Computational prediction of immune cell cytotoxicity

High-throughput screening and molecular dynamics simulations of natural products targeting LuxS/AI-2 system as a novel antibacterial strategy for antibiotic resistance in Helicobacter pylori

The main goal of treating any Helicobacter pylori (H. pylori)-related gastrointestinal disease is completely eradicating infection. Falling eradication efficiency, off-target effects, and patient noncompliance with prolonged and broad spectrums have sparked clinical interest in exploring other effective, safer therapeutic choices. As natural substances are risk-free and privileged with high levels of antibacterial activity, most of these natural chemical's specific modes of action are unknown. With the aid of in silico molecular docking-based virtual screening studies and molecular dynamic simulations, the current study is intended to gather data on numerous such natural chemicals and assess their affinity for the S-ribosyl homocysteine lyase (LuxS) protein of H. pylori. The ligand with the highest binding energy with LuxS, glucoraphanin, catechin gallate and epigallocatechin gallate were rationally selected for further computational analysis. The solution stability of the three compounds' optimal docking postures with LuxS was initially assessed using long-run molecular dynamics simulations. Using molecular dynamics simulation, the epigallocatechin gallate was found to be the most stable molecule with the highest binding free energy, indicating that it might compete with the natural ligand of the inhibitors. According to ADMET analysis, his phytochemical was a promising therapeutic candidate for an antibacterial action since it had a range of physicochemical, pharmacokinetic, and drug-like qualities and had no discernible adverse effects. Additional in vitro, in vivo, and clinical trials are needed to confirm the drug's precise efficacy during H. pylori infection.Communicated by Ramaswamy H. Sarma.

Targeting bladder cancer with Trigonella foenum-graecum: a computational study using network pharmacology and molecular docking

Trigonella foenum-graecum (TF-graecum), known as Hulba or Fenugreek, is one of the oldest known medicinal plants. It has been found to have antimicrobial, antifungal, antioxidant, wound-healing, anti-diarrheal, hypoglycemic, anti-diabetic, and anti-inflammatory activities. In our current report, we have collected and screened the active compounds of TF-graecum and their potential targets via different pharmacology platforms. Network construction shows that eight active compounds may act on 223 potential bladder cancer targets. The pathway enrichment analysis for the seven potential targets of the eight compounds selected, based on KEGG pathway analysis, was conducted to clarify the potential pharmacological effects. Finally, molecular docking and molecular dynamics simulation showed the stability of protein-ligand interactions. This study highlights the need for increased research into the potential medical benefits of this plant.Communicated by Ramaswamy H. Sarma.

Inhibitors of SARS-CoV-2 main protease: Biological efficacy and toxicity aspects

The emergence of the highly contagious respiratory disease, COVID-19, caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has become a significant global public health concern. To combat this virus, researchers have focused on developing antiviral strategies that target specific viral components, such as the main protease (Mpro), which plays a crucial role in SARS-CoV-2 replication. While many compounds have been identified as potent inhibitors of Mpro, only a few have been translated into clinical use due to the potential risk-benefit trade-offs. Development of systemic inflammatory response and bacterial co-infection in patients belong to severe, frequent complications of COVID-19. In this context, we analysed available data on the anti-inflammatory and antibacterial activities of the SARS-CoV-2 Mpro inhibitors for possible implementation in the treatment of complicated and long COVID-19 cases. Synthetic feasibility and ADME properties were calculated and included for better characterisation of the compounds' predicted toxicity. Analysis of the collected data resulted in several clusters pointing to the most prospective compounds for further study and design. The complete tables with collected data are attached in Supplementary material for use by other researchers.

