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The search for bioactive compounds in natural products holds promise for discovering new pharmacologically active molecules. This study explores the anti-inflammatory potential of açaí (Euterpe oleracea Mart.) constituents against the NLRP3 inflammasome using high-throughput molecular modeling techniques. Utilizing methods such as molecular docking, molecular dynamics simulation, binding free energy calculations (MM/GBSA), and in silico toxicology, we compared açaí compounds with known NLRP3 inhibitors, MCC950 and NP3-146 (RM5). The docking studies revealed significant interactions between açaí constituents and the NLRP3 protein, while molecular dynamics simulations indicated structural stabilization. MM/GBSA calculations demonstrated favorable binding energies for catechin, apigenin, and epicatechin, although slightly lower than those of MCC950 and RM5. Importantly, in silico toxicology predicted lower toxicity for açaí compounds compared to synthetic inhibitors. These findings suggest that açaí-derived compounds are promising candidates for developing new anti-inflammatory therapies targeting the NLRP3 inflammasome, combining efficacy with a superior safety profile. Future research should include in vitro and in vivo validation to confirm the therapeutic potential and safety of these natural products. This study underscores the value of computational approaches in accelerating natural product-based drug discovery and highlights the pharmacological promise of Amazonian biodiversity.

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This study reports the first application of in silico methods to assess the toxicity of 4-chloromethcathinone (4-CMC), a novel psychoactive substance (NPS). Employing advanced toxicology in silico tools, it was possible to predict crucial aspects of the toxicological profile of 4-CMC, including acute toxicity (LD50), genotoxicity, cardiotoxicity, and its potential for endocrine disruption. The obtained results indicate significant acute toxicity with species-specific variability, moderate genotoxic potential suggesting the risk of DNA damage, and a notable cardiotoxicity risk associated with hERG channel inhibition. Endocrine disruption assessment revealed a low probability of 4-CMC interacting with estrogen receptor alpha (ER-α), suggesting minimal estrogenic activity. These insights, derived from in silico studies, are critical in advancing the understanding of 4-CMC properties in forensic and clinical toxicology. These initial toxicological findings provide a foundation for future research and aid in the formulation of risk assessment and management strategies in the context of the use and abuse of NPSs.

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Pregnant females may use medications to manage health problems that develop during pregnancy or that they had prior to pregnancy. However, using medications during pregnancy has a potential risk to the fetus. Assessing the fetotoxicity of drugs is essential to ensure safe treatments, but the current process is challenged by ethical issues, time, and cost. Therefore, the need for in silico models to efficiently assess the fetotoxicity of drugs has recently emerged. Previous studies have proposed successful machine learning models for fetotoxicity prediction and even suggest molecular substructures that are possibly associated with fetotoxicity risks or protective effects. However, the interpretation of the decisions of the models on fetotoxicity prediction for each drug is still insufficient. This study constructed machine learning-based models that can predict the fetotoxicity of drugs while providing explanations for the decisions. For this, permutation feature importance was used to identify the general features that the model made significant in predicting the fetotoxicity of drugs. In addition, features associated with fetotoxicity for each drug were analyzed using the attention mechanism. The predictive performance of all the constructed models was significantly high (AUROC: 0.854-0.974, AUPR: 0.890-0.975). Furthermore, we conducted literature reviews on the predicted important features and found that they were highly associated with fetotoxicity. We expect that our model will benefit fetotoxicity research by providing an evaluation of fetotoxicity risks for drugs or drug candidates, along with an interpretation of that prediction.

