Broad-Specificity Chemiluminescence Enzyme ... - ACS Publications

Subscriber access provided by ORTA DOGU TEKNIK UNIVERSITESI KUTUPHANESI

Article

Broad-Specific Chemiluminescence Enzyme Immunoassay for (Fluoro)quinolones: Hapten Design and Molecular Modeling Study of Antibody Recognition Haopeng Zeng, Jiahong Chen, Chijian Zhang, Xinan Huang, Yuanming Sun, Zhenlin Xu, and Hongtao Lei Anal. Chem., Just Accepted Manuscript • DOI: 10.1021/acs.analchem.6b00082 • Publication Date (Web): 15 Mar 2016 Downloaded from http://pubs.acs.org on March 15, 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.

Analytical 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 25

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Analytical Chemistry

Broad-Specific Chemiluminescence Enzyme Immunoassay for (Fluoro)quinolones: Hapten Design and Molecular Modeling Study of Antibody Recognition Haopeng Zeng,† Jiahong Chen,† Chijian Zhang,† Xin-an Huang,*‡ Yuanming Sun,† Zhenlin Xu*,†, Hongtao Lei*,†

†

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

Science, South China Agricultural University, Guangzhou, 510642, P. R. China. ‡

Tropical Medicine Institute & South China Chinese Medicine Collaborative

Innovation Center, Guangzhou University of Chinese Medicine, Guangzhou, 510405, P. R. China.

*

Corresponding authors

†

Phone: 8620-8528-3448; Fax: 8620-8528-0270; E-mail: [email protected]

(Hongtao Lei), [email protected] (Zhenlin Xu). ‡

Phone: 8620-3658-5475; Fax: 8620-8637-3516; E-mail:

[email protected] (Xin-an Huang).

ACS Paragon Plus Environment


1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

ABSTRACT: Based on the structural features of (fluoro)quinolones (FQs), pazufloxacin was firstly used as a generic immunizing hapten to raise a broad-specific antibody. The obtained polyclonal antibody exhibited broad cross-reactivity ranging from 5.19% to 478.77% with 21 FQs. And the antibody was able to recognize these FQs below their maximum residue limits (MRLs) in an indirect competitive chemiluminescence enzyme immunoassay (ic-CLEIA), with the limit of detection (LOD) ranging from 0.10 to 33.83 ng/mL. For simply pre-treated milk samples with spiked FQs, the ic-CLEIA exhibited an excellent recovery ranging 84.6%-106.9% and an acceptable coefficient of variation below 15%, suggested its suitability and reliability for the use of a promising tool to detect FQs. Meanwhile comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) models, with statistically significant correlation coefficients (q2CoMFA = 0.559, r2CoMFA = 0.999; q2CoMSIA = 0.559, r2CoMSIA = 0.994), were established to investigate the antibody recognition mechanism. These two models revealed that in the antibody the active cavity binding FQs’ 7-position substituents worked together with another cavity (binding FQs’ 1-position groups) to crucially endow the high cross-reactivity. This investigation will be significant for better exploring the recognition mechanism and for designing new haptens.


Page 2 of 25

Page 3 of 25

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


INTRODUCTION (Fluoro)quinolones (FQs) are a large group of synthetic antibiotics that derived from nalidixic acid by introducing the piperazinyl group at 7-position and fluorine atom at 6-position (Figure 1). FQs have been widely used to cure various infections caused by Gram-positive and Gram-negative bacteria, due to their broad antibacterial spectrum and high antibacterial efficiency 1. In recent years, some FQs have been specifically used in food-producing animals, not only for treating bacterial infection but also for growth promotion and infection prevention 2. The drug residues in food products of animal origin have become a serious public health issue world-widely 3. Furthermore, the emergence of resistant bacteria strains to FQs has been reported in recent years 4, 5. To minimize the hazards of residues in food products and to preserve the efficacy of these antibiotics, the USA 6, the European Commission in Council Regulation 2377/90/EC 7, the Japanese Positive List System 8 and Ministry of Agriculture of the People’s Republic of China 9 have approved the rules to regulate the use of FQs in food-producing animals, and established the MRLs for FQs in milk and other edible samples 10. For example, the MRLs are set at 75 µg/kg for marbofloxacin and 100 µg/kg for enrofloxacin and ciprofloxacin in milk 11,12. Nowadays, various analytical methods, including high-performance liquid chromatography (HPLC) 13-15, HPLC-mass spectrometry (HPLC-MS) 16-18 and capillary electrophoresis (CE) 19-21, etc., have been reported to quantify FQs in food matrices. These instrumental methods are mostly accurate and sensitive, but their shortcomings, such as expensive, time consuming, labor-intensive, and requiring complicated equipments, complex pretreatment procedures, restrict their widespread use for the rapid detection of FQs in a large number of food samples. In contrast, immunochemical analysis, such as the enzyme-linked immunosorbent assay (ELISA) has been widely employed in practice for fast screening of FQs in food samples due to its rapidity, low cost, high sensitivity, ease of use and detection efficiency 22-27. However, most of these immunoassay methods can only recognize one single or a



1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

small set of FQs with high structure similarity. Therefore, the emerging trend is to develop a generic immunoassay based on broad-specific antibody which can detect multiple FQs derivatives in a single test. Several antibodies were produced using FQs as the immunizing haptens, and their cross-activities were examined 28-36. However, it is still difficult to design desired haptens for developing immunoassay for the simultaneous multi-analyte detection of FQs with both high sensitivity and enough broad-specificity, due to the lack in understanding of the specific interactions between antibodies and haptens or target analytes. In this paper, pazufloxacin was designed to be the generic immunizing hapten to raise broad specific polyclonal antibody for the first time, and a highly sensitive indirect competitive chemiluminescence enzyme immunoassay (ic-CLEIA) method was then developed for the detection of FQs in milk samples. Moreover, in order to investigate the antibody recognition, the three-dimensional quantitative structure-activity relationship (3D QSAR) was studied.

