Sensors 2012, 12(7), 9363-9374; doi:10.3390/s120709363

Article
Investigating the Quantitative Structure-Activity Relationships for Antibody Recognition of Two Immunoassays for Polycyclic Aromatic Hydrocarbons by Multiple Regression Methods
Yan-Feng Zhang 1,*, Li Zhang 1, Zhi-Xian Gao 2 and Shu-Gui Dai 1
1
Key Laboratory for Pollution Process and Environmental Criteria of Ministry of Education, College of Environmental Science and Engineering, Nankai University, Tianjin 300071, China; E-Mails: zhangli@nankai.edu.cn (L.Z.); sgdai@nankai.edu.cn (S.-G.D.)
2
Institute of Hygiene and Environmental Medicine, Tianjin 300050, China; E-Mail: gaozhx@163.com
*
Author to whom correspondence should be addressed; E-Mail: zhangyf@nankai.edu.cn; Tel.: +86-22-2350-3411; Fax: +86-22-2350-8807.
Received: 2 May 2012; in revised form: 12 June 2012 / Accepted: 25 June 2012 /
Published: 9 July 2012

Abstract

: Polycyclic aromatic hydrocarbons (PAHs) are ubiquitous contaminants found in the environment. Immunoassays represent useful analytical methods to complement traditional analytical procedures for PAHs. Cross-reactivity (CR) is a very useful character to evaluate the extent of cross-reaction of a cross-reactant in immunoreactions and immunoassays. The quantitative relationships between the molecular properties and the CR of PAHs were established by stepwise multiple linear regression, principal component regression and partial least square regression, using the data of two commercial enzyme-linked immunosorbent assay (ELISA) kits. The objective is to find the most important molecular properties that affect the CR, and predict the CR by multiple regression methods. The results show that the physicochemical, electronic and topological properties of the PAH molecules have an integrated effect on the CR properties for the two ELISAs, among which molar solubility (Sm) and valence molecular connectivity index (3χv) are the most important factors. The obtained regression equations for RisC kit are all statistically significant (p < 0.005) and show satisfactory ability for predicting CR values, while equations for RaPID kit are all not significant (p > 0.05) and not suitable for predicting. It is probably because that the RisC immunoassay employs a monoclonal antibody, while the RaPID kit is based on polyclonal antibody. Considering the important effect of solubility on the CR values, cross-reaction potential (CRP) is calculated and used as a complement of CR for evaluation of cross-reactions in immunoassays. Only the compounds with both high CR and high CRP can cause intense cross-reactions in immunoassays.
Keywords:
polycyclic aromatic hydrocarbons; immunoassay; enzyme-linked immunosorbent assay; cross-reactivity; quantitative structure-activity relationship; hapten

1. Introduction

Polycyclic aromatic hydrocarbons (PAHs) are ubiquitous contaminants found in air, water, sediment and soil. They are a large and diverse class of compounds consisting of two or more fused aromatic rings produced by both natural and anthropogenic processes. Since many PAHs and the metabolites are mutagens and carcinogens, PAHs have been listed as priority pollutants in many countries. PAHs rarely occur as individual compounds in the environment, but always as a complex mixture of various compounds. Conventional analytical methods for PAHs are gas chromatography (GC) and high-performance liquid chromatography (HPLC) which require time-consuming pretreatment extraction procedures.

In recent years, immunoassays have represented a fast, sensitive, inexpensive and field-portable analytical method to complement traditional chromatographic procedures for PAHs [1]. Some immunoassay techniques, such as enzyme-linked immunosorbent assay (ELISA) [2,3], fluorescence polarization immunoassay [4], chemiluminescent immunoassay [5], real-time immuno-polymerase chain reaction assay [6] and immunosensor [7], were developed for PAHs analysis. Several ELISA test kits for PAHs have been produced and are commercially available, among which RaPID and RisC are the most widely studied and used [8]. They have been used for determination of PAHs in water [9,10], soil [1115], biological [16,17] and some other samples [18]. The RisC kit is used in US EPA method 4035 for rapid screening test of PAHs in soil samples.

