A systematic review: Molecular docking simulation of small molecules as anticancer non-small cell lung carcinoma drug candidates

Non-small cell lung carcinoma (NSCLC) is a type of lung cancer with the highest prevalence and mortality rate worldwide. Many cases of this type of cancer are overexpression on epidermal growth factor receptor (EGFR). The use of currently available EGFR inhibitors as one of the treatment options for NSCLC still shows various shortcomings, especially the high failure rate of therapy due to resistance. It is important to find NSCLC drug candidates with EGFR inhibitory activity. There are various published articles and it is prominent to draw evidence-based scientific conclusions as a basis of decision-making to select potential compounds for further research. Polymer matrix composites and ScienceDirect are used as a database for article screening. Research using molecular docking method targeted to EGFR with parameters of Gibbs energy and amino acid interactions between ligands and drug targets are included in inclusion criteria. Compounds that achieve docking parameters and have comparable activity to NSCLC guideline drugs are conscientiously ranked. There are only 11 compounds that achieved the docking parameters and had comparable EGFR inhibitory potential. Top-rated compounds include 1,3,5-trisubstituted pyrazoline (3c), 1,3,5-trisubstituted pyrazoline (6c), 1,3,5-trisubstituted pyrazoline (8d), N-(3,4-Dimethylphenyl)-2-[(4-oxo-3-(4-sulfamoylphenyl)-3,4-dihydrobenzo[g] quinazolin-2-yl) thio] acetamide. The top-rated compounds can be used and considered for further research processes.

Profiling of phytochemical and antioxidant activity of wild mushrooms: Evidence from the in vitro study and phytoconstituent's binding affinity to the human erythrocyte catalase and human glutathione reductase

This study was undertaken to evaluate the appearance of phytochemicals and antioxidant activity of seven wild mushrooms of the University of Chittagong campus. Phytochemical screening was performed using standard methods, whereas DPPH radical scavenging assay was used to elucidate the antioxidant effect. Besides, in silico studies were implemented using the targets of human erythrocyte catalase 3-amino-1,2,4-triazole, human glutathione reductase, and selected compounds. Again, the absorption, distribution, metabolism, elimination and toxicity (ADME/T) analysis has been determined by using online tools. Both Ganoderma lucidum (Curtis) Karst. and Ganoderma applanatum (Pers.) Pat. showed a significant (p < .001) increase in the percentage of scavenging activity at 400 μg/ml concentration when compared with ascorbic acid. The methanol extract of G. lucidum, G. applanatum, and Rhodofomes cajanderi (P. Karst.) B. K. Cui, M. L. Han & Y. C. Dai showed strong antioxidant activity with an IC50 value. In addition, molecular docking studies of the previously isolated compounds from three selective mushrooms revealed that the targeted compounds along with positive controls were able to interact strongly (range: -3.498 to -8.655) with the enzymes. The study concludes that the G . lucidum, G. applanatum, and R. cajanderi mushrooms can be a strong source in the management of oxidative stress-induced diseases.

Fruit Bromelain-Derived Peptide Potentially Restrains the Attachment of SARS-CoV-2 Variants to hACE2: A Pharmacoinformatics Approach

Before entering the cell, the SARS-CoV-2 spike glycoprotein receptor-binding domain (RBD) binds to the human angiotensin-converting enzyme 2 (hACE2) receptor. Hence, this RBD is a critical target for the development of antiviral agents. Recent studies have discovered that SARS-CoV-2 variants with mutations in the RBD have spread globally. The purpose of this in silico study was to determine the potential of a fruit bromelain-derived peptide. DYGAVNEVK. to inhibit the entry of various SARS-CoV-2 variants into human cells by targeting the hACE binding site within the RBD. Molecular docking analysis revealed that DYGAVNEVK interacts with several critical RBD binding residues responsible for the adhesion of the RBD to hACE2. Moreover, 100 ns MD simulations revealed stable interactions between DYGAVNEVK and RBD variants derived from the trajectory of root-mean-square deviation (RMSD), radius of gyration (Rg), and root-mean-square fluctuation (RMSF) analysis, as well as free binding energy calculations. Overall, our computational results indicate that DYGAVNEVK warrants further investigation as a candidate for preventing SARS-CoV-2 due to its interaction with the RBD of SARS-CoV-2 variants.

