55 research outputs found
Mathematical Model for Cyclodextrin Alteration of Bioavailability of Organic Pollutants
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
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
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
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
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
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
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
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
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
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
- …