Cross-reactions are common phenomena in immunoassays, i.e., the antibody responds with compounds structurally related to the analyte. Cross-reactivity (CR) is an essential character to evaluate the extent of cross-reactions in immunoreactions and immunoassays. Although CR affects the specificity of the immunoassay and possibly results in bias in the test result, sometimes it can be explored to identify metabolites or structurally similar compounds of the analyte for class-specific immunoassays. It is believed that the CR of a cross-reactant is determined by the physicochemical and structural properties of the compound.

In our previous work, we found that CR values of PAHs are significantly correlated with the data of logarithm of octanol-water partition coefficient (log Kow), but it seemed that CR cannot be solely explained by log Kow [19]. In this article, in addition to log Kow, other six representative physicochemical, electronic and topological descriptors are selected to investigate the correlation between CR and the molecular properties of PAHs. Three multiple regression methods, including stepwise multiple linear regression (MLR), principal component regression (PCR) and partial least square regression (PLSR), were employed to develop the quantitative structure-activity relationship (QSAR) models using the data of two commercial PAHs ELISA kits. The objective of this work is to find the most important molecular properties that affect the CR of PAHs in immunoassays, and if possible, to find the feasibility of predicting CR by multiple regression analysis. Moreover, considering the important effect of water solubility on the CR, we calculated cross-reaction potential (CRP) and used it to complement CR for evaluation of cross-reactions in immunoassays.

2. Experimental Section

2.1. Molecular Descriptor Data Set

The data of 16 representative PAHs (Figure 1) analyzed by the two ELISA kits were used for modeling. Seven typical physicochemical, electronic and topological descriptors are selected for developing the QSAR models. They are molar solubility (Sm), the logarithm of octanol-water partition coefficient (log Kow), the gap between the highest occupied molecular orbital energy and the lowest unoccupied molecular orbital energy (EHOMOELUMO), and four valence molecular connectivity indices (0χv, 1χv, 2χv, 3χv). The data of solubility (S) and log Kow were obtained from Mackay et al. [20]. Sm was calculated by dividing S with the molecular weight. The data of EHOMOELUMO were from de Lima Ribeiro and Ferreira [21]. The data of 0χv, 1χv, 2χv and 3χv were cited from Govers and Aiking [22].

2.2. Data Set of Cross-Reactivity

The experimental data of cross-reactivity (CR) for the two commercial PAHs ELISA kits, RaPID and RisC, were obtained from Krämer [8]. Since activity data used for QSARs should be in molar dimensions, CR values were converted to molar cross-reactivity (MCR), i.e., the ratio of the molar IC50 of target analyte and the cross-reactant, for QSAR modeling [19]. Then, the predicted MCR was calculated by the QSAR models and converted to CR for comparison with the experimental CR value. We assumed that the CR values “<0.5%” and “<1.6%” were low enough to describe low levels of cross-reactions in the two ELISA kit tests, and reasonably considered the CR value “<” to be “=” for regression modeling [19].

2.3. Regression Analysis

The data of 14 compounds of the 16 PAHs were submitted as training set for regression analysis, and anthracene and benzo[a]pyrene were used as the test set. In order to reduce the colinearity and the number of the molecular descriptors, the analysis of the quantitative relationship between log MCR and the molecular descriptors was performed by stepwise MLR, PCR and PLSR employing SAS 8.1 software. In the stepwise MLR procedure, the data of the seven descriptors of the 16 PAHs were collected in a single data matrix, and the key descriptors were selected by adding descriptors one by one to perform a multivariable regression calculation. The variables significant at the 0.15 level were left in the model. In PCR analysis, the original descriptors were subjected to principal component analysis, and the subset of principal components explaining more than 90% of the variance was extracted. Then, the principal components extracted were subjected to multiple linear regression analysis. The PLSR method reduced large volume of descriptors to several components that were most correlative with the CR. These components were the linear combinations of the descriptors and used as new variables for regression analysis. The optimum number of components for regression analysis was obtained by the leave-one-out cross-validation procedure.

