QSTR Models Research Articles

Introducing third-generation periodic table descriptors for nano-qRASTR modeling of zebrafish toxicity of metal oxide nanoparticles.

Metal oxide nanoparticles (MONPs) are widely used in medicine and environmental remediation because of their unique properties. However, their size, surface area, and reactivity can cause toxicity, potentially leading to oxidative stress, inflammation, and cellular or DNA damage. In this study, a nano-quantitative structure-toxicity relationship (nano-QSTR) model was initially developed to assess zebrafish toxicity for 24 MONPs. Previously established 23 first- and second-generation periodic table descriptors, along with five newly proposed third-generation descriptors derived from the periodic table, were employed. Subsequently, to enhance the quality and predictive capability of the nano-QSTR model, a nano-quantitative read across structure-toxicity relationship (nano-qRASTR) model was created. This model integrated read-across descriptors with modeled descriptors from the nano-QSTR approach. The nano-qRASTR model, featuring three attributes, outperformed the previously reported simple QSTR model, despite having one less MONP. This study highlights the effective utilization of the nano-qRASTR algorithm in situations with limited data for modeling, demonstrating superior goodness-of-fit, robustness, and predictability (R 2 = 0.81, Q 2 LOO = 0.70, Q 2 F1/R 2 PRED = 0.76) compared to simple QSTR models. Finally, the developed nano-qRASTR model was applied to predict toxicity data for an external dataset comprising 35 MONPs, addressing gaps in zebrafish toxicity assessment.

Quantitative structure-activity relationship predicting toxicity of pesticides towards Daphnia magna.

Global pesticide usage reaching 2.7 million metric tons annually, brings a grave threat to non-target organisms, especially aquatic organisms, resulting in serious concerns. Predicting aquatic toxicity of pesticides towards Daphnia magna is significant. In this work, random forest (RF) algorithm, together with ten Dragon molecular descriptors, was successfully utilized to develop a quantitative structure-activity/toxicity relationship (QSAR/QSTR) model for the toxicity pEC50 of 745 pesticides towards Daphnia magna. The optimal QSTR model (RF Model I) based on the RF parameters of ntree = 50, mtry = 3 and nodesize = 5, yielded R2 = 0.877, MAE = 0.570, rms = 0.739 (training set of 596 pEC50), R2 = 0.807, MAE = 0.732, rms = 0.902 (test set of 149 pEC50), and R2 = 0.863, MAE = 0.602, rms = 0.774 (total set of 745 pEC50), which are accurate and satisfactory. The optimal RF model is comparable to other published QSTR models for Daphnia magna, although the optimal RF model possessed a small descriptor subset and dealt with a large dataset of pesticide toxicity pEC50. Thus, the investigation in this work provides a reliable, applicable QSTR model for predicting the toxicity pEC50 of pesticides towards Daphnia magna.

New QSTR models to evaluation of imidazolium- and pyridinium-contained ionic liquids toxicity

We present machine learning studies devoted to the creation of predictive models for toxicity evaluation of imidazolium- and pyridinium-containing ionic liquids. New created predictive models were developed using the OCHEM. The predictive ability of the models was tested by cross-validation, giving a coefficient of determination q2 = 0.77–0.82. The models were applied to screen a virtual chemical library to the toxicity of ILs in Danio rerio and Daphnia magna bioassays. Models were used to predict toxicity for 25 ILs, which were then synthesized and tested in vivo. The in vivo toxicity studies found that D. magna is a more sensitive aquatic test organism than D. rerio – 67 % of the studied ILs are classified as extremely toxic with an LC50 range from 0.005 to 0.01 mg/l. At the same time, only one IL 1-dodecylpyridinium bromide with an LC50 of 0.08 mg/l is classified as extremely toxic, and 76 % are classified as slightly and moderately toxic compounds using D. rerio as a test organism. The most toxic ILs 5 and 19 were docked into the human AChE active center and demonstrated calculated binding energy values −9.5 and −9.3 kcal/mol that is comparable with the complexation of the human AChE inhibitor Donepezil, which provides insight into the potential molecular mechanisms of ILs toxicity. The created QSTR models are a successful tool for the toxicity analysis of new promising ILs. QSTR models demonstrated not only high predictive indicators but also a high percentage of correctly predicted toxicity values in vivo studies.

