QSARs for Prioritizing PBT Substances to Promote Pollution Prevention

High-throughput prediction of oral acute toxicity in Rat and Mouse of over 100,000 polychlorinated persistent organic pollutants (PC-POPs) by interpretable data fusion-driven machine learning global models

This study utilized available oral acute toxicity data in Rat and Mouse for polychlorinated persistent organic pollutants (PC-POPs) to construct data fusion-driven machine learning (ML) global models. Based on atom-centered fragments (ACFs), the collected high-throughput data overcame the applicability limitations, enabling accurate toxicity prediction for a wide range of PC-POPs series compounds using only single models. The data variances in the Rat training and test sets were 1.52 and 1.34, respectively, while for the Mouse, the values were 1.48 and 1.36, respectively. Genetic algorithm (GA) was used to build multiple linear regression (MLR) models and pre-screen descriptors, addressing the "black-box" problem prevalent in ML and enhancing model interpretability. The best ML models for Rat and Mouse achieved approximately 90 % prediction reliability for over 100,000 true untested compounds. Ultimately, a warning list of highly toxic compounds for eight categories of polychlorinated atom-centered fragments (PCACFs) was generated based on the prediction results. The analysis of descriptors revealed that dioxin analogs generally exhibited higher toxicity, because the heteroatoms and ring systems increased structural complexity and formed larger conjugated systems, contributing to greater oral acute toxicity. The present study provides valuable insights for guiding the subsequent in vivo tests, environmental risk assessment and the improvement of global governance system of pollutants.

Fragments quantum descriptors in classification of bio-accumulative compounds

The aim of the following research is to assess the applicability of calculated quantum properties of molecular fragments as molecular descriptors in machine learning classification task. The research is based on bio-concentration and QM9-extended databases. A number of compounds with results from quantum-chemical calculations conducted with Psi4 quantum chemistry package was also added to the quantum properties database. Classification results are compared with a baseline of random guesses and predictions obtained with the traditional RDKit generated molecular descriptors. Chosen classification metrics show that results obtained with fragments quantum descriptors fall between results from baseline and those provided by molecular descriptors widely applied in cheminformatics. According to the results, the implementation of principal component analysis, causes a drop in categorization metrics.

Molecular Descriptors for Structure-Activity Applications: A Hands-On Approach

Molecular descriptors capture diverse parts of the structural information of molecules and they are the support of many contemporary computer-assisted toxicological and chemical applications. After briefly introducing some fundamental concepts of structure-activity applications (e.g., molecular descriptor dimensionality, classical vs. fingerprint description, and activity landscapes), this chapter guides the readers through a step-by-step explanation of molecular descriptors rationale and application. To this end, the chapter illustrates a case study of a recently published application of molecular descriptors for modeling the activity on cytochrome P450.

Chiral pharmaceuticals: A review on their environmental occurrence and fate processes

More than 50% of pharmaceuticals in current use are chiral compounds. Enantiomers of the same pharmaceutical have identical physicochemical properties, but may exhibit differences in pharmacokinetics, pharmacodynamics and toxicity. The advancement in separation and detection methods has made it possible to analyze trace amounts of chiral compounds in environmental media. As a result, interest on chiral analysis and evaluation of stereoselectivity in environmental occurrence, phase distribution and degradation of chiral pharmaceuticals has grown substantially in recent years. Here we review recent studies on the analysis, occurrence, and fate of chiral pharmaceuticals in engineered and natural environments. Monitoring studies have shown ubiquitous presence of chiral pharmaceuticals in wastewater, surface waters, sediments, and sludge, particularly β-receptor antagonists, analgesics, antifungals, and antidepressants. Selective sorption and microbial degradation have been demonstrated to result in enrichment of one enantiomer over the other. The changes in enantiomer composition may also be caused by biologically catalyzed chiral inversion. However, accurate evaluation of chiral pharmaceuticals as trace environmental pollutants is often hampered by the lack of identification of the stereoconfiguration of enantiomers. Furthermore, a systematic approach including occurrence, fate and transport in various environmental matrices is needed to minimize uncertainties in risk assessment of chiral pharmaceuticals as emerging environmental contaminants.

In silico environmental chemical science: properties and processes from statistical and computational modelling

Quantitative structure-activity relationships (QSARs) have long been used in the environmental sciences. More recently, molecular modeling and chemoinformatic methods have become widespread. These methods have the potential to expand and accelerate advances in environmental chemistry because they complement observational and experimental data with "in silico" results and analysis. The opportunities and challenges that arise at the intersection between statistical and theoretical in silico methods are most apparent in the context of properties that determine the environmental fate and effects of chemical contaminants (degradation rate constants, partition coefficients, toxicities, etc.). The main example of this is the calibration of QSARs using descriptor variable data calculated from molecular modeling, which can make QSARs more useful for predicting property data that are unavailable, but also can make them more powerful tools for diagnosis of fate determining pathways and mechanisms. Emerging opportunities for "in silico environmental chemical science" are to move beyond the calculation of specific chemical properties using statistical models and toward more fully in silico models, prediction of transformation pathways and products, incorporation of environmental factors into model predictions, integration of databases and predictive models into more comprehensive and efficient tools for exposure assessment, and extending the applicability of all the above from chemicals to biologicals and materials.

