Toxicity of pharmaceuticals in binary mixtures: Assessment by additive and non-additive toxicity models

Toxicity of selected pharmaceuticals and their mixtures to the aquatic indicators Daphnia magna and Aliivibrio fischeri

Despite the benefits derived from the use of pharmaceuticals, these compounds are currently considered contaminants of emerging concern because of their presence and persistence in the environment. This study aimed to determine the toxicity of 27 pharmaceuticals and the interaction effects of binary mixtures of selected compounds towards two model organisms: the microcrustacean Daphnia magna and the bacterium Aliivibrio fischeri (Microtox test). Six compounds, namely polymyxin B, polymyxin E, fluoxetine, diphenhydramine, clenbuterol and ketoprofen exhibited moderate toxicity towards D. magna. Additionally, three compounds (cefotaxime, polymyxin B, polymyxin E) also showed a moderate toxic effect on A. fischeri. The comparison of such results with model estimations showed inaccuracy in the predicted data, highlighting the relevance of experimental ecotoxicological assays. The assayed mixtures contained four selected drugs of high-hazard according to their reported concentrations in wastewater and surface water (diphenhydramine, trimethoprim, ketoprofen, and fluoxetine); data revealed interactions only in the fluoxetine-containing mixtures for D. magna, while all mixtures showed interactions (mostly synergistic) for Microtox. Chronic effects on the reproduction of D. magna were observed after exposure to fluoxetine and diphenhydramine, although higher sensitivity was determined for the latter, while the mixture of these compounds (which showed acute synergy in both models) also affected the reproduction patterns. Nonetheless, all the effects described at the acute or chronic level (for individual compounds or mixtures) were determined at concentrations higher than commonly reported at environmental levels. This work provides valuable ecotoxicological information for the risk assessment of pharmaceuticals and their mixtures in the environment.

Ecotoxicological study of seven pharmaceutically active compounds: Mixture effects and environmental risk assessment

Pharmaceutically active compounds (PhACs) have been detected in several aquatic compartments, which has been of environmental concern since PhACs can cause adverse effects on the aquatic ecosystem at low concentrations. Despite the variety of PhACs detected in surface water, ecotoxicological studies are non-existent for many of them, mainly regarding their mixture. In addition, water bodies can continuously receive the discharge of raw or treated wastewater with micropollutants. Thus, PhACs are subject to mixture and interactions, potentiating or reducing their toxicity. Therefore, the present study evaluated the toxicity on Aliivibrio fischeri of seven PhACs, which still needs to be explored in the literature. The effects were evaluated for the PhACs individually and for their binary and tertiary mixture. Also, the experimental effects were compared with the concentration addition (CA) and independent action (IA) models. Finally, an environmental risk assessment was carried out. Fenofibrate (FEN), loratadine (LOR), and ketoprofen (KET) were the most toxic, with EC50 of 0.32 mg L-1, 6.15 mg L-1 and 36.8 mg L-1, respectively. Synergistic effects were observed for FEN + LOR, KET + LOR, and KET + FEN + LOR, showing that the CA and IA may underestimate the toxicity. Environmental risks for KET concerning algae, and LOR e 17α-ethynylestradiol (EE2) for crustaceans and fish were high for several locations. Besides, high removals by wastewater treatment technologies are required to achieve the concentrations necessary for reducing KET and LOR risk quotients. Thus, this study contributed to a better understanding of the toxic interactions and environmental risks of PhACs.