EXPERIMENTAL SECTION Reagents and Chemicals. Pazufloxacin (PAZ), Garenoxacin (GAR), Norfloxacin (NOR), Lomefloxacin (LOM), Pefloxacin (PEF), Rac-ofloxacin (OFL), Clinafloxacin (CLI), Pipemidic acid (PIA), Rufloxacin (RUF), Ciprofloxacin (CIP), Enrofloxacin (ENR), Gatifloxacin (GAT), Danofloxacin (DAN), Marbofloxacin (MAR), Nalidixic acid (NAA), Oxolinic acid (OA), Difloxacin (DIF), Moxifloxacin (MOX), Sarafloxacin (SAR), Tosufloxacin (TOS), Prulifloxacin (PRU) standard were purchased from Beijing HWRK Chem Co., LTD (Beijing, China). Bovine serum albumin (BSA), ovalbumin (OVA), 1-ethyl-3-(3-dimethylaminopropyl) carbodiimide (EDC), Freund’s complete adjuvant and Freund’s incomplete adjuvant were purchased from Sigma Chemical Co. (St. Louis, MO, USA). The Super Signal West Pico CL substrate (luminol/enhancer, A; stable peroxide buffer, B) was obtained from Pierce Protein Research Products (Thermo Fisher Scientific Inc., Illinois, USA).The goat


Page 4 of 25

Page 5 of 25

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


anti-rabbit IgG-HRP was purchased from Boster Biotechnology Co. (Wuhan, China). Deionized water was prepared using a Milli-Q water purification system (Millipore, Bedford, MA). Other chemical reagents were of analytical grade. Apparatus. The chemiluminescence signal was determined by the MP 280 Chemiluminescence Immunoassay Analyzer (Beijing Tai Geke Letter Biological Technology Co., Ltd., China). The UV-vis spectrophotometer (UV-2450 Shimadzu) was used to identify the coupling of protein with PAZ. The 96-well polystyrene microtiter plates (Shenzhen Jincanhua Industrial Co., Ltd., China) were used as the solid-phase support to coat antigen. Plates were washed in a Multiskan MK2 microplate washer (Thermo Scientific, Hudson, USA) to flush unattached substance away. The electrical thermostatic cultivation cabinet (DHG-9146A, Shanghai Jing Hong Laboratory Instrument Co. Ltd., China) and high speed centrifuge (TGL-16, Shanghai medical analysis instrument factory, China) were also used. Buffers and Solution. The following buffers and solution were used: (A) coating buffer was 0.05 mol/L bicarbonate buffer solution, pH 9.6; (B) incubation buffer was 0.01 mol/L sodium phosphate buffered saline (PBS), pH 7.4; (C) washing and dilution buffer consisted of buffer B contained 0.05% (v/v) Tween-20 (PBST); (D) blocking solution consisted of buffer B contained 1% (v/v) BSA and 4% (w/v) cane sugar, pH 7.2; (E) Standard stock solution (1 mg/mL) was prepared by dissolving an appropriate amount of each standard in 0.03 mol/L sodium hydroxide solution and kept at 4 °C until use. Working standards (0.02, 0.14, 1.04, 7.29, 51.02, 357.14, 2500 ng/mL) of each FQs were prepared by diluting the stock solution in PBST buffer. Synthesis of Immunogens and Coating Antigens. PAZ comprises a carboxyl group on 3-position, which can be directly conjugated to carrier protein. The immunogens were prepared by conjugating hapten PAZ to BSA based on the linkage of 2 imine-carbon (EDC) method 29 with changes as follows: (1) PAZ (9.14 mg) and BSA (15 mg) were dissolved in 1 mL of normal saline; (2) EDC (9 mg) were slowly added to the above solution and stirred at room temperature for 10-15 min; (3) the



1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

mixture was dialyzed against normal saline at 4 °C for 72 h with buffer changed every 12 h to remove the unreacted materials; (4) the PAZ–BSA conjugates were identified by UV-vis and stored at -20 °C before use. Meanwhile, the coating antigens were prepared by conjugating PAZ to OVA in the same manner. Antibody Production. To produce polyclonal antibody, two healthy female New Zealand white rabbits were used. For the initial immunization, the immunogen solution (0.5 mg in 0.5 mL normal saline) was emulsified in Freund’s complete adjuvant (0.5 mL) and injected subcutaneously into each rabbit. For subsequent immunizations, each rabbit was boosted intervals of two weeks with the same immunogen solution emulsified in Freund’s incomplete adjuvant. One week after the final boost, rabbits were euthanized and the blood samples were collected. After a centrifugation step at 3500 rpm for 10 min, the supernatant was collected and stored at -20 °C until use. Ic-CLEIA Method. A 96-well polystyrene microtiter plate was coated with PAZ-OVA (100 µL per well), incubated at 37 °C overnight and then washed twice with PBST, The blocking buffer was added (120 µL per well), and then the plate was were incubated at 37 °C for 3 h, and washed again and dried at 37 °C. PAZ standards or samples (50 µL per well) were added followed by the addition of anti-pazufloxacin antibody dilution (50 µL per well). The wells were incubated at 37 °C for 40 min and then washed five times with PBST. After that, 100 µL of goat anti-rabbit IgG-HRP (1:5000 in PBST) was added into each well and the microtitre plate was incubated at 37 °C for 30 min. The CL substrate (50 µL of A and 50 µL of B) was added after washing the wells for five times. The CL intensity was instantly tested by chemiluminescence immunoassay analyzer (expressed as relative light unit, RLU). The logarithm of PAZ or FQs concentration was served as X-axis, while B/B0 (B, the average RLU of the well containing analyte; B0, the average RLU of the well without analyte) was served as Y-axis. And a four-parameter equation was used to fit the sigmoidal curve using Origin 8.5 software (Origin Lab Corp., Northampton, MA,


