A review of the electrophilic reaction chemistry involved in covalent protein binding relevant to toxicity

Development of machine learning models for the prediction of the skin sensitization potential of cosmetic compounds

Background

To enhance the accuracy of allergen detection in cosmetic compounds, we developed a co-culture system that combines HaCaT keratinocytes (transfected with a luciferase plasmid driven by the AKR1C2 promoter) and THP-1 cells for machine learning applications.

Methods

Following chemical exposure, cell cytotoxicity was assessed using CCK-8 to determine appropriate stimulation concentrations. RNA-Seq was subsequently employed to analyze THP-1 cells, followed by differential expression gene (DEG) analysis and weighted gene co-expression net-work analysis (WGCNA). Using two data preprocessing methods and three feature extraction techniques, we constructed and validated models with eight machine learning algorithms.

Results

Our results demonstrated the effectiveness of this integrated approach. The best performing models were random forest (RF) and voom-based diagonal quadratic discriminant analysis (voomDQDA), both achieving 100% accuracy. Support vector machine (SVM) and voom based nearest shrunken centroids (voomNSC) showed excellent performance with 96.7% test accuracy, followed by voom-based diagonal linear discriminant analysis (voomDLDA) at 95.2%. Nearest shrunken centroids (NSC), Poisson linear discriminant analysis (PLDA) and negative binomial linear discriminant analysis (NBLDA) achieved 90.5% and 90.2% accuracy, respectively. K-nearest neighbors (KNN) showed the lowest accuracy at 85.7%.

Conclusion

This study highlights the potential of integrating co-culture systems, RNA-Seq, and machine learning to develop more accurate and comprehensive in vitro methods for skin sensitization testing. Our findings contribute to the advancement of cosmetic safety assessments, potentially reducing the reliance on animal testing.

In Silico Evaluation of Some Computer-Designed Fluoroquinolone-Glutamic Acid Hybrids as Potential Topoisomerase II Inhibitors with Anti-Cancer Effect

Background/Objectives: Fluoroquinolones (FQs) are topoisomerase II inhibitors with antibacterial activity, repositioned recently as anti-cancer agents. Glutamic acid (GLA) is an amino acid that affects human metabolism. Since an anti-cancer mechanism of FQs is human topoisomerase II inhibition, it is expected that FQ-GLA hybrids can act similarly. Methods: We designed 27 hypothetical hybrids of 6 FQs and GLA through amide bonds at the 3- and 7-position groups of FQs or via ethylenediamine/ethanolamine linkers at the carboxyl group of the FQ. Hydroxamic acid derivatives were also theoretically formulated. Computational methods were used to predict their physicochemical, pharmacokinetic, or toxicological properties and their anti-cancer activity. For comparison, etoposide was used as an anti-cancer agent inhibiting topoisomerase II. Molecular docking assessed whether the hybrids could interact with the human topoisomerase II beta in the same binding site and interaction sites as etoposide. Results: All the hybrids acted as potential topoisomerase II inhibitors, demonstrating possible anti-cancer activity on several cancer cell lines. Among all the proposed hybrids, MF-7-GLA would be the ideal candidate as a lead compound. The hybrid OF-3-EDA-GLA and the hydroxamic acid derivatives also stood out. Conclusions: Both FQs and GLA have advantageous structures for obtaining hybrids with favourable properties. Improvements in the hybrids' structure could lead to promising results.

Comprehensive Characterization of Oxidative Stress-Modulating Chemicals Using GPT-Based Text Mining

The screening of hazardous environmental pollutants is hindered by the limited availability of toxicological databases. Large language model (LLM)-based text mining holds the potential to automatically extract complex toxicological information from the literature. Due to its relevance to diseases and the challenge of comprehensive characterization, oxidative stress serves as a suitable case for research by texting mining. In this study, a robust workflow utilizing a LLM (i.e., GPT-4) was developed to extract information on oxidative stress tests, including data collection, text preprocessing, prompt engineering, and performance evaluation procedures. A total of 17,780 relevant records were extracted from 7166 articles, covering 2558 unique compounds. A rising interest in oxidative stress was observed over the past two decades. A list of known prooxidants (n = 1416) and antioxidants (n = 1102) was established, with the leading chemical categories being pharmaceuticals, pesticides, and metals for prooxidants and pharmaceuticals and flavonoids for antioxidants. Structural alert analysis identified potential prooxidant (e.g., chlorobenzene, nitrobenzene, and tertiary amines) and antioxidant (e.g., flavonoid and thiol) substructures. These findings illustrate the feasibility of building toxicological databases through LLM-based text mining in a cost-efficient manner, and the information obtained from the technique holds significant promise for future applications in environmental and health research.

Assessing Aquatic Baseline Toxicity of Plastic-Associated Chemicals: Development and Validation of the Target Plastic Model

We developed a Target Plastic Model (TPM) to estimate the critical plastic burden of organic toxicants in five types of plastics, namely, polydimethylsiloxane (PDMS), polyoxymethylene (POM), polyacrylate (PA), low-density polyethylene (LDPE), and polyurethane ester (PU), following the Target Lipid Model (TLM) framework. By substituting the lipid-water partition coefficient in the TLM with plastic-water partition coefficients to create TPM, we demonstrated that the biomimetic nature of these plastic phases allows for the calculation of critical plastic burdens of toxicants, similar to the notion of critical lipid burdens in TLM. Following this approach, the critical plastic burdens of baseline (n = 115), less-inert (n = 73), and reactive (n = 75) toxicants ranged from 0.17 to 51.33, 0.04 to 26.62, and 1.00 × 10-6 to 6.78 × 10-4 mmol/kg of plastic, respectively. Our study showed that PDMS, PA, POM, PE, and PU are similar to biomembranes in mimicking the passive exchange of chemicals with the water phase. Using the TPM, median lethal concentration (LC50) values for fish exposed to baseline toxicants were predicted, and the results agreed with experimental values, with RMSE ranging from 0.311 to 0.538 log unit. Similarly, for the same data set of baseline toxicants, other widely used models, including the TLM (RMSE: 0.32-0.34), ECOSAR (RMSE: 0.35), and the Abraham Solvation Model (ASM; RMSE: 0.31), demonstrated comparable agreement between experimental and predicted values. For less inert chemicals, predictions were within a factor of 5 of experimental values. Comparatively, ASM and ECOSAR showed predictions within a factor of 2 and 3, respectively. The TLM based on phospholipid had predictions within a factor of 3 and octanol within a factor of 4, indicating that the TPM's performance for less inert chemicals is comparable to these established models. Unlike these methods, the TPM requires only the knowledge of plastic bound concentration for a given plastic phase to calculate baseline toxic units, bypassing the need for extensive LC50 and plastic-water partition coefficient data, which are often limited for emerging chemicals. Taken together, the TPM can provide valuable insights into the toxicities of chemicals associated with environmental plastic phases, assisting in selecting the best polymeric phase for passive sampling and designing better passive dosing techniques for toxicity experiments.

Equations for estimating binary mixture toxicity: 3-methyl-2-butanone with a series of electrophiles

Mixture toxicity was determined for 32 binary combinations. One chemical was the non-reactive, non-polar narcotic 3-methyl-2-butanone (always chemical A) and the other was a potentially reactive electrophile (chemical B). Bioluminescence inhibition in Allovibrio fischeri was measured at 15-, 30-, and 45-minutes of exposure for A, B, and the mixture (MX). Concentration-response curves (CRCs) were developed for each chemical and used to develop predicted CRCs for the concentration addition (CA) and independent action (IA) mixture toxicity models. Also, MX CRCs were generated and compared with model predictions using the 45-minute data. Classification of observed mixture toxicity used three specific criteria: 1) predicted IA EC50 vs. CA EC50 values at 45-minutes, 2) consistency of 45-minute MX CRC fit to IA, CA, or otherwise at three effect levels (EC25, EC50 and EC75), and 3) the known/suspected mechanism of toxicity for chemical B. Mixture toxicity was then classified into one of seven groupings. As a result of the predicted IA EC50 being more toxic than the predicted CA EC50, IA represented the greater toxic hazard. For this reason, non-sham MXs having toxicity consistent with CA were classified as being "coincident" with CA rather than mechanistically-consistent with CA. Multiple linear regression analyses were performed to develop equations that can be used to estimate the toxicity of other 3M2B-containing binary mixtures. These equations were developed from the data for both IA and CA, at each exposure duration and effect level. Each equation had a coefficient of determination (r2) above 0.950 and a variance inflation factor <1.2. This approach can potentially reduce the need for mixture testing and is amenable to other model systems and to assays that evaluate toxicity at low effect levels.