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In this study, for the first time in the literature, a 2-(3-methoxyphenylamino)-2-oxoethyl acrylate (3MPAEA) molecule was synthesized in two steps, and a 2-chloro-N-(3-methoxyphenyl)acetamide (m-acetamide) was obtained in the first step. Experimental results were obtained using FTIR, 1H, and 13C NMR spectroscopy methods for m-acetamide and 3MPAEA compounds created in the laboratory environment and compared with theoretical results. Band gap (BG) energy, chemical hardness, electronegativity, chemical potential, and electrophilicity index were calculated. With vibration spectroscopic analysis, atom-molecule vibrations of the theoretical and experimental peaks of the spectrum were observed. The locations of C and H atoms were determined by nuclear magnetic resonance spectroscopy. The green, blue, and red regions of the potential energy map (MEP) map were examined. Some observed that the energy thermal, heat capacity, and entropy graphs increased in direct proportion to increasing the temperature in Kelvin, which is known as thermochemistry. The changes in the rotation, translation, and vibration of the molecule as its temperature increased were examined. When the thermochemistry surface map was examined, some observed that the temperature was high in the middle binding site of the molecules. Covalent interactions were graphed using the non-covalent interactions (NCIs) calculation method. In silico toxicity studies were carried out for m-acetamide and 3MPAEA molecules: fathead minnow LC50 (96 h), Daphnia magna LC50 (48 h), Tetrahymena pyriformis IGC50 (48 h), oral rat LD50, water solubility, bioconcentration factor, developmental toxicity, mutation, normal boiling point, flash point, melting point, density, thermal conductivity, viscosity, vapor pressure, etc. parameters were investigated.

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In silico toxicology protocols are meant to support computationally-based assessments using principles that ensure that results can be generated, recorded, communicated, archived, and then evaluated in a uniform, consistent, and reproducible manner. We investigated the availability of in silico models to predict the carcinogenic potential of pregabalin using the ten key characteristics of carcinogens as a framework for organizing mechanistic studies. Pregabalin is a single-species carcinogen producing only one type of tumor, hemangiosarcomas in mice via a nongenotoxic mechanism. The overall goal of this exercise is to test the ability of in silico models to predict nongenotoxic carcinogenicity with pregabalin as a case study. The established mode of action (MOA) of pregabalin is triggered by tissue hypoxia, leading to oxidative stress (KC5), chronic inflammation (KC6), and increased cell proliferation (KC10) of endothelial cells. Of these KCs, in silico models are available only for selected endpoints in KC5, limiting the usefulness of computational tools in prediction of pregabalin carcinogenicity. KC1 (electrophilicity), KC2 (genotoxicity), and KC8 (receptor-mediated effects), for which predictive in silico models exist, do not play a role in this mode of action. Confidence in the overall assessments is considered to be medium to high for KCs 1, 2, 5, 6, 7 (immune system effects), 8, and 10 (cell proliferation), largely due to the high-quality experimental data. In order to move away from dependence on animal data, development of reliable in silico models for prediction of oxidative stress, chronic inflammation, immunosuppression, and cell proliferation will be critical for the ability to predict nongenotoxic compound carcinogenicity.

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V-agents are exceedingly toxic nerve agents. Recently, it was highlighted that V-agents constitute a diverse subclass of compounds with most of them not extensively studied. Although chemical weapons have been banned under the Chemical Weapons Convention (CWC), there is an increased concern for chemical terrorism. Thus, it is important to understand their properties and toxicities, especially since some of these agents are not included in the CWC list. Nonetheless, to achieve this goal, the testing of a huge number of compounds is needed. Alternatively, in silico toxicology offers a great advantage for the rapid assessment of toxic compounds. Here, various in silico tools (TEST, VEGA, pkCSM ProTox-II) were used to estimate the acute oral toxicity (LD50) of different V-agents and compare them with experimental values. These programs underestimated the toxicity of V-agents, and certain V-agents were estimated to be relatively non-toxic. TEST was also used to estimate the physical properties and found to provide good approximations for densities, surface tensions and vapor pressures but not for viscosities. Thus, attention should be paid when interpreting and estimating the toxicities of V-agents in silico, and it is necessary to conduct future detailed experiments to understand their properties and develop effective countermeasures.