A multivariate chemical map of industrial chemicals--assessment of various protocols for identification of chemicals of potential concern

In present study the Industrial chemical map was created, and investigated. Molecular descriptors were calculated for 56072 organic substances from the European inventory of existing commercial chemical substances (EINECS). The resulting multivariate dataset was subjected to principal component analysis (PCA), giving five principal components, mainly reflecting size, hydrophobicity, flexibility, halogenation and electronical properties. It is these five PCs that form the basis of the map of organic, industrial chemicals, the Industrial chemical map. The similarities and diversity in chemical characteristics of the substances in relation to their persistence (P), bioaccumulation (B) and long-range transport potential were then examined, by superimposing five sets of entries obtained from other relevant databases onto the Industrial chemical map. These sets displayed very similar diversity patterns in the map, although with a spread in all five PC vectors. Substances listed by the United Nations Environment Program as persistent organic pollutants (UNEP POPs) were on the other hand clearly grouped with respect to each of the five PCs. Illustrating similarities and differences in chemical properties are one of the strengths of the multivariate data analysis method, and to be able to make predictions of, and investigate new chemicals. Further, the results demonstrate that non-testing methods as read-across, based on molecular similarities, can reduce the requirements to test industrial chemicals, provided that they are applied carefully, in combination with sound chemical knowledge.

A QSAR/QSTR Study on the Environmental Health Impact by the Rocket Fuel 1,1-Dimethyl Hydrazine and its Transformation Products

QSAR/QSTR modelling constitutes an attractive approach to preliminary assessment of the impact on environmental health by a primary pollutant and the suite of transformation products that may be persistent in and toxic to the environment. The present paper studies the impact on environmental health by residuals of the rocket fuel 1,1-dimethyl hydrazine (heptyl) and its transformation products. The transformation products, comprising a variety of nitrogen containing compounds are suggested all to possess a significant migration potential. In all cases the compounds were found being rapidly biodegradable. However, unexpected low microbial activity may cause significant changes. None of the studied compounds appear to be bioaccumulating.Apart from substances with an intact hydrazine structure or hydrazone structure the transformation products in general display rather low environmental toxicities. Thus, it is concluded that apparently further attention should be given to tri- and tetramethyl hydrazine and 1-formyl 2,2-dimethyl hydrazine as well as to the hydrazones of formaldehyde and acetaldehyde as these five compounds may contribute to the overall environmental toxicity of residual rocket fuel and its transformation products.

Applicability of a neuroprobabilistic integral risk index for the environmental management of polluted areas: a case study

Recently, we developed a GIS-Integrated Integral Risk Index (IRI) to assess human health risks in areas with presence of environmental pollutants. Contaminants were previously ranked by applying a self-organizing map (SOM) to their characteristics of persistence, bioaccumulation, and toxicity in order to obtain the Hazard Index (HI). In the present study, the original IRI was substantially improved by allowing the entrance of probabilistic data. A neuroprobabilistic HI was developed by combining SOM and Monte Carlo analysis. In general terms, the deterministic and probabilistic HIs followed a similar pattern: polychlorinated biphenyls (PCBs) and light polycyclic aromatic hydrocarbons (PAHs) were the pollutants showing the highest and lowest values of HI, respectively. However, the bioaccumulation value of heavy metals notably increased after considering a probability density function to explain the bioaccumulation factor. To check its applicability, a case study was investigated. The probabilistic integral risk was calculated in the chemical/petrochemical industrial area of Tarragona (Catalonia, Spain), where an environmental program has been carried out since 2002. The risk change between 2002 and 2005 was evaluated on the basis of probabilistic data of the levels of various pollutants in soils. The results indicated that the risk of the chemicals under study did not follow a homogeneous tendency. However, the current levels of pollution do not mean a relevant source of health risks for the local population. Moreover, the neuroprobabilistic HI seems to be an adequate tool to be taken into account in risk assessment processes.

Immobilization of rapeseed press-cake in an alginate matrix for the sorption of atrazine

Due to residual oil retained within it, rapeseed press-cake has been shown to be effective for the removal of atrazine from water through an absorption mechanism. However, it is difficult to put this into practice due to the hygroscopic nature of the press-cake resulting in considerable swelling, together with the formation of a thick paste which hinders phase separation. In order to overcome this, press-cake has been immobilized in an alginate matrix. The kinetics and sorption efficiency of this immobilized press-cake to absorb the model pesticide atrazine, has been studied. The results show that the rate of atrazine removal is slower than for free press-cake, although the total amount of atrazine removed is the same (K(pc/w)=0.25). Phase separation was greatly simplified. The alginate immobilized press-cake could be dried, in order to reduce volume and weight, with no adverse effect on atrazine removal kinetics or sorption properties.