2.4. Cross-Reaction Potential

CR is the ratio of the IC50 (the 50% inhibition concentration) of the target analyte and the IC50 of the cross-reactant. Considering the effect of water solubility on the CR value and the immunoassay results, we defined cross-reaction potential (or cross-reaction probability, CRP), i.e., 100-fold the ratio between the solubility (S) of a cross-reactant and the IC50 value [Equation (1)], and used it as a complement of CR to evaluate the extent of cross-reaction. CRP reflects the relative extent of cross-reaction of a non-target cross-reactant compared with the water solubility. The data set of S was from Mackay et al. [20], and IC50 data were from Krämer [8]. We assumed that the IC50 value “>1,000 μg·L−1” was high enough to be considered as “=1,000 μg·L−1” for CRP calculation:

CRP ( % ) = S I C 50 × 100

3. Results and Discussion

3.1. Effect of Molecular Properties on Cross-Reactivity

The molecular structures of the 16 PAHs analyzed by the two ELISAs are shown in Figure 1. Since antibodies and antigens in immunoreactions are not mass-equivalent but rather molar-equivalent, molar cross-reactivity (MCR) rather than mass cross-reactivity (CR) is applied to investigate the quantitative structure and cross-reactivity relationships. The obtained stepwise MLR, PCR and PLSR equations and statistical parameters are illustrated in Table 1.

It shows that the regression models for RisC kit are all significant (p < 0.005), while the models for RaPID kit are all not significant (p > 0.05). The probable reason is that RisC immunoassay employs a monoclonal antibody, while RaPID kit is based on polyclonal antibody. In the stepwise MLR procedure for RaPID, only Sm enters the regression model, and the other six molecular descriptors are excluded from the regression equation. As for RisC, Sm and 3χv are left in the model. In the PCR procedure, the two most significant principal components (PC1 and PC2) describe respectively 85.7% and 8.3%, and totally 94.0% of the variance. Eigenvectors of the principal components indicate that PC1 demonstrates the integrated character of the seven descriptors, while PC2 mainly represents the character of Sm. The regression equations for RaPID and RisC are Equation (2) and Equation (3) respectively. In the PLSR procedure, the models are optimized by leave-one-out cross-validation, and the optimum numbers of components are found to be 4 and 2 for RaPID and RisC, respectively. The parameter estimates for centered and scaled data (marked by *) are shown in Equation (4) and Equation (5). The results of stepwise MLR, PCR and PLSR imply that Sm plays an important role in affecting the CR property of the PAHs for the two ELISA kits, and 3χv also affects the CR for RisC kit to some extent:

For RaPID : log MCR = 1.349 + 0.07442 × PC 1 - 0.7234 × PC 2
For Ris C : log MCR = 1.178 + 0.002550 × PC 1 - 0.9075 × PC 2
For RaPID : log MCR = - 0.6783 × S m + 1.245 × log K ow - 0.6106 × ( E HOMO - E LUMO ) + 0.2556 × χ 0 v - 0.07527 × χ 1 v 0.6231 × χ 2 v 1.626 × χ 3 v
For Ris C : log MCR = - 1.042 × S m - 0.06846 × log K ow - 0.1012 × ( E HOMO - E LUMO ) - 0.1649 × χ 0 v - 0.1778 × χ 1 v - 0.2025 × χ 2 v - 0.2517 × χ 3 v
In immunoreactions and immunoassays, the interaction between antigen and antibody is caused by the complementary spatial distribution and the strong affinity between the antigen and the antibody, such as hydrogen bonds, electrostatic interactions, van der Waals forces and hydrophobic interactions. The strong effect of Sm on the CR properties of the two ELISAs reflects the important role of hydrophobic interactions in the PAH-antibody reactions, which confirms the previous result [19]. It is commonly believed that lower order molecular connectivity indices encode mainly the bulk of a molecule, whereas higher order indices encode more subtle features such as the presence of rings and branching patterns. The result that 3χv affects CR more than 0χv, 1χv and 2χv implies that molecular shape is more influential than molecular size in the PAH-antibody reactions. It has been reported that EHOMO and ELUMO are responsible for the antibody recognition for phenylurea herbicides and organophosphorus pesticides [2325]. These compounds consist of various functional groups and heteroatoms, while PAHs do not contain substituents and heteroatoms, hence electronic descriptors such as EHOMO and ELUMO may have minor effects on the antibody recognition for PAHs. EHOMOELUMO expresses the necessary energy to excite an electron from the highest occupied molecular orbital to the lowest unoccupied molecular orbital. Since immunoreactions are always not accompanied by a rearrangement of electron density, it is not surprising that EHOMOELUMO does not exhibit strong effect on the CR in the models.

