On the use of electronic descriptors for QSAR modelling of PCDDs, PCDFs and dioxin-like PCBs

Particle and gas phase sampling of PCDD/Fs and dl-PCBs by activated carbon fiber and GC/MS analysis

Polychlorodibenzo-p-dioxins (PCDDs), polychlorodibenzofurans (PCDFs), and polychlorobiphenyls (PCBs) are semi-volatile compounds and can be partitioned in the atmosphere between the gas and particulate phase, due to their physicochemical properties. For this reason, the reference standard methods for air sampling include a quartz fiber filter (QFF) for the particulate and a polyurethane foam (PUF) cartridge for the vapor phase, and it is the classical and most popular sampling method in the air. Despite the presence of the two adsorbing media, this method cannot be used for the study of the gas-particulate distribution, but only for a total quantification. This study presents the results and the performance aim to validate an activated carbon fiber (ACF) filter for the sampling of PCDD/Fs and dioxin-like PCBs (dl-PCBs) using laboratory and field tests. The specificity, precision, and accuracy of the ACF in relation to the QFF + PUF were evaluated through the isotopic dilution technique, the recovery rates, and the standard deviations. Then the ACF performance was assessed on real samples, in a naturally contaminated area, through parallel sampling with the reference method (QFF + PUF). The QA/QC was defined according to the standard methods ISO 16000-13 and -14 and EPA TO4A and 9A. Data confirmed that ACF meets the requirements for the quantification of native POPs compounds in atmospheric and indoor samples. In addition, ACF provided accuracy and precision comparable to those offered by standard reference methods using QFF + PUF, but with significant savings in terms of time and costs.

Revisiting the Use of Quantum Chemical Calculations in LogPoctanol-water Prediction

The partition coefficients of drug and drug-like molecules between an aqueous and organic phase are an important property for developing new therapeutics. The predictive power of computational methods is used extensively to predict partition coefficients of molecules. The application of quantum chemical calculations is used to develop methods to develop structure-activity relationship models for such prediction, either based on molecular fragment methods, or via direct calculation of solvation free energy in solvent continuum. The applicability, merits, and shortcomings of these developments are revisited here.

Process tracing of PCDD/Fs from economizer to APCDs during solid waste incineration: Re-formation and transformation mechanisms

The emission of PCDD/Fs is a crucial factor for the aggravation of the Not-In-My-Back-Yard (NIMBY) syndrome, especially for the incineration plants that fail to meet the emission standard. It is well known that physicochemical processes in the boiler can notably affect the discharge of dioxins, especially under transient, non-steady conditions. However, few studies paid attention to the important operational parameters that influence PCDD/Fs formation and transformation in the boiler when an incinerator is in its daily steady operation. In this study, 36 samples were analyzed to achieve process tracing of PCDD/Fs. The concentration, congener profile and vapor/solid partitions of PCDD/Fs from the economizer to air pollution control devices (APCDs) under two typical steady conditions were investigated. Results indicated that increasing air supply aggravated the formation of PCDD/Fs, disturbed the vapor/solid partitions, and triggered a substandard emission. Quantitative structure-activity relationship (QSAR) modeling was firstly performed for the formation mechanism and orbital energy factors were identified as dominating factors. Besides, the removal rates of PCDD/Fs significantly correlated with the saturated vapor pressure and proportions of different isomers. This study is beneficial for operators to optimize relevant operational parameters of the incineration plants so as to get rid of substandard problems.

Quantum Chemical Investigation of Polychlorinated Dibenzodioxins, Dibenzofurans and Biphenyls: Relative Stability and Planarity Analysis

All the possible polychlorinated aromatic compounds in the classes of dibenzodioxins (PCDDs), dibenzofurans (PCDFs), and biphenyls (PCBs) were studied by the quantum chemical methods of HF/6-311++G(d,p), B3LYP/6-311++G(d,p), and MP2/cc-pVTZ. The calculated stabilities and structures of these compounds were compared with the available data on their abundance and toxicity. Prediction models for trends in energy and planarity among these congeners were proposed. The results discussed here can help contribute to the understanding of the role of dioxin-like compounds (DLCs) in the environment.