Bacterial toxicity of Acetaminophen and Edaravone, and their binary mixtures: experimental and predicted values using traditional and novel Van Laar-based models

In recent years, the presence of Pharmaceutical Active Compounds (PhACs) in ecosystems has become a serious environmental problem due to their capacity to induce harmful effects at extremely low concentrations in both humans and wildlife. Water treatment plants have not been designed to remove these types of compounds efficiently. Thus, the detection of these pollutants is essential to evaluate their negative impacts and is one of the emerging issues in environmental chemistry. The main objective of this study is to determine the bacterial toxicity of two PhACs (both individually and as a mixture) through the quantification of bioluminescence inhibition in the marine bacteria Aliivibrio fischeri, a commonly used method in short-term toxicity tests. In this work, Acetaminophen and Edaravone, two drugs approved by the Food and Drug Administration, have been studied. The acute toxicity of these PhACs has been tested at two exposure times (5 and 15 min) and different concentrations, by estimation of the median effective concentration (EC50) for each individual compound or in combination at different concentrations. Moreover, the EC50 of the binary mixtures Acetaminophen/Edaravone have been forecast using two traditional predictive models, Concentration Addition and Independent Action. The results show that toxicity decreases with exposure time and depends on the concentration tested. Furthermore, a novel semi-empirical Van Laar-based model has been proposed and validated with the experimental data from this study and literature data, obtaining satisfactory estimations of the EC50 for binary mixtures.

Contrasting toxicity response to a mixture of azithromycin and ivermectin between a freshwater and a euryhaline rotifer

Organisms are usually exposed to mixtures of emerging pollutants in aquatic environments. Due to their widespread use and environmental relevance, the individual and combined effects of the drugs azithromycin (AZT) and ivermectin (IVM) on the freshwater rotifer Lecane papuana and the euryhaline rotifer Proales similis were investigated. Rotifers showed greater sensitivity to IVM compared to AZT. The LC50 values of IVM and AZT for L. papuana and P. similis were 0.163 and 0.172 mg/L, and 13.52 and 20.00 mg/L, respectively. Population growth rates, assessed in chronic toxicity assays, responded negatively to increasing concentrations of both toxicants, either individually or in combination. Our results revealed two distinct combined toxicity responses: a strong synergistic effect in the freshwater rotifer and a marked antagonistic impact of the AZT-IVM mixtures in the euryhaline rotifer.

An artificial intelligence-based model for predicting reproductive toxicity of bisphenol analogues mixtures to the rotifer Brachionus calyciflorus

The joint toxicity effects of mixtures, particularly reproductive toxicity, one of the main causes of aquatic ecosystem degradation, are often overlooked as it is impractical to test all mixtures. This study developed and evaluated the following models to predict the concentration response curve concerning the joint reproductive toxicity of mixtures of three bisphenol analogues (BPA, BPF, BPAF) on the rotifer Brachionus calyciflorus: concentration addition (CA), independent action (IA), and two deep neural network (DNN) models. One applied mixture molecular descriptors as input variables (DNN-QSAR), while the other applied the ratios of chemicals in the mixtures (DNN-Ratio). Descriptors related to molecular mass were found to be of greater importance and exhibited a proportional relationship with toxic effects. The results indicate that the range of correlation coefficients (R2) between predicted and measured values for various mixture rays by CA and IA models is 0.372 to 0.974 and - 0.970 to 0.586, respectively. The R2 values for DNN-Ratio and DNN-QSAR were 0.841 to 0.984 and 0.834 to 0.991, respectively, demonstrating that models developed by DNN significantly outperform traditional models in predicting the joint toxicity of mixtures. Furthermore, DNN-QSAR not only predicts mixture toxicity but also provides accurate toxicity predictions for BPA, BPF, and BPAF, with R2 values of 0.990, 0.616, and 0.887, respectively, while DNN-Ratio yields values of 0.920, 0.355, and - 0.495. The study also found that the joint effects of mixtures are primarily influenced by the total concentration of the mixtures, and an increase in total concentration shifts the joint effects towards addition. This study introduces a novel approach to predict joint toxicity and analyze the influencing factors of joint effects, providing a more comprehensive assessment of the ecological risk posed by mixtures.