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The bitter taste perception evolved in human and animals to rapidly perceive and avoid potential toxic compounds. This is mediated by taste receptors type 2 (TAS2R), expressed in various tissues, which recently proved to be involved in roles beyond the bitter perception itself. With this study, the interaction between food-related toxic compounds and TAS2R46 has been investigated via computational approaches, starting with a virtual screening and moving to molecular docking and dynamics simulations. The virtual screening analysis identified trichothecolone and the trichothecenes class it belongs to, which includes mycotoxins widespread in several commodities raising food safety concerns, as possible TAS2R46 binders. Molecular docking and dynamics simulations were performed to further explore the trichotecenes-TAS2R46 interaction. The results indicated that deoxynivalenol and its 15-acetylated derivative could activate TAS2R46. Eventually, this study provided initial evidence supporting the involvement of TAS2R46 in the underpinning mechanisms of deoxynivalenol action highlighting the need of digging into the involvement of TAS2R46 and TAS2Rs in the adverse effects of deoxynivalenol and congeners.

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Gelsedine-type alkaloids are highly toxic plant secondary metabolites produced by shrubs belonging to the Gelsemium genus. Gelsenicine is one of the most concerning gelsedine-type alkaloids with a lethal dose lower than 1 mg/Kg in mice. Several reported episodes of poisoning in livestock and fatality cases in humans due to the usage of Gelsemium plants extracts were reported. Also, gelsedine-type alkaloids were found in honey constituting a potential food safety issue. However, their toxicological understanding is scarce and the molecular mechanism underpinning their toxicity needs further investigations. In this context, an in silico approach based on reverse screening, docking and molecular dynamics successfully identified a possible gelsenicine biological target shedding light on its toxicodynamics. In line with the available crystallographic data, it emerged gelsenicine could target the acetylcholine binding protein possibly acting as a partial agonist against α7 nicotinic acetylcholine receptor (AChR). Overall, these results agreed with evidence previously reported and prioritized AChR for further dedicated analysis.

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In silico methods can be used for an early assessment of arrhythmogenic properties of drug candidates. However, their use for decision-making is conditioned by the possibility to estimate the predictions' uncertainty. This work describes our efforts to develop uncertainty quantification methods for the predictions produced by multi-level proarrhythmia models. In silico models used in this field usually start with experimental or predicted IC50 values that describe drug-induced ion channel blockade. Using such inputs, an electrophysiological model computes how the ion channel inhibition, exerted by a drug in a certain concentration, translates to an altered shape and duration of the action potential in cardiac cells, which can be represented as arrhythmogenic risk biomarkers such as the APD90. Using this framework, we identify the main sources of aleatory and epistemic uncertainties and propose a method based on probabilistic simulations that replaces single-point estimates predicted using multiple input values, including the IC50s and the electrophysiological parameters, by distributions of values. Two selected variability types associated with these inputs are then propagated through the multi-level model to estimate their impact on the uncertainty levels in the output, expressed by means of intervals. The proposed approach yields single predictions of arrhythmogenic risk biomarkers together with value intervals, providing a more comprehensive and realistic description of drug effects on a human population. The methodology was tested by predicting arrhythmogenic biomarkers on a series of twelve well-characterised marketed drugs, belonging to different arrhythmogenic risk classes.

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According to some clinical observations, the use of angiotensin-converting enzyme inhibitors (ACEI) may be associated with an increased risk of cancer. The aim of the present study was to screen for the potential carcinogenicity, mutagenicity and genotoxicity of these drugs using in silico methodology. Delapril, enalapril, imidapril, lisinopril, moexipril, perindopril, ramipril, trandolapril, spirapril were thereby analyzed. In parallel, the corresponding degradation impurities, the diketopiperazine (DKP) derivatives, were also investigated. (Q)SAR computer software (VEGA-GUI and Lazar), available in the public domain, was employed. The obtained predictions suggested that none of the compounds tested (from the group of ACE-Is and DKPs) was mutagenic. Moreover, none of the ACE-Is was carcinogenic. The reliability of these predictions was high to moderate. In contrast, in the DKP group, ramipril-DKP and trandolapril-DKP were found to be potentially carcinogenic, but the reliability of this prediction was low. As for the genotoxicity screening, all compounds tested (ACE-I and DKP) were predicted to be active and genotoxic, with moexipril, ramipril, spirapril, and all DKP derivatives within the highest risk group. They were prioritized for experimental verification studies to confirm or exclude their toxic activity. On the other hand, the lowest risk of carcinogenicity was assigned to imidapril and its DKP. Then, a follow-up in vitro micronucleus assay for ramipril was performed. It showed that this drug was genotoxic via aneugenic activity, but only at concentrations exceeding real-life levels. At concentrations found in human blood after standard dose, ramipril was not genotoxic in vitro. Therefore, ramipril was considered safe for human use with a standard dosing regimen. The other compounds of concern (spirapril, moexipril and all DKP derivatives) should be subjected to analogous in vitro studies. We also concluded that the adopted in silico software was applicable for ACE-I toxicity prediction.

