Structure-toxicity Relationships Research Articles

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.

Comprehensive ecotoxicological assessment of pesticides on multiple avian species: Employing quantitative structure-toxicity relationship (QSTR) modeling and read-across

The rapid increase in the use of pesticides is driven by the growing demand in the agricultural sector. However, the widespread application of these pesticides and their inherent toxicity have significant repercussions on the ecosystem, particularly impacting animal and bird species. In this present study, we have developed four 2D quantitative structure-toxicity relationships (QSTRs) models for four different avian species using the largest number of available experimental data points to date employing the partial least squares (PLS) algorithm. Furthermore, we have also performed the read-across algorithm to improve the test set results. Based on the information derived from the models, it was found that hydrophilic characteristics, the presence of molecular branching and thio imide groups impact negatively to the pesticide toxicity, while the presence of phosphate group, presence of halogens viz. chlorine and bromine atoms, presence of hetero atoms, high molecular weight, presence of bridgehead atoms, presence of secondary aliphatic amide and fragments like RCONHR escalates avian toxicity. The developed QSTR models were further employed to predict the Pesticide Properties DataBase (PPDB) for all four avian species as a measure of data gap-filling and risk assessment. Thus, the developed models can be utilized for eco-toxicological data-gap filling, prediction of toxicity of untested pesticides as well as the development of novel and safe environmental-friendly pesticides.

Chemometrics-driven prediction and prioritization of diverse pesticides on chickens for addressing hazardous effects on public health

The extensive use of various pesticides in the agriculture field badly affects both chickens and humans, primarily through residues in food products and environmental exposure. This study offers the first quantitative structure-toxicity relationship (QSTR) and quantitative read-across-structure toxicity relationship (q-RASTR) models encompassing the LOEL and NOEL endpoints for acute toxicity in chicken, a widely consumed protein. The study's significance lies in the direct link between chemical toxicity in chicken, human intake, and environmental damage. Both the QSTR and the similarity-based read-across algorithms are applied concurrently to improve the predictability of the models. The q-RASTR models were generated by combining read-across derived similarity and error-based parameters, alongside structural and physicochemical descriptors. Machine Learning approaches (SVM and RR) were also employed with the optimization of relevant hyperparameters based on the cross-validation approach, and the final test set prediction results were compared. The PLS-based q-RASTR models for NOEL and LOEL endpoints showed good statistical performance, as traced from the external validation metrics Q2F1: 0.762–0.844; Q2F2: 0.759–0.831 and MAEtest: 0.195–0.214. The developed models were further used to screen the Pesticide Properties DataBase (PPDB) for potential toxicants in chickens. Thus, established models can address eco-toxicological data gaps and development of novel and safe eco-friendly pesticides.

Exploring Structure–Toxicity Relationships in Nitrobenzene and Derivatives: A Multifaceted Biochemical Investigation Using 3D–QSPR, HQSPR, Molecular Docking, and MD Simulation

AbstractNitrobenzene derivatives present an environmental concern to aquatic organisms, as well as a potential adverse effects on human health due to their toxicity. Therefore, monitoring nitrobenzene contamination levels is critical to protect both the ecosystem and the public. This study aims to quantitatively correlate the toxicity of nitrobenzene derivatives to their chemical structures using computational chemistry methods such as 3D‐QSPR, HQSPR, Molecular Docking and MD simulation. Additionally, to investigate the type of interactions and stability of nitrobenzene derivatives with the PP2 protein involved in the Phloem protein 2 (PP2). From each computational method a prediction model was extracted HQSPR (Atomic, Bonds, Donor/Acceptor, 3D‐QSPR (Steric, Electrostatic, Hydrophobic, Acceptor), and used for the design of X1 and X2 new nitrobenzene derivatives, which showed remarkable stability and better interactions. This study contributes to the understanding of the nitrobenzene derivatives’ toxicity and offers materials towards the understanding of the mechanism of action of these substances in protein–ligand interactions.

