In Silico Tools for Sharing Data and Knowledge on Toxicity and Metabolism: Derek for Windows, Meteor, and Vitic

In Silico Phototoxicity Prediction of Drugs and Chemicals by using Derek Nexus and QSAR Toolbox

Phototoxicity testing is crucial for evaluating the potential harmful effects of pharmaceuticals and chemicals on human skin when exposed to sunlight. Traditional in vivo models involving mice, rats, guinea pigs, as well as in vitro assays such as the 3T3 Neutral Red Uptake phototoxicity assay and methods based on the use of reconstructed human epidermis, have been established for phototoxicity testing. While these approaches are extremely valuable, they are costly in terms of both time and resources. Consequently, in silico approaches based on the use of predictive software tools can offer more rapid and cost-effective phototoxicity screening solutions. With this goal in mind, the current study evaluated two in silico tools - Derek Nexus 6.1.0/Derek Knowledge Base 2020 1.0 (Lhasa Limited, UK) and the QSAR Toolbox (v 4.5) developed by the Organisation for Economic Co-operation and Development (OECD) - for their capacity to predict the phototoxicity of several substances from diverse classes. Derek Nexus and the QSAR Toolbox were both found to be very useful for predicting the phototoxicity of drugs and other chemicals. Derek Nexus predicted phototoxicity of the compounds, with a sensitivity of 63%, specificity of 93%, Positive Predictive Values of 90% and Negative Predictive Value of 69%, overall accuracy of 77% and balanced accuracy of 78%. The QSAR Toolbox achieved sensitivity of 73%, specificity of 85%, Positive Predictive Value of 85% and Negative Predictive Value of 74%, overall accuracy of 79% and balanced accuracy of 79%. The results show that Derek Nexus and the QSAR Toolbox can be usefully incorporated in the workflow of phototoxicity testing for pharmaceuticals and chemicals.

Exploring the selectivity of cytochrome P450 for enhanced novel anticancer agent synthesis

Cytochrome P450 monooxygenases are an extensive and unique class of enzymes, which can regio- and stereo-selectively functionalise hydrocarbons by way of oxidation reactions. These enzymes are naturally occurring but have also been extensively applied in a synthesis context, where they are used as efficient biocatalysts. Recently, a biosynthetic pathway where a cytochrome P450 monooxygenase catalyses a critical step of the pathway was uncovered, leading to the production of a number of products that display high antitumour potency. In this work, we use computational techniques to gain insight into the factors that determine the relative yields of the different products. We use conformational search algorithms to understand the substrate stereochemistry. On a machine-learned 3D protein structure, we use molecular docking to obtain a library of favourable poses for substrate-protein interaction. With molecular dynamics, we investigate the most favourable poses for reactivity on a molecular level, allowing us to investigate which protein-substrate interactions favour a given product and thus gain insight into the product selectivity.

GDP-mannose pyrophosphorylase is an efficient target in Xanthomonas citri for citrus canker control

Xanthomonas citri subsp. citri (Xcc) is a bacterium that causes citrus canker, an economically important disease that results in premature fruit drop and reduced yield of fresh fruit. In this study, we demonstrated the involvement of XanB, an enzyme with phosphomannose isomerase (PMI) and guanosine diphosphate-mannose pyrophosphorylase (GMP) activities, in Xcc pathogenicity. Additionally, we found that XanB inhibitors protect the host against Xcc infection. Besides being deficient in motility, biofilm production, and ultraviolet resistance, the xanB deletion mutant was unable to cause disease, whereas xanB complementation restored wild-type phenotypes. XanB homology modeling allowed in silico virtual screening of inhibitors from databases, three of them being suitable in terms of absorption, distribution, metabolism, excretion, and toxicity (ADME/Tox) properties, which inhibited GMP (but not PMI) activity of the Xcc recombinant XanB protein in more than 50%. Inhibitors reduced citrus canker severity up to 95%, similarly to copper-based treatment. xanB is essential for Xcc pathogenicity, and XanB inhibitors can be used for the citrus canker control.

IMPORTANCE

Xcc causes citrus canker, a threat to citrus production, which has been managed with copper, being required a more sustainable alternative for the disease control. XanB was previously found on the surface of Xcc, interacting with the host and displaying PMI and GMP activities. We demonstrated by xanB deletion and complementation that GMP activity plays a critical role in Xcc pathogenicity, particularly in biofilm formation. XanB homology modeling was performed, and in silico virtual screening led to carbohydrate-derived compounds able to inhibit XanB activity and reduce disease symptoms by 95%. XanB emerges as a promising target for drug design for control of citrus canker and other economically important diseases caused by Xanthomonas sp.

MetSim: Integrated Programmatic Access and Pathway Management for Xenobiotic Metabolism Simulators

Xenobiotic metabolism is a key consideration in evaluating the hazards and risks posed by environmental chemicals. A number of software tools exist that are capable of simulating metabolites, but each reports its predictions in a different format and with varying levels of detail. This makes comparing the performance and coverage of the tools a practical challenge. To address this shortcoming, we developed a metabolic simulation framework called MetSim, which comprises three main components. A graph-based schema was developed to allow metabolism information to be harmonized. The schema was implemented in MongoDB to store and retrieve metabolic graphs for subsequent analysis. MetSim currently includes an application programming interface for four metabolic simulators: BioTransformer, the OECD Toolbox, EPA's chemical transformation simulator (CTS), and tissue metabolism simulator (TIMES). Lastly, MetSim provides functions to help evaluate simulator performance for specific data sets. In this study, a set of 112 drugs with 432 reported metabolites were compiled, and predictions were made using the 4 simulators. Fifty-nine of the 112 drugs were taken from the Small Molecule Pathway Database, with the remainder sourced from the literature. The human models within BioTransformer and CTS (Phase I only) and the rat models within TIMES and the OECD Toolbox (Phase I only) were used to make predictions for the chemicals in the data set. The recall and precision (recall, precision) ranked in order of highest recall for each individual tool were CTS (0.54, 0.017), BioTransformer (0.50, 0.008), Toolbox in vitro (0.40, 0.144), TIMES in vivo (0.40, 0.133), Toolbox in vivo (0.40, 0.118), and TIMES in vitro (0.39, 0.128). Combining all of the model predictions together increased the overall recall (0.73, 0.008). MetSim enabled insights into the performance and coverage of in silico metabolic simulators to be more efficiently derived, which in turn should aid future efforts to evaluate other data sets.

Interpreting Neural Network Models for Toxicity Prediction by Extracting Learned Chemical Features

Neural network models have become a popular machine-learning technique for the toxicity prediction of chemicals. However, due to their complex structure, it is difficult to understand predictions made by these models which limits confidence. Current techniques to tackle this problem such as SHAP or integrated gradients provide insights by attributing importance to the input features of individual compounds. While these methods have produced promising results in some cases, they do not shed light on how representations of compounds are transformed in hidden layers, which constitute how neural networks learn. We present a novel technique to interpret neural networks which identifies chemical substructures in training data found to be responsible for the activation of hidden neurons. For individual test compounds, the importance of hidden neurons is determined, and the associated substructures are leveraged to explain the model prediction. Using structural alerts for mutagenicity from the Derek Nexus expert system as ground truth, we demonstrate the validity of the approach and show that model explanations are competitive with and complementary to explanations obtained from an established feature attribution method.

New Approach Methodologies (NAMs) for ad hoc human health risk assessment of food and non-food products - Proceedings of a workshop

RIVM convened a workshop on the use of New Approach Methodologies (NAMs) for the ad hoc human health risk assessment of food and non-food products. Central to the workshop were two case studies of marketed products with a potential health concern: the botanical Tabernanthe iboga which is used to facilitate mental or spiritual insight or to (illegally) treat drug addiction and is associated with cardiotoxicity, and dermal creams containing female sex hormones, intended for use by perimenopausal women to reduce menopause symptoms without medical supervision. The workshop participants recognized that data from NAM approaches added valuable information for the ad hoc risk assessment of these products, although the available approaches were inadequate to derive health-based guidance values. Recommendations were provided on how to further enhance and implement NAM approaches in regulatory risk assessment, specifying both scientific and technical aspects as well as stakeholder engagement aspects.

