Development and assessment of quantitative structure-activity relationship models for bioconcentration factors of organic pollutants

A new perspective to improve the treatment of Lianhuaqingwen on COVID-19 and prevent the environmental health risk of medication

Lianhuaqingwen (LH), one traditional Chinese medicine (TCM), has been used to treat the coronavirus disease 2019 (COVID-19), but its ecotoxicity with potential human health security has not been well investigated. To overcome such adverse effects and improve its medication efficacy, an intelligent multi-method integrated dietary scheme, screening, and performance evaluation approach was developed. Thirteen LH compounds were selected, and the main protease (Mpro) was used as the potential drug target. Resulted information showed that the more compounds of LH added, the higher medication efficacy obtained using multi-method integrated screening system, expert consultation method, and molecular dynamics simulation. Pharmacodynamic mechanism analysis showed that low total energy and polar surface area of LH active compound (i.e., β-sitosterol) will contribute to the best therapeutic effect on COVID-19 using quantitative structure-activity relationships (QSAR) and sensitivity models. Additionally, when mild COVID-19 patients take LH with the optimum dietary scheme (i.e., β-lactoglobulin, α-lactalbumin, vitamin A, vitamin B, vitamin C, carotene, and vitamin E), the medication efficacy were significantly improved (23.58%). Pharmacokinetics and toxicokinetics results showed that LH had certain human health risks and ecotoxicity. This study revealed the multi-compound interaction mechanism of LH treatment on COVID-19, and provided theoretical guidance for improving therapeutic effect, evaluating TCM safety, and preventing human health risk.

Mechanistic studies of congener-specific adsorption and bioaccumulation of polycyclic aromatic hydrocarbons and phthalates in soil by novel QSARs

Polycyclic aromatic hydrocarbons (PAHs) and phthalic acid esters (PAEs) which are structurally featured with one or more aromatic skeletons are often regarded as two important groups of organic pollutants due to the widespread distribution and notorious toxic effects in soils. Relative to the great number of structural analogues or congeners detected in soil, however, the soil adsorption and bioaccumulation of PAHs/PAEs by plant is far less studied for the insufficiency of experimental determinations or lack of insights into the inherent structural requirements. To mechanistically evaluate the congener-specific soil adsorption and bioaccumulation for PAHs/PAEs, the quantitative structure-activity relationships (QSARs) were successfully developed by density functional theory (DFT) computation and partial least squares (PLS) analysis. As verified with the higher cumulative variance coefficients and cross-validated correlation coefficients for strong stability, interpretability and predictability, the QSARs could be used for prediction of unknown adsorption potency or bioavailability within the specified applicability domain, respectively. It was indicated by QSAR that the structural requirements of PAHs/PAEs necessary for strengthening the soil adsorption were mainly attributed to the molecular polarizability and the associated dispersion interaction with soil. As regards the bioaccumulation by carrot, the aggravation of spherical polarity change of molecules and the involved electrostatic interaction with soil entity or electron transfer from the highest occupied molecular orbital (HOMO) of PAHs/PAEs was implied to be inherently decisive for the variance of bioavailability among congeners. Based on the holistic view of negative correlation relationship, the soil adsorption seemed to act as the forceful constraint in decreasing the bioaccumulation of PAHs/PAEs and could also be alternatively gauged as the preliminary evaluation of bioavailability and risks on soil ecosystem. It would thus help better understand the soil adsorption and bioaccumulation with the informative mechanistic insights and provide data support for ecological risk assessment of PAHs/PAEs in soils.

The critical factors affecting typical organophosphate flame retardants to mimetic biomembrane: An integrated in vitro and in silico study

Organophosphate flame retardants (OPFRs) have been reported to induce cytotoxicity in a structure-dependent manner. The toxic effects may be due to the damage of biomembrane integrity and/or the interference of membrane signal pathway. In this study, the damages of fifteen typical OPFRs (chlorinated phosphates, alkyl phosphates, aryl phosphates, and alkoxy phosphates) to mimetic biomembrane were determined by the electrochemical impedance spectroscopy (EIS). The molecular structure descriptors that characterized the action mechanisms were screened by stepwise regression. The six molecular descriptors (MATS7e, DLS_05, Mor19m, Mor22v, Mor12v and MATS8m) were screened to study the actions between OPFRs and mimetic biomembrane. A quantitative structure-activity relationship (QSAR) model was developed by the partial least squares (PLS) method. Statistical results indicated that the QSAR model had good robustness and mechanism interpretability. The distribution of atomic electronegativities (MATS7e) and atomic masses in three dimensional spaces (Mor19m) were the key factors influencing the actions between OPFRs and simulated biofilms. The compounds with strong electron-withdrawing property could invade the inner layer of membrane and destroy its integrity. High levels of steric hindrance could impair the damage capacity caused by electronegativity. Moreover, drug-like index (DLS_05), spatial structures of particle (Mor22v, Mor12v) and atomic masses (MATS8m) also affected the actions. The results revealed the mechanism of the actions of OPFRs with simulated biofilms and elucidated the key structural characteristics affecting the actions of OPFRs, which could provide theoretical basis for ecological risk assessment of OPFRs.

