Ecological risk of synthetic phenolic antioxidants: A study based on their spatial distribution in water, sediment, and soil from the Yangtze River Delta, China.

Environmental occurrence and risks of novel synthetic phenolic antioxidants (SPAs) remain largely unclear. By using a typical algae (Chlorella pyrenoidosa) as model organism, we evaluated the ecological risks of both traditional and novel SPAs, based on their concentrations in water, sediment, and soil collected from the Yangtze River Delta, China. Detection frequencies (DFs) of 10 novel SPAs were 25-100% in water, 3-100% in sediment, and 0-100% in soil, with geometric means (GMs) of 2700 ng/L, 1270 ng/g, and 2440 ng/g, respectively. For 8 traditional SPAs, DFs were 50-100% (GM: 680 ng/L), 3-100% (534 ng/g), and 47-100% (2240 ng/g) in water, sediment, and soil, respectively. AO3114 was the main pollutant in water, while AO1010 dominated in sediment and soil. Notably, low-molecular-weight SPAs showed migration behavior from sediment to water. Four SPAs (AO626, AO1035, AO1098, and AO1076) showed dose- and time-dependent toxicity on Chlorella pyrenoidosa. As time progressed, sediment-released SPAs became more toxic than those in water. Two SPAs (AO1135 and BHT-Q) posed high risks (RQW > 1) to green algae, daphnia, and fish. The SPA mixture exhibited high risks (RQmix > 1) to these organisms, increasing with the trophic level. This research holds valuable guidance for further SPA risk assessments.

Time-dependent hormesis transfer from five high-frequency personal care product components to mixtures.

There is potential for personal care products (PCPs) components and mixtures to induce hormesis. How hormesis is related to time and transmitted from components to mixtures are not clear. In this paper, we conducted determination of components in 16 PCP products and then ran frequent itemset mining on the component data. Five high-frequency components (HFCs), betaine (BET), 1,3-butanediol (BUT), ethylenediaminetetraacetic acid disodium salt (EDTA), glycerol (GLO), and phenoxyethanol (POE), and 14 mixtures were identified. For each mixture system, one mixture ray with the actual mixture ratios in the products was selected. Time-dependent microplate toxicity analysis was used to test the luminescence inhibition toxicity of five HFCs and 14 mixture rays to Vibrio qinghaiensis sp.-Q67 at 12 concentration gradients and eight exposure times. It is showed that BET, EDTA, POE, and 13 mixture rays containing at least one J-type component showed time-dependent hormesis. Characteristic parameters used to describe hormesis revealed that the absolute value of the maximum stimulatory effect (|Emin|) generally increased with time. Notably, mixtures composed of POE and S-type components showed greater |Emin| than POE alone at the same time. Importantly, the maximum stimulatory effective concentration, NOEC/the zero effective concentration point, and EC50 remained relatively stable. Nine hormesis transmission phenomena were observed in different mixture rays. While all mixtures primarily exhibited additive action, varying degrees of synergism and antagonism were noted in binary mixtures, with no strong synergism or antagonism observed in ternary and quaternary mixtures. These findings offer valuable insights for the screening of HFCs and their mixtures, as well as the study of hormesis transmission in personal care products.

Screening for frequently detected quaternary ammonium mixture systems in waters based on frequent itemset mining and prediction of their toxicity.

Screening and prioritizing research on frequently detected mixture systems in the environment is of great significance, as conducting toxicity testing on all mixtures is impractical. Therefore, the frequent itemset mining (FIM) was introduced and applied in this paper to identify variables that commonly co-occur in a dataset. Based on the dataset of the quaternary ammonium compounds (QACs) in the water environment, the four frequent QAC mixture systems with detection rate ≥ 35 % were found, including [BDMM]+Cl--[BTMM]+Cl- (M1), [BDMM]+Cl--[BHMM]+Cl- (M2), [BTMM]+Cl- -[BHMM]+Cl- (M3), and [BDMM]+Cl--[BTMM]+Cl--[BHMM]+Cl- (M4). [BDMM]+Cl-, [BTMM]+Cl-, and [BHMM]+Cl- are benzyl dodecyl dimethyl ammonium chloride, benzyl tetradecyl dimethyl ammonium chloride, and benzyl hexadecyl dimethyl ammonium chloride, respectively. Then, the toxicity of the representative mixture rays and components for the four frequently detected mixture systems was tested using Vibrio qinghaiensis sp.-Q67 (Q67) as a luminescent indicator organism at 0.25 and 12 h. The toxicity of the mixtures was predicted using concentration addition (CA) and independent action (IA) models. It was shown that both the components and the representative mixture rays for the four frequently detected mixture systems exhibited obvious acute and chronic toxicity to Q67, and their median effective concentrations (EC50) were below 7 mg/L. Both CA and IA models predicted the toxicity of the four mixture systems well. However, the CA model had a better predictive ability for the toxicity of the M3 and M4 mixtures than IA at 12 h.