The search for novel proline analogs for viscosity reduction and stabilization of highly concentrated monoclonal antibody solutions

Administration of monoclonal antibodies (mAbs) is currently focused on subcutaneous injection associated with increased patient adherence and reduced treatment cost, leading to sustainable healthcare. The main bottleneck is low volume that can be injected, requiring highly concentrated mAb solutions. The latter results in increased solution viscosity with pronounced mAb aggregation propensity because of intensive protein-protein interactions. Small molecule excipients have been proposed to restrict the protein-protein interactions, contributing to reduced viscosity. The aim of the study was to discover novel compounds that reduce the viscosity of highly concentrated mAb solution. First, the chemical space of proline analogs was explored and 35 compounds were determined. Viscosity measurements revealed that 18 proline analogs reduced the mAb solution viscosity similar to or more than proline. The compounds forming both electrostatic and hydrophobic interactions with mAb reduced the viscosity of the formulation more efficiently without detrimentally effecting mAb physical stability. A correlation between the level of interaction and viscosity-reducing effect was confirmed with molecular dynamic simulations. Structure rigidity of the compounds and aromaticity contributed to their viscosity-reducing effect, dependent on molecule size. The study results highlight the novel proline analogs as an effective approach in viscosity reduction in development of biopharmaceuticals for subcutaneous administration.

Artificial intelligence (AI)-it's the end of the tox as we know it (and I feel fine)

The rapid progress of AI impacts diverse scientific disciplines, including toxicology, and has the potential to transform chemical safety evaluation. Toxicology has evolved from an empirical science focused on observing apical outcomes of chemical exposure, to a data-rich field ripe for AI integration. The volume, variety and velocity of toxicological data from legacy studies, literature, high-throughput assays, sensor technologies and omics approaches create opportunities but also complexities that AI can help address. In particular, machine learning is well suited to handle and integrate large, heterogeneous datasets that are both structured and unstructured-a key challenge in modern toxicology. AI methods like deep neural networks, large language models, and natural language processing have successfully predicted toxicity endpoints, analyzed high-throughput data, extracted facts from literature, and generated synthetic data. Beyond automating data capture, analysis, and prediction, AI techniques show promise for accelerating quantitative risk assessment by providing probabilistic outputs to capture uncertainties. AI also enables explanation methods to unravel mechanisms and increase trust in modeled predictions. However, issues like model interpretability, data biases, and transparency currently limit regulatory endorsement of AI. Multidisciplinary collaboration is needed to ensure development of interpretable, robust, and human-centered AI systems. Rather than just automating human tasks at scale, transformative AI can catalyze innovation in how evidence is gathered, data are generated, hypotheses are formed and tested, and tasks are performed to usher new paradigms in chemical safety assessment. Used judiciously, AI has immense potential to advance toxicology into a more predictive, mechanism-based, and evidence-integrated scientific discipline to better safeguard human and environmental wellbeing across diverse populations.

Identification, trace level quantification, and in silico assessment of potential genotoxic impurity in Famotidine

Potential genotoxic impurities in medications are an increasing concern in the pharmaceutical industry and regulatory bodies because of the risk of human carcinogenesis. To prevent the emergence of these impurities, it is crucial to carefully examine not only the final product but also the intermediates and key starting material (KSM) used in drug synthesis. During the related substances analysis of KSM of Famotidine, an unknown impurity in the range of 0.5-1.0% was found prompting the need for isolation and characterization due to the possibility of its to infiltrate into the final product. In this study, the impurity was isolated and characterized as 5-(2-chloroethyl)-3,3-dimethyl-3,4-dihydro-2H-1,2,4,6-thiatriazine 1,1-dioxide using multiple instrumental analysis, uncovering a structural alert that raises concern. Considering the potential impact of impurity on human health, an in silico genotoxicity assessment was established using Derek and Sarah tool in accordance with ICH M7 guideline. Furthermore, molecular docking and molecular dynamics simulation were performed to evaluate the specific interaction of the impurity with DNA. The findings reveal consistent interaction of the impurity with the dG-rich region of the DNA duplex and binding at the minor groove. Both in silico prediction and molecular dynamic study confirmed the genotoxic character of the impurity. The newly discovered impurity in famotidine has not been reported previously, and there is currently no analytical method available for its identification and control. A highly sensitive HPLC-UV method was developed and validated in accordance with ICH requirements, enabling quantification of the impurity at trace level in famotidine ensuring its safe release.

MolToxPred: small molecule toxicity prediction using machine learning approach

Different types of chemicals and products may exhibit various health risks when administered into the human body. For toxicity reasons, the number of new drugs entering the market through the conventional drug development process has been reduced over the years. However, with the advent of big data and artificial intelligence, machine learning techniques have emerged as a potential solution for predicting toxicity and ensuring efficient drug development and chemical safety. An ML model for toxicity prediction can reduce experimental costs and time while addressing ethical concerns by drastically reducing the need for animals and clinical trials. Herein, MolToxPred, an ML-based tool, has been developed using a stacked model approach to predict the potential toxicity of small molecules and metabolites. The stacked model consists of random forest, multi-layer perceptron, and LightGBM as base classifiers and Logistic Regression as the meta classifier. For training and validation purposes, a comprehensive set of toxic and non-toxic molecules is curated. Different structural and physicochemical-based features in the form of molecular descriptors and fingerprints were employed. MolToxPred utilizes a comprehensive feature selection process and optimizes its hyperparameters through Bayesian optimization with stratified 5-fold cross-validation. In the evaluation phase, MolToxPred achieved an AUROC of 87.76% on the test set and 88.84% on an external validation set. The McNemar test was used as the post-hoc test to determine if the stacked models' performance was significantly different compared to the base learners. The developed stacked model outperformed its base classifiers and an existing tool in the literature, reaffirming its better performance. The hypothesis is that the incorporation of a diverse set of data, the subsequent feature selection, and a stacked ensemble approach give MolToxPred the edge over other methods. In addition to this, an attempt has been made to identify structural alerts responsible for endpoints of the Tox21 data to determine the association of a molecule with a plausible downstream pathway of action. MolToxPred may be helpful for drug discovery and regulatory pipelines in pharmaceutical and other industries for in silico toxicity prediction of small molecule candidates.

Unlocking The Mysteries of DNA Adducts with Artificial Intelligence

Cellular genome is considered a dynamic blueprint of a cell since it encodes genetic information that gets temporally altered due to various endogenous and exogenous insults. Largely, the extent of genomic dynamicity is controlled by the trade-off between DNA repair processes and the genotoxic potential of the causative agent (genotoxins or potential carcinogens). A subset of genotoxins form DNA adducts by covalently binding to the cellular DNA, triggering structural or functional changes that lead to significant alterations in cellular processes via genetic (e. g., mutations) or non-genetic (e. g., epigenome) routes. Identification, quantification, and characterization of DNA adducts are indispensable for their comprehensive understanding and could expedite the ongoing efforts in predicting carcinogenicity and their mode of action. In this review, we elaborate on using Artificial Intelligence (AI)-based modeling in adducts biology and present multiple computational strategies to gain advancements in decoding DNA adducts. The proposed AI-based strategies encompass predictive modeling for adduct formation via metabolic activation, novel adducts' identification, prediction of biochemical routes for adduct formation, adducts' half-life predictions within biological ecosystems, and, establishing methods to predict the link between adducts chemistry and its location within the genomic DNA. In summary, we discuss some futuristic AI-based approaches in DNA adduct biology.