Multi-Endpoint Acute Toxicity Assessment of Organic Compounds Using Large-Scale Machine Learning Modeling

In recent years, alternative animal testing methods such as computational and machine learning approaches have become increasingly crucial for toxicity testing. However, the complexity and scarcity of available biomedical data challenge the development of predictive models. Combining nonlinear machine learning together with multicondition descriptors offers a solution for using data from various assays to create a robust model. This work applies multicondition descriptors (MCDs) to develop a QSTR (Quantitative Structure-Toxicity Relationship) model based on a large toxicity data set comprising more than 80,000 compounds and 59 different end points (122,572 data points). The prediction capabilities of developed single-task multi-end point machine learning models as well as a novel data analysis approach with the use of Convolutional Neural Networks (CNN) are discussed. The results show that using MCDs significantly improves the model and using them with CNN-1D yields the best result (R2train = 0.93, R2ext = 0.70). Several structural features showed a high level of contribution to the toxicity, including van der Waals surface area (VSA), number of nitrogen-containing fragments (nN+), presence of S-P fragments, ionization potential, and presence of C-N fragments. The developed models can be very useful tools to predict the toxicity of various compounds under different conditions, enabling quick toxicity assessment of new compounds.

Modeling mixtures interactions in environmental toxicology

In the environment, organisms are exposed to mixtures of different toxicants, which may interact in ways that are difficult to predict when only considering each component individually. Adapting and expanding tools from pharmacology, the toxicology field uses analytical, graphical, and computational methods to identify and quantify interactions in multi-component mixtures. The two general frameworks are concentration addition, where components have similar modes of action and their effects sum together, or independent action, where components have dissimilar modes of action and do not interact. Other interaction behaviors include synergism and antagonism, where the combined effects are more or less than the additive sum of individual effects. This review covers foundational theory, methods, an in-depth survey of original research from the past 20 years, current trends, and future directions. As humans and ecosystems are exposed to increasingly complex mixtures of environmental contaminants, analyzing mixtures interactions will continue to become a more critical aspect of toxicological research.

Optimizing ciprofloxacin removal through regulations of trophic modes and FNA levels in a moving bed biofilm reactor performing sidestream partial nitritation

The performance of partial nitritation (PN)-moving bed biofilm reactor (MBBR) in removal of antibiotics in the sidestream wastewater has not been investigated so far. In this work, the removal of ciprofloxacin was assessed under varying free nitrous acid (FNA) levels and different trophic modes. For the first time, a positive correlation was observed between ciprofloxacin removal and FNA levels, either in the autotrophic PN-MBBR or in the mixotrophic PN-MBBR, mainly ascribed to the FNA-stimulating effect on heterotrophic bacteria (HB)-induced biodegradation. The maximum ciprofloxacin removal efficiency (∼98 %) and removal rate constant (0.021 L g-1 SS h-1) were obtained in the mixotrophic PN-MBBR at an average FNA level of 0.056 mg-N L-1, which were 5.8 and 51.2 times higher than the corresponding values in the autotrophic PN-MBBR at 0 mg FNA-N L-1. Increasing FNA from 0.006 to 0.056 mg-N L-1 would inhibit ammonia oxidizing bacteria (AOB)-induced cometabolism and metabolism from 10.2 % and 6.9 % to 6.2 % and 6.4 %, respectively, while HB-induced cometabolism and metabolism increased from 31.2 % and 22.7 % to 41.9 % and 34.5 %, respectively. HB-induced cometabolism became the predominant biodegradation pathway (75.9 %-85.8 %) in the mixotrophic mode. Less antimicrobial biotransformation products without the piperazine or fluorine were newly identified to propose potential degradation pathways, corresponding to microbial-induced metabolic types and FNA levels. This work shed light on enhancing antibiotic removal via regulating both FNA accumulation and organic carbon addition in the PN-MBBR process treating sidestream wastewater.