Prediction of the mechanism of action of catechin as superoxide anion antioxidants and natural antivirals for COVID-19 infection with in silico study

Coronavirus disease-2019 (COVID-19) is caused by the severe acute respiratory syndrome coronavirus-2 attacking the lungs, which contain the most oxygen. The involvement of oxidative stress in the body and the role of antioxidant compounds, namely catechins, are thought to be able to prevent various diseases, including the COVID-19 infection virus. An in silico approach was employed between the catechins and the protein NADPH oxidase (Nox), followed by the coronavirus protease protein, to limit the generation of reactive oxygen species. This research using the in silico method seeks to predict the mechanism of action of catechin as a superoxide radical anion inhibitor and as an antiviral for COVID-19. This study carried out molecular docking simulations of catechin compounds against Nox and coronavirus proteases and then compared them with positive controls GKT136901 and remdesivir. The binding energy of catechin and Nox in a docking simulation is - 8.30 kcal/mol, which is somewhat lower than GKT136901's binding value of - 8.72 kcal/mol. Catechin and coronavirus proteases had binding energy of - 7.89 kcal/mol, which was greater than remdesivir's binding energy of - 7.50 kcal/mol. Based on in silico data, catechin as an antioxidant compound can be antiviral for COVID-19.

Exposure Optimization Trial for Patients With Medical Implants During MRI Exposure: Balance Between the Completeness and Efficiency

Elongated conductors, such as pacemaker leads, can couple to the MRI radio-frequency (RF) field during MRI scan and cause dangerous tissue heating. By selecting proper RF exposure conditions, the RF-induced power deposition can be suppressed. As the RF-induced power deposition is a complex function of multiple clinical factors, the problem remains how to perform the exposure selection in a comprehensive and efficient way. The purpose of this work is to demonstrate an exposure optimization trail that allows a comprehensive optimization in an efficient and traceable manner. The proposed workflow is demonstrated with a generic 40 cm long cardio pacemaker, major components of the clinical factors are decoupled from the redundant data set using principle component analysis, the optimized exposure condition can not only reduce the in vivo power deposition but also maintain good image quality.

Assessment of Drug Proarrhythmicity Using Artificial Neural Networks With in silico Deterministic Model Outputs

As part of the Comprehensive in vitro Proarrhythmia Assay initiative, methodologies for predicting the occurrence of drug-induced torsade de pointes via computer simulations have been developed and verified recently. However, their predictive performance still requires improvement. Herein, we propose an artificial neural networks (ANN) model that uses nine multiple input features, considering the action potential morphology, calcium transient morphology, and charge features to further improve the performance of drug toxicity evaluation. The voltage clamp experimental data for 28 drugs were augmented to 2,000 data entries using an uncertainty quantification technique. By applying these data to the modified O'Hara Rudy in silico model, nine features (dVm/dt_max, AP_resting, APD90, APD50, Ca_resting, CaD90, CaD50, qNet, and qInward) were calculated. These nine features were used as inputs to an ANN model to classify drug toxicity into high-risk, intermediate-risk, and low-risk groups. The model was trained with data from 12 drugs and tested using the data of the remaining 16 drugs. The proposed ANN model demonstrated an AUC of 0.92 in the high-risk group, 0.83 in the intermediate-risk group, and 0.98 in the low-risk group. This was higher than the classification performance of the method proposed in previous studies.

Cytotoxic Evaluation and Molecular Docking studies of Aminopyridine derivatives as Potential Anticancer Agents

BACKGROUND

The development of resistance to available anticancer drugs is increasingly becoming a major challenge and new chemical entities could be unveiled to compensate for this therapeutic failure.

OBJECTIVES

The current study demonstrated whether N-protected and deprotected amino acid derivatives of 2-aminopyridine could attenuate tumor development using colorectal cancer cell lines.

METHODS

Biological assays were performed to investigate the anticancer potential of synthesized compounds. The in silico ADME profiling and docking studies were also performed by docking the designed compounds against the active binding site of beta-catenin (CTNNB1) to analyze the binding mode of these compounds. Four derivatives 4a, 4b, 4c, and 4d were selected for investigation of in vitro anticancer potential using colorectal cancer cell line HCT 116. The anti-tumor activities of synthesized compounds were further validated by evaluating the inhibitory effects of these compounds on the target protein beta-catenin through in vitro enzyme inhibitory assay.