Immunotoxicity In Vitro Assays for Environmental Pollutants under Paradigm Shift in Toxicity Tests

With the outbreak of COVID-19, increasingly more attention has been paid to the effects of environmental factors on the immune system of organisms, because environmental pollutants may act in synergy with viruses by affecting the immunity of organisms. The immune system is a developing defense system formed by all metazoans in the course of struggling with various internal and external factors, whose damage may lead to increased susceptibility to pathogens and diseases. Due to a greater vulnerability of the immune system, immunotoxicity has the potential to be the early event of other toxic effects, and should be incorporated into environmental risk assessment. However, compared with other toxicity endpoints, e.g., genotoxicity, endocrine toxicity, or developmental toxicity, there are many challenges for the immunotoxicity test of environmental pollutants; this is due to the lack of detailed mechanisms of action and reliable assay methods. In addition, with the strong appeal for animal-free experiments, there has been a significant shift in the toxicity test paradigm, from traditional animal experiments to high-throughput in vitro assays that rely on cell lines. Therefore, there is an urgent need to build high-though put immunotoxicity test methods to screen massive environmental pollutants. This paper reviews the common methods of immunotoxicity assays, including assays for direct immunotoxicity and skin sensitization. Direct immunotoxicity mainly refers to immunosuppression, for which the assays mostly use mixed immune cells or isolated single cells from animals with obvious problems, such as high cost, complex experimental operation, strong variability and so on. Meanwhile, there have been no stable and standard cell lines targeting immune functions developed for high-throughput tests. Compared with direct immunotoxicity, skin sensitizer screening has developed relatively mature in vitro assay methods based on an adverse outcome pathway (AOP), which points out the way forward for the paradigm shift in toxicity tests. According to the experience of skin sensitizer screening, this paper proposes that we also should seek appropriate nodes and establish more complete AOPs for immunosuppression and other immune-mediated diseases. Then, effective in vitro immunotoxicity assay methods can be developed targeting key events, simultaneously coordinating the studies of the chemical immunotoxicity mechanism, and further promoting the paradigm shift in the immunotoxicity test.

ADME-Tox Prediction and Molecular Docking Studies of Two Lead Flavonoids From the Roots of Tephrosia Egregia Sandw and the Gastroprotective Effects of Its Root Extract in Mice

Background: This study aimed to predict the pharmacokinetic and toxicological properties of lead flavonoids from the roots of T. egregia [praecansone A (1) and pongachalcone (2)], and to assess the gastroprotective effects and possible underlying mechanisms of the root extract in mice.Methods: Quantitative and qualitative data for in silico absorption, distribution, metabolism, and excretion (ADME) analyses of the two flavonoids were acquired from the SwissADME database. Toxicity assessment was performed with the ProTox-II server. To evaluate the putative interactions of both flavonoids with opioid receptors and NO protein, we acquired structures of the targets (&#x03BC;, &#x03BA;, and &#x03B4;-opioid receptors, and iNOS) in Homo sapiens from <a href="https://www.rcsb.org/">https://www.rcsb.org/</a>. For docking studies, AutoDock 4.2 was used for ligand and target arrangement, and AutoDock Vina was used for calculations. For in vivo assays, mice were pretreated (per os) with T. egregia (2, 20, or 200 mg/kg). After 60 min, 99.9% ethanol (0.2 mL) was injected (per os). At 30 min after ethanol injection, the mice were euthanized, and the gastric damage, gastric levels of hemoglobin, glutathione content, and activity of superoxide dismutase and catalase were evaluated. To elucidate T. egregia mechanisms, we used misoprostol, a prostaglandin analog; indomethacin, an inhibitor of prostaglandin synthesis; L-arginine, an NO precursor; L-NAME, an antagonist of NO synthase; naloxone, an opioid antagonist; and morphine, an opioid agonist.Results: In silico results showed that flavonoids (1) and (2) had favorable ADME properties and toxicity profiles, and exhibited satisfactory binding energies data (below &#x2212;6.0 kcal/mol) when docked into their targets (&#x03BC;, &#x03BA;, and &#x03B4;-opioid receptors, and iNOS). T. egregia decreased the ethanol-induced gastric damage and hemoglobin levels, and increased the glutathione content, and activity of superoxide dismutase and catalase. Naloxone and L-NAME, but not indomethacin, prevented T. egregia&#x2019;s effects, thus suggesting that opioid receptors and NO are involved in T. egregia&#x2019;s efficacy.Conclusions: Flavonoids (1) and (2) exhibited favorable pharmacokinetic properties, showing high lethal dose, 50% (LD50; 3,800 and 2,500 mg/kg, respectively) values. Neither flavonoid was found to be hepatotoxic, carcinogenic, or cytotoxic to human cells. In vivo assays indicated that T. egregia ameliorated oxidative stress levels, and its mechanism is at least partially based on opioid receptors and NO. T. egregia may therefore be considered as a new gastroprotective strategy.