Predicting phase-I metabolism of piceatannol: an in silico study

Piceatannol is a natural compound found in plants and can be derived from resveratrol. While resveratrol has been extensively researched for its effects and how the body processes it, there are concerns about its use. These concerns include its limited absorption in the body, the need for specific dosages, potential interactions with other drugs, lack of standardization, and limited clinical evidence to support its benefits. Interestingly, Piceatannol, another compound derived from resveratrol, has received less attention from researchers but appears to offer advantages. It has better bioavailability and seems to have a more favorable therapeutic profile compared to resveratrol. Surprisingly, no previous attempts have been made to explore or predict the metabolites of piceatannol when it interacts with the enzyme cytochrome P450. This study aims to fill that gap by predicting how piceatannol is metabolized by cytochrome P450 and assessing any potential toxicity associated with its metabolites. This research is interesting because it's the first of its kind to investigate the metabolic fate of piceatannol, especially in the context of cytochrome P450. The findings have the potential to significantly contribute to the field of piceatannol research, particularly in the food industry where this compound has applications and implications.

Graphical abstract

Biotoxicity dynamic change and key toxic organics identification of coal chemical wastewater along a novel full-scale treatment process

It is particularly important to comprehensively assess the biotoxicity variation of industrial wastewater along the treatment process for ensuring the water environment security. However, intensive studies on the biotoxicity reduction of industrial wastewater are still limited. In this study, the toxic organics removal and biotoxicity reduction of coal chemical wastewater (CCW) along a novel full-scale treatment process based on the pretreatment process-anaerobic process-biological enhanced (BE) process-anoxic/oxic (A/O) process-advanced treatment process was evaluated. This process performed great removal efficiency of COD, total phenol, NH4+-N and total nitrogen. And the biotoxicity variation along the treatment units was analyzed from the perspective of acute biotoxicity, genotixicity and oxidative damage. The results indicated that the effluent of pretreatment process presented relatively high acute biotoxicity to Tetrahymena thermophila. But the acute biotoxicity was significantly reduced in BE-A/O process. And the genotoxicity and oxidative damage to Tetrahymena thermophila were significantly decreased after advanced treatment. The polar organics in CCW were identified as the main biotoxicity contributors. Phenols were positively correlated with acute biotoxicity, while the nitrogenous heterocyclic compounds and polycyclic aromatic hydrocarbons were positively correlated with genotoxicity. Although the biotoxicity was effectively reduced in the novel full-scale treatment process, the effluent still performed potential biotoxicity, which need to be further explored in order to reduce environmental risk.

Development of a hierarchical support vector regression-based in silico model for the prediction of the cysteine depletion in DPRA

Topical and transdermal treatments have been dramatically growing recently and it is crucial to consider skin sensitization during the drug discovery and development process for these administration routes. Various tests, including animal and non-animal approaches, have been devised to assess the potential for skin sensitization. Furthermore, numerous in silico models have been created, providing swift and cost-effective alternatives to traditional methods such as in vivo, in vitro, and in chemico methods for categorizing compounds. In this study, a quantitative structure-activity relationship (QSAR) model was developed using the innovative hierarchical support vector regression (HSVR) scheme. The aim was to quantitatively predict the potential for skin sensitization by analyzing the percent of cysteine depletion in Direct Peptide Reactivity Assay (DPRA). The results demonstrated accurate, consistent, and robust predictions in the training set, test set, and outlier set. Consequently, this model can be employed to estimate skin sensitization potential of novel or virtual compounds.

Toxicological Profiling of Potential Shikimate Kinase Inhibitors Against Mycobacterium tuberculosis

Over the last decade, Mycobacterium tuberculosis has mutated into a putative 'superbug', as treatments against it have failed due to increasing antimicrobial resistance. As a result, the rising incidence of multidrug-resistant tuberculosis (MDR-TB) is posing a significant public health threat, thus, the need to develop effective drugs for MDR-TB has become an urgent priority. To identify new drug candidates for the treatment of MDR-TB, the present study was based on mycobacterial shikimate kinase (MtSK) as the pharmacological target. One hundred potential MtSK inhibitors were identified from literature and database searches to identify compounds that were designed to specifically function as MtSK antagonists. The ADME properties of these compounds were evaluated by using the SwissADME web tool. ProTox-II software was also used to investigate any potential endocrine disrupting effects, mediated through their interaction with oestrogenic and/or androgenic receptors. This study also aimed to predict LD50 values of potential drug candidates that would be active against the standard H37Rv strain of M. tuberculosis, by using the ProTox-II in silico tool. The molecules for which no structural hazard alerts were identified with these software tools were further subjected to molecular docking analyses and molecular dynamic simulations to estimate their ability to interact with the MtSK enzyme. Preliminary results from SwissADME indicated that 30 molecules were drug-like, due to their physicochemical and pharmacokinetic properties. However, subsequent analysis with ToxTree and ProTox-II indicated that only three of these 30 drug-like molecules were suitable for taking forward into further in vitro experiments. This study, which is based on the use of commonly used open-source in silico tools, identified new MtSK ligands for potential use in the development of new drugs for the therapeutic management of tuberculosis. An initial prediction of their safety profile was also generated.

Augmenting Expert Knowledge-Based Toxicity Alerts by Statistically Mined Molecular Fragments

Structural alerts are molecular substructures assumed to be associated with molecular initiating events in various toxic effects and an integral part of in silico toxicology. However, alerts derived using the knowledge of human experts often suffer from a lack of predictivity, specificity, and satisfactory coverage. In this work, we present a method to build hybrid QSAR models by combining expert knowledge-based alerts and statistically mined molecular fragments. Our objective was to find out if the combination is better than the individual systems. Lasso regularization-based variable selection was applied on combined sets of knowledge-based alerts and molecular fragments, but the variable elimination was only allowed to happen on the molecular fragments. We tested the concept on three toxicity end points, i.e., skin sensitization, acute Daphnia toxicity, and Ames mutagenicity, which covered both classification and regression problems. Results showed the predictive performance of such hybrid models is, indeed, better than the models based solely on expert alerts or statistically mined fragments alone. The method also enables the discovery of activating and mitigating/deactivating features for toxicity alerts and the identification of new alerts, thereby reducing false positive and false negative outcomes commonly associated with generic alerts and alerts with poor coverage, respectively.

The Good, The Bad, and The Perplexing: Structural Alerts and Read-Across for Predicting Skin Sensitization Using Human Data

In our earlier work (Golden et al., 2021), we showed 70-80% accuracies for several skin sensitization computational tools using human data. Here, we expanded the data set using the NICEATM human skin sensitization database to create a final data set of 1355 discrete chemicals (largely negative, ∼70%). Using this expanded data set, we analyzed model performance and evaluated mispredictions using Toxtree (v 3.1.0), OECD QSAR Toolbox (v 4.5), VEGA's (1.2.0 BETA) CAESAR (v 2.1.7), and a k-nearest-neighbor (kNN) classification approach. We show that the accuracy on this data set was lower than previous estimates, with balanced accuracies being 63% and 65% for Toxtree and OECD QSAR Toolbox, respectively, 46% for VEGA, and 59% for a kNN approach, with the lower accuracy likely due to the higher percentage of nonsensitizing chemicals. Two hundred eighty seven chemicals were mispredicted by both Toxtree and OECD QSAR Toolbox, which was approximately 20% of the entire data set, and 84% of these were false positives. The absence or presence of metabolic simulation in OECD QSAR Toolbox made no overall difference. While Toxtree is known for overpredicting, 60% of the chemicals in the data set had no alert for skin sensitization, and a substantial number of these chemicals were in fact sensitizers, pointing to sensitization mechanisms not recognized by Toxtree. Interestingly, we observed that chemicals with more than one Toxtree alert were more likely to be nonsensitizers. Finally, a kNN approach tended to mispredict different chemicals than either OECD QSAR Toolbox or Toxtree, suggesting that there was additional information to be garnered from a kNN approach. Overall, the results demonstrate that while there is merit in structural alerts as well as QSAR or read-across approaches (perhaps even more so in their combination), additional improvement will require a more nuanced understanding of mechanisms of skin sensitization.

