrominated diphenyl ether BDE-121. Anal. Biochem. 2014, 447, 49-54.

503

(23) Sybyl Molecular Modeling Software, Version X-2.1, Tripos Inc., St. Louis, MO.

504

(24) Szurdoki, F.; Bekheit, H. K.; Marco, M. P.; Goodrow, M. H.; Hammock, B. D.,

505

Synthesis of haptens and conjugates for an enzyme immunoassay for analysis of the

506

herbicide bromacil. J. Agr. Food Chem. 1992, 40, 1459-1465.

507

(25) Xu, Z. L.; Xie, G. M.; Li, Y. X.; Wang, B. F.; Beier, R. C.; Lei, H. T.; Wang, H.;

508

Shen, Y. D.; Sun, Y. M., Production and characterization of a broad-specificity

509

polyclonal antibody for O, O-diethyl organophosphorus pesticides and a quantitative

510

structure–activity relationship study of antibody recognition. Anal. Chim. Acta 2009,

511

647, 90-96.

512

(26) Buchberger, W. W., Novel analytical procedures for screening of drug residues

513

in water, waste water, sediment and sludge. Anal. Chim. Acta 2007, 593, 129-139.

514

(27) Wang, Z. H.; Zhu, Y.; Ding, S. Y.; He, F. Y.; Beier, R. C.; Li, J. C.; Jiang, H. Y.;

515

Feng, C. W.; Wan, Y. P.; Zhang, S. X., Development of a monoclonal antibody-based

516

broad-specificity ELISA for fluoroquinolone antibiotics in foods and molecular

517

modeling studies of cross-reactive compounds. Anal. Chem. 2007, 79, 4471-4483.


Page 24 of 38

Page 25 of 38


518

(28) Cui, J. L.; Zhang, K.; Huang, Q. X.; Yu, Y. Y.; Peng, X. Z., An indirect

519

competitive enzyme-linked immunosorbent assay for determination of norfloxacin in

520

waters using a specific polyclonal antibody. Anal. Chim. Acta 2011, 688, 84-89.

521

(29) Holtzapple, C. K.; Carlin, R. J.; Rose, B. G.; Kubena, L. F.; Stanker, L. H.,

522

Characterization of monoclonal antibodies to aflatoxin M1 and molecular modeling

523

studies of related aflatoxins. Mol. Immunol. 1996, 33, 939-946.

524

(30) Muldoon, M. T.; Holtzapple, C. K.; Deshpande, S. S.; Beier, R. C.; Stanker, L.

525

H., Development of a monoclonal antibody-based cELISA for the analysis of

526

sulfadimethoxine. 1. Development and characterization of monoclonal antibodies and

527

molecular modeling studies of antibody recognition. J. Agr. Food Chem. 2000, 48,

528

537-544.

529

(31) Cao, L. M.; Kong, D. X.; Sui, J. X.; Jiang, T.; Li, Z. Y.; Ma, L.; Lin, H.,

530

Broad-specific antibodies for a generic immunoassay of quinolone: development of a

531

molecular model for selection of haptens based on molecular field-overlapping. Anal.

532

Chem. 2009, 81, 3246-3251.

533

(32) Wen, K.; Nölke, G.; Schillberg, S.; Wang, Z. H.; Zhang, S. X.; Wu, C. M.; Jiang,

534

H. Y.; Meng, H.; Shen, J. Z., Improved fluoroquinolone detection in ELISA through

535

engineering of a broad-specific single-chain variable fragment binding simultaneously

536

to 20 fluoroquinolones. Anal. Bioanal. Chem. 2012, 403, 2771-2783.

537

(33) Richmond, N. J.; Abrams, C. A.; Wolohan, P. R.; Abrahamian, E.; Willett, P.;

538

Clark, R. D., GALAHAD: 1. Pharmacophore identification by hypermolecular

539

alignment of ligands in 3D. J. Comput. Aid. Mol. Des. 2006, 20, 567-587.



540

(34) Clark, M.; Cramer, R. D., The probability of chance correlation using partial

541

least squares (PLS). Quantitative Structure‐Activity Relationships 1993, 12, 137-145.

542

(35) Lindgren, F.; Geladi, P.; Rännar, S.; Wold, S., Interactive variable selection

543

(IVS) for PLS. Part 1: Theory and algorithms. J. Chemom. 1994, 8, 349-363.

544

(36) Clark, R. D.; Sprous, D. G.; Leonard, J. M., Validating models based on large

545

data sets. Rational Approaches to Drug Design 2001, 475-485.

546


Page 26 of 38

Page 27 of 38


547

Figure captions

548

Figure 1 The ciELISA standard curve for PAZ.

549

Figure 2 The pharmacophore superimposition by GALAHAD

550

DA1 and DA2: hydrogen bond donor atom; AA3, AA4 and AA5: hydrogen bond

551

acceptor atom (AA4 and AA5 were overlapped with DA1 and DA2, respectively);

552

HY6 and HY7: hydrophobic centers; NC8: negative center; NP9: positive nitrogen.

553

Figure 3 The scatter plots of predicted versus experimental pIC50.

554

Figure 4 The CoMFA contour maps.

555

a, b and c: CoMFA steric contour maps together with PAZ, SOF and ROF,

556

respectively. e, f and g: CoMFA electrostatic contour maps together with PAZ, SOF

557

and TOS, respectively. The energies of all fields were calculated with the weight of

558

the standard deviation and the coefficient. Green, yellow, blue and red contours

559

represented steric bulk desirable, steric bulk undesirable, positive charge desirable and

560

negative charge desirable, respectively; and their contributions in Fig. 5a-5g were

561

30.8%, 18.5%, 11.0% and 3.4%, respectively.

562

Figure 5 The CoMSIA contour maps.

