Hazard of pharmaceuticals for aquatic environment: Prioritization by structural approaches and prediction of ecotoxicity

Pesticides in wastewater treatment plant effluents in the Yeongsan River Basin, Korea: Occurrence and environmental risk assessment

Pesticides are among the main drivers posing risks to aquatic environments, with effluents from wastewater treatment plants (WWTPs) serving as a major source. This study aimed to identify the primary pesticides for which there was a risk of release into aquatic environments through WWTP effluents, thereby enabling more effective contamination management in public water bodies. In this study, monitoring, risk assessment, and risk-based prioritization of 87 pesticides in effluents from three WWTPs in the Yeongsan River Basin, Korea, were conducted. A total of 59 pesticides were detected at concentrations from 0.852 ng/L to 82.044 μg/L and exhibited variable patterns across different WWTP locations. An environmental risk assessment based on the risk quotient (RQ) of individual pesticides identified 13 substances implicated in significant ecotoxicological risks, as they exceeded RQ values of 1 at least once. An optimized risk (RQf)-based prioritization, considering the frequency of the measured environmental concentration (MEC) exceeding the predicted environmental concentration (PNEC), was conducted to identify pesticides that potentially posed risks and thus should be managed as a priority. Four pesticides had an RQf value >1; metribuzin exhibited the highest RQf value of 4.951, followed by 3-phenoxybenzoic acid, atrazin-2-hydroxy, and atrazine. Additionally, five pesticides (terbuthylazine, methabenzthiazuron, diuron, thiacloprid, and fipronil) and another four pesticides (propazine, imidacloprid, hexaconazole, and hexazione) had RQf values >0.1 and > 0.01, respectively. By calculating the contributions of individual pesticides to the RQf of these mixtures (RQf, mix) based on the concentration addition model, it was determined that >95 % of the sum of RQf, mix was driven by the top seven pesticides. These findings highlight the importance of prioritizing pesticides for effective management of contamination sources.

Prediction of chronic toxicity of pharmaceuticals in Daphnia magna by combining ortholog prediction, pharmacological effects, and quantitative structure-activity relationship

To develop a method for predicting chronic toxicity of pharmaceuticals in Daphnia, we investigated the feasibility of combining the presence of drug-target orthologs in Daphnia magna, classification based on pharmacological effects, and ecotoxicity quantitative structure-activity relationship (QSAR) prediction. We established datasets on the chronic toxicity of pharmaceuticals in Daphnia, including information on therapeutic categories, target proteins, and the presence or absence of drug-target orthologs in D. magna, using literature and databases. Chronic toxicity was predicted using ecotoxicity prediction QSAR (Ecological Structure Activity Relationship and Kashinhou Tool for Ecotoxicity), and the differences between the predicted and measured values and the presence or absence of drug-target orthologs were examined. For pharmaceuticals without drug-target orthologs in D. magna or without expected specific actions, the ecotoxicity prediction QSAR analysis yielded acceptable predictions of the chronic toxicity of pharmaceuticals. In addition, a workflow model to assess the chronic toxicity of pharmaceuticals in Daphnia was proposed based on these evaluations and verified using an additional dataset. The addition of biological aspects such as drug-target orthologs and pharmacological effects would support the use of QSARs for predicting the chronic toxicity of pharmaceuticals in Daphnia.

What Approaches Should be Used to Prioritize Pharmaceuticals and Personal Care Products for Research on Environmental and Human Health Exposure and Effects?

Pharmaceuticals and personal care products (PPCPs) are released from multiple anthropogenic sources and thus have a ubiquitous presence in the environment. The environmental exposure and potential effects of PPCPs on biota and humans has aroused concern within the scientific community and the public. Risk assessments are commonly conducted to evaluate the likelihood of chemicals including PPCPs that pose health threats to organisms inhabiting various environmental compartments and humans. Because thousands of PPCPs are currently used, it is impractical to assess the environmental risk of all of them due to data limitations; in addition, new PPCPs are continually being produced. Prioritization approaches, based either on exposure, hazard, or risk, provide a possible means by which those PPCPs that are likely to pose the greatest risk to the environment are identified, thereby enabling more effective allocation of resources in environmental monitoring programs in specific geographical locations and ecotoxicological investigations. In the present review, the importance and current knowledge concerning PPCP occurrence and risk are discussed and priorities for future research are proposed, in terms of PPCP exposure (e.g., optimization of exposure modeling in freshwater ecosystems and more monitoring of PPCPs in the marine environment) or hazard (e.g., differential risk of PPCPs to lower vs. higher trophic level species and risks to human health). Recommended research questions for the next 10 years are also provided, which can be answered by future studies on prioritization of PPCPs. Environ Toxicol Chem 2024;43:488-501. © 2022 SETAC.

In silico ecotoxicity assessment of pharmaceutical residues in wastewater following oxidative treatment

Advanced oxidation processes such as thermal plasma activation and UV-C/H2O2 treatment are considered as applications for the degradation of pharmaceutical residues in wastewater complementary to conventional wastewater treatment. It is supposed that direct oxidative treatment can lower the toxicity of hospital sewage water (HSW). The aim of this study was to predict the ecotoxicity for three aquatic species before and after oxidative treatment of 10 quantified pharmaceuticals in hospital sewage water. With the application of oxidative chemistry, pharmaceuticals are degraded into transformation products before reaching complete mineralization. To estimate the potential ecotoxicity for fish, Daphnia and green algae ECOSAR quantitative structure-activity relationship software was used. Structure information from pristine pharmaceuticals and their oxidative transformation products were calculated separately and in a mixture computed to determine the risk quotient (RQ). Calculated mixture toxicities for 10 compounds found in untreated HSW resulted in moderate-high RQ predictions for all three aquatic species. Compared to untreated HSW, 30-min treatment with thermal plasma activation or UV-C/H2O2 resulted in lowered RQs. For the expected transformation products originating from fluoxetine, cyclophosphamide and acetaminophen increased RQs were predicted. Prolongation of thermal plasma oxidation up to 120 min predicted low-moderate toxicity in all target species. It is anticipated that further degradation of oxidative transformation products will end in less toxic aliphatic and carboxylic acid products. Predicted RQs after UV-C/H2O2 treatment turned out to be still moderate-high. In conclusion, in silico extrapolation of experimental findings can provide useful predicted estimates of mixture toxicity. However due to the complex composition of wastewater this in silico approach is a first step to screen for ecotoxicity. It is recommendable to confirm these predictions with ecotoxic bioassays.

Aquatic toxicity, ecological effects, human exposure pathways and health risk assessment of liquid crystal monomers

Liquid crystal monomers (LCMs), one of the key materials for liquid crystal displays, have been considered as emerging pollutants in recent years. However, the environmental behaviors of LCMs have not yet been well investigated. The toxicity data of 1173 LCMs were calculated by integrated computational simulation methods in this study. It showed that 64.6% LCMs exhibited PBT (persistent, bioaccumulative, and toxic) properties. Based on the results, 1173 LCMs were identified as molecules possessing the highest level of acute toxicity to aquatic organisms. Among which, and a human health risk priority control list about LCMs was generated in this study, among which 435 were classified as requiring priority control LCMs. It was confirmed that LCMs could eventually accumulate in the human body along the aquatic food chain or penetrate the bloodstream through the dermis, thereby causing harm to health by identifying the exposure pathways of LCMs in humans. Additionally, the electronegativity of the side chain group of LCMs is the main factor causing toxicity differences; therefore, the LCMs containing halogens presented significant acute and chronic toxic effects. This study provided a more comprehensive understanding of LCMs for the public and scientific strategies for controlling LCMs.