Electrochemistry coupled with mass spectrometry for the prediction of the environmental fate and elucidation of the degradation mechanisms of pesticides: current status and future prospects

One of the crucial steps in the development of a new pesticide (active molecule) is predicting its environmental and in vivo fate, so as to determine potential consequences to a living organism's health and ecology as a whole. In this regard, pesticides undergo transformation processes in response to biotic and abiotic stress. Therefore, there is a need to investigate pesticide transformation products (TPs) and the formation processes they could undergo during the manufacturing process and when discharged into the ecosystem. Although methods based on biological in vitro and in vivo experimental models are tools of choice for the elucidation of metabolic pathways of pesticides (xenobiotics in general), electrochemistry-based techniques offer numerous advantages such as rapid and low-cost analysis, easy implementation, low sample volume requirement, no matrix effects, and miniaturization to improve the performance of the developed methods. However, for greater efficiency, electrochemistry (EC) should be coupled with analytical techniques such as mass spectrometry (MS) and sometimes liquid chromatography (LC), leading to the so-called EC-MS and EC-LC-MS hybrid techniques. In this review, past studies, current applications and utilization of EC-MS and EC-LC-MS techniques for the simulation of environmental fate/degradation of pesticides were reviewed by selected studies with chemical transformation, structures of metabolites, and some experimental conditions. The current challenges and future trends for the mimicry and prediction of the environmental fate/degradation of pesticides based on electrochemical methods combined with mass spectrometry were highlighted.

Exhaustive in silico design and screening of novel antipsychotic compounds with improved pharmacodynamics and blood-brain barrier permeation properties

Antipsychotic drugs or neuroleptics are widely used in the treatment of psychosis as a manifestation of schizophrenia and bipolar disorder. However, their effectiveness largely depends on the blood-brain barrier (BBB) permeation (pharmacokinetics) and drug-receptor pharmacodynamics. Therefore, in this study, we developed and implemented the in silico pipeline to design novel compounds (n = 260) as leads using the standard drug scaffolds with improved PK/PD properties from the standard scaffolds. As a result, the best candidates (n = 3) were evaluated in molecular docking to interact with serotonin and dopamine receptors. Finally, haloperidol (HAL) derivative (1-(4-fluorophenyl)-4-(4-hydroxy-4-{4-[(2-phenyl-1,3-thiazol-4-yl)methyl]phenyl}piperidin-1-yl)butan-1-one) was identified as a "magic shotgun" lead compound with better affinity to the 5-HT2A, 5-HT1D, D2, D3, and 5-HT1B receptors than the control molecule. Additionally, this hit substance was predicted to possess similar BBB permeation properties and much lower toxicological profiles in comparison to HAL. Overall, the proposed rational drug design platform for novel antipsychotic drugs based on the BBB permeation and receptor binding might be an invaluable asset for a medicinal chemist or translational pharmacologist.Communicated by Ramaswamy H. Sarma.

Impact of formaldehyde, acetaldehyde, and N-(3-(Dimethylamino)propyl)methacrylamide on the efficacy of the human derived coagulation factor IX

Polymer-borne leachables such as formaldehyde, acetaldehyde, and N-3-(Dimethylamino)propyl)methacrylamide (DMAPMA) may interact with therapeutic proteins. In this study, the leachables were spiked into human derived coagulation factor IX (FIX) at concentrations of 1, 10, 50, 100, and 500 µg/mL, corresponding to a leachable - FIX ratio of 0.5, 5, 25, 50 and 250 %, respectively. The spiked samples were visually inspected, and pH was measured. No visual effects were observed, and pH was within the drug product's specified range. Recovery experiments were performed and no loss of leachables was identified. Protein structure analysis revealed that formaldehyde reacted with lysine contained in two different positions of FIX, in a concentration-dependent manner starting at 10 µg/mL (5 %). The clotting activity of FIX was measured. The activity of the samples spiked with 500 µg/mL (250 %) of formaldehyde dropped by more than half. The activity of the samples spiked with acetaldehyde began to drop at 50 µg/mL (25 %) and continued to decline in concentration-dependent manner. DMAPMA did not impair the activity of FIX. The findings conclude that depending on the concentration, some leachables may react with or modify therapeutic proteins, potentially causing an undesired pharmacological effect however, this is specific to each protein.

Assessing machine learning approaches for predicting failures of investigational drug candidates during clinical trials

One of the major challenges in drug development is having acceptable levels of efficacy and safety throughout all the phases of clinical trials followed by the successful launch in the market. While there are many factors such as molecular properties, toxicity parameters, mechanism of action at the target site, etc. that regulates the therapeutic action of a compound, a holistic approach directed towards data-driven studies will invariably strengthen the predictive toxicological sciences. Our quest for the current study is to find out various reasons as to why an investigational candidate would fail in the clinical trials after multiple iterations of refinement and optimization. We have compiled a dataset that comprises of approved and withdrawn drugs as well as toxic compounds and essentially have used time-split based approach to generate the training and validation set. Five highly robust and scalable machine learning binary classifiers were used to develop the predictive models that were trained with features like molecular descriptors and fingerprints and then validated rigorously to achieve acceptable performance in terms of a set of performance metrics. The mean AUC scores for all the five classifiers with the hold-out test set were obtained in the range of 0.66-0.71. The models were further used to predict the probability score for the clinical candidate dataset. The top compounds predicted to be toxic were analyzed to estimate different dimensions of toxicity. Apparently, through this study, we propose that with the appropriate use of feature extraction and machine learning methods, one can estimate the likelihood of success or failure of investigational drugs candidates thereby opening an avenue for future trends in computational toxicological studies. The models developed in the study can be accessed at https://github.com/gnsastry/predicting_clinical_trials.git.

Pathways to Identify Electrophiles In Vivo Using Hemoglobin Adducts: Hydroxypropanoic Acid Valine Adduct and Its Possible Precursors

Analytical methods and tools for the characterization of the human exposome by untargeted mass spectrometry approaches are advancing rapidly. Adductomics methods have been developed for untargeted screening of short-lived electrophiles, in the form of adducts to proteins or DNA, in vivo. The identification of an adduct and its precursor electrophile in the blood is more complex than that of stable chemicals. The present work aims to illustrate procedures for the identification of an adduct to N-terminal valine in hemoglobin detected with adductomics, and pathways for the tracing of its precursor and possible exposure sources. Identification of the adduct proceeded via preparation and characterization of standards of adduct analytes. Possible precursor(s) and exposure sources were investigated by measurements in blood of adduct formation by precursors in vitro and adduct levels in vivo. The adduct was identified as hydroxypropanoic acid valine (HPA-Val) by verification with a synthesized reference. The HPA-Val was measured together with other adducts (from acrylamide, glycidamide, glycidol, and acrylic acid) in human blood (n = 51, schoolchildren). The HPA-Val levels ranged between 6 and 76 pmol/g hemoglobin. The analysis of reference samples from humans and rodents showed that the HPA-Val adduct was observed in all studied samples. No correlation of the HPA-Val level with the other studied adducts was observed in humans, nor was an increase in tobacco smokers observed. A small increase was observed in rodents exposed to glycidol. The formation of the HPA-Val adduct upon incubation of blood with glycidic acid (an epoxide) was shown. The relatively high adduct levels observed in vivo in relation to the measured reactivity of the epoxide, and the fact that the epoxide is not described as naturally occurring, suggest that glycidic acid is not the only precursor of the HPA-Val adduct identified in vivo. Another endogenous electrophile is suspected to contribute to the in vivo HPA-Val adduct level.