Page 6 of 25

Page 7 of 25

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


USA): Y=(A-D)/[1+(x/C)B]+D, where A, B, C and D corresponded to the maximal RLU, the slope of the sigmoid curve, the analyte concentration (IC50) that provided a 50% reduction of the maximum RLU signal (RLUmax), and the minimal RLU, respectively 37. The limit of detection (LOD) of the assay was defined as the analyte concentration (IC10) that provided a 10% reduction of RLUmax, while the dynamic detectable range of the assay was established between the IC20 and IC80 31, 37. Specificity. Twenty FQs were tested with the above ic-CLEIA experiment to evaluate the cross-reactivity (CR), defined as the percentage ratio of IC50 value for PAZ to that for FQs. The CR was calculated using the following equation 38: CR%= IC50 (PAZ, nmol/L)/IC50 (FQs, nmol/L) ×100% Preparation of Milk Samples. Milk samples were purchased from a local supermarket. All samples were confirmed to be free of FQs before use, as analyzed following the normalized procedures of China National Standards (GB 29692-2013). Sample pretreatment procedures were as follows 30, 39. The milk samples were spiked with FQs at three different concentrations (1/2, 1, and 2×MRLs). After vortex-mixed for about 1 min, the mixture were centrifuged for 15 min at 10000 rpm to remove fat in the milk samples. The non-fat liquid in the middle layer was collected and diluted 10-fold with PBST and then analyzed using ic-CLEIA. Method Comparison. The comparison experiment was carried out by comparing CLEIA with HPLC. The HPLC analysis of spiked milk samples was conducted following the normalized procedures of China National Standards (GB 29692-2013). Molecular Modeling. The molecular modeling was conducted using SYBYL-X 2.1 program package. The dataset molecules of GAR, NOR, LOM, PEF, CLI, PAZ, CIP, ENR, GAT, DAN, MAR, NAA, OA, MOX, SAR, TOS, PRU, S-OFL (SOF), R-OFL (ROF), PIA, RUF and DIF were constructed using the “SKETCH” option function; then they were energy minimized using the Powell method. The criteria of the termination and max iterations were set at 0.005 kcal/(mol×Å) and 1000,



1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

respectively. MMFF94 force field and MMFF94 charges were used in the energy minimization, while other parameters were as defaults. The molecular alignment was carried out using PAZ as the template molecule with its C-4, C-5, C-9, C-10 and N-1 as the common atoms (red marked in Figure 1). The training set contained the first 17 molecules, while the test set comprised the other five molecules. Their pIC50 (-log IC50) values of the dataset molecules were used in analysis. In the comparative molecular field analysis (CoMFA), the steric and electrostatic interaction fields of each molecule were calculated on a 3D cubic lattice. A sp3 carbon probe atom with Van der Waal radius of 1.52 Å and +1 charge was used to generate the steric and electrostatic filed energies. The cross-validated correlation coefficient R2 (q2) and the optimum number of components (ONC) were obtained using the partial least-square (PLS) method with leave-one-out (LOO) option. Using the obtained ONC, the final non-cross-validated model was created. In the comparative molecular similarity index analysis (CoMSIA), the five fields of steric (S), electrostatic (E), hydrophobic (H), hydrogen bond donor (D) and hydrogen bond acceptor (A) field, were systemically combined. The combination with higher q2 was chosen to generate the final model.

RESULTS AND DISCUSSION Hapten Design and Antibody Production. Designing a suitable hapten is the critical step for the whole process of producing a broad-specificity antibody and developing a broad-specificity immunoassay. The cross-activity derived from the fact that the antibody could widely recognize a serial of compounds with different but usually related structure, therefore the structure similarity is often proposed as the basis for the immunizing hapten selectiion for induciblly producing a broad-specific antibody 40. The interaction between fluorine (or oxegen) at 8-position and alkane substituent at 1-position in some FQs formed a ring (e.g. PAZ, ROF, MAR, OFL, SOF) or ring-like conformation (e.g. LOM, MOX) 41. This ring feature was infered to be