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Alkenylbenzenes are aromatic compounds found in several vegetable foods that can cause genotoxicity upon bioactivation by members of the cytochrome P450 (CYP) family, forming 1'-hydroxy metabolites. These intermediates act as proximate carcinogens and can be further converted into reactive 1'-sulfooxy metabolites, which are the ultimate carcinogens responsible for genotoxicity. Safrole, a member of this class, has been banned as a food or feed additive in many countries based on its genotoxicity and carcinogenicity. However, it can still enter the food and feed chain. There is limited information about the toxicity of other alkenylbenzenes that may be present in safrole-containing foods, such as myristicin, apiole, and dillapiole. In vitro studies showed safrole as mainly bioactivated by CYP2A6 to form its proximate carcinogen, while for myristicin this is mainly done by CYP1A1. However, it is not known whether CYP1A1 and CYP2A6 can activate apiole and dillapiole. The present study uses an in silico pipeline to investigate this knowledge gap and determine whether CYP1A1 and CYP2A6 may play a role in the bioactivation of these alkenylbenzenes. The study found that the bioactivation of apiole and dillapiole by CYP1A1 and CYP2A6 is limited, possibly indicating that these compounds may have limited toxicity, while describing a possible role of CYP1A1 in the bioactivation of safrole. The study expands the current understanding of safrole toxicity and bioactivation and helps understand the mechanisms of CYPs involved in the bioactivation of alkenylbenzenes. This information is essential for a more informed analysis of alkenylbenzenes toxicity and risk assessment.

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In silico models are often built solely on publicly available data which may mean that they are less predictive for proprietary chemical space. Data sharing initiatives can improve the performance of such models, but organisations are often unable to share their data due to the need to protect their business interests and maintain the confidentiality of the chemicals in their research and development programmes. In silico models like Derek Nexus, which use expert knowledge to develop structural alerts based on chemical toxicity, can use proprietary data to identify new areas of chemical space and/or refine existing alerts whilst still preserving the privacy of the confidential data. Five hundred and thirty seven proprietary chemicals with skin sensitisation data were shared which led to the implementation of 7 new alerts and 5 modified alerts, with a concomitant 19% increase in sensitivity and 3% increase in specificity of the model.

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Neurotoxicology is the study of adverse effects on the structure or function of the developing or mature adult nervous system following exposure to chemical, biological, or physical agents. The development of more informative alternative methods to assess developmental (DNT) and adult (NT) neurotoxicity induced by xenobiotics is critically needed. The use of such alternative methods including in silico approaches that predict DNT or NT from chemical structure (e.g., statistical-based and expert rule-based systems) is ideally based on a comprehensive understanding of the relevant biological mechanisms. This paper discusses known mechanisms alongside the current state of the art in DNT/NT testing. In silico approaches available today that support the assessment of neurotoxicity based on knowledge of chemical structure are reviewed, and a conceptual framework for the integration of in silico methods with experimental information is presented. Establishing this framework is essential for the development of protocols, namely standardized approaches, to ensure that assessments of NT and DNT based on chemical structures are generated in a transparent, consistent, and defendable manner.