Mixture predicted no-effect concentrations derived by independent action model vs concentration addition model based on different species sensitivity distribution models.

In the hazard assessment of mixtures, the mixture predicted no-effect concentration (mPNEC) is always derived by the concentration addition (CA) model (mPNECCA) to assess the risk of mixtures combined with exposure assessment. However, the independent action (IA) model, which is also widely used as the CA model in the prediction and evaluation of mixture toxicity, is always used to calculate the population fraction showing a predefined effect, not mPNEC, and this limits the application of IA model in the mixture risk assessment. In this study, we explored the process of mPNEC derived by the IA method (mPNECIA) based on the species sensitivity distribution (SSD) and compared mPNECIA with mPNECCA. Taking two common pesticides, dimethoate (DIM) and dichlorvos (DIC), exposed in the actual water environment as an example, their SSD models were constructed separately using nine distribution functions after toxicity data screening and quality testing. For both DIC and DIM, all different nine models had passed the Kolmogorov-Smirnov test. Then, the PNECs of two pesticides were derived based on SSD models. Finally, mPNECIA with different concentration ratios was derived and compared to mPNECCA based on 81 combinations of nine SSD models. Most mPNEC values derived by IA model were more conservative than those by CA. It is worth noting that the mPNECIA is more conservative than mPNECCA for the commonly used log-logit distribution (function 7), log-normal distribution (8), and log-Weibull distribution (9). This study provides a new direction for the application of IA in the risk assessment and enriches the framework of mixture risk assessment.

Water quality criteria and ecological risk assessment for ammonia in the Shaying River Basin, China.

Current Chinese surface water environmental quality standard GB3838-2002 for ammonia fails to take water quality factors and native organism distributions in different basins into consideration. In this study, ammonia toxicity tests were performed using three aquatic organisms native to the Shaying River Basin (China). Published ammonia toxicity data with pH and temperature, and toxicity data acquired in this study were used to establish water quality criteria. The final criterion maximum concentration (CMC) and criterion continuous concentration (CCC) for the Shaying River Basin were 5.09 and 1.36 (mg total ammonia nitrogen (TAN))/L (pH 7 and 20 °C), respectively. In addition, based on the corresponding relationship between ammonia toxicity and temperature and pH, the ecological risk assessment of ammonia was conducted in different seasons for the Shaying River using a tiered approach of both hazard quotient (HQ) and the joint probability (JPC) methods. Two methods gave consistent results: the ecological risks of ammonia to aquatic species in the Shaying River Basin were severe and the risk could be ranked as wet season > flat season > dry season. It is therefore indicating that monitoring, evaluation, and early warning of ammonia pollution need to be taken to prevent and control the risks posed by ammonia pollution, especially for wet season (because of high temperatures and pH) or flat season (because of high pH values). We hope the present work could provide valuable information to manage and control ammonia pollution in the Shaying River Basin.

A novel method based on similarity and triangulation for predicting the toxicities of various binary mixtures.

There is currently no generally accepted model to predict the hormesis of mixtures. In order to accurately predict the hormesis of a mixture, we developed a method based on similarity and triangulation, which we named SimTri in this paper. SimTri takes the mixture as scatter points in space, which is constructed by the concentration axes of various components in the mixture system. To test the predictive capability of SimTri, the toxicities of three different types of binary mixtures (no hormetic compound, one hormetic compound, and two hormetic compounds) on Vibrio qinghaiensis sp.-Q67 were determined at 0.25 h and 12 h. For each mixture system, the toxicities of five mixture rays, which were designed by direct equipartition ray design, were used for the internal validation (leave-one-out cross-validation, LOOCV). The toxicities of two mixture rays, which were designed by fixed-ratio ray design on the basis of the NOEC and EC70 ratios, were used for the external validation. The results of LOOCV and external validation indicated that the accuracy of SimTri was greater than 90%, which means that SimTri can accurately predict the toxicity of three different types of binary mixtures and may provide a new way to predict the toxicity of mixtures.