Application and Progress of Machine Learning in Pesticide Hazard and Risk Assessment

Long-term exposure to pesticides is associated with the incidence of cancer. With the exponential increase in the number of new pesticides being synthesized, it becomes more and more important to evaluate the toxicity of pesticides by means of simulated calculations. Based on existing data, machine learning methods can train and model the predictions of the effects of novel pesticides, which have limited available data. Combined with other technologies, this can aid the synthesis of new pesticides with specific active structures, detect pesticide residues, and identify their tolerable exposure levels. This article mainly discusses support vector machines, linear discriminant analysis, decision trees, partial least squares, and algorithms based on feedforward neural networks in machine learning. It is envisaged that this article will provide scientists and users with a better understanding of machine learning and its application prospects in pesticide toxicity assessment.

QSAR and Chemical Read-Across Analysis of 370 Potential MGMT Inactivators to Identify the Structural Features Influencing Inactivation Potency

O6-methylguanine-DNA methyltransferase (MGMT) constitutes an important cellular mechanism for repairing potentially cytotoxic DNA damage induced by guanine O6-alkylating agents and can render cells highly resistant to certain cancer chemotherapeutic drugs. A wide variety of potential MGMT inactivators have been designed and synthesized for the purpose of overcoming MGMT-mediated tumor resistance. We determined the inactivation potency of these compounds against human recombinant MGMT using [3H]-methylated-DNA-based MGMT inactivation assays and calculated the IC50 values. Using the results of 370 compounds, we performed quantitative structure-activity relationship (QSAR) modeling to identify the correlation between the chemical structure and MGMT-inactivating ability. Modeling was based on subdividing the sorted pIC50 values or on chemical structures or was random. A total of nine molecular descriptors were presented in the model equation, in which the mechanistic interpretation indicated that the status of nitrogen atoms, aliphatic primary amino groups, the presence of O-S at topological distance 3, the presence of Al-O-Ar/Ar-O-Ar/R..O..R/R-O-C=X, the ionization potential and hydrogen bond donors are the main factors responsible for inactivation ability. The final model was of high internal robustness, goodness of fit and prediction ability (R2pr = 0.7474, Q2Fn = 0.7375-0.7437, CCCpr = 0.8530). After the best splitting model was decided, we established the full model based on the entire set of compounds using the same descriptor combination. We also used a similarity-based read-across technique to further improve the external predictive ability of the model (R2pr = 0.7528, Q2Fn = 0.7387-0.7449, CCCpr = 0.8560). The prediction quality of 66 true external compounds was checked using the "Prediction Reliability Indicator" tool. In summary, we defined key structural features associated with MGMT inactivation, thus allowing for the design of MGMT inactivators that might improve clinical outcomes in cancer treatment.

Hazard screening of contaminants of emerging concern (CECs) in Sweden's three largest lakes and their associated rivers

Persistent, mobile, and toxic (PMT) substances have recently garnered increased attention by environmental researchers, the water sector and environmental protection agencies. In this study, acute and chronic species sensitivity distributions (SSDs) were retrieved from literature data for previously quantified contaminants of emerging concern (CECs) in Swedish surface waters (n = 92) and risk quotients (RQ) were calculated. To better understand the characteristics of the detected CECs in non-urban lake sites (n = 71), these compounds were checked against established criteria for potentially toxic PMs (PM(T)s) and occurrence in the aquatic environment, respectively. For the CECs with missing SSDs (n = 15 [acute], n = 41 [chronic]), ecotoxicity data were extracted for eight taxonomic groups, and if data were sufficient (n ≥ 3), SSDs were derived. The retrieved and newly developed SSDs were then used in an environmental hazard assessment (EHA) in the investigated Swedish rivers and lakes. In the rivers, 8 CECs had RQ> 1 in at least one location, and 20 CECs posed a moderate risk (0.01 < RQ < 1). In total, 21 of the 71 detected substances had already been identified as PM(T)/vPvM substances. Our study shows the importance of studying field data at large spatial scale to reveal potential environmental hazards far from source areas.

Electrodegradation of cyclophosphamide in artificial urine by combined methods

The degradation of the chemotherapeutic drug cyclophosphamide in artificial urine was evaluated by Electrochemical Advanced Oxidation Processes (EAOP). The system consisted of an electrochemical flow reactor with a commercial DSA^® electrode (nominal composition Ti / Ru_0,3Ti_0,7O₂) and Ti-mesh cathode. In order to assess the best parameters, the effect of current density, time and flow rate were analyzed using an initial 2³ factorial design. The chosen response variable was the energy efficiency to produce free chlorine species (HClO/ClO^-). After obtaining the most significant factors, the Central Composite Design (CCD) was performed, where the optimum conditions were determined for the current density range (11.714 mA cm^-2 and 66.57 mA cm^-2), flow rate (31.33 mL min^-1) and time range (19 and 37 min). Under an optimized condition, the efficiency of other combined methods (photo-assisted electrochemical, photochemical, sonoelectrochemical and photo-assisted sonoelectrochemical) was evaluated. The efficiency of degradation processes was determined by removal of Chemical Oxygen Demand (COD), creatinine and urea. Analysis by HPLC demonstrates that the cyclophosphamide was substantially removed during the treatment process of ∼77%. Based on these results, it can be observed that the coupling between electrochemical and photochemical processes is a promising alternative for the treatment of this effluent, as a marked reduction of organic matter is observed (63, 94% of creatinine, 29.62% of urea, 39.1% of TOC) and a low treatment cost ratio.

Potential Impact of Environmental Pollution by Human Antivirals on Avian Influenza Virus Evolution

Antiviral (AV) drugs are the main line of defense against pandemic influenza. However, different administration policies are applied in countries with different stocks of AV drugs. These policies lead to different occurrences of drug metabolites in the aquatic environment, altering animal behavior with evolutionary consequences on viruses. The aim of this study was to investigate the potential impact of environmental pollution by human antivirals, such as oseltamivir carboxylate (OC), on the evolutionary rate of avian influenza. We used NA, HA, NP, and MP viral segments from two groups of neighboring countries sharing migratory routes of wild birds and characterized by different AV stockpiles. BEAST analyses were performed using the uncorrelated lognormal clock evolutionary model and the Bayesian skyline tree prior model. The ratios between the rate of evolution of the NA gene and the HA, NP, and MP segments were considered. The two groups of countries were compared by analyzing the differences in the ratio distributions. Our analyses highlighted a possible different behavior in the evolution of H5N1 2.3 clade viral strains when OC environmental pollution is present. In conclusion, the widespread consumption of antivirals and their presence in wastewater could influence the selective pressure on viruses.

Development of reliable quantitative structure-toxicity relationship models for toxicity prediction of benzene derivatives using semiempirical descriptors

The Health and environmental hazards of benzene and nitrobenzene (NB) derivatives have remained a topic of interest of researchers. In silico methods for prediction of toxicity of chemicals have proved their worth in accurate forecast of environmental as well as health toxicity and are strongly recommended by regulatory authorities. Two quantitative structure-toxicity relationship (QSTR) models explaining Scenedesmus obliquus toxicity trends among 39 benzene derivatives and Tetrahymena pyriformis toxicity of 103 NB and 392 benzene derivatives are developed using semiempirical quantum chemical parameters. The best constructed QSTR models have good fitting ability (R² = 0.8053, 0.7591, and 0.8283) and robustness (Q²_LOO = 0.7507, 0.7227, and 0.8194; Q²_LMO = 0.7338, 0.7153, and 0.8172). The external predictivity of all the models are quite good (R²_EXT = 0.8256, 0.9349, and 0.8698). Electronegativity, Cosmo volume, total energy, and molecular weight are responsible for the increase and decrease of toxicity of benzene derivatives against S. obliquus while electronegativity, electrophilicity index, the heat of formation, total energy, hydrophobicity, and cosmo volume are responsible for modulation of toxicity of NB and benzene derivatives toward T. pyriformis. These models fulfill the requirements of all the five OECD principles.

