Predictive binary mixture toxicity modeling of fluoroquinolones (FQs) and the projection of toxicity of hypothetical binary FQ mixtures: a combination of 2D-QSAR and machine-learning approaches.

All sorts of chemicals get degraded under various environmental stresses, and the degradates coexist with the parent compounds as mixtures in the environment. Antibiotics emerge as an additional concern due to the bioactive nature of both the parent compound and degradation products and their combined exposure to the environment. Therefore, environmental risk assessment of antibiotics and their degradation products is very much necessary. In this direction, we made use of in silico new approach methodologies (NAMs) and machine-learning algorithms. In this study, we have developed a robust and predictive mixture-quantitative structure-activity relationship (QSAR) model with promising quality and predictability (internal: MAETrain = 0.085, QLOO2 = 0.849, external: MAETest = 0.090, and QF12 = 0.859) for predicting the toxicity of the mixtures of a class of antibiotics and their degradation products. To obtain the predictive model, toxicity data of 78 binary fluoroquinolone mixtures in E. coli (endpoint: log 1/IC50 in molar) have been utilized. We have used only 0D-2D descriptors to efficiently encode the structural features of mixture components without any additional complexities. The optimization of the class of mixture descriptors has been performed in this study by using three different mixing rules (linear combination of molecular contributions, the squared molecular contributions, and the norm of molecular contributions). Different machine-learning approaches namely, random forest (RF), ada boost, gradient boost (GB), extreme gradient boost (XGB), support vector machine (SVM), linear support vector machine (LSVM), and ridge regression (RR) have been employed here apart from the conventional partial least squares (PLS) regression to optimize the modeling approach. A rigorous validation protocol has been used for assessing the goodness-of-fit, robustness, and external predictability of the models. Finally, the toxicity of possible untested mixtures of different photodegradation products of fluoroquinolones has been predicted using the best model reported in this study.

Quantitative Read-across structure-activity relationship (q-RASAR): A new approach methodology to model aquatic toxicity of organic pesticides against different fish species.

We have developed quantitative toxicity prediction models for organic pesticides of agricultural importance considering different fish species using a novel quantitative Read-across structure-activity relationship (q-RASAR) approach. The current study uses experimental (Log 1/LC50) data of organic pesticides to various fish species, including Rainbow trout (RT: Oncorhynchus mykiss: 715 data points), Lepomis (LP: Lepomis macrochirus: 136 data points), and Miscellaneous (Pimephales promelas, Brachydanio rerio: 226 data points). This study has also discussed the validation of the developed models and the analysis of structural features that are important for aquatic toxicity towards fishes. The read-across-derived similarity, error, and concordance measures (RASAR descriptors) have been extracted from the preliminary 0D-2D descriptors; the combined pool of RASAR and selected 0D-2D descriptors have been used to develop the final models by employing partial least squares algorithm. All the q-RASAR models are acceptable in terms of goodness of fit, robustness, and external predictivity, superseding the quality of the respective QSAR models, as seen from the computed validation metrics. The q-RASAR is an effective approach that has the potential to be used as a good alternative way to enhance external predictivity, interpretability, and transferability for aquatic toxicity prediction as well as ecotoxicity potential identification.

Machine learning - based q-RASAR modeling to predict acute contact toxicity of binary organic pesticide mixtures in honey bees.

We have reported here a quantitative read-across structure-activity relationship (q-RASAR) model for the prediction of binary mixture toxicity (acute contact toxicity) in honey bees. Both the quantitative structure-activity relationship (QSAR) and the similarity-based read-across algorithms are used simultaneously for enhancing the predictability of the model. Several similarity and error-based parameters, obtained from the read-across prediction tool, have been put together with the structural and physicochemical descriptors to develop the final q-RASAR model. The calculated statistical and validation metrics indicate the goodness-of-fit, robustness, and good predictability of the partial least squares (PLS) regression model. Machine learning algorithms like ridge regression, linear support vector machine (SVM), and non-linear SVM have been used to further enhance the predictability of the q-RASAR model. The prediction quality of the q-RASAR models outperforms the previously reported quasi-SMILEs-based QSAR model in terms of external correlation coefficient (Q2F1 SVM q-RASAR: 0.935 vs. Q2VLD QSAR: 0.89). In this research, the toxicity values of several new untested binary mixtures have been predicted with the new models, and the reliability of the PLS predictions has been validated by the prediction reliability indicator tool. The q-RASAR approach can be used as reliable, complementary, and integrative to the conventional experimental approaches of pesticide mixture risk assessment.

"Data fusion" quantitative read-across structure-activity-activity relationships (q-RASAARs) for the prediction of toxicities of binary and ternary antibiotic mixtures toward three bacterial species.

