Computational Modeling of Mixture Toxicity

Compositional Optimization for Miticidal Activity, Ecotoxicity, and Phytotoxicity of Rosmarinus officinalis Essential Oils as Biorational Pesticides

Recognizing the challenges in using botanicals as sustainable pest control agents due to compositional variation, this study addresses the limitations of traditional component-based approaches such as Hewlett and Plackett or Wadley's models. Based on the assumption of noninteractivity among constituents, these models often fail to predict outcomes accurately due to dynamic intermolecular interactions. We introduce a whole mixture-based approach, employing a combination of experimental design and polynomial modeling. This technique accurately predicts miticidal activity on Tetranychus urticae, ecotoxicity on Daphnia magna, and phytotoxic activities on Phaseolus vulgaris of Rosemarinus officinalis essential oils with varying composition. The RMSE values from the polynomial model are 66.9 and 5.0 for miticidal activity and ecotoxicity, respectively, while they are much higher in component-based models, up to 1097.7 and 41.3, respectively. Additionally, we utilize multiobjective optimization algorithms to identify the optimal supplementary blending of oils and compounds. This strategy aims to maximize miticidal effectiveness while minimizing ecotoxicity and phytotoxicity. Our approach for predicting multicomponent mixture effects is likely to bridge the knowledge gap between research and commercialization.

Mechanistic insights into the parental co-exposure of T-2 toxin and epoxiconazole on the F1 generation of zebrafish (Danio rerio)

Mycotoxins and pesticides frequently coexist in agricultural commodities on a global scale. The potential transgenerational consequences induced by these substances pose a significant threat to human health. However, there is a lack of data concerning the effects of co-contamination by these chemicals in the F1 generation following parental exposure. This investigation delved into the mixture effects of T-2 toxin (T-2) and epoxiconazole (EPO) on the offspring of zebrafish (Danio rerio). The findings revealed that exposure across generations to a combination of T-2 and EPO resulted in toxicity in the larvae of the F1 generation. This was demonstrated by a significant increase in the levels or activities of malondialdehyde (MDA), thyroxine (T4), Caspase3, and cas9, along with a decrease in the levels of cyp19a, ERα, and ERβ. These outcomes suggested that cross-generational exposure to T-2 and EPO in D. rerio disrupted oxidative balance, induced cell apoptosis, and affected the endocrine system. Moreover, these effects were magnified when the F1 generation was continuously exposed to these compounds. Notably, these adverse effects could persist in subsequent generations without additional exposure. This study underscored the potential dangers associated with the simultaneous presence of T-2 and EPO on the development of fish offspring and the resulting environmental hazards to aquatic ecosystems. These findings emphasized the significant health risks posed by cross-generational exposure and highlighted the need for additional legislative measures to address these concerns.

Cytotoxicity of pharmaceuticals and their mixtures toward scaffold-free 3D spheroid cultures of rainbow trout (Oncorhynchus mykiss) hepatocytes

Pharmaceutical residues are widely detected in surface waters all around the world, causing a range of adverse effects on environmental species, such as fish. Besides population level effects (mortality, reproduction), pharmaceutical residues can bioaccumulate in fish tissues resulting in organ-specific toxicities. In this study, we developed in vitro 3D culture models for rainbow trout (Oncorhynchus mykiss) liver cell line (RTH-149) and cryopreserved, primary rainbow trout hepatocytes (RTHEP), and compared their spheroid formation and susceptibility to toxic impacts of pharmaceuticals. The rapidly proliferating, immortalized RTH-149 cells were shown to form compact spheroids with uniform morphology in just three days, thus enabling higher throughput toxicity screening compared with the primary cells that required acclimation times of about one week. In addition, we screened the cytotoxicity of a total of fourteen clinically used human pharmaceuticals toward the 3D cultures of both RTH-149 cells (metabolically inactive) and primary RTHEP cells (metabolically active), to evaluate the impacts of the pharmaceuticals' own metabolism on their hepatotoxicity in rainbow trout in vitro. Among the test substances, the azole antifungals (clotrimazole and ketoconazole) were identified as the most cytotoxic, with hepatic metabolism indicatively amplifying their toxicity, followed by fluoxetine, levomepromazine, and sertraline, which were slightly less toxic toward the metabolically active primary cells than RTH-149 spheroids. Besides individual pharmaceuticals, the 3D cultures were challenged with mixtures of the eight most toxic substances, to evaluate if their combined mixture toxicities can be predicted based on individual substances' half-maximal effect (EC50) concentrations. As a result, the classical concentration addition approach was concluded sufficiently accurate for preliminarily informing on the approximate effect concentrations of pharmaceutical mixtures on a cellular level. However, direct read-across from human data was proven challenging and inexplicit for prediction of hepatotoxicity in fish in vitro.

Systematic approaches to machine learning models for predicting pesticide toxicity

Pesticides play an important role in modern agriculture by protecting crops from pests and diseases. However, the negative consequences of pesticides, such as environmental contamination and adverse effects on human and ecological health, underscore the importance of accurate toxicity predictions. To address this issue, artificial intelligence models have emerged as valuable methods for predicting the toxicity of organic compounds. In this review article, we explore the application of machine learning (ML) for pesticide toxicity prediction. This review provides a detailed summary of recent developments, prediction models, and datasets used for pesticide toxicity prediction. In this analysis, we compared the results of several algorithms that predict the harmfulness of various classes of pesticides. Furthermore, this review article identified emerging trends and areas for future direction, showcasing the transformative potential of machine learning in promoting safer pesticide usage and sustainable agriculture.

First report on soil ecotoxicity prediction against Folsomia candida using intelligent consensus predictions and chemical read-across

Soil invertebrates serve as an outstanding biological indicator of the terrestrial ecosystem and overall soil quality, considering their high sensitivity when compared to other indicators of soil quality. In this study, the available soil ecotoxicity data (pEC50) against the soil invertebrate Folsomia candida (C. name: Springtail) (n = 45) were collated from the database of ECOTOX (cfpub.epa.gov/ecotox) and subjected to QSAR analysis using 2D descriptors. Four partial least squares (PLS) models were built based on the features selected through genertic algorithm followed by the best subset selection. These four models were then used as inputs for Intelligent Consensus Predictor version 1.2 (PLS version) to get the final consensus predictions, using the best selection of predictions (compound-wise) from four "qualified" individual models. Both internal and external validations metrics of the consensus predictions are well- balanced and within the acceptable range as per the OECD criteria. The consensus model was found to be better than the previous developed models for this endpoint. Predictions were also made using the Chemical Read-across approach, which showed even better external validation metric values than the consensus predictions. From the selected features in the QSAR models, it has been found out that molecular weight and presence of a di-thiophosphate group, electron donor groups, and polyhalogen substitutions have a significant impact on the soil ecotoxicity. The soil ecotoxicological risk assessment of organic chemicals can therefore be prioritized by these features. The models developed from diverse structural organic compounds can be applied to any new query compound for data gap filling.