An End Point-Specific Framework for Read-Across Analog Selection for Human Health Effects

Integrating computational chemistry and toxicology can improve the read-across analog approach to fill data gaps in chemical safety assessment. In read-across, structure-related parameters are compared between a target chemical with insufficient test data and one or more materials with sufficient data. Recent advances have focused on enhancing the grouping or clustering of chemicals to facilitate toxicity prediction via read-across. Analog selection ascertains relevant features, such as physical-chemical properties, toxicokinetic-related properties (bioavailability, metabolism, and degradation pathways), and toxicodynamic properties of chemicals with an emphasis on mechanisms or modes of action. However, each human health end point (genotoxicity, skin sensitization, phototoxicity, repeated dose toxicity, reproductive toxicity, and local respiratory toxicity) provides a different critical context for analog selection. Here six end point-specific, rule-based schemes are described. Each scheme creates an end point-specific workflow for filling the target material data gap by read-across. These schemes are intended to create a transparent rationale that supports the selected read-across analog(s) for the specific end point under study. This framework can systematically drive the selection of read-across analogs for each end point, thereby accelerating the safety assessment process.

Unveiling toxicity profile for food risk components: A manually curated toxicological databank of food-relevant chemicals

Rigorous risk assessment of chemicals in food and feed is essential to address the growing worldwide concerns about food safety. High-quality toxicological data on food-relevant chemicals are fundamental for risk modeling and assessment in the food safety area. The organization and analysis of substantial toxicity information can positively support decision-making by providing insight into toxicity trends. However, it remains challenging to systematically obtain fragmented toxicity data, and related toxicological resources are required to meet the current demands. In this study, we collected 221,439 experimental toxicity records for 5,657 food-relevant chemicals identified from extensive databases and literature, along with their information on chemical identification, physicochemical properties, environmental fates, and biological targets. Based on the aggregated data, a freely available web-based databank, Food-Relevant Available Chemicals Toxicology Databank (FRAC-TD) is presented, which supports multiple browsing ways and search criterions. Applying FRAC-TD for data-driven analysis, we revealed the underlying toxicity profiles of food-relevant chemicals in humans, mammals, and other species in the food chain. Expectantly, FRAC-TD could positively facilitate toxicological studies, toxicity prediction, and risk assessments in the food industry.

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.

Modeling and insights into the structural basis of chemical acute aquatic toxicity

It has become a top global regulatory priority to prevent and control pollution from the release of synthetic chemicals, which continues to affect the aquatic communities. In the past decades, computational tools were largely used to significantly reduce the budget and time cost of chemical acute aquatic toxicity assessment. But the structural basis of toxic compounds was rarely analyzed. In the present study, we collected 1438, 485 and 961 chemicals with acute toxicity data records for three representative aquatic species, including Tetrahymena pyriformis, Daphnia magna, and Fathead minnow, respectively. A series of artificial intelligence models were developed using OCHEM tools. For each aquatic toxicity endpoint, a consensus model was developed based on the top performed individual models. The consensus models provided good performance on external validation sets with total accuracy values 96.88 %, 90.63 %, and 84.90 % for Tetrahymena pyriformis toxicity (TPT), Daphnia magna toxicity (DMT), and Fathead minnow toxicity (FMT), respectively. The models can be freely accessed via https://ochem.eu/article/146910. Moreover, the analysis of physical-chemical properties suggested that several key molecular properties of aquatic toxic compounds were significantly different with those of non-toxic compounds. Thus, these descriptors may be associated to chemical acute aquatic toxicity, and may be useful for the understand of chemical aquatic toxicity. Besides, in this study, the structural alerts for aquatic toxicity were detected using f-score and frequency ratio analysis of predefined substructures. A total of 112, 58 and 33 structural alerts were identified responsible for TPT, DMT, and FMT, respectively. These structural alerts could provide useful information for the mechanisms of chemical aquatic toxicity and visual alerts for environmental assessment. All the structural alerts were integrated in the web-server SApredictor (www.sapredictor.cn).

A scheme to evaluate structural alerts to predict toxicity - Assessing confidence by characterising uncertainties

Structure-activity relationships (SARs) in toxicology have enabled the formation of structural rules which, when coded as structural alerts, are essential tools in in silico toxicology. Whilst other in silico methods have approaches for their evaluation, there is no formal process to assess the confidence that may be associated with a structural alert. This investigation proposes twelve criteria to assess the uncertainty associated with structural alerts, allowing for an assessment of confidence. The criteria are based around the stated purpose, description of the chemistry, toxicology and mechanism, performance and coverage, as well as corroborating and supporting evidence of the alert. Alerts can be given a confidence assessment and score, enabling the identification of areas where more information may be beneficial. The scheme to evaluate structural alerts was placed in the context of various use cases for industrial and regulatory applications. The analysis of alerts, and consideration of the evaluation scheme, identifies the different characteristics an alert may have, such as being highly specific or generic. These characteristics may determine when an alert can be used for specific uses such as identification of analogues for read-across or hazard identification.

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Structural alerts are useful tools for predictive toxicology.

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12 criteria to evaluate structural alerts have been identified.

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A strategy to determine confidence of structural alerts is presented.

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Different use cases require different characteristics of structural alerts.

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A Scheme to Evaluate Structural Alerts to Predict Toxicity – Assessing Confidence By Characterising Uncertainties.

High-Throughput Assessment of the Abiotic Stability of Test Chemicals in In Vitro Bioassays

Abiotic stability of chemicals is not routinely tested prior to performing in vitro bioassays, although abiotic degradation can reduce the concentration of test chemicals leading to the formation of active or inactive transformation products, which may lead to misinterpretation of bioassay results. A high-throughput workflow was developed to measure the abiotic stability of 22 test chemicals in protein-rich aqueous media under typical bioassay conditions at 37 °C for 48 h. These test chemicals were degradable in the environment according to a literature review. The chemicals were extracted from the exposure media at different time points using a novel 96-pin solid-phase microextraction. The conditions were varied to differentiate between various reaction mechanisms. For most hydrolyzable chemicals, pH-dependent degradation in phosphate-buffered saline indicated that acid-catalyzed hydrolysis was less important than reactions with hydroxide ions. Reactions with proteins were mainly responsible for the depletion of the test chemicals in the media, which was simulated by bovine serum albumin (BSA) and glutathione (GSH). 1,2-Benzisothiazol-3(2H)-one, 2-methyl-4-isothiazolinone, and l-sulforaphane reacted almost instantaneously with GSH but not with BSA, indicating that GSH is a good proxy for reactivity with electrophilic amino acids but may overestimate the actual reaction with three-dimensional proteins. Chemicals such as hydroquinones or polyunsaturated chemicals are prone to autoxidation, but this reaction is difficult to differentiate from hydrolysis and could not be simulated by the oxidant N-bromosuccinimide. Photodegradation played a minor role because cells are exposed in incubators in the dark and simulations with high light intensities did not yield realistic degradation. Stability predictions from various in silico prediction models for environmental conditions can give initial indications of the stability but were not always consistent with the experimental stability in bioassays. As the presented workflow can be performed in high throughput under realistic bioassay conditions, it can be used to provide an experimental database for developing bioassay-specific stability prediction models.