RESULTS

The docking analysis revealed favorable binding energies and interactions with the target proteins. The in vitro MTT assay on colorectal cancer cell line HCT 116 and HT29 revealed potential anti-tumor activities with an IC50 range of 3.7-8.1µM and 3.27-7.7 µM, respectively. The inhibitory properties of these compounds on the concentration of beta-catenin by ELISA revealed significant percent inhibition of target protein at 100 µg/ml.

CONCLUSION

In conclusion, the synthesized compounds showed significant anti-tumor activities both in silico and in vitro, having potential for further investigating its role in colorectal cancer.

Dietary Intake of Flavonoids and Carotenoids Is Associated with Anti-Depressive Symptoms: Epidemiological Study and In Silico-Mechanism Analysis

Flavonoids and carotenoids are bioactive compounds that have protective effects against depressive symptoms. Flavonoids and carotenoids are the two main types of antioxidant phytochemicals. This study investigated the association between flavonoid and carotenoid intake and depressive symptoms in middle-aged Korean females. We analyzed the mechanism of these associations using an in silico method. Depressive symptoms were screened using the Beck Depression Inventory-II (BDI-II), and flavonoid and carotenoid intake were assessed using a semi-quantitative food frequency questionnaire. Using a multivariate logistic regression model, we found that flavones, anthocyanins, individual phenolic compounds, lycopene, and zeaxanthin were negatively associated with depressive symptoms. In silico analysis showed that most flavonoids have high docking scores for monoamine oxidase A (MAOA) and monoamine oxidase B (MAOB), which are two important drug targets in depression. The results of the docking of brain-derived neurotrophic factor (BDNF) and carotenoids suggested the possibility of allosteric activation of BDNF by carotenoids. These results suggest that dietary flavonoids and carotenoids can be utilized in the treatment of depressive symptoms.

Assay format diversity in pre-clinical immunogenicity risk assessment: Toward a possible harmonization of antigenicity assays

A major impediment to successful use of therapeutic protein drugs is their ability to induce anti-drug antibodies (ADA) that can alter treatment efficacy and safety in a significant number of patients. To this aim, in silico, in vitro, and in vivo tools have been developed to assess sequence and other liabilities contributing to ADA development at different stages of the immune response. However, variability exists between similar assays developed by different investigators due to the complexity of assays, a degree of uncertainty about the underlying science, and their intended use. The impact of protocol variations on the outcome of the assays, i.e., on the immunogenicity risk assigned to a given drug candidate, cannot always be precisely assessed. Here, the Non-Clinical Immunogenicity Risk Assessment working group of the European Immunogenicity Platform (EIP) reviews currently used assays and protocols and discusses feasibility and next steps toward harmonization and standardization.

In silico analysis of highly conserved cytotoxic T-cell epitopes in the structural proteins of African swine fever virus

Background and Aim:

African swine fever (ASF) is a viral disease of pigs caused by ASF virus (ASFV). High mortality and the lack of available treatments have severely impacted the swine industry resulting in huge global economic losses. In response to the dire necessity for vaccines, this study aims to identify highly conserved cytotoxic T-cell epitopes in ASFV structural proteins pp220, pp62, p72, p30, and CD2v through immunoinformatics approach.

Materials and Methods:

The amino acid sequences of the structural proteins were retrieved from the National Center for Biotechnology Information protein database. The sequences were evaluated in CD-HIT Suite wherein resulting representative sequences were aligned in Clustal Omega. Highly conserved sequences were identified in the Protein Variability Server which were used as reference sequences for the cytotoxic T-cell epitope mapping. Epitopes were predicted using the tools in Immune Epitope Database. Peptides which bind to the swine major histocompatibility complex with IC₅₀ binding scores >500 nM were filtered out. Epitopes which are classified to be potentially toxic and cross-reactive with the swine proteome sequences were all excluded from the study. The epitopes were docked with the swine leukocyte antigen-1*0401 (SLA-1*0401) wherein the binding affinity, the binding energy, and the root-mean-square deviation (RMSD) per residue of epitope-SLA complexes formed were determined and compared with the influenza epitope as positive control.