Page 8 of 25

Page 9 of 25

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


capable to improve the molecular similarity around 1 and 8-positions, and thus the structure feture around 1 and 8-positions was ragarded more important than piperazinyl at 7-position for a promising candidate immunizing hapten to produce broad specific antibody to FQs 41. However, other researchers found that the substructure at the distal end of the coupling site was closely associated with the specific recognition of the resultant antibody 42. This could induce that the structure at 7-position of FQs might play a critical role in the interaction between FQs and the corresponding antibody if the the carboxy group at 3-position in FQs was linked to the carrier protein. Moreover, on basis of the size-exclusion mode, if an antibody has suitable active pocket to accommodate the conformations of big molecules, and then it may have high broad-reactivity to small ones 43. Because both of the anmino and cyclopropyl groups are attached around 7-position of PAZ (Figure 1), the raised antibody could probably produce a large cavity to accommodate the piperazinyl at 7-position if PAZ is used as an immunizing hapten. Under aforementioned consideration PAZ was herein considered as an immunizing hapten and intended to generate an antibody hopefully with a highly broad specificity, although it did not possess a common piperazinyl like most of FQs. To determine the effectiveness of the conjugation reaction, the PAZ-BSA and PAZ-OVA conjugates were identified by UV-vis spectrophotometer. As shown in Figure S1, the strongest absorption peaks for PAZ-BSA (246, 331 nm) and PAZ-OVA (255, 332 nm) were slightly different from the peak for PAZ (242, 333 nm), while the maximum absorbances of BSA and OVA were both observed at 278 nm, indicating the PAZ-BSA and PAZ-OVA conjugates were prepared successfully 25. Meanwhile, antibody titers of two rabbits’ serum were tested by indirect ELISA as described in the literature 12 and the antiserum with a significantly high titer (> 64000) was used for further investigation. Ic-CLEIA. In the immunoassay, the optimum concentrations for coating antigen and antibody were the primary factors influencing the sensitivity of the method.



1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Chessboard titration was applied to optimize the concentration of coating antigen (from 12.5 to 1600 ng/mL) and dilute multiple of antibody (serially diluted from1:4000 to 1:128000). For the apparatus, the ideal RLU detecting range is 106–107, between which the chemiluminescence apparatus was relatively sensitive 37. Therefore, several suitable coating antigen concentrations in combination with antibody dilution multiples were further investigated (Figure S2). The RLUmax/IC50 ratio of calibration curve had been shown as a useful parameter to estimate the sensitivity of CLEIA 44. The higher sensitivity of CLEIA was obtained under high ratio of RLUmax/IC50. Thus, by comparing the values of IC50 and RLUmax/IC50 ratio (Table S1), the optimal coating antigen concentration and dilution multiple of antibody were found to be 100 ng/mL and 1:4000, respectively. Under this optimal condition, a calibration curve for PAZ was developed (Figure 2). The IC50 value, LOD and dynamic detectable range were 13.48 ng/mL, 0.17 ng/mL and 0.76-236.42 ng/mL, respectively. These results showed that this method is more sensitive for the detection of PAZ, compared with the molecularly imprinted polymer solid-phase extraction with flow-injection chemiluminescence (LOD, 0.7 ng/mL) 45 and HPLC (LOD, 33.0 ng/mL) 46. Broad Specificity. The specificity of the antibody was evaluated by performing the above immunoassays using 21 FQs. As seen in Table 1, the anti-pazufloxacin antibody showed desirable affinity with FQs, especially for GAR, NOR, LOM, PEF, OFL, CLI and PIA. As seen in another study 35, the CR data for DIF, SAR and TOS were lower than the other 15 FQs, meaning that the antibody had lower affinity with such substances. The presence of the fluorophenyl group can introduce a big obstacle at 1-position in DIF, SAR or TOS than in other FQs, however, the individual effect of the aromatic ring and electronegative fluorine on antibody binding was unclear. Although the CR data for DIF, SAR, TOS, MOX and PRU were low, they were still detectable since all the LODs were below the MRLs 10. Therefore, this ic-CLEIA method has the potential to be incorporated into a multiresidue programme for simultaneously detecting 21 FQs. Compared with previously published immunoassay


Page 10 of 25

Page 11 of 25

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


methods 28-31, 33, 34, 36, 47, this method could also be applied to a wider range of quinolones without (e.g. NAA, OA) or with fluorine (e.g. FQs). The hapten-antibody interaction is dependent on molecular force-fields, which are associated with hydrogen bond, hydrophobicity, electrostaticity and etc 31,48. Therefore, it is not easy to explain the CR data and estimate the main forces contributing to the binding affinity by viewing the 2D-chemical structures (especially for MOX and PRU). In order to investigate which effects were primarily important for antibody-FQs binding, molecular modeling techniques were used. Molecular Modeling Studies. Molecular modeling can provide insights into molecular structure and biological-activity that are difficult or otherwise impossible to obtain 49, and can simulate the details of the cross-reaction of particular antibody 50-52. Thus, it can be used to improve hapten design 53. In order to investigate the factors affecting the antibody recognition, the 3D QSAR methods of CoMFA and CoMSIA were used here to correlate the force-fields/conformations and their corresponding affinities of 21 FQs. 3D QSAR Statistical Results. OFL was the mixture of SOF and ROF. Here, OFL was used to evaluate the predictive performance of CoMFA and CoMSIA models. In the CoMFA model, the q2 and predictive r2 values were 0.559 and 0.970, and the S and E contributions were 60.2% and 39.8%, respectively. Using the same test set as in CoMFA, CoMSIA showed that the single S field could provide higher q2 than any of the combinations among S, E, A, H and D field. With the single S field, the q2 and the predictive r2 of the CoMSIA model were 0.559 and 0.951, respectively (Table S2). The predicted values were listed in Table S3 and their scatter plots of the predicted versus experimental activity were shown in Figure S3. Compared with the CoMSIA model, the CoMFA model had a higher predictive r2 value as well as a more accurate predictive value for RUF. However, the CoMSIA model gave smaller absolute residuals for DIF, PIA and OFL (Table S3). 3D QSAR Contour Analysis. The contour maps reflected the effect of different