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Many flavor ingredients are often used in potentially reduced-risk tobacco products (such as e-vapor products). Although most are "generally recognized as safe (GRAS)" when used in food, there is limited information available on their long-term health effects when delivered by inhalation. While obtaining route-of-exposure-specific toxicological data on flavor ingredients is critical to product evaluation, the large number of individual flavor ingredients available and their potential combinations render classical toxicological assessment approaches impractical, as they may require years of preclinical investigations and thousands of laboratory animals. Therefore, we propose a pragmatic approach in which flavor ingredients are initially assigned to groups of structurally related compounds (Flavor Groups), from which flavor group representatives (FGR) are then selected and tested individually and as a mixture in vitro and in vivo. The premise is that structurally related compounds would have comparable metabolic and biological activity and that the data generated using FGRs could support the toxicological assessment of other structurally related flavor ingredients of their respective Flavor Groups. This approach is explained in a step-wise manner and exemplified by a case study, along with its strengths, limitations as well as recommendations for further confirmatory testing. Once completed, this FGR approach could significantly reduce the time and resources required for filling the data gap in understanding the health risks of many flavor ingredients while also minimizing the need for laboratory animals.

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Industrial needs and regulatory requirements have played a significant role in accelerating the use of nontesting methods including in silico tools as alternatives to animal testing. The main interest is not solely on the use of in silico tools, or in read-across, but on better toxicological safety assessment of substances, and for this purpose more advanced, integrated strategies have to be implemented. VEGAHUB wants to promote this broader view, not necessarily focused on a specific approach. Applying multiple tools and complementary approaches instead of one technique may provide more elements for a more robust evaluation, but at the same time it is important to have a conceptual scheme to integrate multiple, heterogeneous lines of evidence. We will show how the user can benefit from the diversity of tools available within the platform VEGAHUB for assessing the biological properties of chemical substances on an example of (non)mutagenicity.

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There exists consensus that the traditional means by which safety of chemicals is assessed-namely through reliance upon apical outcomes obtained following in vivo testing-is increasingly unfit for purpose. Whilst efforts in development of suitable alternatives continue, few have achieved levels of robustness required for regulatory acceptance. An array of "new approach methodologies" (NAM) for determining toxic effect, spanning in vitro and in silico spheres, have by now emerged. It has been suggested, intuitively, that combining data obtained from across these sources might serve to enhance overall confidence in derived judgment. This concept may be formalised in the "tiered assessment" approach, whereby evidence gathered through a sequential NAM testing strategy is exploited so to infer the properties of a compound of interest. Our intention has been to provide an illustration of how such a scheme might be developed and applied within a practical setting-adopting for this purpose the endpoint of rat acute oral lethality. Bayesian statistical inference is drawn upon to enable quantification of degree of confidence that a substance might ultimately belong to one of five LD50-associated toxicity categories. Informing this is evidence acquired both from existing in silico and in vitro resources, alongside a purposely-constructed random forest model and structural alert set. Results indicate that the combination of in silico methodologies provides moderately conservative estimations of hazard, conducive for application in safety assessment, and for which levels of certainty are defined. Accordingly, scope for potential extension of approach to further toxicological endpoints is demonstrated.

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Microcystins (MC) are a group of structurally similar cyanotoxins with currently 279 described structural variants. Human exposure is frequent by consumption of contaminated water, food or food supplements. MC can result in serious intoxications, commensurate with ensuing pathology in various organs or in rare cases even mortality. The current WHO risk assessment primarily considers MC-LR, while all other structural variants are treated as equivalent to MC-LR, despite that current data strongly suggest that MC-LR is not the most toxic MC, and toxicity can be very different for MC congeners. To investigate and analyse binding and conformation of different MC congeners, we applied for the first time Molecular Dynamics (MD) simulation to four MC congeners (MC-LR, MC-LF, [Enantio-Adda5]MC-LF, [ß-D-Asp3,Dhb7]MC-RR). We could show that ser/thr protein phosphatase 1 is stable in all MD simulations and that MC-LR backbone adopts to a second conformation in solvent MD simulation, which was previously unknown. We could also show that MC congeners can adopt to different backbone conformation when simulated in solvent or in complex with ser/thr protein phosphatase 1 and differ in their binding behaviour. Our findings suggest that MD Simulation of different MC congeners aid in understanding structural differences and binding of this group of structurally similar cyanotoxins.