Q-RASTR modelling for prediction of diverse toxic chemicals towards T. pyriformis

ABSTRACT A series of diverse organic compounds impose serious detrimental effects on the health of living organisms and the environment. Determination of the structural aspects of compounds that impart toxicity and evaluation of the same is crucial before public usage. The present study aims to determine the structural characteristics of compounds for Tetrahymena pyriformis toxicity using the q-RASTR (Quantitative Read Across Structure–Toxicity Relationship) model. It was developed using RASTR and 2-D descriptors for a dataset of 1792 compounds with defined endpoint (pIGC50) against a model organism, T. pyriformis. For the current study, the whole dataset was divided based on activity/property into the training and test sets, and the q-RASTR model was developed employing six descriptors (three latent variables) having r 2, Q 2 F1 and Q 2 values of 0.739, 0.767, and 0.735, respectively. The generated model was thoroughly validated using internationally recognized internal and external validation criteria to assess the model’s dependability and predictability. It was highlighted that high molecular weight, aromatic hydroxyls, nitrogen, double bonds, and hydrophobicity increase the toxicity of organic compounds. The current study demonstrates the applicability of the RASTR algorithm in QSTR model development for the prediction of toxic chemicals (pIGC50) towards T. pyriformis.

Large Dataset-Based Regression Model of Chemical Toxicity to Vibrio fischeri.

For the first time, a global regression quantitative structure-toxicity/activity relationship (QSTR/QSAR) model was developed for the toxicity of a large data set including 1236 chemicals towards Vibrio fischeri, by using random forest (RF) regression algorithm. The optimal RF model with RF parameters of mtry = 3, ntree = 150 and nodesize = 5 was based on 13 molecular descriptors. It can achieve accurate prediction for the toxicity of 99.1% of 1236 chemicals, and yield coefficients of determination R2 of 0.893 for 930 log(Mw/IBC50) in the training set, 0.723 for 306 log(Mw/IBC50) in the test se, and 0.865 for 1236 toxicity log(Mw/IBC50) in the total set. The optimal RF global model proposed in this work is comparable to other published local QSTR models on small datasets of the toxicity to Vibrio fischeri.

Predicting the ecotoxicity of endocrine disruptive chemicals: Multitasking in silico approaches towards global models

Manufactured substances known as endocrine disrupting chemicals (EDCs) released in the environment, through the use of cosmetic products or pesticides, can cause severe eco and cytotoxicity that may induce trans-generational as well as long-term deleterious effects on several biological species at relatively low doses, unlike other classical toxins. As the need for effective, affordable and fast EDCs environmental risk assessment has become increasingly pressing, the present work introduces the first moving average-based multitasking quantitative structure-toxicity relationship (MA-mtk QSTR) modeling specifically developed for predicting the ecotoxicity of EDCs against 170 biological species belonging to six groups. Based on 2,301 data-points with high structural and experimental diversity, as well as on the usage of various advanced machine learning methods, the novel most predictive QSTR models display overall accuracies > 87% in both training and prediction sets. However, maximum external predictivity was achieved when a new multitasking consensus modeling approach was applied to these models. Additionally, the developed linear model provided means to investigate the determining factors for eliciting higher ecotoxicity by the EDCs towards different biological species, identifying several factors such as solvation, molecular mass and surface area as well as the number of specific molecular fragments (e.g.: aromatic hydroxy and aliphatic aldehyde). The resource to non-commercial open-access tools to develop the models is a useful step towards library screening to speed up regulatory decision on discovery of safe alternatives to reduce the hazards of EDCs.