1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

force-fields on the molecular binding affinity. In Figure 3a, the conformations of the stereoisomers of SOF and ROF differed in the chiral regions, where the former had a methyl group pointing to the green contours (bulky groups favorable), while the methyl group in the latter pointed to the yellow contours (bulky groups unfavorable). This explained the reason that SOF (pred. pIC50 = 7.775 and so forth) had higher activity than ROF (pred.6.990). The ethyl, cyclopropyl and phenyl in PEF, CIP, SAR and DIF were separately connected to the nitrogen at 1-position in quinoline ring. The ethyl or cyclopropyl group toward the green contours was favorable for the affinity. The phenyl group near the yellow and blue contours was a bulky and multi-electron group, which decreased the affinity (Figure 3b). The aforementioned combined effects led to the lower activities of SAR (exp. 6.242 and so forth) and DIF (6.381) than CIP (7.260) and PEF (7.841). The green contours near the piperazinyl group in Figure 3c indicated that proper bulky groups in these areas could increase the affinity, which revealed the fact that most of the dataset molecules with piperazinyl groups, such as NOR, LOM and PEF, had higher activities than NAA (6.930). The red contours around the piperazinyl groups suggested that low electronegative groups in these areas were undesired. PEF had a relative low electronegative methyl group near the red contours, which coincided with its lower activity than NOR (7.963). The blue contours around the piperazinyl groups indicated that high electronegative groups in these areas were undesired. 1, 3-Dioxolen-2-one was negative charged moiety. The unfavorable substructure near the blue contours and the unfavorable methyl near the yellow contours resulted in the low activity (6.088) of PRU (Figure 3d). Compared with the CoMFA model, the CoMSIA model had similar steric contours near the oxazine rings and more elaborate contours around the piperazinyl rings (Figure 3e). In generally, the molecules with substructures that were largely embedded in the yellow regions had lower activities, such as PRU, MOX, DIF, SAR and TOS. The green contours represented that bulky groups in these regions were desired for increasing the activity. 2,3-Dihydro-1,4-thiazine ring in RUF had the


Page 12 of 25

Page 13 of 25

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


similar spatial structure as the oxazine ring in SOF, however, the longer carbon-sulfur bond which made the sulfur closer to the undesired yellow steric area, and the lack of a spatial methyl group as in SOF comprehensively led to its lower activity (pred. 7.715) than SOF (pred.7.872). Recovery. In food samples, chemical compounds such as fat and protein may affect the binding of antibodies and analytes. Thus, it is important to remove or minimize the matrix effects in the immunoassay study. Solid-phase extraction (SPE) was commonly chosen as a purification technique in HPLC, but it was time-consuming, expensive, and not suitable for a rapid and simple screening application 54. Therefore, in the present investigation, the matrix effect of extract was expected to remove just by buffer dilution, mainly because of the highly sensitivity of the developed immunoassay methods. The calibration curve of ic-CLEIA for PAZ in the blank milk extract with 10-fold dilution using PBST was coincided to a large extent with that in PBST (Figure 4). It indicated that a 1:10 dilution with PBST for milk extract can eliminate the matrix interference. In this paper, the PAZ and three FQs (ENR, CIP and MAR) largely used in the animal industry were selected for recovery studies. The recovery for the four FQs ranged from 84.6% to 106.9% for intra-assay and 84.8% to 106.3% for inter-assay (Table 2). The U.S. Environmental Protection Agency (EPA) stated that a screening detection method for sample recoveries of the analyte must be in the range of 70-120% 55

. Therefore, the proposed ic-CLEIA method with simple pre-treatment can meet well

the requirement of EPA. The precision of assay was studies and the coefficient of variation (CV) was below 15%. These results demonstrate that the ic-CLEIA developed for the analysis of FQs in milk was stable and satisfactory. Methods Comparison. The performance of the ic-CLEIA method was compared with a confirmatory HPLC method. It is found that PAZ could be detected at 5.2 min in the HPLC chromatogram, and the linear detectable range is 0.01-0.5 µg/mL (R2= 0.9998). Milk samples, which were spiked at four different



1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

concentrations (50, 100, 150 and 200 ng/mL), were tested by ic-CLEIA and HPLC methods, respectively. The linear regression equation between data obtained using the ic-CLEIA and HPLC was y=1.14x-3.99 with a correlation coefficient (r2) of 0.978 (Figure 5). This showed that the two methods had a good correlation, which indicated the good performance of the ic-CLEIA method. It should be noted that the measure results by HPLC were lower than those by ic-CLEIA, which may due to the loss of analyte during the sample preparation for HPLC. These indicated that the ic-CLEIA method was reliable and could be satisfactorily employed in sample detection.

CONCLUSION Based on the structural features of the FQs, PAZ was designed as a generic immunizing hapten and the obtained broad-specific polyclonal antibody can highly sensitively recognize 21 FQs as expected (LOD ranging from 0.10 to 33.83 ng/mL) . With a confirmed satisfactory recovery (84.6%-106.9%) and good agreement to HPLC method, the proposed ic-CLEIA is suitable and reliable for the determination of FQs commonly existed in milk samples. Meanwhile, the established CoMFA and CoMSIA models exhibited good predictive abilities for CR, and showed the major causative effects of the steric fields on the molecular activities. Particularly, in the antibody the active cavity binding FQs’ 7-position substituents worked together with another cavity (binding FQs’ 1-position groups) to crucially endow the high cross-reactivity. This investigation will be significant for better exploring the recognition mechanism and for designing new haptens for broad specificity immunoassay of FQs.