Artificial Intelligence in Chemistry: Current Trends and Future Directions

The application of artificial intelligence (AI) to chemistry has grown tremendously in recent years. In this Review, we studied the growth and distribution of AI-related chemistry publications in the last two decades using the CAS Content Collection. The volume of both journal and patent publications have increased dramatically, especially since 2015. Study of the distribution of publications over various chemistry research areas revealed that analytical chemistry and biochemistry are integrating AI to the greatest extent and with the highest growth rates. We also investigated trends in interdisciplinary research and identified frequently occurring combinations of research areas in publications. Furthermore, topic analyses were conducted for journal and patent publications to illustrate emerging associations of AI with certain chemistry research topics. Notable publications in various chemistry disciplines were then evaluated and presented to highlight emerging use cases. Finally, the occurrence of different classes of substances and their roles in AI-related chemistry research were quantified, further detailing the popularity of AI adoption in the life sciences and analytical chemistry. In summary, this Review offers a broad overview of how AI has progressed in various fields of chemistry and aims to provide an understanding of its future directions.

Virtual Screening of Citrus Flavonoid Tangeretin: A Promising Pharmacological Tool for the Treatment and Prevention of Zika fever and COVID-19

It is of great importance for the pharmaceutical industry to find therapeutic substances extracted from natural sources, which are abundant, obtained with low costs and presenting the antiviral potential for the treatment of Zika virus (ZIKV) and COVID-19. Tangeretin (TAN) is a citrus polymethoxyflavone from Citrus reticulata peel oil with known antiviral activities, whose physico-chemical properties are not reported. The present study aimed to investigate by a theoretical screening of electronic, structural properties and pharmacodynamic and pharmacokinetic parameters that characterize TAN as a therapeutic drug in the treatment and prevention of zika fever and COVID-19. The molecule reached its minimum energy-forming state of [Formula: see text]795.85747[Formula: see text]kJ/mol and the HOMO and LUMO boundary orbitals reactivity descriptors suggest that the compound is stable and does not tend to be reactive in intermolecular interactions. The ligand connects to the NS1 ZIKV receptor with strong H-bond interactions, also connects with the NS5 ZIKV receptor in a competitive effect with the SAM inhibitor and acts in a supplementary effect with the N3 inhibitor and the BRT drug in the Mpro SARS-CoV-2 receptor. The properties of ADMET shows that the compound suffers few amounts of drug alterations because it inhibits the metabolic enzymes CYP2C9 and CYP3A4 and penetrates the central nervous system, without accumulation of drug residues in the blood or in the lumen in the gastrointestinal tract, without risk of toxicity to the patient. With the results obtained, it is possible to identify TAN as a promising pharmacological tool for the treatment and prevention of Zika fever and COVID-19.

[Once upon a time the hepatotoxicity…]

The liver ensures a large part of xenobiotics metabolism thanks to its sizeable enzymatic equipment, its anatomical localization and its abundant vascularization. However, these various characteristics also make it a privileged target for toxic compounds, particularly in the case of a toxic metabolism. Xenobiotics-induced hepatotoxicity is a major cause of liver damage and a real challenge for clinicians, pharmaceutical industry, and health agencies. Intrinsic, i.e. predictable and reproducible hepatotoxicities occurring at threshold doses are distinguished from idiosyncratic hepatotoxicities, occurring in an unpredictable manner in people with individual susceptibilities. Among them, idiosyncratic immune-mediated hepatotoxicity pathophysiology is still unclear. However, the development of tools to improve the prediction and understanding of these disorders may open avenues to the identification of risk factors and new mechanisms of toxicity.