Risk assessment of aromatic organic chemicals to T. pyriformis in environmental protection using regression-based QSTR and Read-Across algorithm

Hazardous aromatic compounds have a high probability of entering the surrounding environment, posing a threat to humans and other habitats. It is, thus, necessary to estimate the toxicity of these chemicals as a preventive measure before being marketed. The present study involves the development of in silico-based predictive 2D-QSTR models using a PLS regression approach for the exploration of the structural features responsible for the toxicity of T. pyriformis using simple and easily interpretable 2D descriptors employing a dataset containing 892 chemicals. The developed model was extensively validated by evaluating the model's reliability and predictability using internationally accepted external and internal validation metrics. We have also used the “Intelligent Consensus Predictor” tool and Read-Across software, which shows better results for test set compounds as compared with individual PLS models. We have also validated the models using a set of 383 external set compounds which are not used for the development of models. The results suggested that molecules with aliphatic aldehyde groups are much more toxic to the protozoan whereas chemicals containing C–N fragments at the topological distance 3 & 4, polar and alcoholic groups are less toxic to the protozoan. In conclusion, the developed QSTR and Read-Across models might be extremely beneficial as guides for researchers to estimate the toxicity profile of novel compounds against T. pyriformis.

Random forest algorithm-based accurate prediction of chemical toxicity to Tetrahymena pyriformis

The random forest (RF) algorithm, together with ten Dragon descriptors, was used to develop a quantitative structure–toxicity/activity relationship (QSTR/QSAR) model for a larger data set of 1792 chemical toxicity pIGC50 towards Tetrahymena pyriformis. The optimal RF (ntree =300 and mtry =3) model yielded root mean square (rms) errors of 0.261 for the training set (1434 chemicals) and 0.348 for the test set (358 chemicals). Compared with other QSTR models reported in the literature, the optimal RF model in this paper is more accurate. The feasibility of applying the RF algorithm to predict chemical toxicity pIGC50 towards Tetrahymena pyriformis has been verified.

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

: Health and environmental hazards by benzene and nitrobenzene derivatives has remained 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 models explaining Scenedesmus obliquus toxicity trends among 39 benzene derivatives and Tetrahymena pyriformis toxicity of 103 nitrobenzene 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, 0.8283) and robustness (Q2 LOO =0.7507, 0.7227, 0.8194; Q2 LMO =0.7338, 0.7153, 0.8172). The external predictivity of all the models are quite good (R2 EXT =0.8256, 0.9349, 0.8698). Electronegativity, Cosmo volume, total energy and molecular weight are responsible for increase and decrease of toxicity of benzene derivatives against Scenedesmus obliquus while electronegativity, electrophilicity index, heat of formation, total energy, hydrophobicity and cosmo volume are responsible for modulation of toxicity of nitrobenzene and benzene derivatives towards Tetrahymena pyriformis. These models fulfill the requirements of all the five OECD principles.

Ecotoxicological QSTR and QSTTR Modeling for the Prediction of Acute Oral Toxicity of Pesticides against Multiple Avian Species.

The ever-increasing use of pesticides in response to the rising agricultural demand has threatened the existence of nontarget organisms like avian species, disrupting the global ecological integrity. Therefore, it is critical to protect and restore different endangered bird species from the perspective of ecosystem safety. In the present work, we have developed regression-based two-dimensional quantitative structure toxicity relationship (2D QSTR) and quantitative structure toxicity-toxicity relationship (QSTTR) models to estimate the toxicity of pesticides on five different avian species following the Organization for Economic Co-operation and Development (OECD) guidelines. Rigorous validation has been performed using different statistical internal and external validation parameters to ensure the robustness and interpretability of the developed models. From the developed models, it can be stated that the presence of electronegative and lipophilic features greatly enhance pesticide toxicity, whereas the hydrophilic characters are shown to have a detrimental impact on the toxicity of pesticides. Moreover, the developed QSTTR models have been employed to the in silico toxicity prediction of 124, 154, and 250 pesticides against bobwhite quail, ring-necked pheasant, and mallard duck species, respectively, extracted from the Office of Pesticides Program (OPP) Pesticide Ecotoxicity Database. The information obtained from the modeled descriptors might be used for pesticide risk assessment in the future, with the added benefit of providing an early caution of their possible negative impact on birds for regulatory purposes.