Metabolic Activation and Cytotoxicity of Fungicide Carbendazim Mediated by CYP1A2

Carbendazim (CBZ) is a broad-spectrum fungicide widely used in many nations for foliar spray as well as seed and soil treatment. The resulting contamination and environmental pollution have been drawing public attention. In particular, CBZ was reported to cause liver damage in rats and zebrafish, and the mechanisms of its toxicity have not been clarified. The purposes of this study were to investigate the metabolic activation of CBZ and to determine a possible role of the reactive metabolites in CBZ-induced liver injury reported. One oxidative metabolite (M1), one glutathione conjugate (M2), and one N-acetyl cysteine conjugate (M3) were detected in human and rat liver microsomal incubations fortified with glutathione or N-acetyl cysteine after exposure to CBZ. CYP1A2 was the major enzyme responsible for the metabolic activation of CBZ. Biliary M2 and urinary M3 were detected in rats treated with CBZ. CBZ-derived protein adduction was found in cultured rat primary hepatocytes treated with CBZ. The increase of administration concentration intensified not only the cytotoxicity but also protein adduction induced by CBZ, suggesting a correlation of the cytotoxicity with the observed protein modification. The findings facilitate the understanding of the mechanisms of toxic action of CBZ.

Comparison of modes of toxic action between Rana chensinensis tadpoles and Limnodrilus hoffmeisteri worms based on interspecies correlation, excess toxicity and QSAR for class-based compounds

Insecticides, fungicides, dinitrobenzenes, resorcinols, phenols and anilines are widely used in agricultural and industrial productions. However, their modes of toxic action are unclear in some nontarget organisms, such as worms and tadpoles. In this study, acute toxicity data was experimentally collected for Limnodrilus hoffmeisteri worms and Rana chensinensis tadpoles, respectively. Interspecies correlation and excess toxicity were calculated to determine modes of action (MOAs) between the two species for class-based compounds. The result showed that, although the interspecies correlation of toxicity between the tadpoles and worms is significant with a coefficient of determination (R²) of 0.83, tadpoles are more sensitive than the worms and toxicity values between these two species are not identical with an overall 0.43 log unit difference. Regression analysis revealed that the toxicity of nonpolar narcotics or baseline compounds is linearly related to hydrophobicity for both the tadpoles and worms and the two baseline models are parallel, suggesting that these nonpolar narcotics share the same MOA between the two species. The difference of baseline toxicities between the two species is attributed to differences in bioconcentration factors. Analysis of the excess toxicity calculated from the toxicity ratio (TR) suggested that phenols and anilines can be classified as polar narcotics, not only to fish, but also to the tadpoles and worms. These compounds are more toxic than the baseline compounds and quantitative structure-activity relationship (QSAR) models show that their toxicity is linearly related to chemical hydrophobicity and polarity. Analysis of the excess toxicity reveals that aminophenols and resorcinols can be classified as reactive compounds, and insecticides and fungicides can be classified as specifically-acting compounds for both species. These compounds exhibited significantly greater toxic effect to both the tadpoles and worms. QSAR models have been developed to describe the toxic mechanisms for nonpolar narcotics, polar narcotics, reactive chemicals and specifically-acting compounds, and a theoretical equation has been derived to explain the effect of bio-uptake and interaction of the chemical with target receptors for both tadpole and worm toxicity. Our study reveals that tadpole toxicity can be estimated from worm toxicity data and the two species can serve as surrogates for each other in the safety evaluation of organic pollutants.

A Computational Approach for Respiratory Hazard Identification of Flavor Chemicals in Tobacco Products

Flavor chemicals contribute to the appeal and toxicity of tobacco products, including electronic nicotine delivery systems (ENDS). The assortment of flavor chemicals available for use in tobacco products is extensive. In this study, a chemistry-driven computational approach was used to evaluate flavor chemicals based on intrinsic hazardous structures and reactivity of chemicals. A large library of 3012 unique flavor chemicals was compiled from publicly available information. Next, information was computed and collated based on their (1) physicochemical properties, (2) global harmonization system (GHS) health hazard classification, (3) structural alerts linked to the chemical's reactivity, instability, or toxicity, and (4) common substructure shared with FDA's harmful and potentially harmful constituents (HPHCs) flavor chemicals that are respiratory toxicants. Computational analysis of the constructed flavor library flagged 638 chemicals with GHS classified respiratory health hazards, 1079 chemicals with at least one structural alert, and 2297 chemicals with substructural similarity to FDA's established and proposed list of HPHCs. A subsequent analysis was performed on a subset of 173 chemicals in the flavor library that are respiratory health hazards, contain structural alerts as well as flavor HPHC substructures. Four general toxicophore structures with an increased potential for respiratory toxicity were then identified. In summary, computational methods are efficient tools for hazard identification and understanding structure-toxicity relationship. With appropriate context of use and interpretation, in silico methods may provide scientific evidence to support toxicological evaluations of chemicals in or emitted from tobacco products.

Chemistry of Spontaneous Alkylation of Methimazole with 1,2-Dichloroethane

Spontaneous S-alkylation of methimazole (1) with 1,2-dichloroethane (DCE) into 1,2-bis[(1-methyl-1H-imidazole-2-yl)thio]ethane (2), that we have described recently, opened the question about its formation pathway(s). Results of the synthetic, NMR spectroscopic, crystallographic and computational studies suggest that, under given conditions, 2 is obtained by direct attack of 1 on the chloroethyl derivative 2-[(chloroethyl)thio]-1-methyl-1H-imidazole (3), rather than through the isolated stable thiiranium ion isomer, i.e., 7-methyl-2H, 3H, 7H-imidazo[2,1-b]thiazol-4-ium chloride (4a, orthorhombic, space group Pnma), or in analogy with similar reactions, through postulated, but unproven intermediate thiiranium ion 5. Furthermore, in the reaction with 1, 4a prefers isomerization to the N-chloroethyl derivative, 1-chloroethyl-2,3-dihydro-3-methyl-1H-imidazole-2-thione (7), rather than alkylation to 2, while 7 further reacts with 1 to form 3-methyl-1-[(1-methyl-imidazole-2-yl)thioethyl]-1H-imidazole-2-thione (8, monoclinic, space group P 2₁/c). Additionally, during the isomerization of 3, the postulated intermediate thiiranium ion 5 was not detected by chromatographic and spectroscopic methods, nor by trapping with AgBF₄. However, trapping resulted in the formation of the silver complex of compound 3, i.e., bis-{2-[(chloroethyl)thio]-1-methyl-1H-imidazole}-silver(I)tetrafluoroborate (6_, monoclinic, space group P 2₁/c), which cyclized upon heating at 80 °C to 7-methyl-2H, 3H, 7H-imidazo[2,1-b]thiazol-4-ium tetrafluoroborate (4b, monoclinic, space group P 2₁/c). Finally, we observed thermal isomerization of both 2 and 2,3-dihydro-3-methyl-1-[(1-methyl-1H-imidazole-2-yl)thioethyl]-1H-imidazole-2-thione (8), into 1,2-bis(2,3-dihydro-3-methyl-1H-imidazole-2-thione-1-yl)ethane (9), which confirmed their structures.

Amino Reactivity of Glutardialdehyde and Monoaldehydes─Chemoassay Profile vs Skin Sensitization Potency

Chemoassay profiling of organic electrophiles through the direct peptide reactivity assay has become an OECD-accepted nonanimal component in the REACH evaluation of potential skin sensitizers. For aldehydes forming imines (Schiff bases), however, existing chemoassays yielded inconclusive results, indicating issues with their NH₂ sensitivity and the reversibility of the reaction. In the present study, a new kinetic chemoassay employing the N terminus of glycine-para-nitroanilide, Gly-pNA, as a model nucleophile for protein NH₂ groups is introduced and applied to nine aliphatic monoaldehydes and glutardialdehyde (1,5-pentanedial) that have log K_ow (octanol/water partition coefficient) values from 0.63 to 3.99. The Gly-pNA second-order rate constants k₁ range from 8.56 to 150 L·mol^-1·min^-1 for the monoaldehydes. Interestingly, glutardialdehyde with a k₁ of 17 731 L·mol^-1·min^-1 is 170-fold more reactive than its monoaldehyde counterpart pentanal. This can be rationalized by hydration or tautomerization of the dialdehyde to monoaldehydic forms, now facilitating Schiff base formation through an intramolecular H bond. Comparison with murine local lymph node assay data from the literature reveals that adduct stability in terms of reaction thermodynamics (K = k₁/k_-1^pseudo) rather than formation kinetics (k₁) governs the skin sensitization potency of Schiff-base-forming aldehydes. The discussion includes analytically determined adduct patterns, and the impact of α- and β-carbon substitution as well as hydrophobicity on aldehyde reactivity.