Comparison of Cellular Morphological Descriptors and Molecular Fingerprints for the Prediction of Cytotoxicity- and Proliferation-Related Assays

Cell morphology features, such as those from the Cell Painting assay, can be generated at relatively low costs and represent versatile biological descriptors of a system and thereby compound response. In this study, we explored cell morphology descriptors and molecular fingerprints, separately and in combination, for the prediction of cytotoxicity- and proliferation-related in vitro assay endpoints. We selected 135 compounds from the MoleculeNet ToxCast benchmark data set which were annotated with Cell Painting readouts, where the relatively small size of the data set is due to the overlap of required annotations. We trained Random Forest classification models using nested cross-validation and Cell Painting descriptors, Morgan and ErG fingerprints, and their combinations. While using leave-one-cluster-out cross-validation (with clusters based on physicochemical descriptors), models using Cell Painting descriptors achieved higher average performance over all assays (Balanced Accuracy of 0.65, Matthews Correlation Coefficient of 0.28, and AUC-ROC of 0.71) compared to models using ErG fingerprints (BA 0.55, MCC 0.09, and AUC-ROC 0.60) and Morgan fingerprints alone (BA 0.54, MCC 0.06, and AUC-ROC 0.56). While using random shuffle splits, the combination of Cell Painting descriptors with ErG and Morgan fingerprints further improved balanced accuracy on average by 8.9% (in 9 out of 12 assays) and 23.4% (in 8 out of 12 assays) compared to using only ErG and Morgan fingerprints, respectively. Regarding feature importance, Cell Painting descriptors related to nuclei texture, granularity of cells, and cytoplasm as well as cell neighbors and radial distributions were identified to be most contributing, which is plausible given the endpoint considered. We conclude that cell morphological descriptors contain complementary information to molecular fingerprints which can be used to improve the performance of predictive cytotoxicity models, in particular in areas of novel structural space.

Machine Learning in Predictive Toxicology: Recent Applications and Future Directions for Classification Models

In recent times, machine learning has become increasingly prominent in predictive toxicology as it has shifted from in vivo studies toward in silico studies. Currently, in vitro methods together with other computational methods such as quantitative structure-activity relationship modeling and absorption, distribution, metabolism, and excretion calculations are being used. An overview of machine learning and its applications in predictive toxicology is presented here, including support vector machines (SVMs), random forest (RF) and decision trees (DTs), neural networks, regression models, naïve Bayes, k-nearest neighbors, and ensemble learning. The recent successes of these machine learning methods in predictive toxicology are summarized, and a comparison of some models used in predictive toxicology is presented. In predictive toxicology, SVMs, RF, and DTs are the dominant machine learning methods due to the characteristics of the data available. Lastly, this review describes the current challenges facing the use of machine learning in predictive toxicology and offers insights into the possible areas of improvement in the field.

Gamma-hexalactone flavoring causes DNA lesion and modulates cytokines secretion at non-cytotoxic concentrations

BACKGROUND

The γ-hexalactone is a flavoring agent for alcoholic beverages, teas, breads, dairy products, coffees, buttery products among others. It presents low molecular weight and exhibits sweet fruity aroma with nuances of nuts. As far as we know, both literature and government regulations have gaps regarding the safe use of the γ-hexalactone. In this context, the main objective of this work was to evaluate the effects of γ-hexalactone through in silico and in vitro approaches.

METHODS

The in silico analysis was performed through four free online platforms (admetSAR, Osiris Property Explorer^®, pkCSM platform and PreADMET) and consisted of comparative structural analysis with substances present in databases. The computational prediction was performed in the sense of complement and guide the in vitro tests. Regarding in vitro investigations, screening of cytotoxicity (assessed by cell proliferation and viability parameters) in lymphocytes exposed to γ-hexalactone for 72 h were carried out previously to determine non-cytotoxic concentrations. Following this screening, concentrations of 5.15, 0.515, and 0.0515 μM were selected for the study of the respective potentials: genotoxic (assessed by DNA comet assay), chromosomal mutation (analysis of micronucleus frequency) and immunomodulatory (cytokine quantification using ELISA immunoassay). The results of in vitro assays were compared by one-way analysis of variance (ANOVA), followed by Bonferroni's post hoc test, conducted by statistic software.