Quantitative structure–toxicity relationship models for predication of toxicity of ionic liquids toward leukemia rat cell line IPC-81 based on index of ideality of correlation

The application of ionic liquids (ILs) as green solvents has attracted the attention of the scientific community. However, ILs may play the role of toxins. Even though ionic liquids may assist to minimize air pollution, but their discharge into aquatic ecosystems might result in significant water pollution due to their potential toxicity and inaccessibility to biodegradation. Recently, more attention has been paid to the toxicity of ILs on plants, bacteria, and humans. Here, a quantitative structure–toxicity relationship study (QSTR) based on the Monte Carlo method of CORAL software has been applied to estimate the logarithm of the half-maximal effective concentration of toxicity of ILs against leukemia rat cell line IPC-81 (logEC50). A hybrid optimal descriptor is used to build QSTR models for a large set of 304 diverse ILs including ammonium, imidazolium, morpholinium, phosphonium, piperidinium, pyridinium, pyrrolidinium, quinolinium, sulfonium, and protic ILs. The SMILES notations of ILs are utilized to compute the descriptor correlation weight (DCW). Four splits are made from the whole dataset and each split is randomly divided into four sets (training subsets and validation set). The index of ideality of correlation (IIC) is applied to evaluate the authenticity and robustness of the QSTR models. A QSTR model with statistical parameters R 2 = 0.85, CCC = 0.92, Q 2 = 0.84, and MAE = 0.25 for the validation set of the best split is considered as a prime model. The outliers and promoters of increase/decrease of logEC50 are extracted and the mechanistic interpretation of effective descriptors for the model is also offered. Highlights Global SMILES-based QSAR model was developed to predict the toxicity of ILs. The CORAL software is used to model the ILs toxicity on IPC-81 leukemia rat cell line. IIC is tested as a criterion of predictive potential. The toxicological effects of ILs are discussed based on the proposed model.

Identification of concealed structural alerts using QSTR modeling for Pseudokirchneriella subcapitata.

In the present work, QSTR modeling was conducted for microalga Pseudokirchneriella subcapitata using a data set of 271 molecules belonging to different types of chemical classes for the prediction of EC50 for 72 hr based assays. The balanced QSTR model encompasses seven easily interpretable molecular descriptors and possesses statistical robustness with high predictive ability. This Genetic Algorithm Multi-linear regression (GA-MLR) model was subjected to internal validation, Y-randomization test, applicability domain analysis, and external validation as per the recommended OECD guidelines. The newly developed model fulfilled the threshold values for more than 20 recommended validation parameters including R2=0.72, Q2LOO=0.70, etc. The developed QSTR model was successful in identifying the type of hybridization or specific type of atoms of previously reported and newer structural alerts. Thus, the model could be useful for data gap filling and expanding mechanistic interpretation of toxicity for different chemicals.

Support vector machine-based model for toxicity of organic compounds against fish

Predicting the toxicity of chemicals to various fish species through chemometric approach is crucial for ecotoxicological assessment of existing as well as not yet synthesized chemicals. This paper reports a quantitative structure–activity/toxicity relationship (QSAR/QSTR) model for the toxicity pLC50 of organic chemicals against various fish species. Only six descriptors were used to develop the QSTR model, by applying support vector machine (SVM) together with genetic algorithm. The QSTR model was trained and established on a sufficiently large data set of 840 organic compounds and evaluated with a test set (281 compounds). Compared with other QSTRs reported in the literature, the optimal SVM model for fish toxicity produces better statistical results with determination coefficients R2 above 0.70 for both the training set and test set, although the QSTR model in this work possesses fewer molecular descriptors. Applying SVM and genetic algorithm to develop the QSTR model for pLC50 of organic compounds against various fish species is successful.