QSPR of the bioconcentration factors of non-ionic organic compounds in fish using extended topochemical atom (ETA) indices

Bioconcentration refers to the absorption or uptake of a chemical from the media to an organism's tissues leading to greater concentration in tissues than that in the surrounding environment. Considering the importance of bioconcentration from the viewpoint of ecological safety assessment, a QSPR study was conducted based upon log BCF of 122 non-ionic organic compounds in fish using the recently introduced extended topochemical atom (ETA) indices. In deriving the models, principal component factor analysis (FA) followed by multiple linear regression (MLR), stepwise regression, partial least squares (PLS) and principal component regression analysis (PCRA) were applied as statistical tools. This was repeated with non-ETA (topological and physicochemical) descriptors and a combination set including both the ETA and non-ETA descriptors. The ETA indices suggested negative contributions of functionalities of nitro, amino and hydroxy substructures and positive contributions of branching, volume and functionality of chloro substituents. Again, the predictive ability of the developed models was compared with the previously reported models. Finally the validation of all the QSAR models was discussed based on random division, sorted log BCF data and K-means clusters for the factor scores of the original variable (ETA) matrix without the response property values. The results suggest that ETA parameters are sufficiently rich in chemical information to encode the structural features contributing to the bioconcentration of the non-ionic organic compounds in fish and thus these merit further assessment to explore their potential in QSAR/QSPR/QSTR modelling.

Modeling the bioconcentration factors and bioaccumulation factors of polychlorinated biphenyls with posetic quantitative super-structure/activity relationships (QSSAR)

During bioconcentration, chemical pollutants from water are absorbed by aquatic animals via the skin or a respiratory surface, while the entry routes of chemicals during bioaccumulation are both directly from the environment (skin or a respiratory surface) and indirectly from food. The bioconcentration factor (BCF) and the bioaccumulation factor (BAF) for a particular chemical compound are defined as the ratio of the concentration of a chemical inside an organism to the concentration in the surrounding environment. Because the experimental determination of BAF and BCF is time-consuming and expensive, it is efficacious to develop models to provide reliable activity predictions for a large number of chemical compounds. Polychlorinated biphenyls (PCBs) released from industrial activities are persistent pollutants of the environment that produce widespread contamination of water and soil. PCBs can bioaccumulate in the food chain, constituting a potential source of exposure for the general population. To predict the bioconcentration and bioaccumulation factors for PCBs we make use of the biphenyl substitution-reaction network for the sequential substitution of H-atoms by Cl-atoms. Each PCB structure then occurs as a node of this reaction network, which is some sort of super-structure, turning out mathematically to be a partially ordered set (poset). Rather than dealing with the molecular structure via ordinary QSAR we use only this poset, making different quantitative super-structure/activity relationships (QSSAR). Thence we developed cluster expansion and splinoid QSSARs for PCB bioconcentration and bioaccumulation factors. The predictive ability of the BAF and BCF models generated for 20 data sets (representing different conditions and fish species) was evaluated with the leave-one-out cross-validation, which shows that the splinoid QSSAR (r between 0.903 and 0.935) are better than models computed with the cluster expansion (r between 0.745 and 0.887). The splinoid QSSAR models for BAF and BCF yield predictions for the missing PCBs in the investigated data sets.

A combined QSAR and partial order ranking approach to risk assessment

QSAR generated data appear as an attractive alternative to experimental data as foreseen in the proposed new chemicals legislation REACH. A preliminary risk assessment for the aquatic environment can be based on few factors, i.e. the octanol-water partition coefficient (Kow), the vapour pressure (VP) and the potential biodegradability of the compound in combination with the predicted no-effect concentration (PNEC) and the actual tonnage in which the substance is produced. Application of partial order ranking, allowing simultaneous inclusion of several parameters leads to a mutual prioritisation of the investigated substances, the prioritisation possibly being further analysed through the concept of linear extensions and average ranks. The ranking uses endpoint values (log Kow and log VP) derived from strictly linear 'noise-deficient' QSAR models as input parameters. Biodegradation estimates were adopted from the BioWin module of the EPI Suite. The population growth impairment of Tetrahymena pyriformis was used as a surrogate for fish lethality.

Giving molecules an identity. On the interplay between QSARs and partial order ranking

The interplay between 'noise-deficient' QSAR and Partial Order Ranking, including analysis of average linear ranks, constitutes an effective tool in giving substances which have not been investigated experimentally an identity by comparison with experimentally well-characterized, structurally similar compounds. It is disclosed that experimentally well-characterized compounds may serve as substitutes for highly toxic compounds in experimental studies without exhibiting the same extreme toxicity, while from an overall viewpoint they exhibit analogous environmental characteristics.