Revisiting the Landscape of Potential Small and Drug Substance Related Nitrosamines in Pharmaceuticals

N-Nitrosamines are a class of indirect acting mutagens, as their metabolic degradation leads to the formation of the DNA-alkylating diazonium ion. Following up on the in-silico identification of thousands of nitrosamines that can potentially be derived from small molecule drugs and their known impurities described in a previous publication, we have now re-analyzed this dataset to apply EMA's Carcinogenic Potency Categorization Approach (CPCA) introduced with the 16th revision of their Q&A document for Marketing Authorization Holders. We find that the majority of potential nitrosamines from secondary amine precursors belongs to potency categories 4 and 5, corresponding to an acceptable daily intake of 1500 ng, whereas nitrosamines from tertiary amine precursors distribute more evenly among all categories, resulting in a substantial number of structures that are assigned the more challenging acceptable intakes of 18 ng/day and 100 ng/day for potency categories 1 and 2, respectively. However, the nitrosative dealkylation pathway for tertiary amine is generally far slower than the direct nitrosation on secondary amines, with a direct nitrosation mechanism suspected only for structures featuring electron-rich (hetero)aromatic substituents. This allows for greater focus towards those structures that require further review, and we demonstrate that their number is not substantial. In addition, we reflect on the nitrosamine risk posed by secondary amine API impurities and demonstrate that based on the ICH Q3A/B identification threshold unknown impurities may exist that could be transformed to relevant amounts of NA. We also demonstrate that the analytical sensitivity required for the quantification of high potency nitrosamines can be problematic especially for high dose APIs. In summary, the regulatory framework rolled out with the latest Q&A document represents a substantial improvement compared with the previous situation, but further refinement through interaction between manufacturers, regulators, not-for-profit and academic institutions will be required to ensure patient access to vital medicines without compromising safety.

Non-animal approaches for photoallergenicity safety assessment: Needs and perspectives for the toxicology for the 21st century

Certain chemicals and/or their byproducts are photoactivated by UV/VIS and trigger a dermal allergenic response, clinically recognized as photoallergic contact dermatitis (PACD). It is important to identify the chemicals which are potentially photoallergenic, not only for establishing the correct differential diagnosis between PACD and other photodermatoses, but also as causative agents which should be avoided as a preventative measure. Moreover, materials with photoallergenic properties need to be correctly identified to allow thorough safety assessments for their use in finished products (e.g. cosmetics). Development of methods for predicting photoallergenicity potential of chemicals has advanced at slow pace in recent years. To date, there are no validated methods for photosensitisation potential of chemicals for regulatory purposes, although it remains a required endpoint in some regions. The purpose of this review is to explore the mechanisms potentially involved in the photosensitisation process and discuss the methods available in the literature for identification of photosensitisers. The review also explores the possibilities of further research investment required to develop human-relevant new approach methodologies (NAMs) and next generation risk assessment (NGRA) approaches, considering the current perspectives and needs of the Toxicology for the 21st Century.

Predicting routes of phase I and II metabolism based on quantum mechanics and machine learning

1. Unexpected metabolism could lead to the failure of many late-stage drug candidates or even the withdrawal of approved drugs. Thus, it is critical to predict and study the dominant routes of metabolism in the early stages of research. In this study, we describe the development and validation of a 'WhichEnzyme' model that accurately predicts the enzyme families most likely to be responsible for a drug-like molecule's metabolism. Furthermore, we combine this model with our previously published regioselectivity models for Cytochromes P450, Aldehyde Oxidases, Flavin-containing Monooxygenases, UDP-glucuronosyltransferases and Sulfotransferases - the most important Phase I and Phase II drug metabolising enzymes - and a 'WhichP450' model that predicts the Cytochrome P450 isoform(s) responsible for a compound's metabolism. The regioselectivity models are based on a mechanistic understanding of these enzymes' actions, and use quantum mechanical simulations with machine learning methods to accurately predict sites of metabolism and the resulting metabolites. We train heuristic based on the outputs of the 'WhichEnzyme', 'WhichP450', and regioselectivity models to determine the most likely routes of metabolism and metabolites to be observed experimentally. Finally, we demonstrate that this combination delivers high sensitivity in identifying experimentally reported metabolites and higher precision than other methods for predicting in vivo metabolite profiles.

Evaluation of Alectinib Metabolic Stability in HLMs Using Fast LC-MS/MS Method: In Silico ADME Profile, P450 Metabolic Lability, and Toxic Alerts Screening

Alectinib, also known as Alecensa®, is prescribed for the therapeutic treatment of individuals diagnosed with metastatic non-small cell lung cancer (NSCLC) who have a specific genetic mutation referred to as anaplastic lymphoma kinase (ALK) positivity. The Food and Drug Administration granted regular approval to alectinib, a drug developed by Hoffmann-La Roche, Inc. (Basel, Switzerland)/Genentech, Inc. (South San Francisco, CA, USA), on 6 November 2017. The screening of the metabolic stability and identification of hazardous alarms within the chemical structure of ALC was conducted using the StarDrop software package (version 6.6), which incorporates the P450 metabolic module and DEREK software (KB 2018 1.1). The primary aim of this investigation was to develop a high-throughput and accurate LC-MS/MS technique for the quantification of ALC in the metabolic matrix (human liver microsomes; HLMs). The aforementioned methodology was subsequently employed to assess the metabolic stability of ALC in HLMs through in vitro tests, with the obtained results further validated using in silico software. The calibration curve of the ALC showed a linear correlation that exists within the concentration range from 1 to 3000 ng/mL. The LC-MS/MS approach that was recommended exhibited accuracy and precision levels for both inter-day and intra-day measurements. Specifically, the accuracy values ranged from -2.56% to 3.45%, while the precision values ranged from -3.78% to 4.33%. The sensitivity of the established approach was proved by its ability to adhere to an LLOQ of 0.82 ng/mL. The half-life (t1/2) and intrinsic clearance (Clint) of ALC were estimated to be 22.28 min and 36.37 mL/min/kg, correspondingly, using in vitro experiments. The ALC exhibited a moderate extraction ratio. The metabolic stability and safety properties of newly created derivatives can be enhanced by making modest adjustments to the morpholine and piperidine rings or by substituting the substituent, as per computational software. In in silico ADME prediction, ALC was shown to have poor water solubility and high gastrointestinal absorption along with inhibition of some cytochrome P450s (CYP2C19 and CYP2C9) without inhibition of others (CYP1A2, CYP3A4, and CYP2D6) and P-glycoprotein substrate. The study design that involves using both laboratory experiments and different in silico software demonstrates a novel and groundbreaking approach in the establishment and uniformization of LC-MS/MS techniques for the estimation of ALC concentrations, identifying structural alerts and the assessment of its metabolic stability. The utilization of this study strategy has the potential to be employed in the screening and optimization of prospective compounds during the drug creation process. This strategy may also facilitate the development of novel derivatives of the medicine that maintain the same biological action by targeted structural modifications, based on an understanding of the structural alerts included within the chemical structure of ALC.

Enhancing developmental and reproductive toxicity knowledge: A new AOP stemming from glutathione depletion

Integrated approaches to testing and assessments (IATAs) have been proposed as a method to organise new approach methodologies in order to replace traditional animal testing for chemical safety assessments. To capture the mechanistic aspects of toxicity assessments, IATAs can be framed around the adverse outcome pathway (AOP) concept. To utilise AOPs fully in this context, a sufficient number of pathways need to be present to develop fit for purpose IATAs. In silico approaches can support IATA through the provision of predictive models and also through data integration to derive conclusions using a weight-of-evidence approach. To examine the maturity of a developmental and reproductive toxicity (DART) AOP network derived from the literature, an assessment of its coverage was performed against a novel toxicity dataset. A dataset of diverse compounds, with data from studies performed according to OECD test guidelines TG-421 and TG-422, was curated to test the performance of an in silico model based on the AOP network - allowing for the identification of knowledge gaps within the network. One such gap in the knowledge was filled through the development of an AOP stemming from the molecular initiating event 'glutathione reaction with an electrophile' leading to male fertility toxicity. The creation of the AOP provided the mechanistic rationale for the curation of pre-existing structural alerts to relevant key events. Integrating this new knowledge and associated alerts into the DART AOP network will improve its coverage of DART-relevant chemical space. In addition, broadening the coverage of AOPs for a particular regulatory endpoint may facilitate the development of, and confidence in, robust IATAs.

