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

Ukić, Šime; Sigurnjak, Marija; Cvetnić, Matija; Markić, Marinko; Stankov, Mirjana Novak; Rogošić, Marko; Rasulev, Bakhtiyor; Božić, Ana Lončarić; Kušić, Hrvoje; Bolanča, Tomislav

doi:10.1016/j.ecoenv.2019.109696

Cited by 27 publications

(6 citation statements)

References 43 publications

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“…Similarly to previous studies [ 65 , 66 ], our results showed the synergistic effects of diclofenac on other pharmaceuticals; however, DCF also had a toxicity-increasing effect on pesticides. PERMANOVA analysis (based on the CI values), uniquely used for this purpose in our study, confirmed that both diclofenac and carbamazepine, alone and together, acted synergistically in the mixtures.…”

Section: Discussionsupporting

confidence: 89%

“…Several studies have been conducted on binary mixtures of pharmaceuticals, including diclofenac and ibuprofen. Noteworthy among these are investigations by Di Nica et al, Ukić et al, Zuriaga et al, and Drzymała et al, who reported a relatively high incidence of synergy and additive effects [ 65 , 66 , 67 , 68 ]. In contrast, studies by Ge et al (2020) [ 69 ], focusing on the interaction between antibiotics in binary mixtures, did not observe synergic effects; instead, they reported antagonism or additive actions.…”

Section: Discussionmentioning

confidence: 99%

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The Cocktail Effects on the Acute Cytotoxicity of Pesticides and Pharmaceuticals Frequently Detected in the Environment

Göbölös,

Sebők,

Szabó

et al. 2024

Toxics

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Xenobiotics never appear as single, isolated substances in the environment but instead as multi-component mixtures. However, our understanding of the ecotoxicology of mixtures is far from sufficient. In this study, three active pharmaceutical ingredients (carbamazepine, diclofenac, and ibuprofen) and three pesticides (S-metolachlor, terbuthylazine, and tebuconazole) from the most frequently detected emerging micropollutants were examined for their acute cytotoxicity, both individually and in combination, by bioluminescence inhibition in Aliivibrio fischeri (NRRL B-11177). Synergy, additive effects, and antagonism on cytotoxicity were determined using the combination index (CI) method. Additionally, PERMANOVA was performed to reveal the roles of these chemicals in binary, ternary, quaternary, quinary, and senary mixtures influencing the joint effects. Statistical analysis revealed a synergistic effect of diclofenac and carbamazepine, both individually and in combination within the mixtures. Diclofenac also exhibited synergy with S-metolachlor and when mixed with ibuprofen and S-metolachlor. S-metolachlor, whether alone or paired with ibuprofen or diclofenac, increased the toxicity at lower effective concentrations in the mixtures. Non-toxic terbuthylazine showed great toxicity-enhancing ability, especially at low concentrations. Several combinations displayed synergistic effects at environmentally relevant concentrations. The application of PERMANOVA was proven to be unique and successful in determining the roles of compounds in synergistic, additive, and antagonistic effects in mixtures at different effective concentrations.

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Section: Discussionsupporting

confidence: 89%

Section: Discussionmentioning

confidence: 99%

The Cocktail Effects on the Acute Cytotoxicity of Pesticides and Pharmaceuticals Frequently Detected in the Environment

Göbölös,

Sebők,

Szabó

et al. 2024

Toxics

View full text Add to dashboard Cite

show abstract

“…Higher consumption of antibiotics always results in large amounts of unchanged residues excreting into wastewater, posing potential hazards on aquatic ecosystems ( Ukić et al, 2019 ). Although wastewater treatment plants (WWTPs) could remove organic carbon, nitrogen and phosphorus efficiently, lower removal efficiencies were observed for most antibiotics, rendering WWTPs effluent as the major sources for antibiotic residues and their transformation products into the receiving aquatic environment.…”

Section: Introductionmentioning

confidence: 99%

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

Xu,

Wang,

et al. 2024

Water Research X

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“…Recently, machine learning has become one of the most powerful and quickly developing tools for in silico discovery and computer-aided drug development, as well as environmental chemical screening. , Researchers have used Quantitative Structure–Activity Relationship (QSAR) for many applications in toxicity studies. ,− Linear regression, random forest, and support vectors are among the most popular computational algorithms for the development of cheminformatics models. ,− Beyond traditional QSAR methods, artificial neural networks appeared as a beneficial tool in the case of training the model on larger data sets and performing better on unseen test data. In the meantime, the remarkable increase in available biomedical data over the past years led to the generation of big data, which along with advances in screening technology, made it possible to develop extensive toxicity prediction models. ,, When models are trained on small data sets, they are typically unable to show good performance on external data .…”

Section: Introductionmentioning

confidence: 99%

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

Daghighi,

Casanola-Martin,

Iduoku

et al. 2024

Environ. Sci. Technol.

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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 (R 2 train = 0.93, R 2 ext = 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.

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

Cited by 27 publications

References 43 publications

The Cocktail Effects on the Acute Cytotoxicity of Pesticides and Pharmaceuticals Frequently Detected in the Environment

The Cocktail Effects on the Acute Cytotoxicity of Pesticides and Pharmaceuticals Frequently Detected in the Environment

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

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

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