Polyethylene glycol 400 significantly enhances the stimulation of 2-phenoxyethanol on Vibrio qinghaiensis sp.-Q67 bioluminescence.

Previous studies demonstrated long-term stimulation of some commercial personal care products (PCPs) on freshwater luminescent bacteria Vibrio qinghaiensis sp.-Q67 (Q67). However, whether a certain component can affect mixture's hormetic effect is still unknown. In this paper, two of ingredients in PCPs, 2-phenoxyethanol (PhE) and polyethylene glycol 400 (PEG400), were selected as object compounds to explore the relationship between concentration-response (CR) of mixtures and that of a single component. It was found that PEG400 has monotonic CR (MCR) on Q67 both at the short-term (0.25 h) and long-term (12 h) exposures while PhE has MCR at 0.25 h and hormetic CR (HCR) at 12 h. Here, the concentration-response curves (CRCs) of PEG400 at 0.25 and 12 h are overlapped each other and the CRCs of PEG400 are on the right of PhE. If the pEC50 is taken as a toxic index, the toxicities of PEG400 at two times are basically the same, and those of PhE are the same, too, but PhE is twice as toxic as PEG400. For the mixtures of PEG400 and PhE, all rays except R1 have MCRs at 0.25 h while all rays have HCRs at 12 h where the higher the mixture ratio of PhE is, the more negative the maximum stimulation effect is. More importantly, the Emin values of all rays are more negative (1.79-3.17-fold) than that of PhE worked alone, which implies that the introduction of PEG400 significantly enhances stimulative effect of PhE. At 0.25 h, all binary mixture rays but R1 produce a low-concentration additive action and high-concentration synergism. At 12 h, all rays display additive action, antagonism, additive action, and synergism in turn when the concentration changes from low to high. The overall findings suggested toxicological interactions should be considered in the risk assessment of PCPs and their potential impacts on ecological balances.

Commercial personal care product mixtures exhibit hormetic concentration-responses to Vibrio qinghaiensis sp.-Q67.

The biological effects related to personal care products (PCPs) are almost induced by some active ingredients in the PCPs rather than the PCP itself. In this study, 23 common and widely used toner, skin water, and make-up water (TSM) commodities were directly taken as mixture samples, and Vibrio qinghaiensis sp.-Q67 (Q67) was used as the test organism. The toxicities of the TSMs to Q67 were determined via microplate toxicity analysis at 0.25 h and 12 h. Each TSM commodity can be regarded as a complicated mixture (relative concentration is 1). It was shown that the concentration-response curves (CRCs) of 23 TSMs are monotonic sigmoid-shaped (S-shaped) at 0.25 h, the CRCs of six TSMs are also S-shaped but the other 17 TSMs are non-monotonic hormetic or J-shaped at 12 h. In addition, to effectively characterize the nature of the stimulation and inhibition phases, it is suggested that five parameters such as the ECL (the median stimulation effective concentration (left)), Emin (the maximum stimulation effect), ECmin (the maximum stimulation effective concentration), ZEP (zero effect point where the effect is 0 and the concentration is ZEP), and EC50 can depict the non-monotonic CRC. To the best of our knowledge, this is the first study about the hormetic CRCs of commercial PCP mixtures.

Global concentration additivity and prediction of mixture toxicities, taking nitrobenzene derivatives as an example.