Ecotoxicological QSAR modelling of the acute toxicity of fused and non-fused polycyclic aromatic hydrocarbons (FNFPAHs) against two aquatic organisms: Consensus modelling and comparison with ECOSAR

Fused and non-fused polycyclic aromatic hydrocarbons (FNFPAHs) are a type of organic compounds widely occurring in the environment that pose a potential hazard to ecosystem and public health, and thus receive extensive attention from various regulatory agencies. Here, quantitative structure-activity relationship (QSAR) models were constructed to model the ecotoxicity of FNFPAHs against two aquatic species, Daphnia magna and Oncorhynchus mykiss. According to the stringent OECD guidelines, we used genetic algorithm (GA) plus multiple linear regression (MLR) approach to establish QSAR models of the two aquatic toxicity endpoints: D. magna (48 h LC₅₀) and O. mykiss (96 h LC₅₀). The models were established using simple 2D descriptors with explicit physicochemical significance and evaluated using various internal/external validation metrics. The results clearly show that both models are statistically robust (Q_LOO² = 0.7834 for D. magna and Q_LOO² = 0.8162 for O. mykiss), have good internal fitness (R² = 0.8159 for D. magna and R² = 0.8626 for O. mykiss and external predictive ability (D. magna: R_test² = 0.8259, Q_Fn² = 0.7640∼0.8140, CCC_test = 0.8972; O. mykiss:R_test² = 0.8077, Q_Fn² = 0.7615∼0.7722, CCC_test = 0.8910). To prove the predictive performance of the developed models, an additional comparison with the standard ECOSAR tool obviously shows that our models have lower RMSE values. Subsequently, we utilized the best models to predict the true external set compounds collected from the PPDB database to further fill the toxicity data gap. In addition, consensus models (CMs) that integrate all validated individual models (IMs) were more externally predictive than IMs, of which CM2 has the best prediction performance towards the two aquatic species. Overall, the models presented here could be used to evaluate unknown FNFPAHs inside the domain of applicability (AD), thus being very important for environmental risk assessment under current regulatory frameworks.

Healthcare Waste-A Serious Problem for Global Health

Healthcare waste (HCW) is generated in different healthcare facilities (HCFs), such as hospitals, laboratories, veterinary clinics, research centres and nursing homes. It has been assessed that the majority of medical waste does not pose a risk to humans. It is estimated that 15% of the total amount of produced HCW is hazardous and can be infectious, toxic or radioactive. Hazardous waste is a special type of waste which, if not properly treated, can pose a risk to human health and to the environment. HCW contains potentially harmful microorganisms that can be spread among healthcare personnel, hospital patients and the general public, causing serious illnesses. Healthcare personnel are the specialists especially exposed to this risk. The most common medical procedure, which pose the highest risk, is injection (i.e, intramuscular, subcutaneous, intravenous, taking blood samples). The World Health Organization (WHO) estimates that around 16 billion injections are administered worldwide each year. However, if safety precautions are not followed, and needles and syringes are not properly disposed of, the risk of sharps injuries increases among medical staff, waste handlers and waste collectors. What is more, sharps injuries increase the risk of human immunodeficiency virus (HIV), hepatitis B and C viruses (HBV/HCV), tuberculosis (TB), diphtheria, malaria, syphilis, brucellosis and other transmissions. Disposing of medical waste in a landfill without segregation and processing will result in the entry of harmful microorganisms, chemicals or pharmaceuticals into soil and groundwater, causing their contamination. Open burning or incinerator malfunctioning will result in the emission of toxic substances, such as dioxins and furans, into the air. In order to reduce the negative impact of medical waste, waste management principles should be formulated. To minimize health risks, it is also important to build awareness among health professionals and the general public through various communication and educational methods. The aim of this paper is to present a general overwiev of medical waste, its categories, the principles of its management and the risks to human health and the environment resulting from inappropriate waste management.

Aquatic toxicity prediction of diverse pesticides on two algal species using QSTR modeling approach

With the aim of identification of toxic nature of the diverse pesticides on the aquatic compartment, a large dataset of pesticides (n = 325) with experimental toxicity data on two algal test species (Pseudokirchneriella subcapitata (PS) (synonym: Raphidocelis subcapitata, Selenastrum capricornutum) and Scenedemus subspicatus (SS)) was gathered and subjected to quantitative structure toxicity relationship (QSTR) analysis to predict aquatic toxicity of pesticides. The QSTR models were developed by multiple linear regressions (MLRs), and the genetic algorithm (GA) was used for the variable selection. The developed GA-MLR models were statistically robust enough internally (Q²_LOO = 0.620-0.663) and externally (Q²F_n = 0.693-0.868, CCC_ext = 0.843-0.877). The leverage approach of applicability domain (AD) and prediction reliability indicator assured the reliability of the developed models. The mechanistic interpretation highlighted that the presence of SO₂, F and aromatic rings influenced the toxicity of pesticides towards PS species while the presence of alkyl, alkyl halide, aromatic rings and carbonyl was responsible for the toxicity of pesticides towards SS species. Additionally, we have reported the application of developed models to pesticides without experimental value and the cumulative toxicity of pesticides on the aquatic environment by using principal component analysis (PCA). The reliable prediction and prioritization of toxic compounds from the developed models will be useful in the aquatic toxicity assessment of pesticides.

Occurrence and mass flows of contaminants of emerging concern (CECs) in Sweden's three largest lakes and associated rivers

Contaminants of emerging concern (CECs) are a concern in aquatic environments due to possible adverse effects on the environment and humans. This study assessed the occurrence and mass flows of CECs in Sweden's three largest lakes and 24 associated rivers. The occurrence and distribution of 105 CECs was investigated, comprising 71 pharmaceuticals, 13 perfluoroalkyl substances (PFASs), eight industrial chemicals, four personal care products (PCPs), three parabens, two pesticides, and four other CECs (mostly anthropogenic markers). This is the first systematic study of CECs in Sweden's main lakes and one of the first to report environmental concentrations of the industrial chemicals tributyl citrate acetate and 2,2'-dimorpholinyldiethyl-ether. The ∑CEC concentration was generally higher in river water (31-5200 ng/L; median 440 ng/L) than in lake water (36-900 ng/L; median 190 ng/L). At urban lake sites, seasonal variations were observed for PCPs and parabens, and also for antihistamines, antidiabetics, antineoplastic agents, antibiotics, and fungicides. The median mass CEC load in river water was 180 g/day (range 4.0-4300 g/day), with a total mass load of 5000 g/day to Lake Vänern, 510 g/day to Lake Vättern, and 5600 g/day to Lake Mälaren. All three lakes are used as drinking water reservoirs, so further investigations of the impact of CECs on the ecosystem and human health are needed.