Pre-selection and assessment of green organic solvents by clustering chemometric tools

The study presents the result of the application of chemometric tools for selection of physicochemical parameters of solvents for predicting missing variables - bioconcentration factors, water-octanol and octanol-air partitioning constants. EPI Suite software was successfully applied to predict missing values for solvents commonly considered as "green". Values for logBCF, logK_OW and logK_OA were modelled for 43 rather nonpolar solvents and 69 polar ones. Application of multivariate statistics was also proved to be useful in the assessment of the obtained modelling results. The presented approach can be one of the first steps and support tools in the assessment of chemicals in terms of their greenness.

In-silico combinatorial design and pharmacophore modeling of potent antimalarial 4-anilinoquinolines utilizing QSAR and computed descriptors

There are very few studies for combinatorial library design and high throughput screening of 4-anilinoquinoline antimalarial compounds having activities against parasitic strain of P. falciparum. Therefore, an attempt has been made in the present paper to design potent lead compounds in this congener utilizing quantitative structure activity relationship utilizing theoretical molecular descriptors. QSAR models for a series of 4-anilinoquinolines considering various theoretical molecular descriptors including topological, constitutional, geometrical, functional group and atom-centered fragments has been carried out by stepwise forward–backward variable selections assimilating multiple linear regression (MLR) methods showing the topological indices contribute maximum impact on parasitic P. falciparum strain. A combinatorial library of 2160 compounds has been generated and finally, 16 compounds were screened through high throughput screening as promising 4-anilinoquinoline antimalarial hits based on their predicted activities utilizing topological descriptor based validated QSAR model. Highly predicted active compounds were then undergone for pharmacophore modeling to predict mode of binding and to optimize leads having greater affinity towards malarial P. falciparum parasitic strain.

Uptake and translocation of organophosphates and other emerging contaminants in food and forage crops

Emerging contaminants in wastewater and sewage sludge spread on agricultural soil can be transferred to the human food web directly by uptake into food crops or indirectly following uptake into forage crops. This study determined uptake and translocation of the organophosphates tris(1-chloro-2-propyl) phosphate (TCPP) (log Kow 2.59), triethyl-chloro-phosphate (TCEP) (log Kow 1.44), tributyl phosphate (TBP) (log Kow 4.0), the insect repellent N,N-diethyl toluamide (DEET) (log Kow 2.18) and the plasticiser N-butyl benzenesulfonamide (NBBS) (log Kow 2.31) in barley, wheat, oilseed rape, meadow fescue and four cultivars of carrot. All species were grown in pots of agricultural soil, freshly amended contaminants in the range of 0.6-1.0 mg/kg dry weight, in the greenhouse. The bioconcentration factors for root (RCF), leaf (LCF) and seed (SCF) were calculated as plant concentration in root, leaf or seed over measured initial soil concentration, both in dry weight. The chlorinated flame retardants (TCEP and TCPP) displayed the highest bioconcentration factors for leaf and seed but did not show the same pattern for all crop species tested. For TCEP, which has been phased out due to toxicity but is still found in sewage sludge and wastewater, LCF was 3.9 in meadow fescue and 42.3 in carrot. For TCPP, which has replaced TCEP in many products and also occurs in higher residual levels in sewage sludge and wastewater, LCF was high for meadow fescue and carrot (25.9 and 17.5, respectively). For the four cultivars of carrot tested, the RCF range for TCPP and TCEP was 10-20 and 1.7-4.6, respectively. TCPP was detected in all three types of seeds tested (SCF, 0.015-0.110). Despite that DEET and NBBS have log Kow in same range as TCPP and TCEP, generally lower bioconcentration factors were measured. Based on the high translocation of TCPP and TCEP to leaves, especially TCPP, into meadow fescue (a forage crop for livestock animals), ongoing risk assessments should be conducted to investigate the potential effects of these compounds in the food web.