Proteomic and cardiac dysregulation by representative perfluoroalkyl acids of different chemical speciation during early embryogenesis of zebrafish

Perfluoroalkyl acids (PFAAs) of different chemical speciation were previously found to cause diverse toxicity. However, the toxicological mechanisms depending on chemical speciation are still largely unknown. In this follow-up study, zebrafish embryos were acutely exposed to only one concentration at 4.67 μM of the acid and salt of representative PFAAs, including perfluorooctanoic acid (PFOA), perfluorobutane carboxylic acid (PFBA), and perfluorobutanesulfonic acid (PFBS), till 96 h post-fertilization (hpf), aiming to gain more mechanistic insights. High-throughput proteomics found that PFAA acid and salt exerted discriminative effects on protein expression pattern. Bioinformatic analyses based on differentially expressed proteins underlined the developmental cardiotoxicity of PFOA acid with regard to cardiac muscle contraction, vascular smooth muscle contraction, adrenergic signaling in cardiomyocytes, and multiple terms related to myocardial contraction. PFOA salt and PFBS acid merely disrupted the cardiac muscle contraction pathway, while cardiac muscle cell differentiation was significantly enriched in PFBA acid-exposed zebrafish larvae. Consistently, under PFAA exposure, especially PFOA and PFBS acid forms, transcriptional levels of key genes for cardiogenesis and the concentrations of troponin and epinephrine associated with myocardial contraction were significantly dysregulated. Moreover, a transgenic line Tg (my17: GFP) expressing green fluorescent protein in myocardial cells was employed to visualize the histopathology of developing heart. PFOA acid concurrently caused multiple deficits in heart morphogenesis and function, which were characterized by the significant increase in sinus venosus and bulbus arteriosus distance (SV-BA distance), the induction of pericardial edema, and the decrease in heart rate, further confirming the stronger toxicity of PFOA acid than the salt counterpart on heart development. Overall, this study highlighted the developmental cardiotoxicity of PFAAs, with potency ranking PFOA > PFBS > PFBA. The acid forms of PFAAs induced stronger cardiac toxicity than their salt counterparts, providing an additional insight into the structure-toxicity relationship.

A Relationship between the Structures and Neurotoxic Effects of Aβ Oligomers Stabilized by Different Metal Ions.

Oligomeric assemblies of the amyloid β peptide (Aβ) have been investigated for over two decades as possible neurotoxic agents in Alzheimer's disease. However, due to their heterogeneous and transient nature, it is not yet fully established which of the structural features of these oligomers may generate cellular damage. Here, we study distinct oligomer species formed by Aβ40 (the 40-residue form of Aβ) in the presence of four different metal ions (Al3+, Cu2+, Fe2+, and Zn2+) and show that they differ in their structure and toxicity in human neuroblastoma cells. We then describe a correlation between the size of the oligomers and their neurotoxic activity, which provides a type of structure-toxicity relationship for these Aβ40 oligomer species. These results provide insight into the possible role of metal ions in Alzheimer's disease by the stabilization of Aβ oligomers.

Theoretical Studies on the Quantitative Structure-Toxicity Relationship of Polychlorinated Biphenyl Congeners Reveal High Affinity Binding to Multiple Human Nuclear Receptors.

Polychlorinated biphenyls (PCBs) are organic chemicals consisting of a biphenyl structure substituted with one to ten chlorine atoms, with 209 congeners depending on the number and position of the chlorine atoms. PCBs are widely known to be endocrine-disrupting chemicals (EDCs) and have been found to be involved in several diseases/disorders. This study takes various molecular descriptors of these PCBs (e.g., molecular weight) and toxicity endpoints as molecular activities, investigating the possibility of correlations via the quantitative structure-toxicity relationship (QSTR). This study then focuses on molecular docking and dynamics to investigate the docking behavior of the strongest-binding PCBs to nuclear receptors and compares these to the docking behavior of their natural ligands. Nuclear receptors are a family of transcription factors activated by steroid hormones, and they have been investigated to consider the impact of PCBs on humans in this context. It has been observed that the docking affinity of PCBs is comparable to that of the natural ligands, but they are inferior in terms of stability and interacting forces, as shown by the RMSD and total energy values. However, it is noted that most nuclear receptors respond to PCBs similarly to how they respond to their natural ligands-as shown in the RMSF plots-the most similar of which are seen in the ER, THR-β, and RAR-α. However, this study is performed purely in silico and will need experimental verification for validation.