RESULTS

The platform PreADMET pointed out that γ-hexalactone is potentially mutagenic and carcinogenic. The comet assay data corroborate with these results demonstrating that γ-hexalactone at 5.15 μM caused lymphocytes DNA damage. In relation to cytokine secretion, the results indicate that lymphocytes were activated by γ-hexalactone at non-cytotoxic concentrations, involving an increase in the IL-1 levels in all tested concentrations, ranging from approximately 56 to 93%. The γ-hexalactone only at 5.15 μM induced increase in the levels of IL-6 (~ 60%), TNF-α (~ 68%) and IFN-γ (~ 29%), but decreased IL-10 (~ 46%) in comparison with the negative control (p < 0.05). No change was observed in total lymphocytes or in cell viability at the concentrations tested.

CONCLUSIONS

In summary, the γ-hexalactone demonstrated immunomodulatory and genotoxic effects at non-cytotoxic concentrations in healthy lymphocytes.

ProTox-II: a webserver for the prediction of toxicity of chemicals

Advancement in the field of computational research has made it possible for the in silico methods to offer significant benefits to both regulatory needs and requirements for risk assessments, and pharmaceutical industry to assess the safety profile of a chemical. Here, we present ProTox-II that incorporates molecular similarity, pharmacophores, fragment propensities and machine-learning models for the prediction of various toxicity endpoints; such as acute toxicity, hepatotoxicity, cytotoxicity, carcinogenicity, mutagenicity, immunotoxicity, adverse outcomes pathways (Tox21) and toxicity targets. The predictive models are built on data from both in vitro assays (e.g. Tox21 assays, Ames bacterial mutation assays, hepG2 cytotoxicity assays, Immunotoxicity assays) and in vivo cases (e.g. carcinogenicity, hepatotoxicity). The models have been validated on independent external sets and have shown strong performance. ProTox-II provides a freely available webserver for in silico toxicity prediction for toxicologists, regulatory agencies, computational and medicinal chemists, and all users without login at http://tox.charite.de/protox_II. The webserver takes a two-dimensional chemical structure as an input and reports the possible toxicity profile of the chemical for 33 models with confidence scores, and an overall toxicity radar chart along with three most similar compounds with known acute toxicity.

Exploring African Medicinal Plants for Potential Anti-Diabetic Compounds with the DIA-DB Inverse Virtual Screening Web Server

Medicinal plants containing complex mixtures of several compounds with various potential beneficial biological effects are attractive treatment interventions for a complex multi-faceted disease like diabetes. In this study, compounds identified from African medicinal plants were evaluated for their potential anti-diabetic activity. A total of 867 compounds identified from over 300 medicinal plants were screened in silico with the DIA-DB web server (http://bio-hpc.eu/software/dia-db/) against 17 known anti-diabetic drug targets. Four hundred and thirty compounds were identified as potential inhibitors, with 184 plants being identified as the sources of these compounds. The plants Argemone ochroleuca, Clivia miniata, Crinum bulbispermum, Danais fragans, Dioscorea dregeana, Dodonaea angustifolia, Eucomis autumnalis, Gnidia kraussiana, Melianthus comosus, Mondia whitei, Pelargonium sidoides, Typha capensis, Vinca minor, Voacanga africana, and Xysmalobium undulatum were identified as new sources rich in compounds with a potential anti-diabetic activity. The major targets identified for the natural compounds were aldose reductase, hydroxysteroid 11-beta dehydrogenase 1, dipeptidyl peptidase 4, and peroxisome proliferator-activated receptor delta. More than 30% of the compounds had five or more potential targets. A hierarchical clustering analysis coupled with a maximum common substructure analysis revealed the importance of the flavonoid backbone for predicting potential activity against aldose reductase and hydroxysteroid 11-beta dehydrogenase 1. Filtering with physiochemical and the absorption, distribution, metabolism, excretion and toxicity (ADMET) descriptors identified 28 compounds with favorable ADMET properties. The six compounds—crotofoline A, erythraline, henningsiine, nauclefidine, vinburnine, and voaphylline—were identified as novel potential multi-targeted anti-diabetic compounds, with favorable ADMET properties for further drug development.