A synthetical methodology for identifying priority pollutants in reclaimed water based on meta-analysis

Wastewater reclamation and reuse is an increasing global project, while the reclamation treatment on wastewater does not completely remove all pollutants in water. The residual pollutants in reclaimed water would cause potential risk on human health and ecosystem safety during the long-term use. It is impossible to analyze and control all pollutants one by one in practice, therefore, identification and control of priority pollutants will be efficient strategy to ensure the safe use of reclaimed water. An integrated three-step methodology for identifying priority pollutants in reclaimed water was proposed in this study. First, a comprehensive literature survey on the occurrence of pollutants in reclaimed water was conducted, and a dataset D_PR for pollutants occurrence in reclaimed water was established, containing 1,113 pollutants. Second, 611 chemicals that had been recommended as hazardous pollutants for various water bodies in previous literatures were summarized, and a dataset D_HP for hazardous pollutants in water was obtained. Third, meta-analysis on these two datasets (D_PR and D_HP) was performed, a new dataset D_HPR for hazardous pollutants in reclaimed water was established, including 265 candidates. Finally, 59 substances out of dataset D_HPR were identified as priority pollutants for reclaimed water based on their recommendation frequency. It is expected that this synthetical methodology will provide powerful support for scientific evaluating and managing water pollution and ensuring safe use of reclaimed water.

Antibiotics, antibiotic-resistant bacteria, and resistance genes in aquaculture: risks, current concern, and future thinking

Aquaculture is remarkably one of the most promising industries among the food-producing industries in the world. Aquaculture production as well as fish consumption per capita have been dramatically increasing over the past two decades. Shifting of culture method from semi-intensive to intensive technique and applying of antibiotics to control the disease outbreak are the major factors for the increasing trend of aquaculture production. Antibiotics are usually present at subtherapeutic levels in the aquaculture environment, which increases the selective pressure to the resistant bacteria and stimulates resistant gene transfer in the aquatic environment. It is now widely documented that antibiotic resistance genes and resistant bacteria are transported from the aquatic environment to the terrestrial environment and may pose adverse effects on human and animal health. However, data related to antibiotic usage and bacterial resistance in aquaculture is very limited or even absent in major aquaculture-producing countries. In particular, residual levels of antibiotics in fish and shellfish are not well documented. Recently, some of the countries have already decided the maximum residue levels (MRLs) of antibiotics in fish muscle or skin; however, many antibiotics are yet not to be decided. Therefore, an urgent universal effort needs to be taken to monitor antibiotic concentration and resistant bacteria particularly multiple antibiotic-resistant bacteria and to assess the associated risks in aquaculture. Finally, we suggest to take an initiative to make a uniform antibiotic registration process, to establish the MRLs for fish/shrimp and to ensure the use of only aquaculture antibiotics in fish and shellfish farming globally.

Systematic QSAR and iQCCR modelling of fused/non-fused aromatic hydrocarbons (FNFAHs) carcinogenicity to rodents: reducing unnecessary chemical synthesis and animal testing

The prediction of new or untested FNFAHs will reduce unnecessary chemical synthesis and animal testing, and contribute to the design of safer chemicals for production activities.

Unswerving modeling of hepatotoxicity of cadmium containing quantum dots using amalgamation of quasiSMILES, index of ideality of correlation, and consensus modeling

Liver toxicity of quantum dots varies with size, concentration, and other structural as well as experimental parameters. For modeling hepatotoxicity, the eclectic data associated with cadmium containing quantum dots have been used in the creation of quasiSMILES for their representation. The core diameter is normalized for wider applicability and the index of the ideality of correlation is applied to construct the better quantitative features toxicity relationship models. Total eight splits are created and the best model is obtained through split 1 with better prediction criteria of validation set objects. The values of all statistical criteria used in the quality determination of a QSAR model are within the specified range for all the eight toxicity models developed here. Factors like TGA ligand and 0.6-0.7 nm diameter are favorable for liver toxicity while L-cysteine ligand and 0.5-0.6 nm core diameter are helpful in the reduction of toxicity. Further, the intelligent consensus modeling process forms a total of 40 individual and 20 consensus models and the best individual and consensus models are 'Good' in MAE-based criteria. The consensus modeling enhances the prediction ability as well as the accuracy of the developed models and increases the applicability space of the built models for hepatotoxicity prediction of quantum dots.

Performance Comparison between the Specific and Baseline Prediction Models of Ecotoxicity for Pharmaceuticals: Is a Specific QSAR Model Inevitable?

Assessing the ecotoxicity of pharmaceuticals is of urgent need due to the recognition of their possible adverse effects on nontarget organisms in the aquatic environment. The reality of ecotoxicity data scarcity promotes the development and application of quantitative structure activity relationship (QSAR) models. In the present study, we aimed to clarify whether a QSAR model of ecotoxicity specifically for pharmaceuticals is needed considering that pharmaceuticals are a class of chemicals with complex structures, multiple functional groups, and reactive properties. To this end, we conducted a performance comparison of two previously developed and validated QSAR models specifically for pharmaceuticals with the commonly used narcosis toxicity prediction model, i.e., Ecological Structure Activity Relationship (ECOSAR), using a subset of pharmaceuticals produced in China that had not been included in the training datasets of QSAR models under consideration. A variety of statistical measures demonstrated that the pharmaceutical specific model outperformed ECOSAR, indicating the necessity of developing a specific QSAR model of ecotoxicity for the active pharmaceutical contaminants. ECOSAR, which was generally used to predict the baseline or the minimum toxicity of a compound, generally underestimated the ecotoxicity of the analyzed pharmaceuticals. This could possibly be because some pharmaceuticals can react through specific modes of action. Nonetheless, it should be noted that 95% prediction intervals spread over approximately four orders of magnitude for both tested QSAR models specifically for pharmaceuticals.

QSARINS-Chem standalone version: A new platform-independent software to profile chemicals for physico-chemical properties, fate, and toxicity

The new software QSARINS-Chem standalone version is a multiplatform tool, freely downloadable, for the in silico profiling of multiple properties and activities of organic chemicals. This software, which is based on the concept of the QSARINS-chem module embedded in the QSARINS software, has been fully redesigned and redeveloped in the Java™ language. In addition to a selection of models included in the old module, the new software predicts biotransformation rates and aquatic toxicities of pharmaceuticals and personal care products in multiple organisms, and offers a suite of tools for the analysis of predictions. Furthermore, a comprehensive and transparent database of molecular structures is provided. The new QSARINS-Chem standalone version is an informative and solid tool, which is useful to support the assessment of the potential hazard and risks related to organic chemicals and is dedicated to users which are interested in the application of QSARs to generate reliable predictions.

Ecotoxicological risk assessment of contaminants of emerging concern identified by "suspect screening" from urban wastewater treatment plant effluents at a territorial scale

Urban wastewater treatment plants (WWTP) are a major vector of highly ecotoxic contaminants of emerging concern (CECs) for urban and sub-urban streams. Ecotoxicological risk assessments (ERAs) provide essential information to public environmental authorities. Nevertheless, ERAs are mainly performed at very local scale (one or few WWTPs) and on pre-selected list of CECs. To cope with these limits, the present study aims to develop a territorial-scale ERA on CECs previously identified by a "suspect screening" analytical approach (LC-QToF-MS) and quantified in the effluents of 10 WWTPs of a highly urbanized territory during three periods of the year. Among CECs, this work focused on pharmaceutical residue and pesticides. ERA was conducted following two complementary methods: (1) a single substance approach, based on the calculation for each CEC of risk quotients (RQs) by the ratio of Predicted Environmental Concentration (PEC) and Predicted No Effect Concentration (PNEC), and (2) mixture risk assessment ("cocktail effect") based on a concentration addition model (CA), summing individual RQs. Chemical results led to an ERA for 41 CEC (37 pharmaceuticals and 4 pesticides) detected in treated effluents. Single substance ERA identified 19 CECs implicated in at least one significant risk for streams, with significant risks for DEET, diclofenac, lidocaine, atenolol, terbutryn, atorvastatin, methocarbamol, and venlafaxine (RQs reaching 39.84, 62.10, 125.58, 179.11, 348.24, 509.27, 1509.71 and 3097.37, respectively). Mixture ERA allowed the identification of a risk (RQmix > 1) for 9 of the 10 WWTPs studied. It was also remarked that CECs leading individually to a negligible risk could imply a significant risk in a mixture. Finally, the territorial ERA showed a diversity of risk situations, with the highest concerns for 3 WWTPs: the 2 biggest of the territory discharging into a large French river, the Rhône, and for the smallest WWTP that releases into a small intermittent stream.