563

a: CoMSIA steric field and SOF; b: CoMSIA H-bond acceptor field and SOF. The

564

energies of all fields were calculated with the weight of the standard deviation and the

565

coefficient. Green, yellow, magenta and red contours represented steric bulk desirable,

566

steric bulk undesirable, acceptor bulk desirable and acceptor bulk undesirable,

567

respectively; and their contributions were 1.7%, 2.7%, 5.4% and 36%, respectively.

568



569

Table 1Antiseratiters of immunized mice from different groups Groups Immunogen Mouse number Antiserum titer

Carbodiimide coupling method PAZ-D-BSA 1 2 1/32000 1/32000

Glutaraldehyde coupling method PAZ-P-BSA 4 5 6 1/2000 1/8000 1/4000


Page 28 of 38

Page 29 of 38


570

Table 2 Cross reactivity of PAZ-related molecules LOD (ng/mL)

IC50 (nmol/mL)

CR (%)

1.40

0.032

100.0

SOF

0.68

0.046

69.6

OFL

1.10

0.048

66.7

PRU

3.83

0.050

64.0

CIP

1.82

0.058

55.2

RUF

1.28

0.064

50.0

LOM

7.71

0.069

46.4

PEF

0.95

0.070

45.7

ENR

0.45

0.077

41.6

NOR

1.75

0.099

32.3

GAR

2.30

0.104

30.8

GAT

1.72

0.112

28.6

ROF

4.21

0.124

25.8

Molecules

Structure O

O F 6

PAZ

H2N

5 8

7

4 1 N

3 OH 2

O



O

O

F

DAN

Page 30 of 38

OH

1.84

0.135

23.7

0.45

0.178

18.0

1.14

0.198

16.2

CLI

2.54

0.234

13.7

OXO

7.95

0.244

13.1

PIP

1.82

0.356

9.0

SPA

2.26

0.362

8.8

MOX

5.01

0.379

8.4

4.39

0.561

5.7

15.16

0.73

4.4

1.84

6.26

0.5

N

N

N

O

O OH

NAL

N

N

O

O

F

OH

N

DIF

N

N

F

O

O

F

SAR

OH

N

N HCl

HN

F

MAR O

O

F

TOS

H2N

N

OH N

N F

F

571 572


Page 31 of 38


573

Table 3 The determination of the ONC by PLS analyses and progressive scrambling

574

tests PLS with LOO 2

NC

575 576 577 578

q

Progressive scrambling tests 2

SEP STD

RF

Q STD

cSDEP

STD

RF

RF

2

0.550

0.688

0.397

0.330

0.388

0.509

3

0.632

0.718

0.373

0.326

0.434

0.539

4

0.640

0.710

0.373

0.339

0.411

0.491

5

0.668

0.731

0.373

0.346

0.419

0.495

STD

dq2/dr2yy'

RF

STD

RF

0.466

0.420

0.875

1.060

0.468

0.429

0.983

1.042

0.496

0.461

1.237

1.238

0.512

0.479

1.417

1.462

NC, number of components; PLS, partial least square; LOO, leave one out; q2,cross-validated correlation coefficient; SEP, standard error of prediction; Q2, maximal cross-validated correlation coefficient; cSDEP, calculated cross-validated standard error; dq2/dr2yy', dependent variables versus the perturbed dependent variables; STD, Standard CoMFA; RF, CoMFA with region focusing.

579



580 581

Table 4 The summary of the calculated parameters of the 3D QSAR models Parameters

CoMFA

CoMSIA

ONC

3

7

q2

0.718

0.748

2

r

0.991

0.999

Predictive r2

0.827

0.753

Standard error of estimate

0.060

0.028

F-test value

452.137

882.253

S

0.587

0.538

E

0.413

Field’s contribution

A

0.462


Page 32 of 38

Page 33 of 38


583 584

Figure 1 The ciELISA standard curve for pazufloxacin (n=3).

585



586 587

Figure 2 The pharmacophore superimposition by GALAHAD. DA1 and DA2:

588

hydrogen bond donor atom; AA3, AA4 and AA5: hydrogen bond acceptor atom

589

(AA4 and AA5 were overlapped with DA1 and DA2, respectively); HY6 and HY7:

590

hydrophobic centers; NC8: negative center; NP9: positive nitrogen.

591


Page 34 of 38

Page 35 of 38


592

593 594 595

Figure 3 The scatter plots of predicted versus experimental pIC50



596

597

598 599

Figure 4 The CoMFA contour maps.

600

a, b and c: CoMFA steric contour maps together with PAZ, SOF and ROF,

601

respectively. e, f and g: CoMFA electrostatic contour maps together with PAZ, SOF

602

and TOS, respectively. The energies of all fields were calculated with the weight of

603

the standard deviation and the coefficient. Green, yellow, blue and red contours

604

represented steric bulk desirable, steric bulk undesirable, positive charge desirable and

605

negative charge desirable, respectively; and their contributions were 30.8%, 18.5%,

606

11.0% and 3.4%, respectively.

607


Page 36 of 38

Page 37 of 38


608

609 610

Figure 5 The CoMSIA contour maps.

611

a: CoMSIA steric field and SOF; b: CoMSIA H-bond acceptor field and SOF. The

612

energies of all fields were calculated with the weight of the standard deviation and the

613

coefficient. Green, yellow, magenta and red contours represented steric bulk desirable,

614

steric bulk undesirable, acceptor bulk desirable and acceptor bulk undesirable,

615

respectively; and their contributions were 1.7%, 2.7%, 5.4% and 36%, respectively.

616



617 618

Table of Contents Graphic

619


Page 38 of 38

Investigation of an Immunoassay with Broad Specificity to Quinolone

Recommend Documents