Novel computational models offer alternatives to animal testing for assessing eye irritation and corrosion potential of chemicals

Reexamining the acute toxicity of chloropicrin: Comprehensive estimation using in silico methods

Chloropicrin, historically infamous as a chemical warfare agent during World War I, has recently resurfaced in global conflicts, prompting a reevaluation of its acute toxicological significance. This study addresses the historical knowledge gap surrounding chloropicrin by employing in silico toxicology methods to estimate toxicophores and predict acute toxicity across various exposure routes. Allegations of its use in recent conflicts necessitate a deeper understanding of its toxicological profile, particularly in modern warfare scenarios. Qualitative analysis (STopTox and admetSAR) revealed chloropicrin to be toxic for oral, dermal, and inhalation administration, with the nitro group attached to the carbon atom identified as a significant contributor to its toxic profile. Quantitative in silico estimates, using multiple methods (TEST, ProTox-II, ADMETlab, ACD/Labs Percepta and QSAR Toolbox), indicated t-LD50 values of 48.71 mg/kg bw for oral exposure, 130.16 mg/kg bw for dermal exposure, and an inhalation t-LC50 of 0.022 mg/L. However, method inconsistencies and variability in dose conversion guidance highlight the importance of a cautious approach to interpreting results. Furthermore, the study explores the potential of in silico methods to reduce reliance on animal testing, providing a more efficient and humane alternative for toxicity assessments. The findings contribute to a comprehensive understanding of chloropicrin's acute toxicity, emphasising the relevance of in silico methods in guiding future toxicological studies and informing safety assessments in agricultural and wartime scenarios.

Toxicity of ACP-105: a substance used as doping in sports: application of in silico methods for prediction of selected toxicological endpoints

ACP-105 is a novel non-steroidal Selective Androgen Receptor Modulator (SARM) used by athletes. Its action aims to increase muscle mass and is one of the options in testosterone replacement therapy. Its safety profile remains insufficiently explored, particularly regarding its toxicity in humans. The lack of information about the studied compound in the World Anti-Doping Agency (WADA) became the purpose of this study. Given the increasing use of such compounds in sports, a deeper understanding of their biological risks is crucial. This study not only fills the gap in available information but also contributes to the growing body of research on SARMs, providing insights into their potential hazards and guiding future investigations into their safety. This work aimed to use various in silico techniques to predict the toxicity of ACP-105, including acute toxicity, effects on internal organs, genotoxicity based on the Ames test, eye and skin irritation, and cardiotoxicity by testing hERG inhibitors. A preliminary safety analysis of the compound was based on its chemical structure and interactions with biological targets using various in silico techniques: qualitative (STopTox, ADMETlab, admetSAR, ProTox 3.0, and Toxtree 3.1.0) and quantitative (TEST 5.1.2, Percepta, VEGA QSAR 1.2.3, and SL-Tox) to ensure that the prediction results are as accurate as possible.

Toxicity of Bromo-DragonFLY as a New Psychoactive Substance: Application of In Silico Methods for the Prediction of Key Toxicological Parameters Important to Clinical and Forensic Toxicology

Bromo-DragonFLY is a synthetic new psychoactive substance (NPS) that has gained attention due to its powerful and long-lasting hallucinogenic effects, legal status, and widespread availability. This study aimed to use various in silico toxicology methods to predict key toxicological parameters for Bromo-DragonFLY, including acute toxicity (LD50), genotoxicity, cardiotoxicity, health effects, and the potential for endocrine disruption. The results indicate significant acute toxicity with noticeable variations across different species, a low likelihood of genotoxic potential suggesting potential DNA damage, and a notable risk of cardiotoxicity associated with inhibition of the hERG channel. Evaluation of endocrine disruption suggests a low probability of Bromo-DragonFLY interacting with the estrogen receptor α (ER-α), indicating minimal estrogenic activity. These insights from in silico investigations are important for advancing our understanding of this NPS in forensic and clinical toxicology. These initial toxicological examinations establish a foundation for future research efforts and contribute to developing risk assessment and management strategies for using and misusing NPS.

ToxMPNN: A deep learning model for small molecule toxicity prediction

Machine learning (ML) has shown a great promise in predicting toxicity of small molecules. However, the availability of data for such predictions is often limited. Because of the unsatisfactory performance of models trained on a single toxicity endpoint, we collected toxic small molecules with multiple toxicity endpoints from previous study. The dataset comprises 27 toxic endpoints categorized into seven toxicity classes, namely, carcinogenicity and mutagenicity, acute oral toxicity, respiratory toxicity, irritation and corrosion, cardiotoxicity, CYP450, and endocrine disruption. In addition, a binary classification Common-Toxicity task was added based on the aforementioned dataset. To improve the performance of the models, we added marketed drugs as negative samples. This study presents a toxicity predictive model, ToxMPNN, based on the message passing neural network (MPNN) architecture, aiming to predict the toxicity of small molecules. The results demonstrate that ToxMPNN outperforms other models in capturing toxic features within the molecular structure, resulting in more precise predictions with the ROC_AUC testing score of 0.886 for the Toxicity_drug dataset. Furthermore, it was observed that adding marketed drugs as negative samples not only improves the predictive performance of the binary classification Common-Toxicity task but also enhances the stability of the model prediction. It shows that the graph-based deep learning (DL) algorithms in this study can be used as a trustworthy and effective tool to assess small molecule toxicity in the development of new drugs.