QSAR/QSPR models based on quantum chemistry for risk assessment of pesticides according to current European legislation

In Europe, agencies and official organizations involved in the pesticide control such as the EFSA, ECHA, JRC and ECETOC or even the OECD are pointing out that the software tools based on quantitative structure relationship models, i.e. QSAR and QSPR, have a huge potential to improve the pesticide risk assessment process. In this sense, these non-animal test methods can promote the competitiveness of agriculture in this region: the consumer safety is increased with them due to the possibility of perform an overall better risk assessment of the degradation products and metabolites from pesticides. However, the use of theses computational-based (in silico) tools must be much more systematised and harmonised, improving their validation and including case studies to test them. To open databases, incorporating critical data in an orderly manner for building the models, becomes also necessary. Moreover, quantum chemistry through the Density Functional Theory should be promoted as tool for calculation of quantum descriptors, especially for the study of similar compounds with the same carbon skeleton but differing substitution patterns, e.g. isomers.

Metabolome analyses in exposome studies: Profiling methods for a vast chemical space

Metabolic profiling (metabonomics/metabolomics) is now used routinely as a tool to provide information-rich datasets for biomarker discovery, prompting and augmenting detailed mechanistic studies. The experimental design and focus of any individual study will be reflected in the types of biomarkers that can be detected; toxicological studies will likely focus on markers of response to insult, whereas clinical case-control studies may yield diagnostic markers of disease. Population studies can make use of omics analyses, including metabonomics, to provide mechanistically-relevant markers that link environmental exposures to chronic disease endpoints. In this article, examples of how metabolic profiling has played a key role in molecular epidemiological analyses of chronic disease are presented, and how these reflect different aspects of the causal pathway. A commentary on the nature of metabolome analysis as a complex mixture problem as opposed to a coded, sequence or template problem is provided, alongside an overview of current and future analytical platforms that are being applied to meet this analytical challenge. Epidemiological studies are an important nexus for integrating various measures of the human exposome, and the ubiquity, diversity and functions of small molecule metabolites, represent an important way to link individual exposures, genetics and phenotype.

Consensus toxicity factors for polychlorinated dibenzo-p-dioxins, dibenzofurans, and biphenyls combining in silico models and extensive in vitro screening of AhR-mediated effects in human and rodent cells

Consensus toxicity factors (CTFs) were developed as a novel approach to establish toxicity factors for risk assessment of dioxin-like compounds (DLCs). Eighteen polychlorinated dibenzo-p-dioxins, dibenzofurans (PCDD/Fs), and biphenyls (PCBs) with assigned World Health Organization toxic equivalency factors (WHO-TEFs) and two additional PCBs were screened in 17 human and rodent bioassays to assess their induction of aryl hydrocarbon receptor-related responses. For each bioassay and compound, relative effect potency values (REPs) compared to 2,3,7,8-tetrachlorodibenzo-p-dioxin were calculated and analyzed. The responses in the human and rodent cell bioassays generally differed. Most notably, the human cell models responded only weakly to PCBs, with 3,3',4,4',5-pentachlorobiphenyl (PCB126) being the only PCB that frequently evoked sufficiently strong responses in human cells to permit us to calculate REP values. Calculated REPs for PCB126 were more than 30 times lower than the WHO-TEF value for PCB126. CTFs were calculated using score and loading vectors from a principal component analysis to establish the ranking of the compounds and, by rescaling, also to provide numerical differences between the different congeners corresponding to the TEF scheme. The CTFs were based on rat and human bioassay data and indicated a significant deviation for PCBs but also for certain PCDD/Fs from the WHO-TEF values. The human CTFs for 2,3,4,7,8-pentachlorodibenzofuran, 1,2,3,4,7,8-hexachlorodibenzofuran, 1,2,3,4,6,7,8-heptachlorodibenzo-p-dioxin, and 1,2,3,4,7,8,9-heptachlorodibenzofuran were up to 10 times greater than their WHO-TEF values. Quantitative structure-activity relationship models were used to predict CTFs for untested WHO-TEF compounds, suggesting that the WHO-TEF value for 1,2,3,7,8-pentachlorodibenzofuran could be underestimated by an order of magnitude for both human and rodent models. Our results indicate that the CTF approach provides a powerful tool for condensing data from batteries of screening tests using compounds with similar mechanisms of action, which can be used to improve risk assessment of DLCs.