Predicting the cytotoxicity of chemicals using ensemble learning methods and molecular fingerprints

The prediction of compound cytotoxicity is an important part of the drug discovery process. However, it usually appears as poor predictive performance because the datasets are high-throughput and have a class-imbalance problem. In this study, several strategies of performing a structure-activity relationship study for a cytotoxic endpoint in the AID364 dataset were explored to solve the class-imbalance problem. Random forest adaboost was used as the base learners for 10 types of molecular fingerprints and an ensemble method and six data-balancing methods were applied to balance the classes. As a result, the ensemble model using MACCS fingerprint was found to be the best, giving area under the curve of 85.2% ± 0.35%, sensitivity of 81.8% ± 0.65%, and specificity of 76.0% ± 0.12% in fivefold cross-validation and area under the curve of 78.8%, sensitivity of 55.5% and specificity of 78.5% in external validation. Good performance also appeared on other datasets with different sizes/degrees of imbalance. To explore the structural commonality of cytotoxic compounds, several substructures were identified as an important reference for substructure alerts. The convincing results indicate that the proposed models are helpful in predicting the cytotoxicity of chemicals.

Naïve Bayesian Models for Vero Cell Cytotoxicity

PURPOSE

To advance translational research of potential therapeutic small molecules against infectious microbes, the compounds must display a relative lack of mammalian cell cytotoxicity. Vero cell cytotoxicity (CC₅₀) is a common initial assay for this metric. We explored the development of naïve Bayesian models that can enhance the probability of identifying non-cytotoxic compounds.

METHODS

Vero cell cytotoxicity assays were identified in PubChem, reformatted, and curated to create a training set with 8741 unique small molecules. These data were used to develop Bayesian classifiers, which were assessed with internal cross-validation, external tests with a set of 193 compounds from our laboratory, and independent validation with an additional diverse set of 1609 unique compounds from PubChem.

RESULTS

Evaluation with independent, external test and validation sets indicated that cytotoxicity Bayesian models constructed with the ECFP_6 descriptor were more accurate than those that used FCFP_6 fingerprints. The best cytotoxicity Bayesian model displayed predictive power in external evaluations, according to conventional and chance-corrected statistics, as well as enrichment factors.

CONCLUSIONS

The results from external tests demonstrate that our novel cytotoxicity Bayesian model displays sufficient predictive power to help guide translational research. To assist the chemical tool and drug discovery communities, our curated training set is being distributed as part of the Supplementary Material. Graphical Abstract Naive Bayesian models have been trained with publically available data and offer a useful tool for chemical biology and drug discovery to select for small molecules with a high probability of exhibiting acceptably low Vero cell cytotoxicity.

Enalos+ KNIME Nodes: New Cheminformatics Tools for Drug Discovery

In this chapter we present and discuss Enalos+ nodes designed and developed by NovaMechanics Ltd. for the open-source KNIME platform, as a useful aid when dealing with cheminformatics and nanoinformatics problems or medicinal applications. Enalos+ nodes facilitate tasks performed in molecular modeling and allow access, data mining, and manipulation for multiple chemical databases through the KNIME interface. Enalos+ nodes automate common procedures that greatly facilitate the rapid workflow prototyping within KNIME. Μethods and techniques that are included in Enalos+ nodes are presented in order to offer a deeper understanding of the theoretical background of the incorporated functionalities. An emphasis is given to demonstrate the usefulness of Enalos+ nodes in different cheminformatics applications by presenting four indicative case studies. Specifically, we present case studies that underline the value and the effectiveness of the nodes for molecular descriptors calculation and QSAR predictive model development. In addition, case studies are also presented demonstrating the benefits of the use of Enalos+ nodes for database exploitation within a drug discovery project.

MouseTox: An online toxicity assessment tool for small molecules through Enalos Cloud platform

Advances in the drug discovery research substantially depend on in silico methods and techniques that capitalize on experimental data to enable the accurate property/activity assessment by employing a variety of computational techniques. These in silico tools can significantly reduce expensive and time consuming experimental procedures required and are strongly recommended to avoid animal testing, especially as far as toxicity evaluation and risk assessment is concerned. In this context, in the present work we aim to develop a predictive model for the cytotoxic effects of a wide range of compounds based solely on calculated molecular descriptors that account for their topological, geometric and structural characteristics. The developed model was fully validated and was released online via Enalos Cloud platform accessible through http://enalos.insilicotox.com/MouseTox/. This ready-to-use web service offers, through a user-friendly interface, free access to the model results and therefore can act as a toxicity prediction tool for the risk assessment of novel compounds, without any special requirements or prior programming skills.