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.

Prioritization of the ecotoxicological hazard of PAHs towards aquatic species spanning three trophic levels using 2D-QSTR, read-across and machine learning-driven modelling approaches

Polycyclic aromatic hydrocarbons (PAHs) represent a common group of environmental pollutants that endanger various aquatic organisms via various pathways. To better prioritize the ecotoxicological hazard of PAHs to aquatic environment, we used 2D descriptors-based quantitative structure-toxicity relationship (QSTR) to assess the toxicity of PAHs toward six aquatic model organisms spanning three trophic levels. According to strict OECD guideline, six easily interpretable, transferable and reproducible 2D-QSTR models were constructed with high robustness and reliability. A mechanistic interpretation unveiled the key structural factors primarily responsible for controlling the aquatic ecotoxicity of PAHs. Furthermore, quantitative read-across and different machine learning approaches were employed to validate and optimize the modelling approach. Importantly, the optimum QSTR models were further applied for predicting the ecotoxicity of hundreds of untested/unknown PAHs gathered from Pesticide Properties Database (PPDB). Especially, we provided a priority list in terms of the toxicity of unknown PAHs to six aquatic species, along with the corresponding mechanistic interpretation. In summary, the models can serve as valuable tools for aquatic risk assessment and prioritization of untested or completely new PAHs chemicals, providing essential guidance for formulating regulatory policies.

In silico soil degradation and ecotoxicity analysis of veterinary pharmaceuticals on terrestrial species: first report.

With the aim of persistence property analysis and ecotoxicological impact of veterinary pharmaceuticals on different terrestrial species, different classes of veterinary pharmaceuticals (n=37) with soil degradation property (DT50) were gathered and subjected to QSAR and q-RASAR model development. The models were developed from 2D descriptors under organization for economic cooperation and development guidelines with the application of multiple linear regressions along with genetic algorithm. All developed QSAR and q-RASAR were statistically significant (Internal=R2adj: 0.721-0.861, Q2LOO: 0.609-0.757, and external=Q2Fn=0.597-0.933, MAEext=0.174-0.260). Further, the leverage approach of applicability domain assured the model's reliability. The veterinary pharmaceuticals with no experimental values were classified based on their persistence level. Further, the terrestrial toxicity analysis of persistent veterinary pharmaceuticals was done using toxicity prediction by computer assisted technology and in-house built quantitative structure toxicity relationship models to prioritize the toxic and persistent veterinary pharmaceuticals. This study will be helpful in estimation of persistence and toxicity of existing and upcoming veterinary pharmaceuticals.

Computational investigation of pyrazinamide drugs and its transition metal complexes using a DFT approach.

Pyrazinamide, an antituberculosis but documented toxic drug, is subjected to computational investigation along with the metal complexes via a DFT approach to predict the structure-activity and structure-toxicity relationship. 6-31G(d,p) basis set was used for Zn, Ni, Mn, Fe, and Co, while the SDD basis set was applied to Cu, Cr, Cd, and Hg. Several reactivity parameters and charge distribution were calculated and the reactivity profile was estimated. The complexes were found to be soft and polarizable which could be responsible for their binding with bacterial targets to inhibit their growth. In contrast, pyrazinamide which is found to be hard among all is susceptible to being toxic. Moreover, the electronegative nature of the complexes can endow them with a better antibacterial effect. Since metal complexes have been found to be less toxic and more biologically interactive by computational methods, they can be employed as potent drugs for the cure of tuberculosis.