Stability Constant and Potentiometric Sensitivity of Heavy Metal-Organic Fluorescent Compound Complexes: QSPR Models for Prediction and Design of Novel Coumarin-like Ligands

Industrial wastewater often consists of toxic chemicals and pollutants, which are extremely harmful to the environment. Heavy metals are toxic chemicals and considered one of the major hazards to the aquatic ecosystem. Analytical techniques, such as potentiometric methods, are some of the methods to detect heavy metals in wastewaters. In this work, the quantitative structure-property relationship (QSPR) was applied using a range of machine learning techniques to predict the stability constant (logβ_ML) and potentiometric sensitivity (PS_ML) of 200 ligands in complexes with the heavy metal ions Cu²⁺, Cd²⁺, and Pb²⁺. In result, the logβML models developed for four ions showed good performance with square correlation coefficients (R²) ranging from 0.80 to 1.00 for the training and 0.72 to 0.85 for the test sets. Likewise, the PSML displayed acceptable performance with an R² of 0.87 to 1.00 for the training and 0.73 to 0.95 for the test sets. By screening a virtual database of coumarin-like structures, several new ligands bearing the coumarin moiety were identified. Three of them, namely NEW02, NEW03, and NEW07, showed very good sensitivity and stability in the metal complexes. Subsequent quantum-chemical calculations, as well as physicochemical/toxicological profiling were performed to investigate their metal-binding ability and developability of the designed sensors. Finally, synthesis schemes are proposed to obtain these three ligands with major efficiency from simple resources. The three coumarins designed clearly demonstrated capability to be suitable as good florescent chemosensors towards heavy metals. Overall, the computational methods applied in this study showed a very good performance as useful tools for designing novel fluorescent probes and assessing their sensing abilities.

In Silico Prediction of the Toxicity of Nitroaromatic Compounds: Application of Ensemble Learning QSAR Approach

In this work, a dataset of more than 200 nitroaromatic compounds is used to develop Quantitative Structure-Activity Relationship (QSAR) models for the estimation of in vivo toxicity based on 50% lethal dose to rats (LD₅₀). An initial set of 4885 molecular descriptors was generated and applied to build Support Vector Regression (SVR) models. The best two SVR models, SVR_A and SVR_B, were selected to build an Ensemble Model by means of Multiple Linear Regression (MLR). The obtained Ensemble Model showed improved performance over the base SVR models in the training set (R² = 0.88), validation set (R² = 0.95), and true external test set (R² = 0.92). The models were also internally validated by 5-fold cross-validation and Y-scrambling experiments, showing that the models have high levels of goodness-of-fit, robustness and predictivity. The contribution of descriptors to the toxicity in the models was assessed using the Accumulated Local Effect (ALE) technique. The proposed approach provides an important tool to assess toxicity of nitroaromatic compounds, based on the ensemble QSAR model and the structural relationship to toxicity by analyzed contribution of the involved descriptors.

Binary toxicity of polystyrene nanoplastics and polybrominated diphenyl ethers to Arctic Cyanobacteria under ambient and future climates

Cyanobacteria are the predominant biota in the Arctic. Interactive effects on Arctic cyanobacteria between climate-change-shifting parameters and anthropogenic contaminants are largely unknown. We utilized a fractional factorial experiment and Arctic cyanobacteria Pseudanabaena biceps Strain PCCC_O-153 to capture the complexity of interacting climate factors, nano-polystyrene (nano-PS) and 2,2´,4,4´-tetrabromodipenyl ether (BDE-47). The short-term binary toxicity of nano-PS and BDE-47 was then examined through experiments, toxicity units, and reference models. The toxic mechanism was further revealed through biochemical analyses and multivariate statistics. We found that BDE-47 and nano-PS had more hazardous effects than changing climate conditions. The mixture had antagonistic effects on PCCC_O-153, attributing to the aggregation of nano-PS, the adsorption of BDE-47, and the wrapping of both contaminants by released extracellular polymeric substances. Binary toxicity was caused by the chain reactions triggered by combining individual contaminants. Total protein was a sensitive target and positively correlated to chlorophyll pigment. Oxidative stress for the mixture mainly resulted from the presence of nano-PS. This is the first study to access the hazardous effects of a mixture of anthropogenic contaminants on Arctic cyanobacteria under ambient and future climates.