Emerging contaminants detected in aquaculture sites in Java, Indonesia

Pharmaceuticals of emerging concern (acetaminophen (ACM), trimethoprim (TMP), oxytetracycline (OTC), and sulfamethoxazole (SMX)) were detected in water samples from aquaculture environments and nonaquaculture sites in four regions located on the northern coast of Central Java. ACM was the most prevalent pharmaceutical, with a mean concentration ranging from not detected (n.d.) to 5.5 ± 1.9 ngL^-1 (Brebes). Among the target antibiotics (TMP, OTC, SMX), OTC was the most ubiquitous, with a mean concentration varying from n.d. to 8.0 ± 3.3 ngL^-1. Correlation analysis demonstrated that there was a significant correlation between TMP and SMX concentrations. Based on ecological risk assessment evaluation, the use of OTC requires serious consideration, as it presented high health risks to algae, while ACM, TMP, and SMX posed an insignificant to moderate risk to algae, invertebrates, and fish. The findings obtained from this study highlight OTC as an emerging contaminant of prominent concern. More attention needs to be given to managing and planning for the sustainable management of shrimp farms, particularly in the northern part of Central Java.

Suspect screening of environmental contaminants by UHPLC-HRMS and transposable Quantitative Structure-Retention Relationship modelling

A Quantitative Structure-Retention Relationship (QSRR) model is proposed and aims at increasing the confidence level associated to the identification of organic contaminants by Ultra-High Performance Liquid Chromatography hyphenated to High Resolution Mass Spectrometry (UHPLC-HRMS) in environmental samples under a suspect screening approach. The model was built from a selection of 8 easily accessible physicochemical descriptors, and was validated from a set of 274 organic compounds commonly found in environmental samples. The proposed predictive figure approach is based on the mobile phase composition at solute elution (expressed as % acetonitrile), that has the major advantage of making the model reusable by other laboratories, since the elution composition is independent of both the column geometry and the UHPLC-system. The model quality was assessed and was altered neither by the columns from different lots, nor by the complex matrices of environmental water samples. Then, the solute retention of any organic compound present in water samples is expected to be predicted within ± 14.3% acetonitrile by our model. Solute retention can therefore be used as a supplementary tool for the identification of environmental contaminants by UHPLC-HRMS, in addition to mass spectrometry data already used in the suspect screening approach.

Impacts of Oxazepam on Perch (Perca fluviatilis) Behavior: Fish Familiarized to Lake Conditions Do Not Show Predicted Anti-anxiety Response

A current theory in environmental science states that dissolved anxiolytics (oxazepam) from wastewater effluents can reduce anti-predator behavior in fish with potentially negative impacts on prey fish populations. Here, we hypothesize that European perch (Perca fluviatilis) populations being exposed to oxazepam in situ show reduced anti-predator behavior, which has previously been observed for exposed isolated fish in laboratory studies. We tested our hypothesis by exposing a whole-lake ecosystem, containing both perch (prey) and northern pike (Esox lucius; predator), to oxazepam while tracking fish behavior before and after exposure in the exposed lake as well as in an unexposed nearby lake (control). Oxazepam concentrations in the exposed lake ranged between 11 and 24 μg L^-1, which is >200 times higher than concentrations reported for European rivers. In contrast to our hypothesis, we did not observe an oxazepam-induced reduction in anti-predator behavior, inferred from perch swimming activity, distance to predators, distance to conspecifics, home-range size, and habitat use. In fact, exposure to oxazepam instead stimulated anti-predator behavior (decreased activity, decreased distance to conspecifics, and increased littoral habitat use) when using behavior in the control lake as a reference. Shoal dynamics and temperature changes may have masked modest reductions in anti-predator behavior due to oxazepam. Although we cannot fully resolve the mechanism(s) behind our observations, our results indicate that the effects of oxazepam on perch behavior in a familiar natural ecosystem are negligible in comparison to the effects of other environmental conditions.

Ecosystem ecology: Models for acute toxicity of pesticides towards Daphnia magna

Quantitative structure - activity relationships (QSARs) which are obtained with a representation of the molecular architecture via simplified molecular input-line entry system (SMILES) are applied to build up predictive models of acute toxicity of pesticides towards Daphnia magna. The acute toxicity towards Daphnia magna is an adequate measure of the ecological impact of various substances. The Monte Carlo technique is the basis to build up the above QSAR models. The statistical quality of suggested models is good: the best model is characterized by n = 103, R² = 0.76, RMSE = 0.91 (training set); n = 53, R² = 0.82, RMSE = 0.87 (validation set). The approach provides the mechanistic interpretation (e.g. aromaticity and branching of carbon skeleton are promoters of increase for toxicity towards Daphnia magna in the case of the examined set of pesticides). The approach is attractive to build up predictive models since instead of a large number of different molecular descriptors the corresponding model is based on solely one optimal descriptor calculated with SMILES and all necessary calculations can be done using the CORAL software available on the Internet (http://ww.insilico.eu/coral).

QSAR modeling the toxicity of pesticides against Americamysis bahia

The widespread use of pesticides has received increasing attention in regulatory agencies because their extensive overuse and various adverse effects on all living organisms. Organizations such as EPA and ECHA have published laws that pesticides should be fully evaluated before bring them to market. In the present study, we evaluated the pesticides toxicity using the Quantitative Structural-Activity Relationship (QSAR) method. The models for the single class pesticides (herbicides, insecticides and fungicides) as well as the general class pesticides (the combined dataset plus some microbicides, molluscicides, etc.) were developed using the Genetic Algorithm and Multiple Linear Regression method. The internal and external validation results suggested that all the obtained models were stable and predictive. According to the modeling descriptors, the lipophilic descriptors contributed positively while all the electrotopological state descriptors showed a negative contribution, their presences in every model verified the conspicuous influence of molecular lipophilicity and hydrophilicity on the pesticides toxicity. However, the influence of topological structure descriptors was different and varies with the physiochemical information they encode. Finally, the models presented in this paper would help assess the pesticides toxicity against Americamysis bahia, shorten test time, and reduce the cost of pesticides risk assessment.

Principles of QSAR Modeling

At the end of her academic career, the author summarizes the main aspects of QSAR modeling, giving comments and suggestions according to her 23 years' experience in QSAR research on environmental topics. The focus is mainly on Multiple Linear Regression, particularly Ordinary Least Squares, using a Genetic Algorithm for variable selection from various theoretical molecular descriptors, but the comments can be useful also for other QSAR methods. The need for rigorous validation, also external, and for applicability domain check to guarantee predictivity and reliability of QSAR models is particularly highlighted. The commented approach is the “predictive” one, based on chemometrics, and is usefully applied to the prioritization of environmental pollutants. All the discussed points and the author's ideas are implemented in the software QSARINS, as a legacy to the QSAR community.