Results:

A total of 112 highly conserved fragments with Shannon variability index ≤0.1 were identified. These include 66, 12, 26, 6, and 2 highly conserved fragments from ASFV proteins pp220, pp62, p72, p30, and CD2v, respectively. From these reference sequences, 35 nonameric peptides were selected for the list of candidate cytotoxic T-cell epitopes. These include 26 epitopes for pp220, 7 for pp62, 6 for p72, and one each for p30 and CD2v. Bioinformatics analysis classified the peptides as non-toxic. Further evaluations of epitopes showed that these are less likely to cross-react with the domestic swine proteome sequences. This study identified candidate epitopes from pp220 (IADAINQEF, FLNKSTQAY, QIYKTLLEY, and SLYPTQFDY), and pp62 (GTDLYQSAM, FINSTDFLY, and STDFLYTAI) which can bind to at least two widely distributed SLAs in pig populations. The immunogenicity of candidate peptides RSNPGSFYW, DFDPLVTFY, AIPSVSIPF, and VVFHAGSLY was validated by the acceptable binding affinities, binding energies, and RMSD of the peptide-SLA complexes formed. Results were also comparable with the crystal structure of an SLA-epitope complex in the database.

Conclusion:

This is the first study to identify highly conserved cytotoxic T-cell epitopes in the structural proteins of ASFV. Overall, the results of in silico evaluations showed that the identified highly conserved cytotoxic T-cell epitopes may be used as part of future vaccine formulations against ASFV infection in domesticated pigs. Nonetheless, these findings require in vitro and in vivo validation before application.

In Silico Approaches to Identify Polyphenol Compounds as α-Glucosidase and α-Amylase Inhibitors against Type-II Diabetes

Type-II diabetes mellitus (T2DM) results from a combination of genetic and lifestyle factors, and the prevalence of T2DM is increasing worldwide. Clinically, both α-glucosidase and α-amylase enzymes inhibitors can suppress peaks of postprandial glucose with surplus adverse effects, leading to efforts devoted to urgently seeking new anti-diabetes drugs from natural sources for delayed starch digestion. This review attempts to explore 10 families e.g., Bignoniaceae, Ericaceae, Dryopteridaceae, Campanulaceae, Geraniaceae, Euphorbiaceae, Rubiaceae, Acanthaceae, Rutaceae, and Moraceae as medicinal plants, and folk and herb medicines for lowering blood glucose level, or alternative anti-diabetic natural products. Many natural products have been studied in silico, in vitro, and in vivo assays to restrain hyperglycemia. In addition, natural products, and particularly polyphenols, possess diverse structures for exploring them as inhibitors of α-glucosidase and α-amylase. Interestingly, an in silico discovery approach using natural compounds via virtual screening could directly target α-glucosidase and α-amylase enzymes through Monte Carto molecular modeling. Autodock, MOE-Dock, Biovia Discovery Studio, PyMOL, and Accelrys have been used to discover new candidates as inhibitors or activators. While docking score, binding energy (Kcal/mol), the number of hydrogen bonds, or interactions with critical amino acid residues have been taken into concerning the reliability of software for validation of enzymatic analysis, in vitro cell assay and in vivo animal tests are required to obtain leads, hits, and candidates in drug discovery and development.

Integration of data from the in vitro long-term exposure study on human endothelial cells and the in silico analysis: A case of dibutyl phthalate-induced vascular dysfunction

General population is exposed to dibutyl phthalate (DBP) through continuous use of various consumer products. DBP exhibits its effects mainly on the endocrine and reproductive system but it can also affect the function of the vasculature; however, the underlying mechanisms behind DBP-induced vascular dysfunction are not fully understood. To infer pathways, molecular functions, biological processes, and human diseases associated with DBP exposure, we integrated the toxicogenomic data obtained from the 4-week-long exposure of human vascular endothelial cells (ECs) to three environmentally relevant concentrations of DBP with the in silico analysis. Nine genes were affected by DBP exposure: six of the integrin family, VCAM1, ICAM1, and MMP2. As shown by the in silico analysis, changes in DBP-affected genes could affect extracellular matrix and binding of molecules and cells to ECs, thereby altering cell adhesion and migration. Several pathways, molecular functions, and biological processes were further identified to provide insight into the DBP-vascular disease relationships and the potential mechanism of action. The top three human disease categories associated with DBP exposure and vascular dysfunction include cardiovascular, cerebrovascular, and immune system diseases. Integration of experimental and in silico approaches may offer better understanding of the potential human health risks associated with DBP exposure.