Environmental health and risk assessment metrics with special mention to biotransfer, bioaccumulation and biomagnification of environmental pollutants

The environment pollutants, which are landed up in environment because of human activities like urbanization, mining and industrializations, affects human health, plants and animals. The living organisms present in environment are constantly affected by the toxic pollutants through direct contact or bioaccumulation of chemicals from the environment. The toxic and hazardous pollutants are easily transferred to different environmental matrices like land, air and water bodies such as surface and ground waters. This comprehensive review deeply discusses the routes and causes of different environmental pollutants along with their toxicity, impact, occurrences and fate in the environment. Environment health and risk assessment tools that are used to evaluate the harmfulness, exposure of living organisms to pollutants and the amount of pollutant accumulated are explained with help of bio-kinetic models. Biotransfer, toxicity factor, biomagnification and bioaccumulation of different pollutants in the air, water and marine ecosystems are critically addressed. Thus, the presented survey would be collection of correlations those addresses the factors involved in assessing the environmental health and risk impacts of distinct environmental pollutants.

Mixture toxicity of six pharmaceuticals towards Aliivibrio fischeri, Daphnia magna, and Lemna minor

As the knowledge on the joint effects of pharmaceuticals towards different non-target organisms is still limited, the aim of our study was to evaluate the toxicity of mixtures of pharmaceuticals, as well as their baseline toxicity towards three selected organisms, namely the bioluminescent bacteria Aliivibrio fischeri, the crustacean Daphnia magna, and the duckweed Lemna minor. Different mixtures composed of three up to five pharmaceuticals having the same or different mechanisms of action in terms of their therapeutic activity (non-steroidal anti-inflammatory drugs, opioid analgesic, antibacterial and anti-epileptic drugs) were investigated. The observed EC₅₀s were compared with those predicted using the concentration addition (CA) and independent action (IA) models. In general, the EC₅₀ values for mixtures predicted with the CA model were lower than those obtained with the IA model, although, in some cases, test predictions of these two models were almost identical. Most of the experimentally determined EC₅₀ values for the specific mixtures were slightly higher than those predicted with the CA model; hence, a less than additive effect was noted. Based on the obtained results, it might be concluded that the CA model assumes the worst-case scenario and gives overall closer predictions; therefore, it should be recommended also for modeling the mixture toxicity of pharmaceuticals with different modes of action.

Predicting cytotoxicity of binary pollutants towards a human cell panel in environmental water by experimentation and deep learning methods

Biological assays are useful in water quality evaluation by providing the overall toxicity of chemical mixtures in environmental waters. However, it is impossible to elucidate the source of toxicity and some lethal combination of pollutants simply using biological assays. As facile and cost-effective methods, computation model-based toxicity assessments are complementary technologies. Herein, we predicted the human health risk of binary pollutant mixtures (i.e., binary combinations of As(III), Cd(II), Cr(VI), Pb(II) and F(I)) in water using in vitro biological assays and deep learning methods. By employing a human cell panel containing human stomach, colon, liver, and kidney cell lines, we assessed the human health risk mimicking cellular responses after oral exposures of environmental water containing pollutants. Based on the experimental cytotoxicity data in pure water, multi-task deep learning was applied to predict cellular response of binary pollutant mixtures in environmental water. Using additive descriptors and single pollutant toxicity data in pure water, the established deep learning model could predict the toxicity of most binary mixtures in environmental water, with coefficient of determination (R²) > 0.65 and root mean squared error (RMSE) < 0.22. Further combining the experimental data on synergistic and antagonistic effects of pollutant mixtures, deep learning helped improve the predictive ability of the model (R² > 0.74 and RMSE <0.17). Moreover, predictive models allowed us identify a number of toxicity source-related physiochemical properties. This study illustrates the combination of experimental findings and deep learning methods in the water quality evaluation.