Occurrence and distribution of priority pharmaceuticals in the Yellow River and the Huai River in Henan, China

The occurrence and spatial distribution of priority pharmaceuticals (PPs) in water samples from the Yellow River and the Huai River in the Henan region of China were investigated in this study. The concentration of the total PPs (ΣPPs; sum of the 10 observed PPs) ranged from not detected to 3474 ng L^-1 in samples from the Yellow River and from 4.35 to 146 ng L^-1 in samples from the Huai River. The level of the ΣPPs in the Huai River was much lower than that found in the Yellow River. The composition of the PPs differed between the two rivers. Norfloxacin, carbamazepine, and 5,5-diphenylhydantoin were detected at high concentrations in the Yellow River, whereas sulfamethazine, ampicillin trihydrate, carbamazepine, and 5,5-diphenylhydantoin were the dominant species in the Huai River, suggesting there were different pollution sources. In comparison to other studies around China, most of the PPs in water samples from the Yellow River and the Huai River were at low concentrations, except for norfloxacin and ofloxacin. There were significant seasonal variations among the PPs in water samples from the Huai River, whereas spatial distinctions were recorded among the PPs in the Yellow River. Dissolved organic carbon content did not correlate with the PPs in the studied area.

Application of chemometric methods and QSAR models to support pesticide risk assessment starting from ecotoxicological datasets

The EFSA 'Guidance on tiered risk assessment for edge-of-field surface waters' underscores the importance of in silico models to support the pesticide risk assessment. The aim of this work was to use in silico models starting from an available, structured and harmonized pesticide dataset that was developed for different purposes, in order to stimulate the use of QSAR models for risk assessment. The present work focuses on the development of a set of in silico models, developed to predict the aquatic toxicity of heterogeneous pesticides with incomplete/unknown toxic behavior in the water compartment. The generated models have good fitting performances (R²: 0.75-0.99), they are internally robust (Q²loo: 0.66-0.98) and can handle up to 30% of perturbation of the training set (Q² lmo: 0.64-0.98). The absence of chance correlation was guaranteed by low values of R² calculated on scrambled responses (R² Y_scr: 0.11-0.38). Different statistical parameters were used to quantify the external predictivity of the models (CCC_ext: 0.73-0.91, Q² ext-Fn: 0.53-0.96). The results indicate that all the best models are predictive when applied to chemicals not involved in the models development. In addition, all models have similar accuracy both in fitting and in prediction and this represents a good degree of generalization. These models may be useful to support the risk assessment procedure when experimental data for key species are missing or to create prioritization lists for the general a priori assessment of the potential toxicity of existing and new pesticides which fall in the applicability domain.

Modeling pesticides toxicity to Sheepshead minnow using QSAR

Nowadays, the environmental risk caused by the widespread use of pesticides and their ubiquitous residuals has received more and more attention in academia and regulatory agencies. Due to the large number of pesticides used in agriculture and their adverse effects on all living organisms and the numerous end-points, it is necessary to employ the in silico tools to quickly highlight hazardous pesticides. In this study, we have evaluated the toxicity of pesticides against Sheepshead minnow with the Quantitative Structure-Activity Relationship (QSAR) approach. The models for the specific-type (insecticides, herbicides and fungicides) as well as the general-type (combing all the specific-type pesticides and some microbicides, nematicides, etc.) pesticides were developed using the Genetic Algorithm and the Multiple Linear Regression method, subsequently validated with various metrics. The validation results suggested that the obtained models were highly robust, externally predictive and characterized by a broad applicability domain. Considering the modeling descriptors, the toxicity of pesticides would increase with the lipophilicity and decrease with the polarity and hydrophilicity. Most electrotopological state descriptors contribute negatively to the toxicity, while the influence of topological structure descriptors mainly depends on the physiochemical information they encode. The models proposed in this paper would be useful in filling the data gaps, prioritizing and then focusing experiments on more hazardous pesticides.

Estimate ecotoxicity characterization factors for chemicals in life cycle assessment using machine learning models

In life cycle assessment, characterization factors are used to convert the amount of the chemicals and other pollutants generated in a product's life cycle to the standard unit of an impact category, such as ecotoxicity. However, as a widely used impact assessment method, USEtox (version 2.11) only has ecotoxicity characterization factors for a small portion of chemicals due to the lack of laboratory experiment data. Here we develop machine learning models to estimate ecotoxicity hazardous concentrations 50% (HC₅₀) in USEtox to calculate characterization factors for chemicals based on their physical-chemical properties in EPA's CompTox Chemical Dashborad and the classification of their mode of action. The model is validated by ten randomly selected test sets that are not used for training. The results show that the random forest model has the best predictive performance. The average root mean squared error of the estimated HC₅₀ on the test sets is 0.761. The average coefficient of determination (R²) on the test set is 0.630, meaning 63% of the variability of HC₅₀ in USEtox can be explained by the predicted HC₅₀ from the random forest model. Our model outperforms a traditional quantitative structure-activity relationship (QSAR) model (ECOSAR) and linear regression models. We also provide estimates of missing ecotoxicity characterization factors for 552 chemicals in USEtox using the validated random forest model.

Reliable and representative in silico predictions of freshwater ecotoxicological hazardous concentrations

A reliable quantification of the potential effects of chemicals on freshwater ecosystems requires ecotoxicological response data for a large set of species which is typically not available in practice. In this study, we propose a method to estimate hazardous concentrations (HCs) of chemicals on freshwater ecosystems by combining two in silico approaches: quantitative structure activity relationships (QSARs) and interspecies correlation estimation (ICE) models. We illustrate the principle of our QSAR-ICE method by quantifying the HCs of 51 chemicals at which 50% and 5% of all species are exposed above the concentration causing acute effects. We assessed the bias of the HCs, defined as the ratio of the HC based on measured ecotoxicity data and the HC based on in silico data, as well as the statistical uncertainty, defined as the ratio of the 95th and 5th percentile of the HC. Our QSAR-ICE method resulted in a bias that was comparable to the use of measured data for three species, as commonly used in effect assessments: the average bias of the QSAR-ICE HC50 was 1.2 and of the HC5 2.3 compared to 1.2 when measured data for three species were used for both HCs. We also found that extreme statistical uncertainties (>10⁵) are commonly avoided in the HCs derived with the QSAR-ICE method compared to the use of three measurements with statistical uncertainties up to 10¹². We demonstrated the applicability of our QSAR-ICE approach by deriving HC50s for 1,223 out of the 3,077 organic chemicals of the USEtox database. We conclude that our QSAR-ICE method can be used to determine HCs without the need for additional in vivo testing to help prioritise which chemicals with no or few ecotoxicity data require more thorough assessment.

Prioritization of pharmaceuticals in water environment in China based on environmental criteria and risk analysis of top-priority pharmaceuticals

Numerous studies have shown that a wide range of pharmaceuticals are present in the environment and many of their adverse biological effects on the aquatic ecosystem and human health are unknown. Due to the high population density and large number of pharmaceuticals produced and consumed in China, a systematic approach is needed to identify pharmaceuticals that require greater attention. The present study provides a ranking of pharmaceuticals in China in terms of their occurrence (O), persistence, bioaccumulation, and toxicity (PBT) based on the predicted environmental concentration (PEC). The total and partial ranking method implemented in the decision analysis by ranking techniques (DART) tool was used, which is an easy-to-use tool for the analysis of datasets. Using the DART approach, 10 pharmaceuticals were selected as priority compounds. These pharmaceuticals included antibiotics, anti-inflammatory and antilipidemic. In order to identify the characteristics of the priority pharmaceuticals, ecotoxicological endpoints were considered. The results of this study and the priority list facilitate the selection of candidate pollutants in future monitoring studies.