3.2. Predicting Cross-Reactivity

CR is one of the most important characteristics of an ELISA test, and influences the extent of cross-reaction and the results of ELISAs significantly. However, due to the difficulty and expense in term of cost and time, not all of the CR data of the cross-reactants are available. Moreover, it is impractical to directly measure the CR of the cross-reactants which are not commercially available, so predicted CR values of the PAHs for the two ELISA kits were calculated using the obtained MLR, PCR and PLSR models, and compared with the experimental data (Table 2, Figure 2). The predicted CR values for RisC agree very well with the experimental data, while the predicted and experimental data for RaPID do not agree well with each other. The models were further external validated using the data of three-ringed anthracene and five-ringed benzo[a]pyrene as test set. The range of predicting error for anthracene is from −1.6% to +8.1%, and for benzo[a]pyrene is from −40.8% to −8.3%. It appears that the obtained models can successfully predict the CR for RisC kit, but present poor predicting ability for RaPID kit.

3.3. Cross-Reaction Potential

Generally speaking, higher CR values imply higher levels of immunoreactions. However, the antigen-antibody reactions in immunoassays are carried out in water or buffer solutions, so if the solubility of a cross-reactant is much lower than the IC50 value, it cannot possibly cause intense cross-reactions in the immunoassays. That is to say, the concentration of this compound in real water samples cannot be high enough to evoke high extent cross-reaction in immunoassays, even though the CR is very high. Some of the 16 PAHs are very hydrophobic compounds, and the solubility is much lower than the tested IC50 value. For example, the IC50 referring to water analysis for benzo[a]pyrene for the RaPID ELISA kit is 6.9 μg·L−1, while the solubility of benzo[a]pyrene is 3.8 μg·L−1 (Table 3), so although benzo[a]pyrene has a high CR of 239% in the RaPID ELISA, the concentration of benzo[a]pyrene in water samples cannot be possibly high enough to evoke a high level of cross-reaction. Hence, considering the important effect of water solubility on immunoreactions and immunoassays, cross-reaction potential (or cross-reaction probability, CRP), i.e., the relative IC50 of a non-target cross-reactant compared with its water solubility, was defined and used as a complement of CR to evaluate the potential and probability that a cross-reaction would occur.

The IC50 and CRP data for the 16 PAHs for RaPID kit are shown in Table 3. The CRP values for RisC kit are not calculated because the IC50 values are not available. In addition to the target analyte of phenanthrene, the 15 cross-reactants in RaPID ELISA can be divided into four groups according to CR and CRP (Figure 3): (I) CR > 100%, CRP > 100%; (II) CR > 100%, CRP < 100%; (III) CR < 100%, CRP > 100%; and (IV) CR < 100%, CRP < 100%. The compounds of group (I) might cause intense cross-reactions and affect the determination of phenanthrene, while the group (IV) compounds have little cross-reaction effect on the analysis results. As for the group (II) compounds, the CR is high, while the CPR is low because of the relatively low solubility. The group (III) compounds are two-ringed and three-ringed PAHs, and less cross-reactive but more water soluble. The extent of the cross-reactions of group (II) and (III) compounds depends on both the CR and the CRP properties.