ASSOCIATED CONTENT Supporting Information. Description of the identification of PAZ-BSA and PAZ-OVA conjugates, effect of concentration of coating antigen and dilute multiple of the antibody in RLU, determination of the optimal concentrations of coating antigen


Page 14 of 25

Page 15 of 25

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


and dilute multiple of the antibody in ic-CLEIA, the summary of the calculated parameters of the 3D QSAR models, the predicted values of different 3D QSAR models and the scatter plots of predicted versus experimental pIC50.

ACKNOWLEDGMENTS This work was supported by Natural Science Foundation of China (U1301214), Guangdong Natural Science Foundation (S2013030013338, 2015A030313366), Guangdong Planed Program in Science and Technology (2014TX 01N250, 2010A032000001-4, 2013B051000072). We also greatly thank Dr Kai Wang and Professor Yingju Liu for their kind help in manuscript revision.

REFERENCES (1) Wolfson, J. S.; Hooper, D. C. Am. J. Med. 1991, 91, 153-161. (2) Zhou, J. H.; Xue, X. F.; Chen, F.; Zhang, J. Z.; Li, Y.; Wu, L. M.; Chen, L. Z.; Zhao, J. J. Sep. Sci. 2009, 32, 955-964. (3) Blasco, C.; Torres, C. M.; Pico, Y. Trend. Anal. Chem. 2007, 26, 895-913. (4) Rupali, P.; Abraham, O. C.; Jesudason, M. V.; John, T. J.; Zachariah, A.; Sivaram, S.; Mathai, D. Diagn. Micr. Infec. Dis. 2004, 49, 1-3. (5) Threlfall, E. J.; Fisher, I. S. T.; Berghold, C.; Gerner-Smidt, P.; Tschape, H.; Cormican, M.; Luzzi, I.; Schnieder, F.; Wannet, W.; Machado, J.; Edwards, G. Int. J. Antimicrob. Ag. 2003, 22, 487-491. (6) Code of Federal Regulations 21.530.41 Drugs prohibited for extra label use in animals. (7) Official Journal of the European Union, L22418 August 1990 Council Regulation 2377/90/EC of 26 June 1990 laying down a Community procedure for the establishment of maximum residue limits of veterinary medicinal products in foodstuffs of animal origin, Brussels, Belgium, 1990. (8) The Positive List System for Agricultural Chemical Residues in Foods, Department of Food Safety, Ministry of Health, Labour and Welfare, June 2006. (9) Ministry of Agriculture of the People’s Republic of China No. 235/2002. (10) Andreu, V.; Blasco, C.; Picó, Y. Trend. Anal. Chem. 2007, 26, 534-556. (11) Hermo, M. P.; Nemutlu, E.; Kir, S.; Barron, D.; Barbosa, J. Anal. Chim. Acta. 2008, 613, 98-107. (12) Els, V. C.; Jan, D. B.; Wim, R. J. Agr. Food. Chem. 2004, 52, 4975-4978. (13) Phonkeng, N.; Burakham, R. Chromatographia. 2012, 75, 233-239. (14) Kirbis, A.; Marinsek, J.; Flajs, V. C. Biomed. Chromatogr. 2005, 19, 259-265.