A MULTIVARIATE INTERPOLATION APPROACH FOR PREDICTING DRUG LD50 VALUE

Objective: The present study aimed to develop a multivariate interpolation based on the quantitative structure-toxicity relationship (QSTR) that can accurately predict the oral median lethal dose (LD50) values of drugs in mice by considering five different toxicologic endpoints. Material and Method: A mathematical model was created using a comprehensive dataset comprising LD50 values from 319 pharmaceuticals belonging to various pharmacological classes. We developed a polynomial model that can predict the range of LD50 values for pharmaceuticals. We employed a technique called two-variable polynomial interpolation. This method allowed us to estimate the approximate values of a function at any point within a two-dimensional (2D) space by utilizing a polynomial equation. Result and Discussion: The resulting model demonstrated the ability to predict LD50 values for new or untested drugs, rendering it a valuable tool in the early stages of drug development. The Ghose-Crippen-Viswanadhan octanol-water partition coefficient (ALogP) and Molecular Weight (MW) were selected as suitable descriptors for building the best QSAR model. Based on our evaluation, the model achieved an overall success rate of 86.73%. Compared to traditional experimental methods for LD50 determination, this innovative approach offers time and cost efficiency while reducing animal testing requirements. Our model can improve drug safety, optimize dosage regimens, and assist decision-making processes during preclinical studies and drug development. This approach provided a reliable and efficient method for preliminary acute toxicity assessments.

Predicting the acute toxicity of organophosphate esters (OPEs) to aquatic organisms by modelling the structure-toxicity relationships using partial least square regression

Organophosphate esters (OPEs) have been used worldwide as organophosphate flame retardants (OPFRs) since brominated flame retardants (BFRs) were banned. Due to the toxicity of these OPEs, environmental concerns and ecological risks arose. However, there are still large gaps in the understanding of their toxicity to organisms and the mechanisms of toxicity. After collecting the existing toxicity information and obtaining molecular descriptors of OPEs, a partial least square (PLS) regression model was used in this study to quantify the structure-toxicity relationships of OPEs. Based on the regression results, the acute toxicity of the remaining OPEs lacking acute toxicity data was predicted, and the risk level of total common OPEs was classified. The acute toxicity of 15 chemicals was collected, and >1660 molecular structure descriptors were obtained. The cross-validation results of the partial least square regression indicated that two principal components met the regression requirements with the selected features, and the regression equations of these chemicals were generated with selected molecular descriptors. The influence of physicochemical properties, such as hydrophobicity/molecular weight, in traditional perception of OPE toxicity was not that obvious, and acute toxicity was mainly influenced by the autocorrelation coefficients. However, the regression results indicated that the correlation between autocorrelation coefficients calculated based on different physicochemical properties and toxicity was different. According to the prediction result based on PLS regression, CDP may pose a high risk and halogenated alkyl-substituted OPEs such as TCEP may be less toxic. The results of the present study may help inform the environmental management and risk assessment of emerging chemicals such as OPEs.

The Number of Ethylene Oxide Groups of Sulphate-Based Surfactants Influences the Cytotoxicity of Mixed Micelles to an Amphibian Cell Line

Sodium lauryl ether sulphate (SLEnS) is an anionic surfactant that is widely used in many fields, such as cosmetics and detergents, among others. This study evaluated the influence of the number of ethylene oxide (EO) units, present in the head group of SLEnS, on its cytotoxicity to the A6 cell line of Xenopus laevis using MTT and resazurin assays. The A6 cell line was exposed for 48 h to six SLEnS variants: SLE0S, SLE1S, SLE4S, SLE11S, SLE30S, and SLE50S (subscript values correspond to the number of EO units). Overall, the six variants impaired the A6 cells’ viability at low concentrations for the MTT assay, with the median lethal concentrations (LC50,48h) ranging between 0.398 and 0.554 mg/L and for the resazurin assay between 0.557 and 0.969 mg/L. Further, the obtained results indicate SLEnS variants with fewer EO units to be the most cytotoxic in the resazurin assay; although a similar cytotoxicity pattern was observed with the MTT assay, a significant association between the number of EO units and the values of LC50 was not found. This result highlights the usefulness of in vitro assays with A6 cell lines as a first screening tool for assessing the structure–toxicity relationship of this type of surfactant, also providing a baseline for the development of environmentally friendlier chemical compounds while still maintaining their efficiency.