It should be pointed out that the RaPID PAHs ELISA kit is applied not only for water samples [9,10], but more often for soil samples [11,14,15]. In the pretreatment procedure, PAHs were usually extracted from the soil samples by methanol and diluted by buffer. PAHs are very hydrophobic molecules and can be adsorbed to soils at very high concentration. In the immunoassays of PAHs, much attention should be paid to the solubility of the compounds during the procedures of solvent extraction and buffer dilution.

3.4. Comparison of the Two Kits

The comparison of RaPID and RisC PAHs ELISA kits based on the character and the applicability are illustrated in Table 4. It seems that RisC ELISA is more specific, while RaPID ELISA is applied for more kinds of environmental samples. The selection of appropriate ELISAs for PAHs depends on the objective and request of the analysis.

4. Conclusions

RaPID and RisC are two widely studied ELISA kits used for analysis of PAHs. Three regression methods, including stepwise MLR, PCR and PLSR, were successfully applied to investigate the correlation between the molecular properties and the CR properties of PAHs for the two ELISA kits. It seems that the physicochemical, electronic and topological properties of the PAH molecules have an integrated effect on the CR properties for the two kits. Sm and 3χv show especially strong effects on CR, which implies the important role of hydrophobic interactions and molecular shape in the PAH-antibody reactions. The obtained regression equations for RisC kit are all statistically significant (p < 0.005) and show satisfactory ability for predicting CR values, while equations for RaPID kit are all not significant (p > 0.05) and not suitable for prediction. It is probably because that the RisC immunoassay employs a monoclonal antibody, while the RaPID kit is based on polyclonal antibody. Considering the important effect of solubility on CR for the two PAHs ELISAs, cross-reaction potential (CRP) is defined and used as a complement of CR to evaluate the extent of cross-reaction in immunoassays. We believe that only the compounds with both high CR and high CRP can cause intense cross-reactions in immunoassays. This work demonstrated the feasibility of multiple regression methods in investigating the quantitative structure-CR relationships and predicting CR in immunoassays.

The authors thank Hui-Xuan Gao of the School of Mathematical Sciences of Peking University, China, for her help in statistical analysis.

References

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Figure 1. Molecular structures of the 16 studied PAHs.

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Figure 1. Molecular structures of the 16 studied PAHs.
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Figure 2. Plotting of predicted cross-reactivity (CR) versus experimental values of the PAHs ELISAs.

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Figure 2. Plotting of predicted cross-reactivity (CR) versus experimental values of the PAHs ELISAs.
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Figure 3. Cross-reaction potential (CRP) and cross-reactivity (CR) of 16 PAHs for RaPID ELISA kit.

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Figure 3. Cross-reaction potential (CRP) and cross-reactivity (CR) of 16 PAHs for RaPID ELISA kit.
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Table Table 1. Regression models for quantitative structure and molar cross-reactivity (MCR) relationships for the PAHs ELISAs.

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Table 1. Regression models for quantitative structure and molar cross-reactivity (MCR) relationships for the PAHs ELISAs.
ELISA kit.Regression methodRegression EquationStatistics