Identification of potential Staphylococcus aureus dihydrofolate reductase inhibitors using QSAR, molecular docking, dynamics simulations and free energy calculation

Herein we describe the use of molecular docking simulations, quantitative structure-activity relationships studies and ADMETox predictions to analyse the molecular recognition of a series of 7-aryl-2,4-diaminoquinazoline derivatives on the inhibition of Staphylococcus aureus dihydrofolate reductase and conducted a virtual screening to discover new potential inhibitors. A quantitative structure-activity relationship model was developed using 40 compounds and two selected descriptors. These descriptors indicated the importance of pKa and molar refractivity for the inhibitory activity against SaDHFR. The values of R2train, CVLOO and R2test generated by the model were 0.808, 0.766, and 0.785, respectively. The integration between QSAR, molecular docking, ADMETox analysis and molecular dynamics simulations with binding free energies calculation, yielded the compounds PC-124127620, PC-124127795 and PC-124127805 as promising candidates to SaDHFR inhibitors. These compounds presented high potency, good pharmacokinetics and toxicological profile. Thus, these molecules are good potential antimicrobial agent to treatment of infect disease caused by S. aureus.Communicated by Ramaswamy H. Sarma.

Predicting Regioselectivity of Cytosolic Sulfotransferase Metabolism for Drugs

Cytosolic sulfotransferases (SULTs) are a family of enzymes responsible for the sulfation of small endogenous and exogenous compounds. SULTs contribute to the conjugation phase of metabolism and share substrates with the uridine 5'-diphospho-glucuronosyltransferase (UGT) family of enzymes. UGTs are considered to be the most important enzymes in the conjugation phase, and SULTs are an auxiliary enzyme system to them. Understanding how the regioselectivity of SULTs differs from that of UGTs is essential from the perspective of developing novel drug candidates. We present a general ligand-based SULT model trained and tested using high-quality experimental regioselectivity data. The current study suggests that, unlike other metabolic enzymes in the modification and conjugation phases, the SULT regioselectivity is not strongly influenced by the activation energy of the rate-limiting step of the catalysis. Instead, the prominent role is played by the substrate binding site of SULT. Thus, the model is trained only on steric and orientation descriptors, which mimic the binding pocket of SULT. The resulting classification model, which predicts whether a site is metabolized, achieved a Cohen's kappa of 0.71.

Investigation of Fenebrutinib Metabolism and Bioactivation Using MS3 Methodology in Ion Trap LC/MS

Fenebrutinib is an orally available Bruton tyrosine kinase inhibitor. It is currently in multiple phase III clinical trials for the management of B-cell tumors and autoimmune disorders. Elementary in-silico studies were first performed to predict susceptible sites of metabolism and structural alerts for toxicities by StarDrop WhichP450™ module and DEREK software; respectively. Fenebrutinib metabolites and adducts were characterized in-vitro in rat liver microsomes (RLM) using MS3 method in Ion Trap LC-MS/MS. Formation of reactive and unstable intermediates was explored using potassium cyanide (KCN), glutathione (GSH) and methoxylamine as trapping nucleophiles to capture the transient and unstable iminium, 6-iminopyridin-3(6H)-one and aldehyde intermediates, respectively, to generate a stable adducts that can be investigated and analyzed using mass spectrometry. Ten phase I metabolites, four cyanide adducts, five GSH adducts and six methoxylamine adducts of fenebrutinib were identified. The proposed metabolic reactions involved in formation of these metabolites are hydroxylation, oxidation of primary alcohol to aldehyde, n-oxidation, and n-dealkylation. The mechanism of reactive intermediate formation of fenebrutinib can provide a justification of the cause of its adverse effects. Formation of iminium, iminoquinone and aldehyde intermediates of fenebrutinib was characterized. N-dealkylation followed by hydroxylation of the piperazine ring is proposed to cause the bioactivation to iminium intermediates captured by cyanide. Oxidation of the hydroxymethyl group on the pyridine moiety is proposed to cause the generation of reactive aldehyde intermediates captures by methoxylamine. N-dealkylation and hydroxylation of the pyridine ring is proposed to cause formation of iminoquinone reactive intermediates captured by glutathione. FBB and several phase I metabolites are bioactivated to fifteen reactive intermediates which might be the cause of adverse effects. In the future, drug discovery experiments utilizing this information could be performed, permitting the synthesis of new drugs with better safety profile. Overall, in silico software and in vitro metabolic incubation experiments were able to characterize the FBB metabolites and reactive intermediates using the multistep fragmentation capability of ion trap mass spectrometry.

Prioritization of mycotoxins based on mutagenicity and carcinogenicity evaluation using combined in silico QSAR methods

Mycotoxins and their metabolites are a family of compounds that contains a great diversity of both structure and biological properties. Information on their toxicity is spread within several databases and in scientific literature. Due to the number of molecules and their structure diversity, the cost and time required for hazard evaluation of each compound is unrealistic. In that purpose, new approach methodologies (NAMs) can be applied to evaluate such large set of molecules. Among them, quantitative structure-activity relationship (QSAR) in silico models could be useful to predict the mutagenic and carcinogenic properties of mycotoxins. First, a complete list of 904 mycotoxins and metabolites was built. Then, some known mycotoxins were used to determine the best QSAR tools for mutagenicity and carcinogenicity predictions. The best tool was further applied to the whole list of 904 mycotoxins. At the end, 95 mycotoxins were identified as both mutagen and carcinogen and should be prioritized for further evaluation.

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

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

Supplementary Information

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

Quantifying Analogue Suitability for SAR-Based Read-Across Toxicological Assessment

Structure activity relationship (SAR)-based read-across often is an integral part of toxicological safety assessment, and justification of the prediction presents the most challenging aspect of the approach. It has been established that structural consideration alone is inadequate for selecting analogues and justifying their use, and biological relevance must be incorporated. Here we introduce an approach for considering biological and toxicological related features quantitatively to compute a similarity score that is concordant with suitability for a read-across prediction for systemic toxicity. Fingerprint keys for comparing metabolism, reactivity, and physical chemical properties are presented and used to compare these attributes for 14 case study chemicals each with a list of potential analogues. Within each case study, the sum of these nonstructural similarity scores is consistent with suitability for read-across established using an approach based on expert judgment. Machine learning is applied to determine the contributions from each of the similarity attributes revealing their importance for each structure class. This approach is used to quantify and communicate the differences between a target and a potential analogue as well as rank analogue quality when more than one is relevant. A numerical score with easily interpreted fingerprints increases transparency and consistency among experts, facilitates implementation by others, and ultimately increases chances for regulatory acceptance.

Toxicity evaluation of main zopiclone impurities based on quantitative structure-activity relationship models and in vitro tests

Toxicity evaluation of main zopiclone impurities can provide a basis for safety assessment and quality standards of zopiclone. In this study, the impurity profile of zopiclone was analyzed using forced degradation and related substances of zopiclone tablets using high-performance liquid chromatography (HPLC). Furthermore, various quantitative structure-activity relationship (QSAR) models were used to compare the toxicity, especially genotoxicity of two main zopiclone degradation impurities, namely, impurity B and 2-amino-5-chloropyridine. The predictive genotoxicity results were verified using an in vitro bacterial reverse mutation (Ames) test. Meanwhile, using zebrafish embryos as an animal model, zopiclone and its main impurities were analyzed at different concentrations, and their effects on zebrafish development, including embryonic teratogenesis and lethality, were examined. The results showed that impurity B and 2-amino-5-chloropyridine were the main degradation impurities of zopiclone; the latter's content increased with increase in the solution storage time. QSAR prediction and in vitro test results confirmed that both impurity B and 2-amino-5-chloropyridine were non-mutagenic and classified in the fifth impurity category. According to ICH M7 guidelines, these could be controlled as general non-mutagenic impurities. The relative toxicity to zebrafish embryo development was the highest for 2-amino-5-chloropyridine, followed by impurity B and zopiclone, and the malformation rate and mortality of embryos were concentration dependent. In conclusion, an increase in the control limit of 2-amino-5-chloropyridine is recommended when the quality standards of zopiclone materials and preparations are revised to ensure safety and quality control. The specific limit value of this impurity should be determined through further evaluation and research.