In silico approaches in organ toxicity hazard assessment: Current status and future needs for predicting heart, kidney and lung toxicities

The kidneys, heart and lungs are vital organ systems evaluated as part of acute or chronic toxicity assessments. New methodologies are being developed to predict these adverse effects based on in vitro and in silico approaches. This paper reviews the current state of the art in predicting these organ toxicities. It outlines the biological basis, processes and endpoints for kidney toxicity, pulmonary toxicity, respiratory irritation and sensitization as well as functional and structural cardiac toxicities. The review also covers current experimental approaches, including off-target panels from secondary pharmacology batteries. Current in silico approaches for prediction of these effects and mechanisms are described as well as obstacles to the use of in silico methods. Ultimately, a commonly accepted protocol for performing such assessment would be a valuable resource to expand the use of such approaches across different regulatory and industrial applications. However, a number of factors impede their widespread deployment including a lack of a comprehensive mechanistic understanding, limited in vitro testing approaches and limited in vivo databases suitable for modeling, a limited understanding of how to incorporate absorption, distribution, metabolism, and excretion (ADME) considerations into the overall process, a lack of in silico models designed to predict a safe dose and an accepted framework for organizing the key characteristics of these organ toxicants.

Discrimination of active and inactive substances in cytotoxicity based on Tox21 10K compound library: Structure alert and mode of action

In vitro cytotoxicity assay is an ideal alternative method for the in vivo toxicity in the risk assessment of pollutants in environment. However, modes of action (MOAs) of cytotoxicity have not been investigated for a wide range of compounds. In this paper, binomial and recursive partitioning analysis were carried out between the cytotoxicity and molecular descriptors for 8981 compounds. The results showed that cytotoxicity is strongly related to the chemical hydrophobicity and excess molar refraction, indicating the bio-uptake and chemical-receptor interaction through π and n electron pair play important roles in the cytotoxicity. The decision tree derived from recursive partitioning analysis revealed that the studied compounds could be divided into 25 groups and their structural characteristics could be used as structure alert to identify active and inactive compounds in cytotoxicity. The descriptors used in the decision tree revealed that chemical ionization and bioavailability could affect the cytotoxicity for ionizable and highly hydrophobic compounds. Comparison of MOAs based on Verhaar's classification scheme showed that many inert or less inert compounds were inactive substance, and many reactive or specifically-acting compounds were active substances in the cytotoxicity. In vitro toxicity assay instead of in vivo toxicity assay can be used in the environmental hazard and risk assessment of organic pollutants. The descriptors used in the binomial equation and decision tree reveal that chemical hydrophobicity, ionization and solubility play very important roles for identification of active and inactive compounds. The results obtained in this paper are valuable for understanding the modes of action in cytotoxicity and in vivo-in vitro toxicity relationship.

COSMOS next generation - A public knowledge base leveraging chemical and biological data to support the regulatory assessment of chemicals

The COSMOS Database (DB) was originally established to provide reliable data for cosmetics-related chemicals within the COSMOS Project funded as part of the SEURAT-1 Research Initiative. The database has subsequently been maintained and developed further into COSMOS Next Generation (NG), a combination of database and in silico tools, essential components of a knowledge base. COSMOS DB provided a cosmetics inventory as well as other regulatory inventories, accompanied by assessment results and in vitro and in vivo toxicity data. In addition to data content curation, much effort was dedicated to data governance - data authorisation, characterisation of quality, documentation of meta information, and control of data use. Through this effort, COSMOS DB was able to merge and fuse data of various types from different sources. Building on the previous effort, the COSMOS Minimum Inclusion (MINIS) criteria for a toxicity database were further expanded to quantify the reliability of studies. COSMOS NG features multiple fingerprints for analysing structure similarity, and new tools to calculate molecular properties and screen chemicals with endpoint-related public profilers, such as DNA and protein binders, liver alerts and genotoxic alerts. The publicly available COSMOS NG enables users to compile information and execute analyses such as category formation and read-across. This paper provides a step-by-step guided workflow for a simple read-across case, starting from a target structure and culminating in an estimation of a NOAEL confidence interval. Given its strong technical foundation, inclusion of quality-reviewed data, and provision of tools designed to facilitate communication between users, COSMOS NG is a first step towards building a toxicological knowledge hub leveraging many public data systems for chemical safety evaluation. We continue to monitor the feedback from the user community at support@mn-am.com.

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

A decision-scheme outlining the steps for identifying the appropriate chemical category and subsequently appropriate tested source analog(s) for data gap filling of a target chemical by read-across is described. The primary features used in the grouping of the target chemical with source analogues within a database of 10,039 discrete organic substances include reactivity mechanisms associated with protein interactions and specific-acute-oral-toxicity-related mechanisms (e.g., mitochondrial uncoupling). Additionally, the grouping of chemicals making use of the in vivo rat metabolic simulator and neutral hydrolysis. Subsequently, a series of structure-based profilers are used to narrow the group to the most similar analogues. The scheme is implemented in the OECD QSAR Toolbox, so it automatically predicts acute oral toxicity as the rat oral LD50 value in log [1/mol/kg]. It was demonstrated that due to the inherent variability in experimental data, classification distribution should be employed as more adequate in comparison to the exact classification. It was proved that the predictions falling in the adjacent GSH categories to the experimentally-stated ones are acceptable given the variation in experimental data. The model performance estimated by adjacent accuracy was found to be 0.89 and 0.54 while based on R². The mechanistic and predictive coverages were >0.85.

QSAR analysis of the acute toxicity of avermectins towards Tetrahymena pyriformis

Avermectins have been effectively used in medicine, veterinary medicine, and agriculture as antiparasitic agents for many years. However, there are still no reliable data on the main ecotoxicological characteristics of most individual avermectins. Although many QSAR models have been proposed to describe the acute toxicity of organic compounds towards Tetrahymena pyriformis (T. pyriformis), avermectins are outside the applicability domain of these models. The influence of the molecular structures of various organic compounds on the acute toxicity towards T. pyriformis was studied using the OCHEM web platform (https://ochem.eu). A data set of 1792 toxicants was used to create models. The QSAR (Quantitative Structure-Activity Relationship) models were developed using the molecular descriptors Dragon, ISIDA, CDK, PyDescriptor, alvaDesc, and SIRMS and machine learning methods, such as Least Squares Support Vector Machine and Transformer Convolutional Neural Network. The HYBOT descriptors and Random Forest were used for a comparative QSAR investigation. Since the best predictive ability was demonstrated by the Transformer Convolutional Neural Network model, it was used to predict the toxicity of individual avermectins towards T. pyriformis. During a structural interpretation of the developed QSAR model, we determined the significant molecular transformations that increase and decrease the acute toxicity of organic compounds.

In silico Prediction of Skin Sensitization: Quo vadis?

Skin direct contact with chemical or physical substances is predisposed to allergic contact dermatitis (ACD), producing various allergic reactions, namely rash, blister, or itchy, in the contacted skin area. ACD can be triggered by various extremely complicated adverse outcome pathways (AOPs) remains to be causal for biosafety warrant. As such, commercial products such as ointments or cosmetics can fulfill the topically safe requirements in animal and non-animal models including allergy. Europe, nevertheless, has banned animal tests for the safety evaluations of cosmetic ingredients since 2013, followed by other countries. A variety of non-animal in vitro tests addressing different key events of the AOP, the direct peptide reactivity assay (DPRA), KeratinoSens™, LuSens and human cell line activation test h-CLAT and U-SENS™ have been developed and were adopted in OECD test guideline to identify the skin sensitizers. Other methods, such as the SENS-IS are not yet fully validated and regulatorily accepted. A broad spectrum of in silico models, alternatively, to predict skin sensitization have emerged based on various animal and non-animal data using assorted modeling schemes. In this article, we extensively summarize a number of skin sensitization predictive models that can be used in the biopharmaceutics and cosmeceuticals industries as well as their future perspectives, and the underlined challenges are also discussed.