FPR2nPRESS
RaPIDMLRlog MCR = 1.542 − 0.00676 × Sm4.300.06030.26381411.30
PCRlog MCR = 4.907 − 0.01004 × Sm − 0.07337 × log Kow − 0.2108 × (EHOMOELUMO) − 0.04482 × 0χv − 0.06256 × 1χv − 0.06708 × χv − 0.4319 × 3χv3.170.08190.36561416.68
PLSRlog MCR = 10.49 − 0.008925 × Sm + 1.015 × log Kow − 1.132 × (EHOMOELUMO) + 0.1073 × 0χv − 0.04788 × 1χv − 0.4657 × 2χv − 8.925 × 3χv3.300.06340.59431410.88
RisCMLRlog MCR = 3.439 − 0.01270 × Sm − 4.041 × 3χv12.190.00500.6603144.62
PCRlog MCR = 5.800 − 0.01231 × Sm − 0.1162 × log Kow − 0.2169 × (EHOMOELUMO) − 0.06868 × 0χv − 0.09743 × 1χv − 0.1065 × 2χv − 0.7045 × 3χv8.950.00490.6193145.13
PLSRlog MCR = 5.485 − 0.01245 × Sm − 0.05071 × log Kow − 0.1704 × (EHOMOELUMO) − 0.06290 × 0χv − 0.1027 × 1χv − 0.1375 × 2χv − 1.255 × 3χv9.710.00370.6383144.92
Table Table 2. Experimental and predicted cross-reactivity (CR) of the PAHs ELISAs by regression analysis.

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Table 2. Experimental and predicted cross-reactivity (CR) of the PAHs ELISAs by regression analysis.
NumberCompoundCR (%) of RaPIDCR (%) of RisC


Experimental aPredicted by MLRPredicted by PCRPredicted by PLSRExperimental aPredicted by MLRPredicted by PCRPredicted by PLSR
1Naphthalene<1.61.00.71.20.50.60.50.5
2Acenaphthene2.427525.412586866
3Acenaphthylene3.77.8116.5137.91110
4Phenanthrene1003157711001237282
5Anthracene15035102550123122121133
6Fluorene4731921526781118113
7Benzo[a]anthracene58272915864251921
8Chrysene212272610484311720
9Fluoranthene35130425873413739
10Pyrene21430577629335047
11Benzo[b]fluoranthene3025154.9228.97.47.3
12Benzo[k]fluoranthene3.1251712117.27.87.5
13Benzo[a]pyrene239252141127.1119.2
14Dibenzo[a,h]anthracene<1.6229.613<0.55.43.53.6
15Indeno[1,2,3-cd]pyrene6122118.09.42.13.33.0
16Benzo[g,h,i]perylene<1.622104.8<0.52.03.22.9

aData are from [8].

Table Table 3. Cross-reaction potential (CRP) of PAHs for RaPID ELISA kit.

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Table 3. Cross-reaction potential (CRP) of PAHs for RaPID ELISA kit.
NumberCompoundS (mg·L1) aIC50 g·L1) bCRP (%)
1Naphthalene31.7>1,0003,170
2Acenaphthene3.93688571
3Acenaphthylene16.14473,602
4Phenanthrene1.2916.57,818
5Anthracene0.07311664
6Fluorene1.9835.25,625
7Benzo[a]anthracene0.01428.449
8Chrysene0.0027.826
9Fluoranthene0.264.75,532
10Pyrene0.1357.71,753
11Benzo[b]fluoranthene1.5 × 10−354.22.8
12Benzo[k]fluoranthene8.1 × 10−45240.155
13Benzo[a]pyrene3.8 × 10−36.955
14Dibenzo[a,h]anthracene5.6 × 10−4>1,0000.056
15Indeno[1,2,3-cd]pyrene1.9 × 10−427.20.699
16Benzo[g,h,i]perylene2.6 × 10−4>1,0000.026

aData are from [20];bData are referring to water analysis and from [8].

Table Table 4. Comparison of the Two PAHs ELISA kits.

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Table 4. Comparison of the Two PAHs ELISA kits.
RaPIDRisC
Reference compoundPhenanthrenePhenanthrene
Analysis modeCompetitive heterogeneous ELISA, antibody is coated on tubesCompetitive heterogeneous ELISA, antibody is immobilized to magnetic particles
Cross-reactivity1.6%∼351%0.5%∼123%
SpecificityNot so specificRelatively specific
AntibodyPolyclonalMonoclonal
Cross-reactantAnthracene, chrysene, fluoranthene, pyrene and benzo[a]pyreneAnthracene
SamplesWater, soil, biological and some other samplesMostly soil samples till now
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