The estimation of acute oral toxicity (LD50) of G-series organophosphorus-based chemical warfare agents using quantitative and qualitative toxicology in silico methods

The idea of this study was the estimation of the theoretical acute toxicity (t-LD50, rat, oral dose) of organophosphorus-based chemical warfare agents from the G-series (n = 12) using different in silico methods. Initially identified in Germany, the G-type nerve agents include potent compounds such as tabun, sarin, and soman. Despite their historical significance, there is a noticeable gap in acute toxicity data for these agents. This study employs qualitative (STopTox and AdmetSAR) and quantitative (TEST; CATMoS; ProTox-II and QSAR Toolbox) in silico methods to predict LD50 values, offering an ethical alternative to animal testing. Additionally, we conducted quantitative extrapolation from animals, and the results of qualitative tests confirmed the acute toxicity potential of these substances and enabled the identification of toxicophoric groups. According to our estimations, the most lethal agents within this category were GV, soman (GD), sarin (GB), thiosarin (GBS), and chlorosarin (GC), with t-LD50 values (oral administration, extrapolated from rat to human) of 0.05 mg/kg bw, 0.08 mg/kg bw, 0.12 mg/kg bw, 0.15 mg/kg bw, and 0.17 mg/kg bw, respectively. On the contrary, compounds with a cycloalkane attached to the phospho-oxygen linkage, specifically methyl cyclosarin and cyclosarin, were found to be the least toxic, with values of 2.28 mg/kg bw and 3.03 mg/kg bw. The findings aim to fill the knowledge gap regarding the acute toxicity of these agents, highlighting the need for modern toxicological methods that align with ethical considerations, next-generation risk assessment (NGRA) and the 3Rs (replacement, reduction and refinement) principles.

The acute toxicity of Novichok's degradation products using quantitative and qualitative toxicology in silico methods

The emergence of Novichok agents, potent organophosphorus nerve agents, has spurred the demand for advanced analytical methods and toxicity assessments as a result of their involvement in high-profile incidents. This study focuses on the degradation products of Novichok agents, particularly their potential toxic effects on biological systems. Traditional in vivo methods for toxicity evaluation face ethical and practical constraints, prompting a shift toward in silico toxicology research. In this context, we conducted a comprehensive qualitative and quantitative analysis of acute oral toxicity (AOT) for Novichok degradation products, using various in silico methods, including TEST, CATMoS, ProTox-II, ADMETlab, ACD/Labs Percepta, and QSAR Toolbox. Adopting these methodologies aligns with the 3Rs principle, emphasising Replacement, Reduction, and Refinement in the realm of toxicological studies. Qualitative assessments with STopTox and admetSAR revealed toxic profiles for all degradation products, with predicted toxicophores highlighting structural features responsible for toxicity. Quantitative predictions yielded varied estimates of acute oral toxicity, with the most toxic degradation products being EOPAA, MOPGA, MOPAA, MPGA, EOPGA, and MPAA, respectively. Structural modifications common to all examined hydrolytic degradation products involve substituting the fluorine atom with a hydroxyl group, imparting consequential effects on toxicity. The need for sophisticated analytical techniques for identifying and quantifying Novichok degradation products is underscored due to their inherent reactivity. This study represents a crucial step in unravelling the complexities of Novichok toxicity, highlighting the ongoing need for research into its degradation processes to refine analytical methodologies and fortify readiness against potential threats.

FDA Modernization Act 2.0: An insight from nondeveloping country

Animal testing is required in drug development research and is crucial for assessing the efficacy and safety of medications before they are commercialized. However, the newly furnished Food and Drug Administration Modernization Act 2.0 has given new insight into drug development. It opens a new door by offering an alternative testing method for developing a new drug without using animals. This newly proposed system may potentially significantly impact nondeveloped countries worldwide. In this study, we explore the alternative testing options such as in silico modeling, human tissue-on-chip engineering, animal-free recombinant antibodies, tissue engineering, and artificial intelligence presented by this act and discuss its implications for nondeveloped countries.

Machine-learning based prediction models for assessing skin irritation and corrosion potential of liquid chemicals using physicochemical properties by XGBoost

Skin irritation test is an essential part of the safety assessment of chemicals. Recently, computational models to predict the skin irritation draw attention as alternatives to animal testing. We developed prediction models on skin irritation/corrosion of liquid chemicals using machine learning algorithms, with 34 physicochemical descriptors calculated from the structure. The training and test dataset of 545 liquid chemicals with reliable in vivo skin hazard classifications based on UN Globally Harmonized System [category 1 (corrosive, Cat 1), 2 (irritant, Cat 2), 3 (mild irritant, Cat 3), and no category (nonirritant, NC)] were collected from public databases. After the curation of input data through removal and correlation analysis, every model was constructed to predict skin hazard classification for liquid chemicals with 22 physicochemical descriptors. Seven machine learning algorithms [Logistic regression, Naïve Bayes, k-nearest neighbor, Support vector machine, Random Forest, Extreme gradient boosting (XGB), and Neural net] were applied to ternary and binary classification of skin hazard. XGB model demonstrated the highest accuracy (0.73-0.81), sensitivity (0.71-0.92), and positive predictive value (0.65-0.81). The contribution of physicochemical descriptors to the classification was analyzed using Shapley Additive exPlanations plot to provide an insight into the skin irritation of chemicals.

Supplementary Information

The online version contains supplementary material available at 10.1007/s43188-022-00168-8.