Identifying xenobiotic metabolites with in silico prediction tools and LCMS suspect screening analysis

Understanding the metabolic fate of a xenobiotic substance can help inform its potential health risks and allow for the identification of signature metabolites associated with exposure. The need to characterize metabolites of poorly studied or novel substances has shifted exposure studies towards non-targeted analysis (NTA), which often aims to profile many compounds within a sample using high-resolution liquid-chromatography mass-spectrometry (LCMS). Here we evaluate the suitability of suspect screening analysis (SSA) liquid-chromatography mass-spectrometry to inform xenobiotic chemical metabolism. Given a lack of knowledge of true metabolites for most chemicals, predictive tools were used to generate potential metabolites as suspect screening lists to guide the identification of selected xenobiotic substances and their associated metabolites. Thirty-three substances were selected to represent a diverse array of pharmaceutical, agrochemical, and industrial chemicals from Environmental Protection Agency's ToxCast chemical library. The compounds were incubated in a metabolically-active in vitro assay using primary hepatocytes and the resulting supernatant and lysate fractions were analyzed with high-resolution LCMS. Metabolites were simulated for each compound structure using software and then combined to serve as the suspect screening list. The exact masses of the predicted metabolites were then used to select LCMS features for fragmentation via tandem mass spectrometry (MS/MS). Of the starting chemicals, 12 were measured in at least one sample in either positive or negative ion mode and a subset of these were used to develop the analysis workflow. We implemented a screening level workflow for background subtraction and the incorporation of time-varying kinetics into the identification of likely metabolites. We used haloperidol as a case study to perform an in-depth analysis, which resulted in identifying five known metabolites and five molecular features that represent potential novel metabolites, two of which were assigned discrete structures based on in silico predictions. This workflow was applied to five additional test chemicals, and 15 molecular features were selected as either reported metabolites, predicted metabolites, or potential metabolites without a structural assignment. This study demonstrates that in some-but not all-cases, suspect screening analysis methods provide a means to rapidly identify and characterize metabolites of xenobiotic chemicals.

In silico and in vitro ADME-Tox analysis and in vivo pharmacokinetic study of representative pan-PDE inhibitors from the group of 7,8-disubstituted derivatives of 1,3-dimethyl-7H-purine-2,6-dione

Phosphodiesterase (PDE) inhibitors represent a wide class of chemically different compounds that have been extensively studied in recent years. Their anti-inflammatory and anti-fibrotic effects are particularly desirable in the treatment of chronic respiratory diseases, including asthma and chronic obstructive pulmonary disease (COPD). Due to diversified expression of individual PDEs within cells and/or tissues as well as PDE signaling compartmentalization, pan-PDE inhibitors (compounds capable of simultaneously blocking various PDE subtypes) are of particular interest. Recently, a large group of 7,8-disubstituted derivatives of 1,3-dimethyl-7H-purine-2,6-dione (theophylline) was designed and synthesized. These compounds were characterized as potent pan-PDE inhibitors and their prominent anti-inflammatory and anti-fibrotic activity in vitro has been proved. Herein, we investigated a general in vitro safety profile and pharmacokinetic characteristics of two leading compounds from this group: a representative compound with N'-benzylidenebutanehydrazide moiety (38) and a representative derivative containing N-phenylbutanamide fragment (145). Both tested pan-PDE inhibitors revealed no cytotoxic, mutagenic, and genotoxic activity in vitro, showed moderate metabolic stability in mouse and human liver microsomes, as well as fell into the low or medium permeation category. Additionally, 38 and 145 revealed a lack of interaction with adenosine receptors, including A1, A2A, and A2B. Pharmacokinetic analysis revealed that both tested 7,8-disubstituted derivatives of 1,3-dimethyl-7H-purine-2,6-dione were effectively absorbed from the peritoneal cavity. Simultaneously, they were extensively distributed to mouse lungs and after intraperitoneal (i.p.) administration were detected in bronchoalveolar lavage fluid. These findings provide evidence that investigated compounds represent a new drug candidates with a favorable in vitro safety profile and satisfactory pharmacokinetic properties after a single i.p. administration. As the next step, further pharmacokinetic studies after multiple i.p. and p.o. doses will be conducted to ensure effective 38 and 145 serum and lung concentrations for a longer period of time. In summary, 7,8-disubstituted derivatives of 1,3-dimethyl-7H-purine-2,6-dione represent a promising compounds worth testing in animal models of chronic respiratory diseases, the etiology of which involves various PDE subtypes.

Applications of In Silico Models to Predict Drug-Induced Liver Injury

Drug-induced liver injury (DILI) is a major cause of the withdrawal of pre-marketed drugs, typically attributed to oxidative stress, mitochondrial damage, disrupted bile acid homeostasis, and innate immune-related inflammation. DILI can be divided into intrinsic and idiosyncratic DILI with cholestatic liver injury as an important manifestation. The diagnosis of DILI remains a challenge today and relies on clinical judgment and knowledge of the insulting agent. Early prediction of hepatotoxicity is an important but still unfulfilled component of drug development. In response, in silico modeling has shown good potential to fill the missing puzzle. Computer algorithms, with machine learning and artificial intelligence as a representative, can be established to initiate a reaction on the given condition to predict DILI. DILIsym is a mechanistic approach that integrates physiologically based pharmacokinetic modeling with the mechanisms of hepatoxicity and has gained increasing popularity for DILI prediction. This article reviews existing in silico approaches utilized to predict DILI risks in clinical medication and provides an overview of the underlying principles and related practical applications.

In Silico Identification of Multi-Target Ligands as Promising Hit Compounds for Neurodegenerative Diseases Drug Development

The conventional treatment of neurodegenerative diseases (NDDs) is based on the "one molecule-one target" paradigm. To combat the multifactorial nature of NDDs, the focus is now shifted toward the development of small-molecule-based compounds that can modulate more than one protein target, known as "multi-target-directed ligands" (MTDLs), while having low affinity for proteins that are irrelevant for the therapy. The in silico approaches have demonstrated a potential to be a suitable tool for the identification of MTDLs as promising drug candidates with reduction in cost and time for research and development. In this study more than 650,000 compounds were screened by a series of in silico approaches to identify drug-like compounds with predicted activity simultaneously towards three important proteins in the NDDs symptomatic treatment: acetylcholinesterase (AChE), histone deacetylase 2 (HDAC2), and monoamine oxidase B (MAO-B). The compounds with affinities below 5.0 µM for all studied targets were additionally filtered to remove known non-specifically binding or unstable compounds. The selected four hits underwent subsequent refinement through in silico blood-brain barrier penetration estimation, safety evaluation, and molecular dynamics simulations resulting in two hit compounds that constitute a rational basis for further development of multi-target active compounds against NDDs.