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Thousands of intentionally added substances can be used in printing inks and adhesives applied to plastic food packaging. Some of them can be transferred to foodstuffs through a phenomenon called migration, arising concerns on the potential adverse health effects derived from the exposure to chemicals that have not yet been assessed for their risks to humans. The large number of the substances concerned and the lack of prioritisation strategies hamper the work of control authorities, since it is not clear which substances should be monitored as first priority. In this study, a hazard prioritisation strategy is proposed. An inventory listing more than 6,000 substances used in inks and adhesives applied to plastic food packaging was compiled and filtered using several exclusion criteria aimed to set apart those substances for which there is no apparent need for further evaluation or because fall into one of the exclusion categories of the Threshold of Toxicological Concern (TTC) approach. Additionally, substances with a molecular weight >1,000 Da were removed. Approximately 2,300 substances were retained, for which a comprehensive hazard profiling was conducted based on the general scheme for the application of the TTC approach. First, structural alerts for genotoxic and non-genotoxic carcinogenicity were investigated. If a substance was neither genotoxic nor belonging to the chemical classes of organophosphates and carbamates, the Cramer classification was used. Furthermore, the substances were searched for their presence in three so-called 'Substances of Concern' lists and RASFF notifications. Groups of high, medium and low priority substances were established, resulting in 1,660 substances classified as high and medium priority. A panel of five experts evaluated these substances with respect to their relevance for further risk evaluations. By applying this hazard prioritisation strategy, 696 substances were identified as 'Very High Priority Substances' (VHPS) for which further assessments should be performed.

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This article describes Flame, an open source software for building predictive models and supporting their use in production environments. Flame is a web application with a web-based graphic interface, which can be used as a desktop application or installed in a server receiving requests from multiple users. Models can be built starting from any collection of biologically annotated chemical structures since the software supports structural normalization, molecular descriptor calculation, and machine learning model generation using predefined workflows. The model building workflow can be customized from the graphic interface, selecting the type of normalization, molecular descriptors, and machine learning algorithm to be used from a panel of state-of-the-art methods implemented natively. Moreover, Flame implements a mechanism allowing to extend its source code, adding unlimited model customization. Models generated with Flame can be easily exported, facilitating collaborative model development. All models are stored in a model repository supporting model versioning. Models are identified by unique model IDs and include detailed documentation formatted using widely accepted standards. The current version is the result of nearly 3 years of development in collaboration with users from the pharmaceutical industry within the IMI eTRANSAFE project, which aims, among other objectives, to develop high-quality predictive models based on shared legacy data for assessing the safety of drug candidates.

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The current strategy for treating the Covid-19 coronavirus disease involves the repurposing of existing drugs or the use of convalescent plasma therapy, as no specific therapeutic intervention has yet received regulatory approval. However, severe adverse effects have been reported for some of these repurposed drugs. Recently, several in silico studies have identified compounds that are potential inhibitors of the main protease (3-chymotrypsin-like cysteine protease) and the nucleocapsid protein of SARS-CoV-2. An essential step of drug development is the careful evaluation of toxicity, which has a range of associated financial, temporal and ethical limitations. In this study, a number of in silico tools were used to predict the toxicity of 19 experimental compounds. A range of web-based servers and applications were used to predict hepatotoxicity, mutagenicity, acute oral toxicity, carcinogenicity, cardiotoxicity, and other potential adverse effects. The compounds were assessed based on the consensus of results, and were labelled as positive or negative for a particular toxicity endpoint. The compounds were then categorised into three classes, according to their predicted toxicity. Ten compounds (52.6%) were predicted to be non-mutagenic and non-hERG inhibitors, and exhibited zero or low level hepatotoxicity and carcinogenicity. Furthermore, from the consensus of results, all 19 compounds were predicted to be non-mutagenic and negative for acute oral toxicity. Overall, most of the compounds displayed encouraging toxicity profiles. These results can assist further lead optimisation studies and drug development efforts to combat Covid-19.

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