Automated read-across workflow for predicting acute oral toxicity: I. The decision scheme in the QSAR toolbox

Cheminformatic Read-Across Approach Revealed Ultraviolet Filter Cinoxate as an Obesogenic Peroxisome Proliferator-Activated Receptor γ Agonist

This study introduces a novel cheminformatic read-across approach designed to identify potential environmental obesogens, substances capable of disrupting metabolism and inducing obesity by mainly influencing nuclear hormone receptors (NRs). Leveraging real-valued two-dimensional features derived from chemical fingerprints of 8435 Tox21 compounds, cluster analysis and subsequent statistical testing revealed 385 clusters enriched with compounds associated with specific NR targets. Notably, one cluster exhibited selective enrichment in peroxisome proliferator-activated receptor γ (PPARγ) agonist activity, prominently featuring methoxy cinnamate ultraviolet (UV) filters and obesogen-related compounds. Experimental validation confirmed that 2-ethoxyethyl 4-methoxycinnamate, an organic UV filter cinoxate, could selectively bind to PPARγ (Ki = 18.0 μM), eliciting an obesogenic phenotype in human bone marrow-derived mesenchymal stem cells during adipogenic differentiation. Molecular docking and further experiments identified cinoxate as a potent PPARγ full agonist, demonstrating a preference for coactivator SRC3 recruitment. Moreover, cinoxate upregulated transcription levels of genes encoding lipid metabolic enzymes in normal human epidermal keratinocytes as primary cells exposed during clinical usage. This study provides compelling evidence for the efficacy of cheminformatic read-across analysis in prioritizing potential obesogens, showcasing its utility in unveiling cinoxate as an obesogenic PPARγ agonist.

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.

The prediction of hydrolysis and biodegradation of organophosphorus-based chemical warfare agents (G-series and V-series) using toxicology in silico methods

Nerve agents (G- and V-series) are a group of extremely toxic organophosphorus chemical warfare agents that we have had the opportunity to encounter many times on a massive scale (Matsumoto City, Tokyo subway and Gulf War). The threat of using nerve agents in terrorist attacks or military operations is still present, even with establishing the Chemical Weapons Convention as the legal framework. Understanding their environmental sustainability and health risks is critical to social security. Due to the risk of contact with dangerous nerve agents and animal welfare considerations, in silico methods were used to assess hydrolysis and biodegradation safely. The environmental fate of the examined nerve agents was elucidated using QSAR models. The results indicate that the investigated compounds released into the environment hydrolyse at a different rate, from extremely fast (<1 day) to very slow (over a year); V-agents undergo slower hydrolysis compared to G-agents. V-agents turned out to be relatively challenging to biodegrade, the ultimate biodegradation time frame of which was predicted as weeks to months, while for G-agents, the overwhelming majority was classified as weeks. In silico methods for predicting various parameters are critical to preparing for the forthcoming application of nerve agents.

The prediction of acute toxicity (LD50) for organophosphorus-based chemical warfare agents (V-series) using toxicology in silico methods

Nerve agents are organophosphate chemical warfare agents that exert their toxic effects by irreversibly inhibiting acetylcholinesterase, affecting the breakdown of the neurotransmitter acetylcholine in the synaptic cleft. Due to the risk of exposure to dangerous nerve agents and for animal welfare reasons, in silico methods have been used to assess acute toxicity safely. The next-generation risk assessment (NGRA) is a new approach for predicting toxicological parameters that can meet modern requirements for toxicological research. The present study explains the acute toxicity of the examined V-series nerve agents (n = 9) using QSAR models. Toxicity Estimation Software Tool (ver. 4.2.1 and ver. 5.1.2), QSAR Toolbox (ver. 4.6), and ProTox-II browser application were used to predict the median lethal dose. The Simplified Molecular Input Line Entry Specification (SMILES) was the input data source. The results indicate that the most deadly V-agents were VX and VM, followed by structural VX analogues: RVX and CVX. The least toxic turned out to be V-sub x and Substance 100A. In silico methods for predicting various parameters are crucial for filling data gaps ahead of experimental research and preparing for the upcoming use of nerve agents.

