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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. Th...
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... IC 50 and CRP data for the 16 PAHs for RaPID kit are shown in Table 3. The CRP values for Ris C kit are not calculated because the IC 50 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 ...
Citations
The recent advances in “omics” technologies have generated various in silico approaches for toxicity assessment. In silico-based toxicity predictions can overcome certain major drawbacks of laboratory experiments, including the limitation of conducting experiments in a chemical-by-chemical basis that can be expensive. This chapter discusses some recent applications of in silico approaches utilizing xenobiotic metabolism that can be used to assess the impact of cigarette smoke (CS). We first outline recent studies using quantum mechanics/molecular modeling and quantitative structure-activity relationships that focus on smoking-relevant cytochrome P450 (CYP) enzymes. Subsequently, we describe several network-based approaches for toxicity assessment and relevant use cases leveraging a xenobiotic metabolism network model for a quantitative assessment of CS impact.