Norm index in QSTR work for predicting toxicity of ionic liquids on Vibrio fischeri

Ionic liquids have been becoming new ‘green solvent’ because of the low saturation vapor pressure, less volatilization and more recycling utilization. Since most ILs are soluble in water, it should be indispensable to evaluate the ecotoxicology effect of ILs on aquatic environment before using them widely. Based on the concept of norm index, a set of norm descriptors were proposed for anions, cations and ILs. The whole IL structure optimization method has been used to build a predictive norm index-based quantitative structure-toxicity relationship model for the toxicity of ILs on Vibrio fischeri. Statistical results indicated that norm descriptors were reliable and robust in expressing the relationship between structural information and toxicity of ILs. Meanwhile, a series of ILs without experimental values were predicted based on this stable QSTR model. The results indicated that for imidazole-based ILs, an increase in the length of substituent in the branch could enhance the toxicity of ILs on Vibrio fischeri, and the branch contains hydroxyl group, double bond or triple bonds might reduce the toxicity of ILs. Results obtained in this present work would be valuable for the molecular design and the toxicity evaluation toward aquatic organism of ILs.

QSAR, QSTR, and molecular docking studies of the anti-proliferative activity of phenylpiperazine derivatives against DU145 prostate cancer cell lines

BackgroundProstate cancer is the most common non-cutaneous cancer in males and accounts for about 4% of all cancer-related deaths in males annually. In silico methods provide faster, economical, and environmentally friendly alternatives to the traditional trial and error method of lead identification and optimization. This study, therefore, was aimed at building a robust QSAR and QSTR model to predict the anti-proliferate activity and toxicity of some phenylpiperazine compounds against the DU145 prostate cancer cell lines and normal prostate epithelial cells as well as carry out molecular docking studies between the compounds and the androgen receptor.ResultsGenetic Function Algorithm–Multilinear Regression approach was employed in building the QSAR and QSTR model. The QSAR model built had statistical parameters R2 = 0.7792, R2adj. = 0.7240, Q2cv = 0.6607, and R2ext = 0.6049 and revealed the anti-proliferate activity to be strongly dependent on the molecular descriptors: VR3_Dzp, VE3_Dzi, Kier3, RHSA, and RDF55v. The QSTR model, on the other hand, had statistical parameters R2 = 0.8652, R2adj. = 0.8315, Q2cv = 0.7788, and R2ext = 0.6344. The toxicity of the compounds was observed to be dependent on the descriptors MATS8c, MATS3s, ETA_EtaP_F, and RDF95m. The molecular descriptors in both models were poorly correlated (R < 0.4) and had variance inflation factors < 3. Molecular docking studies between the androgen receptor and compounds 25 and 32 revealed the compounds primarily formed hydrogen, halogen, and hydrophobic interactions with the receptor.ConclusionFindings from this study can be employed in in silico design of novel phenylpiperazine compounds. It can also be employed in predicting the toxicity and anti-proliferate activity of other phenylpiperazine compounds against DU145 prostate cancer cell lines.

Is intraspecies QSTR model answer to toxicity data gap filling: Ecotoxicity modeling of chemicals to avian species

Interspecies model represents an established approach for the response data gap filling for regulatory agencies and researchers. We propose a novel approach of intraspecies modeling within the animals of the same species, instead of animals from different species. The proposed intraspecies model is capable of more precise extrapolation of data than the interspecies model, as animals under the same species share a similar mechanism of action (MOA) and target sites for the response. Along with the advantage of better prediction over the interspecies model, the intraspecies model has all the significant features like recognition of MOA, species-specific toxicity, reduction of animal experimentation, and money and time. To establish and test the intraspecies modeling approach, we have modeled ecotoxicity of organic chemicals to three avian species: Anas platyrhynchos, Colinus virginianus, and Phasianus colchicus. The intraspecies models offer to identify the mechanistic interpretation of the ecotoxicity of the studied chemicals along with the toxicity data gap filling. The success of the intraspecies modeling relies on connecting the missing dots of toxicity for the regulatory purposes, especially when there is a scarcity of ecotoxicity experimental data and in silico models for avian species.