Uncertainty in the results from oral repeated dose toxicity tests: Impact on regulatory classifications

The Lowest Observed (Adverse) Effect Level (LO(A)EL) values are point-of-departure (PoD) values that quantify repeat dose toxicity (RDT). Here, the uncertainty in the regulatory classification of these PoDs is investigated. In the application stage, the dose-response was approximated for a large set of series, giving an account of the possible presence of a hormesis zone. The minimal effect dose (MED) or dose was computed, and the ratio MED/LO(A)EL was used to represent the two components of the experimental uncertainty. The uncertainty estimations were calculated for any combination of gender and reported examination item. Subsequently, how this uncertainty affects the possible classifications was analyzed, and the percentage of the chemicals receiving ambiguous classification was determined. It was shown that more than 40% of the investigated chemicals cannot be classified unambiguously in the Globally Harmonized System (GHS) classification scheme and bear a potential for misclassification when a regulatory decision is based on a single LO(A)EL value. A table containing grey zones for different risk levels and a table with GHS classification distributions for various LO(A)EL values were prepared to facilitate the use of the RDT uncertainty in the practice.

The application of acute oral toxicity computational models in dangerous goods classification

Acute oral toxicity (AOT) data inform the acute toxicity potential of a compound and guides occupational safety and transportation practices. AOT data enable the categorization of a chemical into the appropriate AOT Globally Harmonized System (GHS) category based on the severity of the hazard. AOT data are also utilized to identify compounds that are Dangerous Goods (DGs) and subsequent transportation guidance for shipping of these hazardous materials. Proper identification of DGs is challenging for novel compounds that lack data. It is not feasible to err on the side of caution for all compounds lacking AOT data and to designate them as DGs, as shipping a compound as a DG has cost, resource, and time implications. With the wealth of available historical AOT data, AOT testing approaches are evolving, and in silico AOT models are emerging as tools that can be utilized with confidence to assess the acute toxicity potential of de novo molecules. Such approaches align with the 3R principles, offering a reduction or even replacement of traditional in vivo testing methods and can also be leveraged for product stewardship purposes. Utilizing proprietary historical in vivo AOT data for 210 pharmaceutical compounds (PCs), we evaluated the performance of two established in silico AOT programs: the Leadscope AOT Model Suite and the Collaborative Acute Toxicity Modeling Suite. These models accurately identified 94% and 97% compounds that were not DGs (GHS categories 4, 5, and not classified (NC)) suggesting that the models are fit-for-purpose in identifying PCs with low acute oral toxicity potential (LD50 >300 mg/kg). Utilization of these models to identify compounds that are not DGs can enable them to be de-prioritized for in vivo testing. This manuscript provides a detailed evaluation and assessment of the two models and recommends the most suitable applications of such models.

Estimating uncertainty in LLNA EC3 data and its impact on regulatory classifications

The murine Local Lymph Node Assay (LLNA) is a test that produces numerical results (EC3 values) quantifying the sensitization potency of chemicals. These results are broadly used in toxicology and serve as a basis for various classifications, which determine subsequent regulatory decisions. The continuing interest in LLNA data and the diminished likelihood of new experimental EC3 data being generated sparked this investigation of uncertainty. Instead of using the Gaussian distribution as a default choice for assessing variability in a data set, two strictly positive distributions were proposed and their performance over the available experimental EC3 values was tested. In the application stage, how the uncertainty in EC3 values affects the possible classifications was analyzed, and the percentage of the chemicals receiving ambiguous classification was determined. It was shown that this percentage is high, which increases the risk of improper classification. Two approaches were suggested in regulatory practice to address the uncertainty in the EC3 data: the approaches based on "grey zones" and the classification distribution. If a chemical cannot be classified unambiguously, the latter appears to be an acceptable means to assess the level of sensitization potency of chemicals and helps provide better regulatory decisions.