Migration of styrene oligomers from food contact materials: in silico prediction of possible genotoxicity

Styrene oligomers (SO) are well-known side products formed during styrene polymerization. They consist mainly of dimers (SD) and trimers (ST) that have been shown to be still residual in polystyrene (PS) materials. In this study migration of SO from PS into sunflower oil at temperatures between 5 and 70 °C and contact times between 0.5 h and 10 days was investigated. In addition, the contents of SD and ST in the fatty foodstuffs créme fraiche and coffee cream, which are typically enwrapped in PS, were measured and the amounts detected (of up to 0.123 mg/kg food) were compared to literature data. From this comparison, it became evident, that the levels of SO migrating from PS packaging into real food call for a comprehensive risk assessment. As a first step towards this direction, possible genotoxicity has to be addressed. Due to technical and experimental limitations, however, the few existing in vitro tests available are unsuited to provide a clear picture. In order to reduce uncertainty of these in vitro tests, four different knowledge and statistics-based in silico tools were applied to such SO that are known to migrate into food. Except for SD4 all evaluated SD and ST showed no alert for genotoxicity. For SD4, either the predictions were inconclusive or the substance was assigned as being out of the chemical space (out of domain) of the respective in silico tool. Therefore, the absence of genotoxicity of SD4 requires additional experimental proof. Apart from SD4, in silico studies supported the limited in vitro data that indicated the absence of genotoxicity of SO. In conclusion, the overall migration of all SO together into food of up to 50 µg/kg does not raise any health concerns, given the currently available in silico and in vitro data.

Enhanced database creation with in silico workflows for suspect screening of unknown tebuconazole transformation products in environmental samples by UHPLC-HRMS

The search and identification of organic contaminants in agricultural watersheds has become a crucial effort to better characterize watershed contamination by pesticides. The past decade has brought a more holistic view of watershed contamination via the deployment of powerful analytical strategies such as non-target and suspect screening analysis that can search more contaminants and their transformation products. However, suspect screening analysis remains broadly confined to known molecules, primarily due to the lack of analytical standards and suspect databases for unknowns such as pesticide transformation products. Here we developed a novel workflow by cross-comparing the results of various in silico prediction tools against literature data to create an enhanced database for suspect screening of pesticide transformation products. This workflow was applied on tebuconazole, used here as a model pesticide, and resulted in a suspect screening database counting 291 transformation products. The chromatographic retention times and tandem mass spectra were predicted for each of these compounds using 6 models based on multilinear regression and more complex machine-learning algorithms. This comprehensive approach to the investigation and identification of tebuconazole transformation products was retrospectively applied on environmental samples and found 6 transformation products identified for the first time in river water samples.

Implementation of QbD Approach to the Development of Chromatographic Methods for the Determination of Complete Impurity Profile of Substance on the Preclinical and Clinical Step of Drug Discovery Studies

The purpose of this work was to demonstrate the use of the AQbD with the DOE approach to the methodical step-by-step development of a UHPLC method for the quantitative determination of the impurity profile of new CPL409116 substance (JAK/ROCK inhibitor) on the preclinical and clinical step of drug discovery studies. The critical method parameters (CMPs) have been tested extensively: the kind of stationary phase (8 different columns), pH of the aqueous mobile phase (2.6, 3.2, 4.0, 6.8), and start (20–25%) and stop (85–90%) percentage of organic mobile phase (ACN). The critical method attributes (CMAs) are the resolution between the peaks (≥2.0) and peak symmetry of analytes (≥0.8 and ≤1.8). In the screening step, the effects of different levels of CMPs on the CMAs were evaluated based on a full fractional design 22. The robustness tests were established from the knowledge space of the screening step and performed by application fractional factorial design 2(4−1). Method operable design region (MODR) was generated. The probability of meeting the specifications for the CMAs was calculated by Monte-Carlo simulations. In relation to literature such a complete AQbD approach including screening, optimization, and validation steps for the development of a new method for the quantitative determination of the full profile of nine impurities of an innovative pharmaceutical substance with the structure-based pre-development pointed out the novelty of our work. The final working conditions were as follows: column Zorbax Eclipse Plus C18, aqueous mobile phase 10 mM ± 1 mM aqueous solution of HCOOH, pH 2.6, 20% ± 1% of ACN at the start and 85% ± 1% of ACN at the end of the gradient, and column temperature 30 °C ± 2 °C. The method was validated in compliance with ICH guideline Q2(R1). The optimized method is specified, linear, precise, and robust. LOQ is on the reporting threshold level of 0.05% and LOD at 0.02% for all impurities.

Impact of Established and Emerging Software Tools on the Metabolite Identification Landscape

Scientists’ ability to detect drug-related metabolites at trace concentrations has improved over recent decades. High-resolution instruments enable collection of large amounts of raw experimental data. In fact, the quantity of data produced has become a challenge due to effort required to convert raw data into useful insights. Various cheminformatics tools have been developed to address these metabolite identification challenges. This article describes the current state of these tools. They can be split into two categories: Pre-experimental metabolite generation and post-experimental data analysis. The former can be subdivided into rule-based, machine learning-based, and docking-based approaches. Post-experimental tools help scientists automatically perform chromatographic deconvolution of LC/MS data and identify metabolites. They can use pre-experimental predictions to improve metabolite identification, but they are not limited to these predictions: unexpected metabolites can also be discovered through fractional mass filtering. In addition to a review of available software tools, we present a description of pre-experimental and post-experimental metabolite structure generation using MetaSense. These software tools improve upon manual techniques, increasing scientist productivity and enabling efficient handling of large datasets. However, the trend of increasingly large datasets and highly data-driven workflows requires a more sophisticated informatics transition in metabolite identification labs. Experimental work has traditionally been separated from the information technology tools that handle our data. We argue that these IT tools can help scientists draw connections via data visualizations and preserve and share results via searchable centralized databases. In addition, data marshalling and homogenization techniques enable future data mining and machine learning.

Development of a PFAS reaction library: identifying plausible transformation pathways in environmental and biological systems

Perfluoroalkyl and polyfluoroalkyl substances (PFAS) are used in many consumer applications due to their stain repellency, surfactant properties, ability to form water-proof coatings and use in fire suppression. The production, application, transport, use and disposal of PFAS and PFAS-treated products have resulted in their wide-spread occurrence in environmental and biological systems. Concern over exposure to PFAS and their transformation products and metabolites has necessitated the development of tools to predict the transformation of PFAS in environmental systems and metabolism in biological systems. We have developed reaction libraries for predicting transformation products and metabolites in a variety of environmental and biological reaction systems. These reaction libraries are based on generalized reaction schemes that encode the process science of PFAS reported in the peer-reviewed literature. The PFAS reaction libraries will be executed through the Chemical Transformation Simulator, a web-based tool that is available to the public. These reaction libraries are intended for predicting the environmental transformation and metabolism of PFAS only.

BioTransformer 3.0-a web server for accurately predicting metabolic transformation products

BioTransformer 3.0 (https://biotransformer.ca) is a freely available web server that supports accurate, rapid and comprehensive in silico metabolism prediction. It combines machine learning approaches with a rule-based system to predict small-molecule metabolism in human tissues, the human gut as well as the external environment (soil and water microbiota). Simply stated, BioTransformer takes a molecular structure as input (SMILES or SDF) and outputs an interactively sortable table of the predicted metabolites or transformation products (SMILES, PNG images) along with the enzymes that are predicted to be responsible for those reactions and richly annotated downloadable files (CSV and JSON). The entire process typically takes less than a minute. Previous versions of BioTransformer focused exclusively on predicting the metabolism of xenobiotics (such as plant natural products, drugs, cosmetics and other synthetic compounds) using a limited number of pre-defined steps and somewhat limited rule-based methods. BioTransformer 3.0 uses much more sophisticated methods and incorporates new databases, new constraints and new prediction modules to not only more accurately predict the metabolic transformation products of exogenous xenobiotics but also the transformation products of endogenous metabolites, such as amino acids, peptides, carbohydrates, organic acids, and lipids. BioTransformer 3.0 can also support customized sequential combinations of these transformations along with multiple iterations to simulate multi-step human biotransformation events. Performance tests indicate that BioTransformer 3.0 is 40–50% more accurate, far less prone to combinatorial ‘explosions’ and much more comprehensive in terms of metabolite coverage/capabilities than previous versions of BioTransformer.

