55 research outputs found

    Mathematical Model for Cyclodextrin Alteration of Bioavailability of Organic Pollutants

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    While many cyclodextrin-based applications have been developed to assess or enhance bioavailability of organic pollutants, the choice of cyclodextrin (CD) is largely empirical, with little consideration of pollutant diversity and environmental matrix effects. This study aimed at developing a mathematical model for quantifying CD alteration of bioavailability of organic pollutants. Cyclodextrin appears to have multiple effects, together contributing to its bioavailability-enhancing property. Cyclodextrin is adsorbed onto the adsorbent matrix to different extents. The adsorbed CD is capable of sequestrating organic pollutants, highlighting the role of a pseudophase similar to solid environmental matrix. Aqueous CD can reduce adsorption of organic pollutants via inclusion complexation. The two effects cancel each other to a certain degree, which determines the levels of organic pollutants dissolved (comprising freely dissolved and CD-included forms). Additionally, the CD-included form is nearly identical in biological activity to the free form. A mathematical model of one variable (i.e., CD concentration) was derived to quantify effects of CD on the bioavailability of organic pollutants. Model analysis indicates that alteration of bioavailability of organic pollutants by CD depends on both CD (type and level) and environmental matrix. The selection of CD type and amendment level for a given application may be predicted by the model

    Probing the Stereochemistry of Successive Sulfoxidation of the Insecticide Fenamiphos in Soils

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    Successive sulfoxidation is widely recognized as a general characteristic of the metabolism of chiral or prochiral thioethers, producing sulfoxides, and sulfones. However, information related to the stereochemistry of this process in soils is rare. In this study, the biotic transformation of the insecticide fenamiphos (a model thioether) was followed over two months in three soils, through separate incubations with fenamiphos parent, the sulfoxide intermediate (FSO), the sulfone intermediate (FSO<sub>2</sub>), and their respective stereoisomers. The results showed that the successive sulfoxidation involved oxidation of fenamiphos to FSO and subsequently to FSO<sub>2</sub> as well as diastereomerization/enantiomerization of FSO, all of which were primarily biotic and stereoselective. The concomitant hydrolysis of fenamiphos, FSO, and FSO<sub>2</sub> to phenols that occurred at lower rates was biotically favorable, but not stereoselective. The stereochemistry of this successive sulfoxidation transferred principally through two parallel systems, <i>R</i>(+)-fenamiphos → S<i>R</i>P<i>R</i>(+)-/S<i>S</i>P<i>R</i>(−)-FSO → <i>R</i>(+)-FSO<sub>2</sub> and <i>S</i>(−)-fenamiphos → S<i>R</i>P<i>S</i>(+)-/S<i>S</i>P<i>S</i>(−)-FSO → <i>S</i>(−)-FSO<sub>2</sub>, between which unidirectional intersystem crossing occurred at FSO via isomeric conversions and created a system of <i>S</i>(−)-fenamiphos → S<i>R</i>P<i>R</i>(+)-/S<i>S</i>P<i>R</i>(−)-FSO → <i>R</i>(+)-FSO<sub>2</sub>. This pattern accounts for the enrichment of the intermediates S<i>S</i>P<i>R</i>(−)-/S<i>S</i>P<i>S</i>(−)-FSO and <i>R</i>(+)-FSO<sub>2</sub> that are toxicologically close to the highly toxic <i>S</i>(−)-fenamiphos, associated with soil application of fenamiphos. Selective formation/depletion of these intermediate stereoisomers leads to dramatic variations in the ecotoxicological effects of the thioether insecticide

    Anionic Phenolic Compounds Bind Stronger with Transthyretin than Their Neutral Forms: Nonnegligible Mechanisms in Virtual Screening of Endocrine Disrupting Chemicals

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    The molecular structures of many endocrine-disrupting chemicals (EDCs) contain groups that ionize under physiological pH conditions. It is unclear whether the neutral and ionic forms have different binding mechanisms with the macromolecular targets. We selected phenolic compounds and human transthyretin (hTTR) as a model system and employed molecular docking with quantum mechanics/molecular mechanics optimizations to probe the mechanisms. The binding patterns of ionizable ligands in hTTR crystal structures were also analyzed. We found that the anionic forms of the phenolic compounds bind stronger than the corresponding neutral forms with hTTR. Electrostatic and van de Waals interactions are the dominant forces for most of the anionic and neutral forms, respectively. Because of the dominant and orientational electrostatic interactions, the −O<sup>–</sup> groups point toward the entry port of the binding site. The aromatic rings of the compounds also form cation–π interactions with the −NH<sub>3</sub><sup>+</sup> group of Lys 15 residues in hTTR. Molecular descriptors were selected to characterize the interactions and construct a quantitative structure–activity relationship model on the relative competing potency of chemicals with T<sub>4</sub> binding to hTTR. It is concluded that the effects of ionization should not be neglected when constructing <i>in silico</i> models for screening of potential EDCs