The toxicity of a mixture depends not only on the mixture concentration level but also on the mixture ratio. For a multiple-component mixture (MCM) system with a definite chemical composition, the mixture toxicity can be predicted only if the global concentration additivity (GCA) is validated. The so-called GCA means that the toxicity of any mixture in the MCM system is the concentration additive, regardless of what its mixture ratio and concentration level. However, many mixture toxicity reports have usually employed one mixture ratio (such as the EC50 ratio), the equivalent effect concentration ratio (EECR) design, to specify several mixtures. EECR mixtures cannot simulate the concentration diversity and mixture ratio diversity of mixtures in the real environment, and it is impossible to validate the GCA. Therefore, in this paper, the uniform design ray (UD-Ray) was used to select nine mixture ratios (rays) in the mixture system of five nitrobenzene derivatives (NBDs). The representative UD-Ray mixtures can effectively and rationally describe the diversity in the NBD mixture system. The toxicities of the mixtures to Vibrio qinghaiensis sp.-Q67 were determined by the microplate toxicity analysis (MTA). For each UD-Ray mixture, the concentration addition (CA) model was used to validate whether the mixture toxicity is additive. All of the UD-Ray mixtures of five NBDs are global concentration additive. Afterwards, the CA is employed to predict the toxicities of the external mixtures from three EECR mixture rays with the NOEC, EC30, and EC70 ratios. The predictive toxicities are in good agreement with the experimental toxicities, which testifies to the predictability of the mixture toxicity of the NBDs.

Prediction of placental barrier permeability: a model based on partial least squares variable selection procedure.

Assessing the human placental barrier permeability of drugs is very important to guarantee drug safety during pregnancy. Quantitative structure-activity relationship (QSAR) method was used as an effective assessing tool for the placental transfer study of drugs, while in vitro human placental perfusion is the most widely used method. In this study, the partial least squares (PLS) variable selection and modeling procedure was used to pick out optimal descriptors from a pool of 620 descriptors of 65 compounds and to simultaneously develop a QSAR model between the descriptors and the placental barrier permeability expressed by the clearance indices (CI). The model was subjected to internal validation by cross-validation and y-randomization and to external validation by predicting CI values of 19 compounds. It was shown that the model developed is robust and has a good predictive potential (r2=0.9064, RMSE=0.09, q2=0.7323, rp2=0.7656, RMSP=0.14). The mechanistic interpretation of the final model was given by the high variable importance in projection values of descriptors. Using PLS procedure, we can rapidly and effectively select optimal descriptors and thus construct a model with good stability and predictability. This analysis can provide an effective tool for the high-throughput screening of the placental barrier permeability of drugs.

Hybrid in silico models for drug-induced liver injury using chemical descriptors and in vitro cell-imaging information.

Drug-induced liver injury (DILI) is a major adverse drug reaction that accounts for one-third of post-marketing drug withdrawals. Several classifiers for human hepatotoxicity using chemical descriptors with limited prediction accuracies have been published. In this study, we developed predictive in silico models based on a set of 156 DILI positive and 136 DILI negative compounds for DILI prediction. First, models based on a chemical descriptor (CDK, Dragon and MOE) and in vitro cell-imaging endpoints [human hepatocyte imaging assay technology (HIAT) descriptors] were built using random forest (RF) and five-fold cross-validation procedure. Then three hybrid models were built using HIAT and a single type of chemical descriptors. Generally, the models based only on chemical descriptors were poor, with a correct classification rate (CCR) around 0.60 when the default threshold value (i.e. threshold = 0.50) was used. The hybrid models afforded a CCR of 0.73 with a specificity of 0.74 and a better true positive rate (sensitivity of 0.71), which is crucial in drug toxicity screening for the purpose of patient safety. The benefit of hybrid models was even more drastic when stricter classification thresholds were employed (e.g. CCR would be 0.83 when double thresholds (non-toxic < 0.40 and toxic > 0.60) were used for the hybrid model). We have developed rigorously validated hybrid models which can be used in virtual screening of lead compounds with potential hepatotoxicity. Our study also showed a chemical structure and in vitro biological data can be complementary in enhancing the prediction accuracy of human hepatotoxicity and can afford rational mechanistic interpretation.

Identifying the component responsible for antagonism within ionic liquid mixtures using the up-to-down procedure integrated with a uniform design ray method.