Machine Learning-Based Prediction of Drug-Induced Hepatotoxicity: An OvA-QSTR Approach.

Drug-induced hepatotoxicity, also known as drug-induced liver injury (DILI), is among the possible adverse effects of pharmacotherapy. This clinical condition is accepted as one of the factors leading to patient mortality and morbidity. The LiverTox database was built by the National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK) to predict potential liver damage from medications and take appropriate precautions. The database has classified medicines into seven risk categories (A, B, C, D, E, E*, and X) to avoid medicine-induced liver toxicity. The hepatic damage risk decreases from group A to group E. This study did not include the E* and X classes because they contained unverified and unknown data groups. Our study aims to predict potential liver damage of new drug molecules without using experimental animals. We predict which of the LiverTox risk category drugs with unknown liver toxicity potential will fall into using our one-vs-all quantitative structure-toxicity relationship (OvA-QSTR) model. Our dataset, consisting of 678 organic drug molecules from different pharmacological classes, was collected from LiverTox. The OvA-QSTR models implemented by Bayesian Network (BayesNet) performed well based on the selected descriptors, with the precision-recall curve (PRC) areas ranging from 0.718 to 0.869. Our OvA-QSTR models provide a reliable premarketing risk evaluation of pharmaceutical-induced liver damage potential and offer predictions for different risk levels in DILI.

Implementing comprehensive machine learning models of multispecies toxicity assessment to improve regulation of organic compounds

Machine learning has made significant progress in assessing the risk associated with hazardous chemicals. However, most models were constructed by randomly selecting one algorithm and one toxicity endpoint towards single species, which may cause biased regulation of chemicals. In the present study, we implemented comprehensive prediction models involving multiple advanced machine learning and end-to-end deep learning to assess the aquatic toxicity of chemicals. The generated optimal models accurately unravel the quantitative structure-toxicity relationships, with the correlation coefficients of all training sets from 0.59 to 0.81 and of the test sets from 0.56 to 0.83. For each chemical, its ecological risk was determined from the toxicity information towards multiple species. The results also revealed the toxicity mechanism of chemicals was species sensitivity, and the high-level organisms were faced with more serious side effects from hazardous substances. The proposed approach was finally applied to screen over 16,000 compounds and identify high-risk chemicals. We believe that the current approach can provide a useful tool for predicting the toxicity of diverse organic chemicals and help regulatory authorities make more reasonable decisions.

The use of global and local reactivity descriptors of conceptual DFT to describe toxicity of benzoic acid derivatives

The present study explores the use of global and local quantum reactivity descriptors and conceptual density functional theory (cDFT), as well as descriptors of atomic-quantum topology (QTAIM), to describe the structure–toxicity relationship (STR) for 37 compounds derived from benzoic acid, employing the median lethal dose logarithm (log LD50) as a parameter. The method was validated using a coefficient of determination (R2) of 0.8285 and a leave-one-out cross-validation (Q2LOO) of 0.7138, thus fulfilling the statistical criteria indicated for models generated in QSARINS software. The descriptors closely related to the toxicity of the compounds were EHOMO(N−1), ∇2ρ(r)(BCP2), ∇2ρ(r)(BCP5), QH4Hirshfeld, QYY(C1), and ∑q-meta. This finding both enabled the reactivity of the benzoic acid derivatives to be interpreted and facilitated an understanding of the potential chemical bonds with enzymes, such as serine, in a biological environment.