In-Vitro Approaches to Predict and Study T-Cell Mediated Hypersensitivity to Drugs

Mitigating the risk of drug hypersensitivity reactions is an important facet of a given pharmaceutical, with poor performance in this area of safety often leading to warnings, restrictions and withdrawals. In the last 50 years, efforts to diagnose, manage, and circumvent these obscure, iatrogenic diseases have resulted in the development of assays at all stages of a drugs lifespan. Indeed, this begins with intelligent lead compound selection/design to minimize the existence of deleterious chemical reactivity through exclusion of ominous structural moieties. Preclinical studies then investigate how compounds interact with biological systems, with emphasis placed on modeling immunological/toxicological liabilities. During clinical use, competent and accurate diagnoses are sought to effectively manage patients with such ailments, and pharmacovigilance datasets can be used for stratification of patient populations in order to optimise safety profiles. Herein, an overview of some of the in-vitro approaches to predict intrinsic immunogenicity of drugs and diagnose culprit drugs in allergic patients after exposure is detailed, with current perspectives and opportunities provided.

A novel study for joint toxicity of typical aromatic compounds in coal pyrolysis wastewater by Tetrahymena thermophile

The coal pyrolysis wastewater (CPW) contributed to aquatic environment contamination with amount of aromatic pollutants, and the research on joint toxicity of the mixture of aromatic compounds was vital for environmental protection. By using Tetrahymena thermophile as non-target organism, the joint toxicity of typical nonpolar narcotics and polar narcotics in CPW was investigated. The results demonstrated that the nonpolar narcotics exerted chronic and reversible toxicity by hydrophobicity-based membrane perturbation, while polar narcotics performed acute toxicity by irreversible damage of cells. As the most hydrophobic nonpolar narcotics, indole and naphthalene caused the highest joint toxicity in 24 h with the lowest EC_50mix (24.93 mg/L). For phenolic compounds, the combination of p-cresol and p-nitrophenol also showed the top toxicity (EC_50mix = 10.9 mg/L) with relation to high hydrophobicity, and the joint toxicity was obviously stronger and more acute than that of nonpolar narcotics. Furthermore, by studying the joint toxicity of nonpolar narcotics and polar narcotics, the hydrophobicity-based membrane perturbation was the first step of toxicity effects, and afterwards the acute toxicity induced by electrophilic polar substituents of phenols dominated joint toxicity afterwards. This toxicity investigation was critical for understanding universal and specific effects of CPW to aquatic organisms.

Development of an Enhanced Mechanistically Driven Mode of Action Classification Scheme for Adverse Effects on Environmental Species

This study developed a novel classification scheme to assign chemicals to a verifiable mechanism of (eco-)toxicological action to allow for grouping, read-across, and in silico model generation. The new classification scheme unifies and extends existing schemes and has, at its heart, direct reference to molecular initiating events (MIEs) promoting adverse outcomes. The scheme is based on three broad domains of toxic action representing nonspecific toxicity (e.g., narcosis), reactive mechanisms (e.g., electrophilicity and free radical action), and specific mechanisms (e.g., associated with enzyme inhibition). The scheme is organized at three further levels of detail beyond broad domains to separate out the mechanistic group, specific mechanism, and the MIEs responsible. The novelty of this approach comes from the reference to taxonomic diversity within the classification, transparency, quality of supporting evidence relating to MIEs, and that it can be updated readily.

Identification of additives in polymers from single-use bioprocessing bags by accelerated solvent extraction and ultra-high performance liquid chromatography coupled with high-resolution mass spectrometry

Single-use technologies are increasingly used in biopharmaceutical manufacturing. Despite their advantages, these plastic assemblies draw concern because they are a potential source of contamination due to extractable and leachable compounds (E&Ls). Characterising E&Ls from such materials is a necessary step in establishing their suitability for use. Therefore, there is an urgent need for sensitive methods to identify and quantitatively assess compounds in plastic materials. Accelerated solvent extraction (ASE) is a powerful technique that can be reliably used for this purpose. In this study, ASE followed by liquid chromatography and Orbitrap-based High Resolution Accurate Mass (HRAM) mass analysis was found to be an efficient and versatile method for the determination of additives in different multilayer polymer systems from single-use bags. ASE optimisation was performed using a design of experiments approach. The type of solvent, temperature, swelling agent addition, static time and number of cycles were the selected variables. Optimum conditions were dependent on the type of plastic film. Ethyl acetate and cyclohexane were selected individually as optimum solvents. Optimum temperatures were 90-100 °C. Pressure was set at 1500 psi and extraction time was 30 min in 2 cycles. Swelling agent addition was necessary with polar extraction solvents. More than 100 additives and degradation products were confidently identified by HRAM MS. Correlations between the type and levels of identified additives and the type of polymer system were established. In addition, degradation behaviour and pathways for some additives can be addressed.

Improving predictive capacity of the Amino acid Derivative Reactivity Assay test method for skin sensitization potential with an optimal molar concentration of test chemical solution

The Amino acid Derivative Reactivity Assay (ADRA) is a convenient and effective in chemico test method for assessing covalent binding of test chemicals with protein-derived nucleophilic reagents as a means of predicting skin sensitization potential. Although the original molar-concentration approach to ADRA testing was not suitable for testing multiconstituent substances of an unknown composition, a weight-concentration approach that is suitable for such substances was developed, which also led to the realization that test chemical solutions prepared to molar concentrations higher than the original 1 mM would reduce false negative results as well as enhance predictive capacity. The present study determined an optimal molar-concentration that achieves even higher predictive capacity than the original ADRA. Eight chemicals that were false negatives when tested with 1 mM test chemical solutions were retested with test chemical solutions between 2 and 5 mM, which showed 4 mM to be the optimal molar-concentration for ADRA testing. When 82 chemicals used in the original development were retested with 4 mM test chemical solutions, false negative results were reduced by four. When an additional 85 chemicals used to evaluate the weight-concentration approach to ADRA were retested, the results essentially replicated those obtained with 0.5 mg/ml test chemical solutions and gave 10 fewer false negatives than original ADRA with 1 mM solutions. A comparison of these results for 136 chemicals showed that ADRA testing with 4 mM solutions achieved a four percentage point improvement in accuracy over original ADRA and a two percentage point improvement over DPRA testing.

A Review of In Silico Tools as Alternatives to Animal Testing: Principles, Resources and Applications

Across the spectrum of industrial sectors, including pharmaceuticals, chemicals, personal care products, food additives and their associated regulatory agencies, there is a need to develop robust and reliable methods to reduce or replace animal testing. It is generally recognised that no single alternative method will be able to provide a one-to-one replacement for assays based on more complex toxicological endpoints. Hence, information from a combination of techniques is required. A greater understanding of the time and concentration-dependent mechanisms, underlying the interactions between chemicals and biological systems, and the sequence of events that can lead to apical effects, will help to move forward the science of reducing and replacing animal experiments. In silico modelling, in vitro assays, high-throughput screening, organ-on-a-chip technology, omics and mathematical biology, can provide complementary information to develop a complete picture of the potential response of an organism to a chemical stressor. Adverse outcome pathways (AOPs) and systems biology frameworks enable relevant information from diverse sources to be logically integrated. While individual researchers do not need to be experts across all disciplines, it is useful to have a fundamental understanding of what other areas of science have to offer, and how knowledge can be integrated with other disciplines. The purpose of this review is to provide those who are unfamiliar with predictive in silico tools, with a fundamental understanding of the underlying theory. Current applications, software, barriers to acceptance, new developments and the use of integrated approaches are all discussed, with additional resources being signposted for each of the topics.