Antibiotics are often found in the environment as pollutants. They are usually found as mixtures in the environment and may produce toxicity against different ecological species due to joint exposure in the sub-optimal range. Sometimes the degradation products of parent chemicals also interact with it and cause mixture toxicity. In this study, we have developed three different mixture-Quantitative Structure-Activity Relationship (mixture-QSAR) models for three different bacterial species (Vibrio fischeri, Escherichia coli, and Bacillus subtilis). The toxicity data were collected from a previous experimental report in the literature, which comprised binary and ternary mixtures of sulfonamides (SAs), sulfonamide potentiators (SAPs), and tetracyclines (TCs). We have also explored the interspecies modeling to find inter-correlation among the toxicity of these studied organisms and have developed quantitative structure activity-activity relationship (QSAAR) models by employing the "data fusion" quantitative read-across structure-activity-activity relationship (q-RASAAR) and partial least squares (PLS) regression algorithms. All the models are strictly validated using both internal and external validation tests as suggested in the OECD guidelines. Three different mixing rules have been used in this study for descriptor computations to incorporate the additive and interaction effects among the mixture components. To the best of our knowledge, this is the first report of interspecies mixture toxicity models which can predict the cellular toxicity of binary and ternary mixtures against any of the three above-mentioned organisms.

Predictive Quantitative Read-Across Structure-Property Relationship Modeling of the Retention Time (Log tR) of Pesticide Residues Present in Foods and Vegetables.

The retention time (log tR) of pesticidal compounds in a reverse-phase high-performance liquid chromatography (HPLC) analysis has a direct relationship with lipophilicity, which could be related to the ecotoxicity potential of the compounds. The novel quantitative read-across structure-property relationship (q-RASPR) modeling approach uses similarity-based descriptors for predictive model generation. These models have been shown to enhance external predictivity in previous studies for several end points. The current study describes the development of a q-RASPR model using experimental retention time data (log tR) in the HPLC experiments of 823 environmentally significant pesticide residues collected from a large compound database. To model the retention time (log tR) end point, 0D-2D descriptors have been used along with the read-across-derived similarity descriptors. The developed partial least squares (PLS) model was rigorously validated by various internal and external validation metrics as recommended by the Organization for Economic Co-operation and Development (OECD). The final q-RASPR model is proven to be a good fit, robust, and externally predictive (ntrain = 618, R2 = 0.82, Q2LOO = 0.81, ntest = 205, and Q2F1 = 0.84) that literally outperforms the external predictivity of the previously reported quantitative structure-property relationship (QSPR) model. From the insights of modeled descriptors, lipophilicity is found to be the most important chemical property, which positively correlates with the retention time (log tR). Various other characteristics, such as the number of multiple bonds (nBM), graph density (GD), etc., have a substantial and inversely proportionate relationship with the retention time end point. The software tools utilized in this study are user-friendly, and most of them are free, which makes our methodology quite cost-effective when compared to experimentation. In a nutshell, to obtain better external predictivity, interpretability, and transferability, q-RASPR is an efficient technique that has the potential to be employed as a good alternative approach for retention time prediction and ecotoxicity potential identification.

Chemical similarity and machine learning-based approaches for the prediction of aquatic toxicity of binary and multicomponent pharmaceutical and pesticide mixtures against Aliivibrio fischeri.

Different classes of chemicals are present in the environment as mixtures. Among them, pharmaceuticals and pesticides are of major concern due to their improper use and disposal, and subsequent additive and non-additive effects. To assess the environmental risk posed by the mixtures of pharmaceuticals and pesticides, a quantitative structure-activity relationship (QSAR) model has been developed in this study using the pEC50 values of 198 binary and multi-component mixtures against the marine bacterium Aliivibrio fischeri. The developed partial least squares (PLS) model has been rigorously validated and proved to be a robust and extremely predictive one. To address the chances of overestimation of validation metrics, three cross-validation tests (mixtures out, compounds out, and everything out) have been applied, and the results were satisfactory. The use of simple 2-dimensional descriptors makes the prediction much quick, and also makes the model easily interpretable. A machine learning-based chemical read-across prediction has also been performed to justify the effectiveness of selected structural features in this study. In a nutshell, this study proves QSAR and chemical read-across as effective alternative approaches for the toxicity prediction of pharmaceutical and pesticide mixtures and also approves the use of mixture descriptors for modelling mixtures successfully.

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.

Dynamic surface patterns on cells.

