QSPR modeling of octanol-water partition coefficient and organic carbon normalized sorption coefficient of diverse organic chemicals using Extended Topochemical Atom (ETA) indices

Environmental impact of PFAS: Filling data gaps using theoretical quantum chemistry and QSPR modeling

Per- and polyfluorinated alkyl substances (PFAS), known for their widespread environmental presence and slow degradation, pose significant concerns. Of the approximately 10,000 known PFAS, only a few have undergone comprehensive testing, resulting in limited experimental data. In this study, we employed a combination of physics-based methods and data-driven models to address gaps in PFAS bioaccumulation potential. Using the COnductor-like Screening MOdel for Realistic Solvents (COSMO-RS) method, we predicted n-octanol/water partition coefficients (logKOW), crucial for PFAS bioaccumulation. Our developed Quantitative Structure-Property Relationship (QSPR) model exhibited high accuracy (R2 = 0.95, RMSEC = 0.75) and strong predictive ability (Q2LOO = 0.93, RMSECV = 0.83). Leveraging the extensive NORMAN, we predicted logKOW for over 4,000 compounds, identifying 244 outliers out of 4519. Further categorizing the database into eight Organisation for Economic Co-operation and Development (OECD) categories, we confirmed fluorine atoms role in enhanced bioaccumulation. Utilizing predicted logKOW, water solubility logSW, and vapor pressure logVP values, we calculated additional physicochemical properties that are responsible for the transport and dispersion of PFAS in the environment. Parameters such as Henry's Law (kH), air-water partition coefficient (KAW), octanol-air coefficient (KOA), and soil adsorption coefficient (KOC) exhibited favorable correlations with literature data (R2 > 0.66). Our study successfully filled data gaps, contributing to the understanding of ubiquitous PFAS in the environment and estimating missing physicochemical data for these compounds.

Glyphosate uses, adverse effects and alternatives: focus on the current scenario in Brazil

Brazil, a global frontrunner in pesticide consumption and sales, particularly glyphosate, appears to be at odds with other countries that increasingly ban these products in their territories. This study gathers the values of Acceptable Daily Intake and Maximum Residue Limits (MRL) in the European Union for dozens of substances and subsequently contrasts them with the corresponding benchmarks upheld in Brazil concerning its predominant crops. Furthermore, this study delves into the toxicity levels and the potential health ramifications of glyphosate on humans through the ingestion of food containing its residues. The findings from this research underscore a notable surge in glyphosate and pesticide sales and usage within Brazil over the past decade. In stark contrast to its European counterparts, Brazil not only sanctioned the sale and application of 474 new pesticides in 2019, but extended the authorization for glyphosate sales while downgrading its toxicity classification. Finally, this review not only uncovers disparities among research outcomes but also addresses the complexities of replacing glyphosate and introduces environmentally friendlier alternatives that have been subject to evaluation in the existing literature.

Developing a QSPR Model of Organic Carbon Normalized Sorption Coefficients of Perfluorinated and Polyfluoroalkyl Substances

Perfluorinated and polyfluoroalkyl substances (PFASs) are known for their long-distance migration, bioaccumulation, and toxicity. The transport of PFASs in the environment has been a source of increasing concerned. The organic carbon normalized sorption coefficient (Koc) is an important parameter from which to understand the distribution behavior of organic matter between solid and liquid phases. Currently, the theoretical prediction research on log Koc of PFASs is extremely limited. The existing models have limitations such as restricted application fields and unsatisfactory prediction results for some substances. In this study, a quantitative structure–property relationship (QSPR) model was established to predict the log Koc of PFASs, and the potential mechanism affecting the distribution of PFASs between two phases from the perspective of molecular structure was analyzed. The developed model had sufficient goodness of fit and robustness, satisfying the model application requirements. The molecular weight (MW) related to the hydrophobicity of the compound; lowest unoccupied molecular orbital energy (ELUMO) and maximum average local ionization energy on the molecular surface (ALIEmax), both related to electrostatic properties; and the dipole moment (μ), related to the polarity of the compound; are the key structural variables that affect the distribution behavior of PFASs. This study carried out a standardized modeling process, and the model dataset covered a comprehensive variety of PFASs. The model can be used to predict the log Koc of conventional and emerging PFASs effectively, filling the data gap of the log Koc of uncommon PFASs. The explanation of the mechanism of the model has proven to be of great value for understanding the distribution behavior and migration trends of PFASs between sediment/soil and water, and for estimating the potential environmental risks generated by PFASs.

[Prediction of n-octanol/water partition coefficient of strongly ionized compounds by ion-pair reversed-phase liquid chromatography with silica-based stationary phase]

反相液相色谱(RPLC)是测定正辛醇/水分配系数(log P)的有效方法,但由于缺少同类型模型化合物,RPLC在测定强离解化合物的log P时遇到挑战。该文在硅胶基质C18色谱柱上,采用离子抑制反相液相色谱(IS-RPLC)和离子对反相液相色谱(IP-RPLC)分别对中性化合物、酚酸、羧酸、磺酸及部分两性化合物的保留行为进行了系统研究。在IS-RPLC模式下,利用中性化合物、弱离解的酚酸和苯羧酸作为模型化合物,建立了表观正辛醇/水分配系数(log D)与纯水相保留因子对数值(log k_w)的定量结构-保留行为关系(QSRR)模型,测定了19种离解化合物的log D值,作为后续IP-RPLC的模型化合物及验证化合物。在IP-RPLC模式下,将中性、弱离解和强离解化合物作为混合模型组,以溶质静电荷n_e、氢键酸碱性参数A和B为桥梁,建立了线性良好的log D-log k_w-IP模型,采用3种不同类型的离解化合物进行了外部验证实验,预测值误差低于10%,证实了模型的可靠性。在此基础上,预测了8种强离解化合物的log D_7.0值(pH 7.0条件下的log D值)。研究表明,利用结构相关参数沟通不同类型的模型化合物,是实现IP-RPLC测定强离解化合物log D值的一种行之有效的方法。与聚乙烯醇基质色谱柱相比,通用型的硅胶基质色谱柱上尽管存在着更多的次级作用,但可以为强离解化合物log D的测定提供更灵活的选择。

Chemometric QSAR modeling of acute oral toxicity of Polycyclic Aromatic Hydrocarbons (PAHs) to rat using simple 2D descriptors and interspecies toxicity modeling with mouse

The information of the acute oral toxicity for most polycyclic aromatic hydrocarbons (PAHs) in mammals are lacking due to limited experimental resources, leading to a need to develop reliable in silico methods to evaluate the toxicity endpoint. In this study, we developed the quantitative structure-activity relationship (QSAR) models by genetic algorithm (GA) and multiple linear regression (MLR) for the rat acute oral toxicity (LD₅₀) of PAHs following the strict validation principles of QSAR modeling recommended by OECD. The best QSAR model comprised eight simple 2D descriptors with definite physicochemical meaning, which showed that maximum atom-type electrotopological state, van der Waals surface area, mean atomic van der Waals volume, and total number of bonds are main influencing factors for the toxicity endpoint. A true external set (554 compounds) without rat acute oral toxicity values, and 22 limit test compounds, were firstly predicted along with reliability assessment. We also compared our proposed model with the OPERA predictions and recently published literature to prove the prediction reliability. Furthermore, the interspecies toxicity (iST) models of PAHs between rat and mouse were also established, validated and employed for filling data gap. Overall, our developed models should be applicable to new or untested or not yet synthesized PAHs falling within the applicability domain (AD) of the models for rapid acute oral toxicity prediction, thus being important for environmental or personal exposure risk assessment under regulatory frameworks.