The Influence of Structural Patterns on Acute Aquatic Toxicity of Organic Compounds

Investigation of the influence of molecular structure of different organic compounds on acute toxicity towards Fathead minnow, Daphnia magna, and Tetrahymena pyriformis has been carried out using 2D simplex representation of molecular structure and two modelling methods: Random Forest (RF) and Gradient Boosting Machine (GBM). Suitable QSAR (Quantitative Structure - Activity Relationships) models were obtained. The study was focused on QSAR models interpretation. The aim of the study was to develop a set of structural fragments that simultaneously consistently increase toxicity toward Fathead minnow, Daphnia magna, Tetrahymena pyriformis. The interpretation allowed to gain more details about known toxicophores and to propose new fragments. The results obtained made it possible to rank the contributions of molecular fragments to various types of toxicity to aquatic organisms. This information can be used for molecular optimization of chemicals. According to the results of structural interpretation, the most significant common mechanisms of the toxic effect of organic compounds on Fathead minnow, Daphnia magna and Tetrahymena pyriformis are reactions of nucleophilic substitution and inhibition of oxidative phosphorylation in mitochondria. In addition acetylcholinesterase and voltage-gated ion channel of Fathead minnow and Daphnia magna are important targets for toxicants. The on-line version of the OCHEM expert system (https://ochem.eu) were used for a comparative QSAR investigation. The proposed QSAR models comply with the OECD principles and can be used to reliably predict acute toxicity of organic compounds towards Fathead minnow, Daphnia magna and Tetrahymena pyriformis with allowance for applicability domain estimation.

Reactivity prediction in aza-Michael additions without transition state calculations: the Ames test for mutagenicity

Animal testing remains a contentious ethical issue in predictive toxicology. Thus, a fast, versatile, low-cost quantum chemical model is presented for predicting the risk of Ames mutagenicity in a series of 1,4 Michael acceptor type compounds. This framework eliminates the need for transition state calculations, and uses an intermediate structure to probe the reactivity of aza-Michael acceptors. This model can be used in a variety of settings e.g., the design of targeted covalent inhibitors and polyketide biosyntheses.

Comparison of the predictive nature of the Genomic Allergen Rapid Detection (GARD) assay with mammalian assays in determining the skin sensitisation potential of agrochemical active ingredients

Alternatives to mammalian testing are highly desirable to predict the skin sensitisation potential of agrochemical active ingredients (AI). The GARD assay, a stimulated, dendritic cell-like, cell line measuring genomic signatures, was evaluated using twelve AIs (seven sensitisers and five non-sensitisers) and the results compared with historical results from guinea pig or local lymph node assay (LLNA) studies. Initial GARD results suggested 11/12 AIs were sensitisers and six concurred with mammalian data. Conformal predictions changed one AI to a non-sensitiser. An AI identified as non-sensitising in the GARD assay was considered a potent sensitiser in the LLNA. In total 7/12 GARD results corresponded with mammalian data. AI chemistries might not be comparable to the GARD training set in terms of applicability domains. Whilst the GARD assay can replace mammalian tests for skin sensitisation evaluation for compounds including cosmetic ingredients, further work in agrochemical chemistries is needed for this assay to be a viable replacement to animal testing. The work conducted here is, however, considered exploratory research and the methodology needs further development to be validated for agrochemicals. Mammalian and other alternative assays for regulatory safety assessments of AIs must provide confidence to assign the appropriate classification for human health protection.

1,4-Disubstituted-1,2,3-Triazole Compounds Induce Ultrastructural Alterations in Leishmania amazonensis Promastigote: An in Vitro Antileishmanial and in Silico Pharmacokinetic Study

The current standard treatment for leishmaniasis has remained the same for over 100 years, despite inducing several adverse effects and increasing cases of resistance. In this study we evaluated the in vitro antileishmanial activity of 1,4-disubstituted-1,2,3 triazole compounds and carried out in silico predictive study of their pharmacokinetic and toxicity properties. Ten compounds were analyzed, with compound 6 notably presenting IC₅₀: 14.64 ± 4.392 µM against promastigotes, IC₅₀: 17.78 ± 3.257 µM against intracellular amastigotes, CC₅₀: 547.88 ± 3.256 µM against BALB/c peritoneal macrophages, and 30.81-fold selectivity for the parasite over the cells. It also resulted in a remarkable decrease in all the parameters of in vitro infection. Ultrastructural analysis revealed lipid corpuscles, a nucleus with discontinuity of the nuclear membrane, a change in nuclear chromatin, and kinetoplast swelling with breakdown of the mitochondrial cristae and electron-density loss induced by 1,4-disubstituted-1,2,3-triazole treatment. In addition, compound 6 enhanced 2.3-fold the nitrite levels in the Leishmania-stimulated macrophages. In silico pharmacokinetic prediction of compound 6 revealed that it is not recommended for topical formulation cutaneous leishmaniasis treatment, however the other properties exhibited results that were similar or even better than miltefosine, making it a good candidate for further in vivo studies against Leishmania parasites.

Scopy: an integrated negative design python library for desirable HTS/VS database design

BACKGROUND

High-throughput screening (HTS) and virtual screening (VS) have been widely used to identify potential hits from large chemical libraries. However, the frequent occurrence of 'noisy compounds' in the screened libraries, such as compounds with poor drug-likeness, poor selectivity or potential toxicity, has greatly weakened the enrichment capability of HTS and VS campaigns. Therefore, the development of comprehensive and credible tools to detect noisy compounds from chemical libraries is urgently needed in early stages of drug discovery.

RESULTS

In this study, we developed a freely available integrated python library for negative design, called Scopy, which supports the functions of data preparation, calculation of descriptors, scaffolds and screening filters, and data visualization. The current version of Scopy can calculate 39 basic molecular properties, 3 comprehensive molecular evaluation scores, 2 types of molecular scaffolds, 6 types of substructure descriptors and 2 types of fingerprints. A number of important screening rules are also provided by Scopy, including 15 drug-likeness rules (13 drug-likeness rules and 2 building block rules), 8 frequent hitter rules (four assay interference substructure filters and four promiscuous compound substructure filters), and 11 toxicophore filters (five human-related toxicity substructure filters, three environment-related toxicity substructure filters and three comprehensive toxicity substructure filters). Moreover, this library supports four different visualization functions to help users to gain a better understanding of the screened data, including basic feature radar chart, feature-feature-related scatter diagram, functional group marker gram and cloud gram.

CONCLUSION

Scopy provides a comprehensive Python package to filter out compounds with undesirable properties or substructures, which will benefit the design of high-quality chemical libraries for drug design and discovery. It is freely available at https://github.com/kotori-y/Scopy.

Role of functional groups in reaction kinetics of dithiothreitol with secondary organic aerosols

The toxicity of organic aerosols has been largely ascribed to the generation of reactive oxygen species, which could subsequently induce oxidative stress in biological systems. The reaction of DTT with redox-active species in PM has been generally assumed to be pseudo-first order, with the oxidative potential of PM being represented by the DTT consumption per minute of reaction time per μg of PM. Although catalytic reactive species such as transition metals and quinones are long believed to be the main contributors of DTT responses, the role of non-catalytic DTT reactive species such as organic hydroperoxides (ROOH) and electron-deficient alkenes (e.g., conjugated carbonyls) in DTT consumption has been recently highlighted. Thus, understanding the reaction kinetics and mechanisms of DTT consumption by various PM components is required to interpret the oxidative potential measured by DTT assays more accurately. In this study, we measured the DTT consumptions over time and characterized the reaction products using model compounds and secondary organic aerosols (SOA) with varying initial concentrations. We observed that the DTT consumption rates linearly increased with both initial DTT and sample concentrations. The overall reaction order of DTT with non-catalytic reactive species and SOA in this study is second order. The reactions of DTT with different functional groups have significantly different rate constants. The reaction rate constant of isoprene SOA with DTT is mainly determined by the concentration of ROOH. For toluene SOA, both ROOH and electron-deficient alkenes may dominate its DTT reaction rates. These results provide some insights into the interpretation of DTT-based aerosol oxidative potential and highlight the need to study the toxicity mechanism of ROOH and electron-deficient alkenes in PM for future work.