    Simulating Adsorption of Organic Pollutants on Finite (8,0) Single-Walled Carbon Nanotubes in Water

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    Understanding the mechanism and thermodynamics of the adsorption of chemicals on carbon nanotubes (CNTs) is important to risk assessment and pollution control of both CNTs and chemicals. We computed the adsorption of cyclohexane, benzene derivatives, and polycyclic aromatic hydrocarbons (PAHs) on (8,0) single-walled carbon nanotubes by the M05–2X of density functional theory. The computed adsorption energies (<i>E</i><sub>a</sub>) in the aqueous phase are lower than those in the gaseous phase, indicating that the adsorption in the aqueous phase is more favorable. The contribution of π–π interactions and the enhancing effect of a −NO<sub>2</sub> substituent on the adsorption were quantified. For a hypothetical aromatic with the same hydrophobicity (log<i>K</i><sub>OW</sub>) to cyclohexane, π–π interactions contribute ca. 24% of the total interactions as indicated by <i>E</i><sub>a</sub>. −NO<sub>2</sub> enhances the π–π interactions due to its electron withdrawing effects, and contributes 24% to the value of <i>E</i><sub>a</sub>. Simple linear regression showed the computed Gibbs free energy changes for the adsorption correlate significantly with the experimental values (<i>r</i> = 0.97, <i>p</i> < 0.01). The correlation together with the computed thermodynamic parameters may be employed to predict the adsorption affinity of other chemicals. The study may pave a new way for evaluating/predicting the adsorption affinity of organic compounds on SWNTs and probing the adsorption mechanisms

    Prediction of Hydrolysis Pathways and Kinetics for Antibiotics under Environmental pH Conditions: A Quantum Chemical Study on Cephradine

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    Understanding hydrolysis pathways and kinetics of many antibiotics that have multiple hydrolyzable functional groups is important for their fate assessment. However, experimental determination of hydrolysis encounters difficulties due to time and cost restraint. We employed the density functional theory and transition state theory to predict the hydrolysis pathways and kinetics of cephradine, a model of cephalosporin with two hydrolyzable groups, two ionization states, two isomers and two nucleophilic attack directions. Results showed that the hydrolysis of cephradine at pH = 8.0 proceeds via opening of the β-lactam ring followed by intramolecular amidation. The predicted rate constants at different pH conditions are of the same order of magnitude as the experimental values, and the predicted products are confirmed by experiment. This study identified a catalytic role of the carboxyl group in the hydrolysis, and implies that the carboxyl group also plays a catalytic role in the hydrolysis of other cephalosporin and penicillin antibiotics. This is a first attempt to quantum chemically predict hydrolysis of an antibiotic with complex pathways, and indicates that to predict hydrolysis products under the environmental pH conditions, the variation of the rate constants for different pathways with pH should be evaluated

    A Bioenvironment-Responsive Versatile Nanoplatform Enabling Rapid Clearance and Effective Tumor Homing for Oxygen-Enhanced Radiotherapy

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    Nanoradiosensitizer-augmented cancer radiotherapy (RT) has been widely investigated, whereas the desirable nanoradiosensitizer with the characteristics of rapid clearance, effective tumor homing, and tumor hypoxia relief is still lacking. Herein, bismuth sulfide–albumin composite nanospheres followed by catalase conjugation (denoted as BSNSs-CAT) have been well constructed as a bioenvironment-responsive nanoradiosensitizer platform. The BSNSs-CAT phagocytosed by normal cells demonstrate architectural disintegration into small ones in response to physiological pH, achieving small size-favored rapid clearance, which largely mitigates the concern of long-term toxicity of BSNSs-CAT in normal tissues. More importantly, benefiting from their large size-favored enhanced permeability and retention effect, BSNSs-CAT accumulate efficiently in tumors and remain architecturally stable in a mildly acidic tumor microenvironment (TME), which strongly favors a response with H<sub>2</sub>O<sub>2</sub> overproduced TME. As a result, the produced intratumor oxygen could overcome tumor hypoxia-associated RT resistance, together with the radiosensitization effect of bismuth, collectively enhancing RT efficacy. This research demonstrates a versatile material solution by fully exploring its bioenvironment-responsive nature, offering a new strategy for nanomedicine design and application

    Multimodal Model to Predict Tissue-to-Blood Partition Coefficients of Chemicals in Mammals and Fish