1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

(15) Li, Y.; Zhang, Z.; Li, J.; Li, H.; Chen, Y.; Liu, Z. Talanta. 2011, 84, 690-695. (16) Paschoal, J. A. R.; Reyes, F. G. R.; Rath, S. Food. Addit. Contam. A. 2009, 26, 1331-1340. (17) Tang, C. M.; Yu, Y. Y.; Huang, Q. X.; Peng, X. Z. Int. J. Environ. An. Ch. 2012, 92, 1389-1402. (18) Li, Y. L.; Hao, X. L.; Ji, B. Q.; Xu, C. L.; Chen, W.; Shen, C. Y.; Ding, T. Food. Addit. Contam. A. 2009, 26, 306-313. (19) Faria, A. F.; de Souza, M. V. N.; de Almeida, M. V.; de Oliveira, M. A. L. Anal. Chim. Acta. 2006, 579, 185-192. (20) Herrera-Herrera, A. V.; Hernandez-Borges, J.; Borges-Miquel, T. M.; Rodriguez-Delgado, M. A. Electrophoresis. 2010, 31, 3457-3465. (21) Lombardo-Agui, M.; Gamiz-Gracia, L.; Garcia-Campana, A. M.; Cruces-Blanco, C. Anal. Bioanal. Chem. 2010, 396, 1551-1557. (22) Wang, Z.; Zhang, H.; Ni, H.; Zhang, S.; Shen, J. Anal. Chim. Acta. 2014, 820, 152-158. (23) Hu, K.; Huang, X. Y.; Jiang, Y. S.; Fang, W.; Yang, X. L. Aquaculture. 2010, 310, 8-12. (24)Hou, X. L.; Guo, K. J.; Bao, J.; Wu, Y. N.; Li, H. R. J. Food. Agric. Environ. 2011, 9, 779-783. (25) Yu, F.; Wu, Y.; Yu, S. C.; Zhang, H. L.; Zhang, H. Q.; Qu, L. B.; Harrington, P. D. B. Spectrochim. Acta. A. 2012, 93, 164-168. (26) Yu, F.; Yu, S.; Yu, L.; Li, Y.; Wu, Y.; Zhang, H.; Qu, L.; Harrington P. D. B. Food. Chem. 2014, 149, 71-75. (27) Yu, S. C.; Yu, F.; Zhang, H. Q.; Qu, L. B.; Wu, Y. J. Spectrochim. Acta. A. 2014, 127, 47-51. (28) Jiang, J. Q. J. Food. Agric. Environ. 2014, 12, 70-73. (29) Kato, M.; Ihara, Y.; Nakata, E.; Miyazawa, M.; Sasaki, M.; Kodaira, T.; Nakazawa, H. Food. Agr. Immunol. 2007, 18, 179-187. (30) Fan, G. Y.; Yang, R. S.; Jiang, J. Q.; Chang, X. Y.; Chen, J. J.; Qi, Y. H.; Wu, S. X.;Yang, X. F. J. Zhejiang. Uni. Sci. B. 2012, 13, 545-554. (31) Wang, Z. H.; Zhu, Y.; Ding, S. Y.; He, F. Y.; Beier, R. C.; Li, J. C.; Jiang, H. Y.; Feng, C. W.; Wan, Y. P.; Zhang, S. X.; Kai, Z. P.; Yang, X. L.; Shen, J. Z. Anal. Chem.2007, 79, 4471-4483. (32)Tao, X. Q.; Chen, M.; Jiang, H. Y.; Shen, J. Z.; Wang, Z. H.; Wang, X.; Wu, X. Q.; Wen, K. Anal. Bioanal. Chem. 2013, 405, 7477-7484. (33)Li, Y. L.; Ji, B. Q.; Chen, W.; Liu, L. Q.; Xu, C. L.; Peng, C. F.; Wang, L. B. Food. Agr. Immunol. 2008, 19, 251-264. (34) Huet, A. C.; Charlier, C.; Singh, G.; Godefroy, S. B.; Leivo, J.; Vehniainen, M.; Nielen, M. W.; Weigel, S.; Delahaut, P. Anal. Chim. Acta. 2008, 623, 195-203. (35) Wen, K.; Nolke, G.; Schillberg, S.; Wang, Z.; Zhang, S.; Wu, C.; Jiang, H.; Meng, H.; Shen, J. Anal. Bioanal. Chem. 2012, 403, 2771-2783. (36) Huet, A. C.; Charlier, C.; Tittlemier, S. A.; Singh, G.; Benrejeb, S.; Delahaut, P. J.


Page 16 of 25

Page 17 of 25

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


Agr. Food. Chem. 2006, 54, 2822-2827. (37) Yang, W. Y.; Dong, J. X.; Shen, Y. D.; Yang, J. Y.; Wang, H.; Xu, Z. L.; Yang, X. X.; Sun, Y. M. Chinese. J. Anal. Chem. 2012, 40, 1816-1821. (38) Pinacho, D. G.; Sanchez-Baeza, F.; Marco, M. P. Anal. Chem. 2012, 84, 4527-4534. (39) Liu, B. H.; Xiong, N.; Wang, X. L.; Shi, D. S.; Li, X. Y.; Peng, D. P. Chin J. Vet. Drug. 2008, 42, 16-19. (40) Xu, Z. L.; Zeng, D. P.; Yang, J. Y.; Shen, Y. D.; Beier, R. C.; Lei, H. T.; Wang, H.; Sun, Y. M. J. Environ. Monit. 2011, 13, 3040-3048. (41) Cao, L. M.; Kong, D. X.; Sui, J. X.; Jiang, T.; Li, Z. Y.; Ma, L.; Lin, H. Anal. Chem. 2009, 81, 3246-3251. (42) Banminger, S.; Kohen, F.; Lindner, H. R. J. Steroid Biochem. 1974, 5, 739-747. (43) Goodrow, M. H.; Hammock, B. D. Anal. Chim. Acta. 1998, 376, 83-91. (44) Quan, Y.; Zhang, Y.; Wang, S.; Lee, N. J.; Kennedy, I. R. Anal. Chim. Acta. 2006, 580, 1-8. (45) Yang, C. Y.; Zhang, Z. J.; Chen, S. M.; Yang, F. Microchim. Acta. 2007, 159, 299-304. (46) Wang, Y.; Zheng, L.; Liu, F. Chinese. J. Antibiotics. 2006, 31, 420-422. (47) Sheng, W.; Li, Y. Z.; Xu, X.; Yuan, M.; Wang, S. Microchim. Acta. 2011, 173, 307-316. (48) Sugawara, Y.; Gee, S. J.; Sanborn, J. R.; Gilman, S. D.; Hammock, B. D. Anal. Chem. 1998, 70,1092-1099. (49) Xu, Z. L.; Shen, Y. D.; Zheng, W. X.; Beier, R. C.; Xie, G. M.; Dong, J. X.; Yang, J. Y.; Wang, H.; Lei, H. T.; She, Z. G.; Sun, Y. M. Anal. Chem. 2010, 82, 9314-9321. (50) Spinks, C. A.; Wyatt, G. M.; Lee, H. A.; Morgan, M. R. A. Bioconjugate. Chem. 1999, 10, 583-588. (51) Muldoon, M. T.; Holtzapple, C. K.; Deshpande, S. S.; Beier, R. C.; Stanker, L. H. J. Agr. Food Chem. 2000, 48, 537-544. (52) Holtzapple, C. K.; Carlin, R. J.; Rose, B. G.; Kubena, L. F.; Stanker, L. H. Mol. Immunol. 1996, 33, 939-946. (53) Spinks, C. A. Trends Food Sci. Tech. 2000, 11, 210-217. (54)Mi, T.; Wang, Z.; Eremin, S. A.; Shen, J.; Zhang, S. J. Agr. Food. Chem. 2013, 61, 9347-9355. (55) Krotzky, A. J.; Zeeh, B. Pure. Appl. Chem. 1995, 67, 2065-2088.