Toxicity of binary mixtures of pesticides and pharmaceuticals toward Vibrio fischeri: Assessment by quantitative structure-activity relationships

Pollutants in real aquatic systems commonly occur as chemical mixtures. Yet, the corresponding risk assessment is still mostly based on information on single-pollutant toxicity, accepting the assumption that pollutant mixtures exhibit additive toxicity effect which is often not the case. Therefore, it is still better to use the experimental approach. Unfortunately, experimental determination of toxicity for each mixture is practically unfeasible. In this study, quantitative structure-activity relationship (QSAR) models for the prediction of toxicity of binary mixtures towards bioluminescent bacteria Vibrio fischeri were developed at three toxicity levels (EC₁₀, EC₃₀ and EC₅₀). For model development, experimentally determined toxicity values of 14 pollutants (pharmaceuticals and pesticides) were correlated with their structural features, applying multiple linear regression together with genetic algorithm. Statistical analysis, internal validation and external validation of the models were carried out. The toxicity is accurately predicted by all three models. EC₃₀ and EC₅₀ values are mostly influenced by geometrical distances between nitrogen and sulfur atoms. Furthermore, the simultaneous presence of oxygen and chlorine atoms in mixture can induce the increase in toxicity. At lower effect levels (EC₁₀), nitrogen atom bonded to different groups has the highest impact on mixture toxicity. Thus, the analysis of the descriptors involved in the developed models can give insight into toxic mechanisms of the binary systems.

Modification of the Microtox® Basic Solid Phase Test: A new application for the ecotoxicological studies on poorly soluble antihypertensive drugs

Increasing consumption of angiotensin II receptor blockers (ARBs: valsartan, losartan potassium, telmisartan) is inevitably associated with their appearance in the environment and impact on aquatic and terrestrial organisms. Since the pharmaceuticals do not occur as pure substances in the environment, but as complex mixtures with other active pharmaceutical ingredients (APIs) and excipients used in pharmaceutical formulations, we compared the ecotoxicity of ARBs in various forms: as pure APIs, in pharmaceutical formulations and in mixtures with hydrochlorothiazide (HCT). Because the studied APIs are poorly water-soluble, the Microtox® Basic Solid Phase Test, utilizing bacteria Aliivibrio fischeri, has been modified by using a neutral matrix. Thus, this test, which is correlated with other tests for higher aquatic organisms, may be applied for the ecotoxicological evaluation of poorly soluble APIs. This is the first study reflecting the real situation in the environment, where non-target species are exposed to the pharmaceuticals, which can be dissolved/suspended in the liquid medium or adsorbed on the solid matrix. The results obtained indicate that the excipients are not inert substances and their presence in the environment may cause an increased risk to non-target organisms. Moreover, antagonistic effects were observed for two-component drug-drug (ARBs-HCT) mixtures.

Diazepam, metformin, omeprazole and simvastatin: a full discussion of individual and mixture acute toxicity

High consumption of drugs, combined with their presence in the environment, raises concerns about its consequences. Even though researches are often engaged in analyzing substances separately, that is not the environmental reality. Therefore, the aim of this study was to investigate the acute toxicity of the pharmaceuticals simvastatin, metformin, omeprazole and diazepam, and all possible mixtures between them, to the organism Aliivibrio fischeri, verifying possible synergistic or antagonistic effects and assessing byproducts formation. In terms of individual toxicity, omeprazole is the most toxic of the active ingredients, followed by simvastatin, diazepam and, finally, metformin. When the toxicity of mixtures was tested, synergism, antagonism and hormesis were perceived, most probably generated due to byproducts formation. Moreover, it was observed that even when compounds are at concentrations below the non-observed effect concentration (NOEC), there may be toxicity to the mixture. Hence, this work points to the urgent need for more studies involving mixtures, since chemicals are subject to interactions and modifications, can mix, and potentiate or nullify the toxic effect of each other.