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    Tissue-to-blood partition coefficients (Ptb) are key parameters for assessing toxicokinetics of xenobiotics in organisms, yet their experimental data were lacking. Experimental methods for measuring Ptb values are inefficient, underscoring the urgent need for prediction models. However, most existing models failed to fully exploit Ptb data from diverse sources, and their applicability domain (AD) was limited. The current study developed a multimodal model capable of processing and integrating textual (categorical features) and numerical information (molecular descriptors/fingerprints) to simultaneously predict Ptb values across various species, tissues, blood matrices, and measurement methods. Artificial neural network algorithms with embedding layers were used for the multimodal modeling. The corresponding unimodal models were developed for comparison. Results showed that the multimodal model outperformed unimodal models. To enhance the reliability of the model, a method considering categorical features, weighted molecular similarity density, and weighted inconsistency in molecular activities of structure–activity landscapes was used to characterize the AD. The model constrained by the AD exhibited better prediction accuracy for the validation set, with the determination coefficient, root mean-square error, and mean absolute error being 0.843, 0.276, and 0.213 log units, respectively. The multimodal model coupled with the AD characterization can serve as an efficient tool for internal exposure assessment of chemicals

    Multimodal Model to Predict Tissue-to-Blood Partition Coefficients of Chemicals in Mammals and Fish

    No full text
    Tissue-to-blood partition coefficients (Ptb) are key parameters for assessing toxicokinetics of xenobiotics in organisms, yet their experimental data were lacking. Experimental methods for measuring Ptb values are inefficient, underscoring the urgent need for prediction models. However, most existing models failed to fully exploit Ptb data from diverse sources, and their applicability domain (AD) was limited. The current study developed a multimodal model capable of processing and integrating textual (categorical features) and numerical information (molecular descriptors/fingerprints) to simultaneously predict Ptb values across various species, tissues, blood matrices, and measurement methods. Artificial neural network algorithms with embedding layers were used for the multimodal modeling. The corresponding unimodal models were developed for comparison. Results showed that the multimodal model outperformed unimodal models. To enhance the reliability of the model, a method considering categorical features, weighted molecular similarity density, and weighted inconsistency in molecular activities of structure–activity landscapes was used to characterize the AD. The model constrained by the AD exhibited better prediction accuracy for the validation set, with the determination coefficient, root mean-square error, and mean absolute error being 0.843, 0.276, and 0.213 log units, respectively. The multimodal model coupled with the AD characterization can serve as an efficient tool for internal exposure assessment of chemicals

    Antibiotic Pollution in Marine Food Webs in Laizhou Bay, North China: Trophodynamics and Human Exposure Implication

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    Little information is available about the bioaccumulation and biomagnification of antibiotics in marine food webs. Here, we investigate the levels and trophic transfer of 9 sulfonamide (SA), 5 fluoroquinolone (FQ), and 4 macrolide (ML) antibiotics, as well as trimethoprim in nine invertebrate and ten fish species collected from a marine food web in Laizhou Bay, North China in 2014 and 2015. All the antibiotics were detected in the marine organisms, with SAs and FQs being the most abundant antibiotics. Benthic fish accumulated more SAs than invertebrates and pelagic fish, while invertebrates exhibited higher FQ levels than fish. Generally, SAs and trimethoprim biomagnified in the food web, while the FQs and MLs were biodiluted. Trophic magnification factors (TMF) were 1.2–3.9 for SAs and trimethoprim, 0.3–1.0 for FQs and MLs. Limited biotransformation and relatively high assimilation efficiencies are the likely reasons for the biomagnification of SAs. The pH dependent distribution coefficients (log <i>D</i>) but not the lipophilicity (log <i>K</i><sub>OW</sub>) of SAs and FQs had a significant correlation (<i>r</i> = 0.73; <i>p</i> < 0.05) with their TMFs. Although the calculated estimated daily intakes (EDI) for antibiotics suggest that consumption of seafood from Laizhou Bay is not associated with significant human health risks, this study provides important insights into the guidance of risk management of antibiotics

    Unveiling Adsorption Mechanisms of Organic Pollutants onto Carbon Nanomaterials by Density Functional Theory Computations and Linear Free Energy Relationship Modeling

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    Predicting adsorption of organic pollutants onto carbon nanomaterials (CNMs) and understanding the adsorption mechanisms are of great importance to assess the environmental behavior and ecological risks of organic pollutants and CNMs. By means of density functional theory (DFT) computations, we investigated the adsorption of 38 organic molecules (aliphatic hydrocarbons, benzene and its derivatives, and polycyclic aromatic hydrocarbons) onto pristine graphene in both gaseous and aqueous phases. Polyparameter linear free energy relationships (pp-LFERs) were developed, which can be employed to predict adsorption energies of aliphatic and aromatic hydrocarbons on graphene. Based on the pp-LFERs, contributions of different interactions to the overall adsorption were estimated. As suggested by the pp-LFERs, the gaseous adsorption energies are mainly governed by dispersion and electrostatic interactions, while the aqueous adsorption energies are mainly determined by dispersion and hydrophobic interactions. It was also revealed that curvature of single-walled carbon nanotubes (SWNTs) exhibits more significant effects than the electronic properties (metallic or semiconducting) on gaseous adsorption energies, and graphene has stronger adsorption abilities than SWNTs. The developed models may pave a promising way for predicting adsorption of environmental chemicals onto CNMs with in silico techniques
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