1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 18 of 25

Table 1. Specificity and sensitivity of the ic-CLEIA FQs

LOD (ng/mL)

IC50 (ng/mL)

CR (%)

FQs

LOD (ng/mL)

IC50 (ng/mL)

CR (%)

PAZ

0.17

13.48

100.00

GAT

1.43

30.26

52.53

GAR

0.10

3.77

478.77

DAN

1.07

30.61

49.45

NOR

0.06

3.48

388.35

MAR

1.56

36.95

41.53

LOM

0.48

4.45

334.66

NAA

1.74

27.29

36.04

PEF

0.60

4.81

293.54

OA

0.52

31.70

34.90

OFL

0.17

6.40

239.28

DIF

5.03

166.10

10.18

CLI

0.16

8.91

173.86

MOX

0.59

220.70

7.70

PIA

0.53

8.70

147.69

SAR

9.42

220.90

7.39

RUF

1.09

19.72

78.06

TOS

33.83

247.30

6.92

CIP

0.66

18.21

77.07

PRU

0.16

376.46

5.19

ENR

0.76

20.28

75.05


Page 19 of 25

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


Table 2. Recovery for FQs at different spiked levels in milk samples

Compounds

PAZ

ENO

CIP

MAR

a b

Spiked level (ng/mL)

Intra-assaya Average Measured recovery (ng/mL) (%)

CV (%)

Inter-assay b Average Measured recovery (ng/mL) (%)

CV (%)

50

43.7±2.2

87.4

5.0

43.6±0.2

87.2

0.5

100

99.3±8.5

99.3

8.6

104.5±7.3

104.5

7.0

200

195.0±19.3

97.5

9.9

189.2±8.2

94.6

4.3

50

50.1±4.7

100.2

9.4

51.0±2.1

102.0

4.1

100

106.9±3.7

106.9

3.5

106.3±3.6

106.3

3.4

200

197.4±13.9

98.7

7.0

202.2±6.1

101.1

3.0

50

47.4±4.3

94.8

9.1

49.0±2.9

98.0

5.9

100

98.3±14.5

98.3

14.8

96.5±8.6

96.5

8.9

200

193.8±18.7

96.9

9.6

192.6±13.2

96.3

6.9

37.5

37.4±3.5

99.7

9.4

37.7±3.0

100.5

8.0

75

63.5±3.1

84.6

4.9

63.6±2.0

84.8

3.1

150

130.8±4.7

87.2

3.6

134.0±13.3

89.3

9.9

Intra-assay variation was determined by 3 replicates on a single day. Inter-assay variation was determined by 3 replicates on three different days



1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

For TOC only


Page 20 of 25

Page 21 of 25

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


Figure 1. The structures of FQs.



0.8

Y = -0.24X + 0.79 R2=0.998

0.7

1.0 B/B0

0.6

0.8

0.5 0.4 0.3 0.2

B/B0

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 22 of 25

0.6

0.1 1

10

100

Concentration of PAZ

0.4

0.2

0.0 0.01

IC50=13.48 ng/mL LOD=0.17 ng/mL

0.1

1

10

100

1000

10000

Concentration of PAZ (ng/mL)

Figure 2. Calibration curve for PAZ with ic-CLEIA (n=5).


Page 23 of 25

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


Figure 3. The 3D QSAR contour maps. Green and blue contours indicated the regions where bulky groups and positive charges favored activity, respectively. Similarly, yellow and red contours indicated the regions where bulky groups and positive charges were undesired, respectively. (a-d): favorable and unfavorable cutoff energies were set at the 80% and 20% for the CoMFA steric and electrostatic contributions, respectively. (e, f): favorable and unfavorable cutoff energies were set at the 65% and 35% for the CoMSIA steric contributions. (a): embedded SOF and ROF, hydrogen hidden. (b): embedded PEF, CIP, SAR and DIF, hydrogen hidden. (c): embedded PEF and NOR. (d): embedded PRU. (e): embedded all dataset molecules. (f): embedded RUF and SOF, hydrogen hidden.



1.0

PBST 1/10 Milk Extract

0.8

0.6

B/B0

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 24 of 25

0.4

0.2

0.0 0.01

0.1

1

10

100

1000

10000

Concentration of PAZ (ng/mL) Figure 4. Calibration curves of ic-CLEIA for PAZ in PBST and 1/10 milk extract.


Page 25 of 25

220

y=1.14x-3.99 R2=0.978

200 180

CLEIA (ng/mL)

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


160 140 120 100 80 60 40 20 20

40

60

80

100

120

140

160

180

HPLC (ng/mL)

Figure 5. Comparison of determination of PAZ in milk samples by ic-CLEIA and HPLC (n=3).


Broad-Specificity Chemiluminescence Enzyme ... - ACS Publications

Recommend Documents