The VEGAHUB Platform: The Philosophy and the Tools

VEGAHUB (www.vegahub.eu) is a repository of freely available, downloadable tools based on computational toxicology methodologies. The main software tool available in VEGAHUB is VEGA QSAR software encoding more than 90 quantitative structure-activity relationship (QSAR) models for tens of endpoints for human toxicology, ecotoxicology, environmental, physico-chemical and toxicokinetic properties. However, beyond VEGA QSAR, VEGAHUB offers several other tools. Here, we present these resources, the possibilities to fully exploit them and the ways in which to integrate results provided by different VEGAHUB tools. Read-across and weight-of-evidence represent a major advantage of VEGAHUB. Integration between hazard and exposure is provided within innovative tools, which are specific for well-defined scenarios, such as those for cosmetic products. Prioritisation can be achieved by integrating results from 48 models. Finally, we highlight how some tools may not only fit predefined endpoints but also could be applied to general problems and research applications in the QSAR field. A couple of examples are provided, in which a critical assessment of the predictions and the documentation associated with the prediction are considered, in order to properly assess the quality of the results. These results may be associated with different levels of uncertainty or even be conflicting.

Tools for computational design and high-throughput screening of therapeutic enzymes

Therapeutic enzymes are valuable biopharmaceuticals in various biomedical applications. They have been successfully applied for fibrinolysis, cancer treatment, enzyme replacement therapies, and the treatment of rare diseases. Still, there is a permanent demand to find new or better therapeutic enzymes, which would be sufficiently soluble, stable, and active to meet specific medical needs. Here, we highlight the benefits of coupling computational approaches with high-throughput experimental technologies, which significantly accelerate the identification and engineering of catalytic therapeutic agents. New enzymes can be identified in genomic and metagenomic databases, which grow thanks to next-generation sequencing technologies exponentially. Computational design and machine learning methods are being developed to improve catalytically potent enzymes and predict their properties to guide the selection of target enzymes. High-throughput experimental pipelines, increasingly relying on microfluidics, ensure functional screening and biochemical characterization of target enzymes to reach efficient therapeutic enzymes.

ToxDP2 Database: Toxicity prediction of dietary polyphenols

Polyphenols are bioactive substances that minimize the risk of a variety of chronic diseases. Exposure to polyphenol bioactive compounds in our diet has increased across the globe, with amplified expectations from consumers, industry, and regulators centered on the potential benefits and essential safety of these compounds. Several data resources for beneficial properties of dietary polyphenols are present; however, toxicological information remains partial. We present a dynamic web-based database to assess dietary polyphenols' safety and fulfill the toxicity data gaps in the domain of food safety. The database (ToxDP2) comprises 415 dietary polyphenolic compounds, distributed into 15 subclasses with 25,792 collected and predicted data points. This web server facilitates the exploration of polyphenols for divergent applications. The data-driven approach on the ToxDP2 provides researchers with an understanding of polyphenols structure-function-toxicity relationships beneficial for developing nutraceuticals, pharmaceuticals, herbal supplements, and formulations.

Discovery of compounds with viscosity-reducing effects on biopharmaceutical formulations with monoclonal antibodies

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Computational screening yielded 44 new viscosity-reducing agents on two model mAbs.

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Dual excipients for viscosity reduction and solution buffering were discovered.

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Compounds with three or more charges reduce the viscosity of model mAb formulations.

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Filtering based on physicochemical properties can be applied to other mAb formulations.

For the development of concentrated monoclonal antibody formulations for subcutaneous administration, the main challenge is the high viscosity of the solutions. To compensate for this, viscosity reducing agents are commonly used as excipients. Here, we applied two computational chemistry approaches to discover new viscosity-reducing agents: fingerprint similarity searching, and physicochemical property filtering. In total, 94 compounds were selected and experimentally evaluated on two model monoclonal antibodies, which led to the discovery of 44 new viscosity-reducing agents. Analysis of the results showed that using a simple filter that selects only compounds with three or more charge groups is a good ‘rule of thumb’ for selecting potential viscosity-reducing agents for two model monoclonal antibody formulations.

Use of (Q)SAR genotoxicity predictions and fuzzy multicriteria decision-making for priority ranking of ethoxyquin transformation products

Ethoxyquin (EQ; 6-ethoxy-2,2,4-trimethyl-1,2-dihydroquinoline) has been used as an antioxidant in feed for pets and food-producing animals, including farmed fish such as Atlantic salmon. In Europe, the authorization for use of EQ as a feed additive was suspended, due to knowledge gaps concerning the presence and toxicity of EQ transformation products (TPs). Recent analytical studies focusing on the detection of EQ TPs in farmed Atlantic salmon feed and fillets reported the detection of a total of 27 EQ TPs, comprising both known and previously not described EQ TPs. We devised and applied an in silico workflow to rank these EQ TPs according to their genotoxic potential and their occurrence data in Atlantic salmon feed and fillet. Ames genotoxicity predictions were obtained applying a suite of five (quantitative) structure-activity relationship ((Q)SAR) tools, namely VEGA, TEST, LAZAR, Derek Nexus and Sarah Nexus. (Q)SAR Ames genotoxicity predictions were aggregated using fuzzy analytic hierarchy process (fAHP) multicriteria decision-making (MCDM). A priority ranking of EQ TPs was performed based on combining both fAHP ranked (Q)SAR predictions and analytical occurrence data. The applied workflow prioritized four newly identified EQ TPs for further investigation of genotoxicity. The fAHP-based prioritization strategy described here, can easily be applied to other toxicity endpoints and groups of chemicals for priority ranking of compounds of most concern for subsequent experimental and mechanistic toxicology analyses.

Rational Design of Highly Potent, Selective, and Bioavailable SGK1 Protein Kinase Inhibitors for the Treatment of Osteoarthritis

The serine/threonine kinase SGK1 is an activator of the β-catenin pathway and a powerful stimulator of cartilage degradation that is found to be upregulated under genomic control in diseased osteoarthritic cartilage. Today, no oral disease-modifying treatments are available and chronic treatment in this indication sets high requirements for the drug selectivity, pharmacokinetic, and safety profile. We describe the identification of a highly selective druglike 1H-pyrazolo[3,4-d]pyrimidine SGK1 inhibitor 17a that matches both safety and pharmacokinetic requirements for oral dosing. Rational compound design was facilitated by a novel hSGK1 co-crystal structure, and multiple ligand-based computer models were applied to guide the chemical optimization of the compound ADMET and selectivity profiles. Compounds were selected for subchronic proof of mechanism studies in the mouse femoral head cartilage explant model, and compound 17a emerged as a druglike SGK1 inhibitor, with a highly optimized profile suitable for oral dosing as a novel, potentially disease-modifying agent for osteoarthritis.

Discovery of new acetylcholinesterase inhibitors for Alzheimer's disease: virtual screening and in vitro characterisation

For more than two decades, the development of potent acetylcholinesterase (AChE) inhibitors has been an ongoing task to treat dementia associated with Alzheimer's disease and improve the pharmacokinetic properties of existing drugs. In the present study, we used three docking-based virtual screening approaches to screen both ZINC15 and MolPort databases for synthetic analogs of physostigmine and donepezil, two highly potent AChE inhibitors. We characterised the in vitro inhibitory concentration of 11 compounds, ranging from 14 to 985 μM. The most potent of these compounds, S-I 26, showed a fivefold improved inhibitory concentration in comparison to rivastigmine. Moderate inhibitors carrying novel scaffolds were identified and could be improved for the development of new classes of AChE inhibitors.

Artificial Intelligence, Machine Learning, and Deep Learning in Real-Life Drug Design Cases

The discovery and development of drugs is a long and expensive process with a high attrition rate. Computational drug discovery contributes to ligand discovery and optimization, by using models that describe the properties of ligands and their interactions with biological targets. In recent years, artificial intelligence (AI) has made remarkable modeling progress, driven by new algorithms and by the increase in computing power and storage capacities, which allow the processing of large amounts of data in a short time. This review provides the current state of the art of AI methods applied to drug discovery, with a focus on structure- and ligand-based virtual screening, library design and high-throughput analysis, drug repurposing and drug sensitivity, de novo design, chemical reactions and synthetic accessibility, ADMET, and quantum mechanics.