Dynamic pattern formations are commonly observed in multicellular systems, such as cardiac tissue and slime molds, and modeled using reaction-diffusion systems. Recent experiments have revealed dynamic patterns in the concentration profile of various cortical proteins at a much smaller scale, namely, embryos at their single-cell stage. Spiral waves of Rho and F-actin proteins have been reported in Xenopus frog and starfish oocytes [Bement et al., Nat. Cell Biol. 17, 1471 (2015)], while a pulsatile pattern of Rho and myosin proteins has been found in C. elegans embryo [Nishikawa et al., eLife 6, e30537 (2017)]. Here, we propose that these two seemingly distinct dynamic patterns are signatures of a single reaction-diffusion network involving active-Rho, inactive-Rho, actin, and myosin. We show that a small variation in the concentration of other ancillary proteins can give rise to different dynamical states from the same chemical network.

Dynamics and Stability of the Contractile Actomyosin Ring in the Cell.

Contraction of the cytokinetic ring during cell division leads to physical partitioning of a eukaryotic cell into two daughter cells. This involves flows of actin filaments and myosin motors in the growing membrane interface at the midplane of the dividing cell. Assuming boundary driven alignment of the actomyosin filaments at the inner edge of the interface, we explore how the resulting active stresses influence the flow. Using the continuum gel theory framework, we obtain exact axisymmetric solutions of the dynamical equations. These solutions are consistent with experimental observations on closure rate. Using these solutions, we perform linear stability analysis for the contracting ring under nonaxisymmetric deformations. Our analysis shows that few low wave number modes, which are unstable during onset of the constriction, later on become stable when the ring shrinks to smaller radii, which is a generic feature of actomyosin ring closure. Our theory also captures how the effective tension in the ring decreases with its radius, causing significant slowdown in the contraction process at later times.

Computational Modeling of Mixture Toxicity.

Environmental pollution has become an inevitable problem and a relevant global issue of the twenty-first century. The fast industrial growth has caused the production and release of various chemical species and multicomponent mixtures to the environment which affect the entire living world adversely. Various industrial regulatory agencies are working in this domain to regulate the production of chemical entities, proper release of chemical wastes, and the risk assessment of the industrial and hazardous chemicals; however, they mostly rely upon the single chemical risk assessment instead of considering the toxicity of multicomponent mixtures. In this era of chemical advances, single chemical exposure is a myth. The entire living world is always being exposed to the environmental chemical mixtures but the scarcity of toxicity data of chemical mixtures is a serious concern. The nature of toxicity of mixtures is entirely different and complex from the individual chemicals because of the interactions (synergism/antagonism) among the mixture components. Various regulatory authorities and the scientific world have come up with a handful of methodologies and guidelines for evaluating the harmful effects of the multicomponent mixtures, though there is no such significant, standard, and reliable approach for the toxicity evaluation of chemical mixtures and their management across diverse fields. Toxicity experimentations on laboratory animals are troublesome, time-consuming, costly, and unethical. Thus, to reduce the animal experimentations, the scientific communities, regulatory agencies, and the industries are now depending upon the already proven computational alternatives. The computational approaches are capable of predicting toxicities, prioritizing chemicals, and their risk assessment. Besides these, the in silico methods are cost-effective, less time-consuming, and easy to understand. It has been found out that most of the in silico toxicity predictions are on single chemicals and till date there are very few computational studies available for chemical mixtures in the scientific literature. Therefore, the current chapter illustrates the importance of determination of toxicity of mixtures, the conventional methods for toxicity evaluation of chemical mixtures, and the role of in silico methods to assess the toxicity, followed by the types of various computational methods used for such purpose. Additionally, few successful applications of computational tools in toxicity prediction of mixtures have been discussed in detail. At the end of this chapter, we have discussed some future perspectives toward the role and applications of in silico techniques for toxicity prediction of mixtures.

Prediction of aquatic toxicity of chemical mixtures by the QSAR approach using 2D structural descriptors.

The rapid industrialization has led to the generation of various organic chemicals and multi-component mixtures which affect the environment adversely. Although organic chemicals are often exposed to the environment as a form of chemical mixtures rather than individual compounds, there is insufficient toxicity data available for the chemical mixtures due to the associated complexities. Most importantly, the nature of toxicity of mixtures is completely different from the individual chemicals, which makes the evaluation more difficult and challenging. In this paper, we have developed QSAR models for various individual and mixture data sets for the prediction of the aquatic toxicity. We have used Partial Least Squares (PLS) regression as a statistical tool to build the models. The various structural features of the individual chemicals and the mixture components have been modeled against the toxicity end point pEC50 (negative logarithm of median effective concentration in molar scale) of the aquatic organisms Photobacterium phosphoreum (marine bacterium) and Selenastrum capricornutum (freshwater algae). The mixture descriptors have been calculated by the weighted descriptor generation approach. The models were developed in accordance with OECD guidelines, and the quality of each model has been adjudged by strict validation parameters. The final models are robust, extremely predictive and interpretable mechanistically which can be used for the prediction of toxicity of untested chemical mixtures under the domain of applicability of the developed models.