Prioritization of chemicals in the aquatic environment based on risk assessment: analytical, modeling and regulatory perspective

The extensive and intensive use of chemicals in our developed, highly technological society includes more than 100,000 chemical substances. Significant scientific evidence has lead to the recognition that their improper use and release may result in undesirable and harmful side-effects on both the human and ecosystem health. To cope with them, appropriate risk assessment processes and related prioritization schemes have been developed in order to provide the necessary scientific support for regulatory procedures. In the present paper, two of the elements that constitute the core of risk assessment, namely occurrence and hazard effects, have been discussed. Recent advances in analytical chemistry (sample pre-treatment and instrumental equipment, etc.) have allowed for more comprehensive monitoring of environmental pollution reaching limits of detection up to sub ng L(-1). Alternative to analytical measurements, occurrence models can provide risk managers with a very interesting approach for estimating environmental concentrations from real or hypothetical scenarios. The most representative prioritization schemes used for issuing lists of concerning chemicals have also been examined and put in the context of existing environmental policies for protection strategies and regulations. Finally, new challenges in the field of risk-assessment have been outlined, including those posed by new materials (i.e., nanomaterials), transformation products, multi-chemical exposure, or extension of the risk assessment process to the whole ecosystem.

Linear and non-linear relationships between bioconcentration and hydrophobicity: theoretical consideration

A non-linear relationship (e.g. Gaussian-type) between measured bioconcentration factor (BCF) and octanol/water partition coefficient (K(OW)) was noted many years ago. Many studies have focused on the cause of the breakdown in the log BCF/log K(OW) curve for highly hydrophobic chemicals with log K(OW)>6. However, there has been little investigation on the theoretical background of this feature for highly hydrophilic chemicals. In this paper, the cause of linear and non-linear relationships between log BCF and log K(OW) has been investigated on the basis of the partitioning-based mechanism for classified non-ionic and ionisable compounds. For highly hydrophilic compounds, lipid tissue in fish is not the major storage site of chemicals. Uptake from other tissues/organs plays a much more important role than the lipid content, leading to a variation of measured log BCF around 0.5. For hydrophobic chemicals with 0.5<log K(OW)<6, hydrophobicity is the principal driving force of bioconcentration and log BCF increases with increasing log K(OW). The log BCF/log K(OW) curve breaks down for highly hydrophobic chemicals with log K(OW)>6. The main reason for this is attributed to the reduced bioavailability of chemicals in water. A linear solvation energy relationship shows that the bioconcentration increases with increasing molecular size by increasing the dispersion interactions between the chemical and lipid content. Bioconcentration decreases with increasing the basicity of hydrophobic compounds by increasing the H-bonding of chemicals with water. Principal component analysis shows that the octanol/water system is the closest system, but not an ideal surrogate, to describe the bioconcentration for hydrophobic compounds as compared with other solvent/water partition systems.

QSAR model for the prediction of bio-concentration factor using aqueous solubility and descriptors considering various electronic effects

The in silico modelling of bio-concentration factor (BCF) is of considerable interest in environmental sciences, because it is an accepted indicator for the accumulation potential of chemicals in organisms. Numerous QSAR models have been developed for the BCF, and the majority utilize the octanol/water partition coefficient (log P) to account for the penetration characteristics of the chemicals. The present work used descriptors from a variety of software packages for the development of a multi-linear regression model to estimate BCF. The modelled data set of 473 diverse compounds covers a wide range of log BCF values. In the proposed QSAR model, most of the variation is described by the calculated solubility in water. Other contributing descriptors describe, for instance, hydrophobic surface area, hydrogen bonding and other electronic effects. The model was validated internally by using a variety of statistical approaches. Two external validations were also performed. For the former validation, a subset from the same data source was used. The 2nd external validation was based on an independent data set collected from different resources. All validations showed the consistency of the model. The applicability domain of the model was discussed and described and a thorough outlier analysis was performed.

QSPR Modeling of Bioconcentration Factors of Nonionic Organic Compounds

The terms bioaccumulation and bioconcentration refer to the uptake and build-up of chemicals that can occur in living organisms. Experimental measurement of bioconcentration is time-consuming and expensive, and is not feasible for a large number of chemicals of potential regulatory concern. A highly effective tool depending on a quantitative structure-property relationship (QSPR) can be utilized to describe the tendency of chemical concentration organisms represented by, the important ecotoxicological parameter, the logarithm of Bio Concentration Factor (log BCF) with molecular descriptors for a large set of non-ionic organic compounds. QSPR models were developed using multiple linear regression, partial least squares and neural networks analyses. Linear and non-linear QSPR models to predict log BCF of the compounds developed for the relevant descriptors. The results obtained offer good regression models having good prediction ability. The descriptors used in these models depend on the volume, connectivity, molar refractivity, surface tension and the presence of atoms accepting H-bonds.