Simultaneous Determination of the Size and Shape of Single α-Synuclein Oligomers in Solution.

Soluble oligomers of amyloid-forming proteins are implicated as toxic species in the context of several neurodegenerative diseases. Since the size and shape of these oligomers influence their toxicity, their biophysical characterization is essential for a better understanding of the structure-toxicity relationship. Amyloid oligomers are difficult to characterize by conventional approaches due to their heterogeneity in size and shape, their dynamic aggregation process, and their low abundance. This work demonstrates that resistive pulse measurements using polymer-coated solid-state nanopores enable single-particle-level characterization of the size and shape of individual αSyn oligomers in solution within minutes. A comparison of the resulting size distribution with single-particle analysis by transmission electron microscopy and mass photometry reveals good agreement with superior resolution by nanopore-based characterization. Moreover, nanopore-based analysis has the capability to combine rapid size analysis with an approximation of the oligomer shape. Applying this shape approximation to putatively toxic oligomeric species that range in size from 18 ± 7 aggregated monomers (10S) to 29 ± 10 aggregated monomers (15S) and in concentration from picomolar to nanomolar revealed oligomer shapes that agree well with previous estimates by cryo-EM with the added advantage that nanopore-based analysis occurs rapidly, in solution, and has the potential to become a widely accessible technique.

Bio-sourced alternatives to diglycidyl ether of bisphenol A in epoxy–amine thermosets

Since the 1940s, bisphenol A (BPA) has been used in the plastic industry, reaching production of 10 Mt in 2022. More than 30% of the produced BPA is used in the production of epoxy resins. Decades of research has now provided enough evidence that BPA has endocrine-disrupting activity. Hence, it is an urgent matter to replace BPA in the production of epoxy resins. In the past years, considerable efforts have been put into finding alternatives to the toxic BPA. However, not only does the diglycidyl ether of BPA exhibit high polymerization reactivity, but also the presence of aromatic rings confers interesting thermomechanical resistance to epoxy networks therefrom. Hence, these properties are also expected from potential alternatives to BPA. In this review, first, the elements leading to toxicity of BPA are explained and then thorough accounts of possible bio-sourced aromatic alternatives to BPA are gathered. The reported synthetic routes for each of these alternatives and their toxicity are described. Also, their use in synthesis of epoxy resins and how the new alternatives influence the mechanical properties are discussed. This is a concise summary of the structure–property and structure–toxicity relationship for possible bio-sourced substitutes of BPA in synthesis of epoxy resins.

First report on ecotoxicological QSTR and i-QSTR modeling for the prediction of acute ecotoxicity of diverse organic chemicals against three protozoan species

The recent years have witnessed an upsurge of interest to assess the toxicity of organic chemicals exhibiting harmful impacts on the environment. In this investigation, we have developed regression-based quantitative structure-toxicity relationship (QSTR) models against three protozoan species (Entosiphon sulcantum, Uronema parduczi, and Chilomonas paramecium) using three sets of descriptor combinations such as ETA indices only, non-ETA descriptors only, and both ETA and non-ETA descriptors to examine the key structural features that determine the toxic properties of protozoa. The interspecies QSTR models (i-QSTRs) were also generated for efficient data gap-filling of toxicity databases. The statistical results of the validated models in terms of both internal and external validation metrics suggested that the models are statistically reliable and robust. Additionally, using these validated models, we screened the DrugBank database containing 11,300 pharmaceuticals for assessing the ecotoxicological properties. The features appearing in the models suggested that non-polar characteristics, electronegativity, hydrogen bonding, π–π, and hydrophobic interactions are responsible for chemical toxicity toward protozoan. The validated models may be utilized for the development of eco-friendly drugs & chemicals, data gap-filling of toxicity databases for regulatory purposes and research, as well as to decrease the use of toxic and hazardous chemicals in the environment.