Quantitative structure-toxicity relationships of organic chemicals against Pseudokirchneriella subcapitata

Predicting the toxicity of organic toxicants to aquatic life through chemometric approach is challenging area. In this paper, a six-descriptor quantitative structure–activity/toxicity relationship (QSAR/QSTR) model was successfully developed for the toxicity pEC10 of organic chemicals against Pseudokirchneriella subcapitata, by applying support vector machine (SVM) together with genetic algorithm. A sufficiently large data set consisting of 334 organic chemicals was randomly divided into a training set (167 compounds) and a test set (167 compounds) with a ratio of 1:1. The optimal SVM model possesses coefficient of determination R2 of 0.76 and mean absolute error (MAE) of 0.60 for the training set and R2 of 0.75 and MAE of 0.61 for the test set. Compared with other models reported in the literature, our SVM model for the toxicity pEC10 shows significant statistical quality and satisfactory predictive ability, although it has fewer molecular descriptors and more samples in the test set. A QSTR model for pEC50 of organic chemicals against Pseudokirchneriella subcapitata was also developed with the same subsets and molecular descriptors.

Identification of structural fingerprints for in vivo toxicity by using Monte Carlo based QSTR modeling of nitroaromatics

Monte Carlo based method by using either SMILES based or combination of SMILES and Graph-based descriptors is an important strategy to build the QSAR/QSTR model for prediction of different biological endpoints. In this study, Monte Carlo based QSTR approach was applied to the dataset of 90 nitroaromatic compounds related to their in vivo toxicity, represented by 50% lethal dose concentration for rats (LD50). Both classification and regression-based QSTR models were developed to get an idea about different fingerprints for promoters and hinderers of nitroaromatics toxicity. The best classification model was obtained by using SMILES and graph-based (GAO) descriptor with 1ECK connectivity (sensitivity = 0.7143, specificity = 1.0000, accuracy = 0.8889, and MCC = 0.7774). The best regression model calculated by using SMILES and hydrogen-suppressed graph descriptors with 0ECk connectivity (R2 = 0.7386, Q2 = 0.6315, S = 0.467, and MAE = 0.340). Finally, a consensus QSTR model was generated to predict efficiently the toxicity of new compounds. The study highlighted that the comparative QSTR models by using the Monte Carlo method can also be generated and will be a useful tool for structural fingerprint analysis in case of nitroaromatics for preliminary evaluation of its toxicity to mammals.

QSAR Models for Cytotoxicity of Chlorinated Alkanes

Knowledge on the degree of toxicity of aliphatic hydrocarbons and their derivatives has become essential for environmental safety and security. In the present study, an effort has been put forth to analyze the toxicity of aliphatic hydrocarbons. This work focuses on development of new QSAR models with various DFT based descriptors. Cytotoxicity of Chlorinated alkanes with experimental activity log EC50 Values are utilized to obtain QSTR models

Norm index-based QSTR model to predict the eco-toxicity of ionic liquids towards Leukemia rat cell line

The evaluation of eco-toxicity of ionic liquids (ILs) in the aquatic environment is essential for their safe utilization and QSTR approach plays an important role in obtaining the eco-toxicity data of ILs with diverse structures. Usually, the descriptors used to build QSTR model were made up of anion and cation descriptors, and their interactions were often neglected to some extent. In this work, based on the optimization of the ILs structure, a new set of descriptors were proposed to describe the interaction between anions and cations, and some new atomic distribution matrices were constructed to calculate norm descriptors of ILs, anion and cation. A norm index-based QSTR model was built to predict the eco-toxicity of ILs toward Leukemia rat cell line (IPC-81). This model has satisfactory statistical results with the R2 of 0.954 and RMSE of 0.241, respectively. Furthermore, leave-one-out cross-validation and applicability domain results showed good stability and predictability of this model. This approach showed that the interaction between cations and anions could be reflected by optimizing the whole structure of ILs which might play an important role for describing the eco-toxicity of ILs. Therefore, it is further suggested that the norm descriptors would be applicable to predict the eco-toxicity of ILs towards IPC-81.