Prioritizing environmental risks of pharmaceuticals and personal care products in reclaimed water on urban green space in Beijing

Pharmaceuticals and personal care products (PPCPs) in reclaimed water can enter into soil, groundwater, and air during the irrigation of urban green spaces, leading to potential risks due to their negative effects of feminization, on root elongation, and as carcinogens. In this study, a validated HYDRUS-1D model by field experiments and an exposure model were used to simulate the distributions of 67 PPCPs detected in the effluent from municipal wastewater treatment plants of Beijing under two scenarios (1, uniform irrigation concentrations; 2, detected irrigation concentrations) in soil, groundwater, and air. To determine the priority ranks of the 67 PPCPs, the effect values of the PPCPs in soil, groundwater, and air were calculated on the basis of distributions and toxicity data, and then weighted and scored. Under scenario 1, roxithromycin, medroxyprogesterone acetate, and megestrol acetate, characterized by high adsorption and low volatilization and degradation, had the highest accumulations in soil, and ofloxacin, characterized by the lowest degradation and adsorption, had the highest leaching to groundwater. The highest volatilization was observed for galaxolide abbalide, tonalid, and dioctyl phthalate. Under scenario 2, based on their overall scores and priority ranks, the 67 PPCPs were divided into three groups: I, high priority; II, moderate priority; III, low priority. Of the 67 PPCPs, 17 were classified in group I, with the highest priority rankings for ofloxacin, 17α-ethynylestradiol, dibutyl phthalate, dioctyl phthalate, and sulfamethoxazole. In group III (total 33 PPCPs), 28 of the PPCPs were not of urgent concern under reclaimed water irrigation in Beijing.

Panorama da pesquisa sobre tratamento e reúso de efluentes da indústria de antibióticos

RESUMO Este trabalho realizou uma revisão integrativa de artigos científicos indexados entre 2007 e 2017 em diferentes bases de dados sobre o tratamento e o reúso de efluentes provenientes da indústria de antibióticos. Foram encontrados 31 artigos, sendo que somente 4 abordaram o reúso de efluente, e 1 utilizou um sistema de tratamento em escala real. A maior parte desses estudos foi realizado na Ásia, com destaque para a China. Observa-se que, no Brasil, que é um dos grandes produtores e consumidores de fármacos do mundo, esse tipo de pesquisa ainda é incipiente. Os processos mais encontrados foram os oxidativos avançados que mostraram maior eficiência na remoção de antibióticos, mas podem gerar subprodutos, o que pode representar um risco ainda maior dependendo da substância formada. Os processos biológicos devem ser primeiramente aclimatados aos antibióticos para não serem impactados, entretanto, a liberação desses micro-organismos resistentes no corpo receptor também apresenta um risco ambiental. Os sistemas integrados de membranas ao biológico também foram bem eficientes, mas atenta-se ao risco na destinação final dessas membranas que foram capazes de reter esses compostos. No geral, são necessários mais estudos sobre essa abordagem para reduzir os riscos no desenvolvimento de micro-organismos multirresistentes no meio ambiente.

Bioinspired metal oxide particles as efficient wet air oxidation and photocatalytic oxidation catalysts for the degradation of acetaminophen in aqueous phase

The catalytic performances of the biomimetic metal oxides were tested in photo Fenton-like oxidation and catalytic wet air oxidation processes. Biomimetic copper oxide, iron oxide, and cobalt oxide catalysts were prepared by using pollen grains as biotemplate. The surface characteristics of the biomimetic metal oxides were characterized. SEM micrographs of the biomimetic catalysts demonstrated that pollen grains were successfully mimicked by metal oxide structures. The influences of UV light intensity, catalyst loading, and the initial hydrogen peroxide concentration on acetaminophen degradation were investigated in the photo Fenton-like oxidation process whereas the effects of reaction temperature and catalyst loading were investigated in catalytic wet air oxidation process. The biomimetic copper oxide was the most effective catalyst for the removal of acetaminophen in both of the advanced oxidation processes. The highest acetaminophen degradation efficiency was 86.9% in photo Fenton-like oxidation process when the initial acetaminophen concentration, catalyst loading, and the initial H₂O₂ concentrations were 10 mg/L, 0.1 g/L and 1 mM, respectively, at room temperature. In the catalytic wet air oxidation process, 98.3% degradation was achieved for the treatment of 100 mg/L acetaminophen solutions at 180 °C and 10 bar by using 1 g/L of catalyst loading at the same reaction time as photo Fenton-like oxidation. Mineralization analysis and the toxicity tests indicated that the biomimetic copper oxide catalysts were promising for the acetaminophen removal in catalytic wet air oxidation processes.

Computational prediction of toxicity of small organic molecules: state-of-the-art

The field of computational prediction of various toxicity end-points has evolved over last two decades significantly. Availability of newer modelling techniques, powerful computational resources and good-quality data have made it possible to generate reliable predictions for new chemical entities, impurities, chemicals, natural products and a lot of other substances. The field is still undergoing metamorphosis to take into account molecular complexities underlying toxicity end-points such as teratogenicity, mutagenicity, carcinogenicity, etc. Expansion of the applicability domain of these predictive models into areas other than life sciences, such as environmental and materials sciences have received a great deal of attention from all walks of life, fuelling further development and growth of the field. The present chapter discusses the state-of-the-art computational prediction of toxicity end-points of small organic molecules to balance the trade-off between the molecular complexity and the quality of such predictions, without compromising their immense utility in many fields.

Prioritization Approaches for Substances of Emerging Concern in Groundwater: A Critical Review

Risks from emerging contaminants (ECs) in groundwater to human health and aquatic ecology remain difficult to quantify. The number of ECs potentially found in groundwater presents challenges for regulators and water managers regarding selection for monitoring. This study is the first systematic review of prioritization approaches for selecting ECs that may pose a risk in groundwater. Online databases were searched for prioritization approaches relating to ECs in the aquatic environment using standardized key word search combinations. From a total of 672, 33 studies met the eligibility criteria based primarily on the relevance to prioritizing ECs in groundwater. The review revealed the lack of a groundwater specific contaminant prioritization methodology in spite of widely recognized differences between groundwater and surface water environments with regard to pathways to receptors. The findings highlight a lack of adequate evaluation of methodologies for predicting the likelihood of an EC entering groundwater and knowledge gaps regarding the occurrence and fate of ECs in this environment. The review concludes with a proposal for a prioritization framework for ECs in groundwater monitoring that enables priority lists to be updated as new information becomes available for substances with regard to their usage, physicochemical properties, and hazards.

Predicting the cytotoxicity of disinfection by-products to Chinese hamster ovary by using linear quantitative structure-activity relationship models

A suitable model to predict the toxicity of current and continuously emerging disinfection by-products (DBPs) is needed. This study aims to establish a reliable model for predicting the cytotoxicity of DBPs to Chinese hamster ovary (CHO) cells. We collected the CHO cytotoxicity data of 74 DBPs as the endpoint to build linear quantitative structure-activity relationship (QSAR) models. The linear models were developed by using multiple linear regression (MLR). The MLR models showed high performance in both internal (leave-one-out cross-validation, leave-many-out cross-validation, and bootstrapping) and external validation, indicating their satisfactory goodness of fit (R² = 0.763-0.799), robustness (Q²_LOO = 0.718-0.745), and predictive ability (CCC = 0.806-0.848). The generated QSAR models showed comparable quality on both the training and validation levels. Williams plot verified that the obtained models had wide application domains and covered the 74 structurally diverse DBPs. The molecular descriptors used in the models provided comparable information that influences the CHO cytotoxicity of DBPs. In conclusion, the linear QSAR models can be used to predict the CHO cytotoxicity of DBPs.