Various chemicals in the environment always exist as mixtures. Toxicity interaction within mixtures may pose potential hazards and risks to the environmental safety and human health. Recent studies showed that toxicity interaction by ionic liquid (IL) mixtures can be related to a certain component. To identify the component, we developed a novel procedure integrating an up-to-down process with the uniform design-based ray method (UDUD) and applied it into an IL mixture system of four 1-butyl-3-methylimidazolium ILs (simply [bmim]X) where X=Cl(-), Br(-), CH3OSO3(-) and CH3(CH2)7OSO3(-). It was shown that two mixture rays in the quaternary system exhibited significant antagonistic interaction. In this paper, the UDUD was first employed to design four ternary mixture systems. The microplate toxicity analysis was used to determine the toxicities of various mixtures to a freshwater photobacterium Vibrio qinghaiensis sp.-Q67. The concentration addition was taken as an additive reference to assess the toxicity interactions taking place in mixtures. The results revealed that some ternary mixture rays including [bmim]CH3(CH2)7OSO3 display antagonism while the ternary rays without [bmim]CH3(CH2)7OSO3 exhibit additivity. On these grounds, we again designed all binary mixtures containing [bmim]CH3(CH2)7OSO3, determined their toxicities and assessed toxicity interaction. The results showed that three binary mixture systems produce antagonism. Thus, it may be concluded that [bmim]CH3(CH2)7OSO3 is indeed a key component inducing mixture antagonism.

Two-stage prediction of the effects of imidazolium and pyridinium ionic liquid mixtures on luciferase.

The predicted toxicity of mixtures of imidazolium and pyridinium ionic liquids (ILs) in the ratios of their EC50, EC10, and NOEC (no observed effect concentration) were compared to the observed toxicity of these mixtures on luciferase. The toxicities of EC50 ratio mixture can be effectively predicted by two-stage prediction (TSP) method, but were overestimated by the concentration addition (CA) model and underestimated by the independent action (IA) model. The toxicities of EC10 ratio mixtures can be basically predicted by TSP and CA, but were underestimated by IA. The toxicities of NOEC ratio mixtures can be predicted by TSP and CA in a certain concentration range, but were underestimated by IA. Our results support the use of TSP as a default approach for predicting the combined effect of different types of ILs at the molecular level. In addition, mixtures of ILs mixed at NOEC and EC10 could cause significant effects of 64.1% and 97.7%, respectively. Therefore, we should pay high attention to the combined effects in mixture risk assessment.

Derivation of water quality criteria for paraquat, bisphenol A and carbamazepine using quantitative structure-activity relationship and species sensitivity distribution (QSAR-SSD).

The risk assessment of an expanding array of emerging contaminants in aquatic ecosystems and the establishment of water quality criteria rely on species sensitivity distribution (SSD), necessitating ample multi-trophic toxicity data. Computational methods, such as quantitative structure-activity relationship (QSAR), enable the prediction of specific toxicity data, thus mitigating the need for costly experimental testing and exposure risk assessment. In this study, robust QSAR models for four aquatic species (Rana pipiens, Crassostrea virginica, Asellus aquaticus, and Lepomis macrochirus) were developed using leave-one-out (LOO) screening variables and the partial least squares algorithm to predict toxicity data for paraquat, bisphenol A, and carbamazepine. These predicted data can be integrated with experimental data to construct SSD models and derive hazardous concentration for 5 % of species (HC5) for the criterion maximum concentration. The chronic water quality criterion for paraquat, bisphenol A, and carbamazepine were determined at 6.7, 11.1, and 3.5 µg/L, respectively. The QSAR-SSD approach presents a viable and cost-effective method for deriving water quality criteria for other emerging contaminants.

A new parameter for quantitatively characterizing antibiotic hormesis: QSAR construction and joint toxic action judgment.

Antibiotics usually induce the hormetic effects on bacteria, featured by low-dose stimulation and high-dose inhibition, which challenges the central belief in toxicity assessment and environmental risk assessment of antibiotics. However, there are currently no ideal parameters to quantitatively characterize hormesis. In this study, an effective area in hormesis (AH) was developed to quantify the biphasic dose-responses of single antibiotics (sulfonamides (SAs), sulfonamides potentiators (SAPs), and tetracyclines (TCs)) and binary mixtures (SAs-SAPs, SAs-TCs, and SAs-SAs) to the bioluminescence of Aliivibrio fischeri. Using Ebind (the lowest interaction energy between antibiotic and target protein) and Kow (octanol-water partition coefficient) as the structural descriptors, the reliable quantitative structure-activity relationship (QSAR) models were constructed for the AH values of test antibiotics and mixtures. Furthermore, a novel method based on AH was established to judge the joint toxic actions of binary antibiotics, which mainly exhibited synergism. The results also indicated that SAPs (or TCs) contributed more than SAs in the hormetic effects of antibiotic mixtures. This study proposes a new quantitative parameter for characterizing and predicting antibiotic hormesis, and considers hormesis as an integrated whole to reveal the combined effects of antibiotics, which will promote the development of risk evaluation for antibiotics and their mixtures.