Towards a comprehensive characterisation of the human internal chemical exposome: Challenges and perspectives

The holistic characterisation of the human internal chemical exposome using high-resolution mass spectrometry (HRMS) would be a step forward to investigate the environmental ætiology of chronic diseases with an unprecedented precision. HRMS-based methods are currently operational to reproducibly profile thousands of endogenous metabolites as well as externally-derived chemicals and their biotransformation products in a large number of biological samples from human cohorts. These approaches provide a solid ground for the discovery of unrecognised biomarkers of exposure and metabolic effects associated with many chronic diseases. Nevertheless, some limitations remain and have to be overcome so that chemical exposomics can provide unbiased detection of chemical exposures affecting disease susceptibility in epidemiological studies. Some of these limitations include (i) the lack of versatility of analytical techniques to capture the wide diversity of chemicals; (ii) the lack of analytical sensitivity that prevents the detection of exogenous (and endogenous) chemicals occurring at (ultra) trace levels from restricted sample amounts, and (iii) the lack of automation of the annotation/identification process. In this article, we discuss a number of technological and methodological limitations hindering applications of HRMS-based methods and propose initial steps to push towards a more comprehensive characterisation of the internal chemical exposome. We also discuss other challenges including the need for harmonisation and the difficulty inherent in assessing the dynamic nature of the internal chemical exposome, as well as the need for establishing a strong international collaboration, high level networking, and sustainable research infrastructure. A great amount of research, technological development and innovative bio-informatics tools are still needed to profile and characterise the "invisible" (not profiled), "hidden" (not detected) and "dark" (not annotated) components of the internal chemical exposome and concerted efforts across numerous research fields are paramount.

Prioritization of Mycotoxins Based on Their Genotoxic Potential with an In Silico-In Vitro Strategy

Humans are widely exposed to a great variety of mycotoxins and their mixtures. Therefore, it is important to design strategies that allow prioritizing mycotoxins based on their toxic potential in a time and cost-effective manner. A strategy combining in silico tools (Phase 1), including an expert knowledge-based (DEREK Nexus®, Lhasa Limited, Leeds, UK) and a statistical-based platform (VEGA QSAR©, Mario Negri Institute, Milan, Italy), followed by the in vitro SOS/umu test (Phase 2), was applied to a set of 12 mycotoxins clustered according to their structure into three groups. Phase 1 allowed us to clearly classify group 1 (aflatoxin and sterigmatocystin) as mutagenic and group 3 (ochratoxin A, zearalenone and fumonisin B1) as non-mutagenic. For group 2 (trichothecenes), contradictory conclusions were obtained between the two in silico tools, being out of the applicability domain of many models. Phase 2 confirmed the results obtained in the previous phase for groups 1 and 3. It also provided extra information regarding the role of metabolic activation in aflatoxin B1 and sterigmatocystin mutagenicity. Regarding group 2, equivocal results were obtained in few experiments; however, the group was finally classified as non-mutagenic. The strategy used correlated with the published Ames tests, which detect point mutations. Few alerts for chromosome aberrations could be detected. The SOS/umu test appeared as a good screening test for mutagenicity that can be used in the absence and presence of metabolic activation and independently of Phase 1, although the in silico-in vitro combination gave more information for decision making.

Constructing xenobiotic maps of metabolism to predict enzymes catalyzing metabolites capable of binding to DNA

Background

The liver plays a major role in the metabolic activation of xenobiotics (drugs, chemicals such as pollutants, pesticides, food additives...). Among environmental contaminants of concern, heterocyclic aromatic amines (HAA) are xenobiotics classified by IARC as possible or probable carcinogens (2A or 2B). There exist little information about the effect of these HAA in humans. While HAA is a family of more than thirty identified chemicals, the metabolic activation and possible DNA adduct formation have been fully characterized in human liver for only a few of them (MeIQx, PhIP, A\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\alpha$$\end{document}αC).

Results

We have developed a modeling approach in order to predict all the possible metabolites of a xenobiotic and enzymatic profiles that are linked to the production of metabolites able to bind DNA. Our prediction of metabolites approach relies on the construction of an enriched and annotated map of metabolites from an input metabolite.The pipeline assembles reaction prediction tools (SyGMa), sites of metabolism prediction tools (Way2Drug, SOMP and Fame 3), a tool to estimate the ability of a xenobotics to form DNA adducts (XenoSite Reactivity V1), and a filtering procedure based on Bayesian framework. This prediction pipeline was evaluated using caffeine and then applied to HAA. The method was applied to determine enzymes profiles associated with the maximization of metabolites derived from each HAA which are able to bind to DNA. The classification of HAA according to enzymatic profiles was consistent with their chemical structures.

Conclusions

Overall, a predictive toxicological model based on an in silico systems biology approach opens perspectives to estimate the genotoxicity of various chemical classes of environmental contaminants. Moreover, our approach based on enzymes profile determination opens the possibility of predicting various xenobiotics metabolites susceptible to bind to DNA in both normal and physiopathological situations.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12859-021-04363-6.

NICEdrug.ch, a workflow for rational drug design and systems-level analysis of drug metabolism

The discovery of a drug requires over a decade of intensive research and financial investments - and still has a high risk of failure. To reduce this burden, we developed the NICEdrug.ch resource, which incorporates 250,000 bioactive molecules, and studied their enzymatic metabolic targets, fate, and toxicity. NICEdrug.ch includes a unique fingerprint that identifies reactive similarities between drug-drug and drug-metabolite pairs. We validated the application, scope, and performance of NICEdrug.ch over similar methods in the field on golden standard datasets describing drugs and metabolites sharing reactivity, drug toxicities, and drug targets. We use NICEdrug.ch to evaluate inhibition and toxicity by the anticancer drug 5-fluorouracil, and suggest avenues to alleviate its side effects. We propose shikimate 3-phosphate for targeting liver-stage malaria with minimal impact on the human host cell. Finally, NICEdrug.ch suggests over 1300 candidate drugs and food molecules to target COVID-19 and explains their inhibitory mechanism for further experimental screening. The NICEdrug.ch database is accessible online to systematically identify the reactivity of small molecules and druggable enzymes with practical applications in lead discovery and drug repurposing.

Combined Urinary Biomarkers to Assess Coffee Intake Using Untargeted Metabolomics: Discovery in Three Pilot Human Intervention Studies and Validation in Cross-Sectional Studies

Coffee is a widely consumed beverage worldwide and has a high content of chlorogenic acids, polyphenols, methylxanthines, and volatile flavor compounds. Scientific evidence to support the beneficial health effects of coffee is limited, and validated urinary biomarkers of coffee intake are therefore needed. We observed 23 common putative biomarkers of coffee intake in three separate parallel intervention studies by ultra-high-performance liquid chromatography-electrospray ionization-quadrupole time-of-flight-mass spectrometry (UHPLC-ESI-QTOF-MS) and multivariate analyses. Baseline samples from the NU-AGE study were used to confirm and validate 16 of these candidate biomarkers, including their robustness, time response, and dose response. These validated candidate biomarkers are N-methylpyridinium cation, 1-methyl-1H-pyrrole-2-carboxaldehyde, 1H-pyrrole-2-carboxaldehyde sulfate, 3-piperidinemethanol, furfurylidene-furfurylamine, 2-furoylglycine, N-substituted-5-(aminoethyl) furan-2-carbaldehyde derivative, 3',4'-dihydroxyacetophenone sulfate, caffeine, dihydroxystyrene glucuronide, ferulic acid sulfate, 4-ethylcatechol glucuronide, 3-feruloylquinic acid, 3,4-dihydroxystyrene sulfate, one unknown glucuronide, and one unknown sulfate. Combinations of candidate biomarkers gave a better prediction of coffee consumption than individual biomarkers. The robustness of the combined biomarkers requires additional validation in cohort studies covering other populations.