Comparison of Prioritisation Schemes for Human Pharmaceuticals in the Aquatic Environment

Only a small proportion of pharmaceuticals available for commercial use have been monitored in the aquatic environment, and even less is known about the effects on organisms. With thousands of pharmaceuticals in use, it is not feasible to monitor or assess the effects of all of these compounds. Prioritisation schemes allow the ranking of pharmaceuticals based on their potential as environmental contaminants, allowing resources to be appropriately used on those which are most likely to enter the environment and cause greatest harm. Many different types of prioritisation schemes exist in the literature and those utilising predicted environmental concentrations (PECs), the fish plasma model (FPM), critical environmental concentrations (CECs) and acute ecotoxicological data were assessed in the current study using the 50 most prescribed drugs in the UK. PECs were found to be overestimates of mean measured environmental concentrations but mainly underestimations of maximum concentrations. Acute ecological data identified different compounds of concern to the other effects assessments although the FPM and CECs methods were more conservative. These schemes highlighted antidepressants, lipid regulators, antibiotics, antihypertensive compounds and ibuprofen as priority compounds for further study and regulation.

Removal of pharmaceutically active compounds from synthetic and real aqueous mixtures and simultaneous disinfection by supported TiO2/UV-A, H2O2/UV-A, and TiO2/H2O2/UV-A processes

Pharmaceutically active compounds are carried into aquatic bodies along with domestic sewage, industrial and agricultural wastewater discharges. Psychotropic drugs, which can be toxic to the biota, have been detected in natural waters in different parts of the world. Conventional water treatments, such as activated sludge, do not properly remove these recalcitrant substances, so the development of processes able to eliminate these compounds becomes very important. Advanced oxidation processes are considered clean technologies, capable of achieving high rates of organic compounds degradation, and can be an efficient alternative to conventional treatments. In this study, the degradation of alprazolam, clonazepam, diazepam, lorazepam, and carbamazepine was evaluated through TiO₂/UV-A, H₂O₂/UV-A, and TiO₂/H₂O₂/UV-A, using sunlight and artificial irradiation. While using TiO₂ in suspension, best results were found at [TiO₂] = 0.1 g L^-1. H₂O₂/UV-A displayed better results under acidic conditions, achieving from 60 to 80% of removal. When WWTP was used, degradation decreased around 50% for both processes, TiO₂/UV-A and H₂O₂/UV-A, indicating a strong matrix effect. The combination of both processes was shown to be an adequate approach, since removal increased up to 90%. H₂O₂/UV-A was used for disinfecting the aqueous matrices, while mineralization was obtained by TiO₂-photocatalysis.

Ozonation of valsartan: Structural elucidation and environmental properties of transformation products

The pharmaceutical valsartan is classified as a trace organic compound and is released into wastewater from human consumption. Trace organic compounds are not completely removed during conventional wastewater treatment. In order to prevent their release into the aquatic environment, advanced wastewater treatment technologies such as ozonation are currently implemented. Ozonation leads to the formation of transformation products (TPs), which then enter the receiving waters. In the present work, laboratory-scale ozonation experiments of valsartan solutions were performed. The resulting TPs were analyzed by HPLC-MS and searched for using a non-targeted approach. Of the 51 compounds detected, 27 have tentative structural suggestions based on MS/MS experiments. Ozonation of valsartan does not lead to the formation of TPs with higher toxicity towards A. fischeri than the parent compound. According to QSAR-based environmental behavior estimations, most TPs reveal lower lipophilicity, increased biodegradability as well as decreased acute and chronic toxicities concerning fish, daphnia and algae compared to their parent compound valsartan.

Consensus QSAR modeling of toxicity of pharmaceuticals to different aquatic organisms: Ranking and prioritization of the DrugBank database compounds

In the present work, quantitative structure-activity relationship (QSAR) models have been developed for ecotoxicity of pharmaceuticals on four different aquatic species namely Pseudokirchneriella subcapitata, Daphnia magna, Oncorhynchus mykiss and Pimephales promelas using genetic algorithm (GA) for feature selection followed by Partial Least Squares regression technique according to the Organization for Economic Co-operation and Development (OECD) guidelines. Double cross-validation methodology was employed for selecting suitable models. Only 2D descriptors were used for capturing chemical information and model building, whereas validation of the models was performed by considering various stringent internal and external validation metrics. Interestingly, models could be developed even without using any LogP terms in contrary to the usual dependence of toxicity on lipophilicity. However, the current manuscript proposes highly robust and more predictive models employing computed logP descriptors. The applicability domain study was performed in order to set a predefined chemical zone of applicability for the obtained QSAR models, and the test compounds falling outside the domain were not taken for further analysis while making a prioritized list. An additional comparison was made with ECOSAR, an online expert system for toxicity prediction of organic pollutants, in order to prove predictability of the obtained models. The obtained robust consensus models were utilized to predict the toxicity of a large dataset of approximately 9300 drug-like molecules in order to prioritize the existing drug-like substances in accordance to their acute predicted aquatic toxicities following a scaling technique. Finally, prioritized lists of 500 most toxic chemicals obtained by respective consensus models and those predicted from ECOSAR tool have been reported.

Occurrence and ecological risk of pharmaceuticals in river surface water of Bangladesh

Pharmaceutical contamination in the aquatic environment is a global issue that affects aquatic animals, micro-organisms and human health. The occurrence and preliminary ecological risk of 12 (11 antibiotics and 1 antiepileptic drug) pharmaceuticals were investigated for the first time in the surface water of the old Brahmaputra River, where open-water-fed aquaculture activities are being practiced in Bangladesh. The pharmaceuticals were quantified by high-performance liquid chromatography tandem mass spectrometry (HPLC-MS/MS), operated with positive electrospray ionization (ESI⁺) and a multiple reaction monitoring (MRM) mode. Nine pharmaceuticals were detected in the river surface water, whereas three were below the limit of detection (LOD). Metronidazole was detected in all the samples with concentrations ranging from 0.05 to 13.51 ng L^-1. Trimethoprim had the second highest frequency of detection (95%) with the highest concentration (17.20 ng L^-1). The ranges of concentration and detection frequency of sulfonamides and macrolides were <LOD-11.35 and <LOD-16.68 ng L^-1; 35-70 and 60-85%, respectively, whereas carbamazepine was in the range of <LOD-8.80 ng L^-1 and had a detection frequency of 65%. The concentrations of sulfamethoxazole, trimethoprim, erythromycin-H₂O and tylosin were distinctly higher in the fed aquaculture areas. The principal component analysis confirmed that fed aquaculture activities contributed most of the pharmaceutical contamination in the river surface water. Hospitals, nursing homes, sewage wastewater or surface runoff from the surrounding areas might all contribute to the presence of metronidazole and carbamazepine. The preliminary ecological risk assessment revealed that sulfamethoxazole, erythromycin-H₂O and tylosin showed medium risk, and carbamazepine displayed low risk to sensitive aquatic organisms for maximum measured concentrations. Thus, this study suggests that pharmaceutical contamination in different rivers and seasons needs to be quantified, and ecological as well as human health risks need to be assessed in Bangladesh.