Time-dependent nonmonotonic concentration-response and synergism of alkyl glycosides with different alkyl side chain to Vibrio qinghaiensis sp. -Q67.

Alkyl glycosides (AGs), commonly used nonionic surfactants, may have toxic effects on the environmental organisms. However, the complex concentration-response patterns of AGs with varying alkyl side chains and their mixtures have not been thoroughly studied. Therefore, the luminescence inhibition toxicities of six AGs with different alkyl side chains, namely, ethyl (AG02), butyl (AG04), hexyl (AG06), octyl (AG08), decyl (AG10), and dodecyl (AG12) glucosides, were determined in Vibrio qinghaiensis sp. -Q67 (Q67) at 0.25, 3, 6, 9, and 12 h. The six AGs exhibited time- and side-chain-dependent nonmonotonic concentration- responses toward Q67. AG02, with a short side chain, presented a concentration-response curve (CRC) with two peaks after 6 h and stimulated the luminescence of Q67 at both 6 and 9 h. AG04, AG06, and AG08 showed S-shaped CRCs at five exposure time points, and their toxicities increased with the side-chain length. AG10 and AG12, with long side chains, exhibited hormesis at 9 and 12 h. Molecular docking was performed to explore the mechanism governing the possible influence of AGs on the luminescence response. The effects of AGs on Q67 could be attributed to multiple luminescence-regulatory proteins, including LuxA, LuxC, LuxD, LuxG, LuxI, and LuxR. Notably, LuxR was identified as the primary binding protein among the six AGs. Given that they may co-exist, binary mixtures of AG10 and AG12 were designed to explore their concentration-response patterns and interactions. The results revealed that all AG10-AG12 binary mixture rays showed time-dependent hormesis on Q67, similar to that shown by their individual components. The interactions of these binary mixtures were mainly characterized by low-concentration additive action and high-concentration synergism at different times.

Acute toxicity of binary mixtures for quorum sensing inhibitors and sulfonamides against Aliivibrio fischeri: QSAR investigations and joint toxic actions.

Quorum sensing inhibitors (QSIs), as a kind of ideal antibiotic substitutes, have been recommended to be used in combination with traditional antibiotics in medical and aquaculture fields. Due to the co-existence of QSIs and antibiotics in environmental media, it is necessary to evaluate their joint risk. However, there is little information about the acute toxicity of mixtures for QSIs and antibiotics. In this study, 10 QSIs and 3 sulfonamides (SAs, as the representatives for traditional antibiotics) were selected as the test chemicals, and their acute toxic effects were determined using the bioluminescence of Aliivibrio fischeri (A. fischeri) as the endpoint. The results indicated that SAs and QSIs all induced S-shaped dose-responses in A. fischeri bioluminescence. Furthermore, SAs possessed greater acute toxicity than QSIs, and luciferase (Luc) might be the target protein of test chemicals. Based on the median effective concentration (EC50) for each test chemical, QSI-SA mixtures were designed according to equitoxic (EC50(QSI):EC50(SA) = 1:1) and non-equitoxic ratios (EC50(QSI):EC50(SA) = 1:10, 1:5, 1:0.2, and 1:0.1). It could be observed that with the increase of QSI proportion, the acute toxicity of QSI-SA mixtures enhanced while the corresponding TU values decreased. Furthermore, QSIs contributed more to the acute toxicity of test binary mixtures. The joint toxic actions of QSIs and SAs were synergism for 23 mixtures, antagonism for 12 mixtures, and addition for 1 mixture. Quantitative structure-activity relationship (QSAR) models for the acute toxicity QSIs, SAs, and their binary mixtures were then constructed based on the lowest CDOCKER interaction energy (Ebind-Luc) between Luc and each chemical and the component proportion in the mixture. These models exhibited good robustness and predictive ability in evaluating the toxicity data and joint toxic actions of QSIs and SAs. This study provides reference data and applicable QSAR models for the environmental risk assessment of QSIs, and gives a new perspective for exploring the joint effects of QSI-antibiotic mixtures.

Spontaneous interactions between typical antibiotics and soil enzyme: Insights from multi-spectroscopic approaches, XPS technology, molecular modeling, and joint toxic actions.

A large amount of antibiotics enters the soil environment and accumulates therein as individuals and mixtures, threatening the soil safety. However, there is little information regarding the influence of single and mixed antibiotics on key soil proteins at molecular level. In this study, setting sulfadiazine (SD) and tetracycline hydrochloride (TC) as the representative antibiotics, the interactions between these agents and α-amylase (an important hydrolase in soil carbon cycle) were investigated through multi-spectroscopic approaches, X-ray photoelectron spectrometry, and molecular modeling. It was found that both SD and TC spontaneously bound to α-amylase with 1:1 stoichiometry mainly via forming stable chemical bonds. The interactions altered the polarity of aromatic amino acids, protein backbone, secondary structure, hydrophobicity and activity of α-amylase. The SD-TC mixtures were designed based on the direct equipartition ray to comprehensively characterize the possible concentration distribution, and interactive effects indicated that the mixtures antagonistically impacted α-amylase. These findings reveal the binding characteristics between α-amylase and typical antibiotics, which probably influence the ecological functions of α-amylase in soil. This study clarifies the potential harm of antibiotics on soil functional enzyme, which is significant for the environmental risk assessment of antibiotics and their mixtures.

An innovative mixture sampling strategy with uniform design: Application to global sensitivity analysis of mixture toxicity.

Global sensitivity analysis combined with quantitative high-throughput screening (GSA-qHTS) uses random starting points of the trajectories in mixture design, which may lead to potential contingency and a lack of representativeness. Moreover, a scenario in which all factor levels were at stimulatory effects was not considered, thereby hindering a comprehensive understanding of GSA-qHTS. Accordingly, this study innovatively introduced an optimised experimental design, uniform design (UD), to generate non-random and representative sample points with smaller uniformity deviation as starting points of multiple trajectories. By combining UD with the previously optimised one-factor-at-a-time (OAT) method, a novel mixture design method was developed (UD-OAT). The single toxicity tests showed that three pyridinium and five imidazolium ionic liquids (ILs) exerted stimulatory effects on Vibrio qinghaiensis sp.-Q67; thus, four stimulatory effective concentrations of each IL were selected as factor levels. The UD-OAT generated 108 mixture samples with equal frequency and without repetition. High-throughput microplate toxicity analysis revealed that all 108 mixtures exhibited inhibitory effects. Among these, type B mixtures exhibited increasing toxicities that subsequently decreased, unlike type C mixtures, which consistently increased over time. GSA successfully identified three of the eight ILs as important factors influencing the toxicities of the mixtures. When individual ILs produced stimulatory effects, mixtures containing two to three ILs exhibited either stimulatory effects or none. In contrast, mixtures containing five to eight ILs exhibited inhibitory effects, while those containing four ILs showed a transition from stimulatory to inhibitory effects. This study provides a novel mixture design method for studying mixture toxicity and fills the application gap of GSA-qHTS. The phenomenon of individuals being beneficial while mixtures can be harmful challenges traditional mixture risk assessments.

The use of pseudo-equilibrium constant affords improved QSAR models of human plasma protein binding.

PURPOSE: To develop accurate in silico predictors of Plasma Protein Binding (PPB). METHODS: Experimental PPB data were compiled for over 1,200 compounds. Two endpoints have been considered: (1) fraction bound (%PPB); and (2) the logarithm of a pseudo binding constant (lnKa) derived from %PPB. The latter metric was employed because it reflects the PPB thermodynamics and the distribution of the transformed data is closer to normal. Quantitative Structure-Activity Relationship (QSAR) models were built with Dragon descriptors and three statistical methods. RESULTS: Five-fold external validation procedure resulted in models with the prediction accuracy (R²) of 0.67 ± 0.04 and 0.66 ± 0.04, respectively, and the mean absolute error (MAE) of 15.3 ± 0.2% and 13.6 ± 0.2%, respectively. Models were validated with two external datasets: 173 compounds from DrugBank, and 236 chemicals from the US EPA ToxCast project. Models built with lnKa were significantly more accurate (MAE of 6.2-10.7 %) than those built with %PPB (MAE of 11.9-17.6 %) for highly bound compounds both for the training and the external sets. CONCLUSIONS: The pseudo binding constant (lnKa) is more appropriate for characterizing PPB binding than conventional %PPB. Validated QSAR models developed herein can be applied as reliable tools in early drug development and in chemical risk assessment.