Appraisal of Bioactive Compounds of Betel Fruit as Antimalarial Agents by Targeting Plasmepsin 1 and 2: A Computational Approach

In many countries, the fruit of betel (Piper betle Linn) is traditionally used as medicine for treating malaria. It is a fatal disease, and existing medications are rapidly losing potency, necessitating the development of innovative pharmaceutics. The current study attempted to determine the compounds in the n-hexane fraction of betel fruit extract and investigate the potential inhibition of bioactive compounds against aspartic protease plasmepsin 1 (PDB ID: 3QS1) and plasmepsin 2 (PDB ID: 1LEE) of Plasmodium falciparum using a computational approach. The ethanol extract was fractionated into n-hexane and further analyzed using gas chromatography-mass spectrometry (GC-MS) to obtain information regarding the compounds contained in betel fruit. Each compound's potential antimalarial activity was evaluated using AutoDock Vina and compared to artemisinin, an antimalarial drug. Molecular dynamics simulations (MDSs) were performed to evaluate the stability of the interaction between the ligand and receptors. Results detected 20 probable compounds in the n-hexane extract of betel fruit based on GC-MS analysis. The docking study revealed that androstan-17-one,3-ethyl-3-hydroxy-, (5 alpha)- has the highest binding affinity for plasmepsin 1 and plasmepsin 2. The compound exhibits a similar interaction with artemisinin at the active site of the receptors. The compound does not violate Lipinski's rules of five. It belongs to class 5 toxicity with an LD₅₀ of 3000 mg/kg. MDS results showed stable interactions between the compound and the receptors. Our study concluded that androstan-17-one,3-ethyl-3-hydroxy-, (5 alpha)- from betel fruit has the potential to be further investigated as a potential inhibitor of the aspartic protease plasmepsin 1 and plasmepsin 2 of Plasmodium falciparum.

In silico prediction of volume of distribution of drugs in man using conformal prediction performs on par with animal data-based models

Volume of distribution at steady state (V_ss) is an important pharmacokinetic endpoint. In this study we apply machine learning and conformal prediction for human V_ss prediction, and make a head-to-head comparison with rat-to-man scaling, allometric scaling and the Rodgers-Lukova method on combined in silico and in vitro data, using a test set of 105 compounds with experimentally observed V_ss.The mean prediction error and % with <2-fold prediction error for our method were 2.4-fold and 64%, respectively. 69% of test compounds had an observed V_ss within the prediction interval at a 70% confidence level. In comparison, 2.2-, 2.9- and 3.1-fold mean errors and 69, 64 and 61% of predictions with <2-fold error was reached with rat-to-man and allometric scaling and Rodgers-Lukova method, respectively.We conclude that our method has theoretically proven validity that was empirically confirmed, and showing predictive accuracy on par with animal models and superior to an alternative widely used in silico-based method. The option for the user to select the level of confidence in predictions offers better guidance on how to optimise V_ss in drug discovery applications.

Computational Screening for the Anticancer Potential of Seed-Derived Antioxidant Peptides: A Cheminformatic Approach

Some seed-derived antioxidant peptides are known to regulate cellular modulators of ROS production, including those proposed to be promising targets of anticancer therapy. Nevertheless, research in this direction is relatively slow owing to the inevitable time-consuming nature of wet-lab experimentations. To help expedite such explorations, we performed structure-based virtual screening on seed-derived antioxidant peptides in the literature for anticancer potential. The ability of the peptides to interact with myeloperoxidase, xanthine oxidase, Keap1, and p47^phox was examined. We generated a virtual library of 677 peptides based on a database and literature search. Screening for anticancer potential, non-toxicity, non-allergenicity, non-hemolyticity narrowed down the collection to five candidates. Molecular docking found LYSPH as the most promising in targeting myeloperoxidase, xanthine oxidase, and Keap1, whereas PSYLNTPLL was the best candidate to bind stably to key residues in p47^phox. Stability of the four peptide-target complexes was supported by molecular dynamics simulation. LYSPH and PSYLNTPLL were predicted to have cell- and blood-brain barrier penetrating potential, although intolerant to gastrointestinal digestion. Computational alanine scanning found tyrosine residues in both peptides as crucial to stable binding to the targets. Overall, LYSPH and PSYLNTPLL are two potential anticancer peptides that deserve deeper exploration in future.

First Glimpse at the Diverse Aquaporins of Amphipod Crustaceans

The importance of aquaporins (AQPs) in the transport of water and solutes through cell membranes is well recognized despite being relatively new. To date, despite their abundance, diversity, and presence in disparate environments, amphipods have only been mentioned in studies about the AQPs of other animals and have never been further investigated. In this work, we aimed to recover from public data available AQPs of these crustaceans and reconstruct phylogenetic affinities. We first performed BLAST searches with several queries of diverse taxa against different NCBI databases. Then, we selected the clades of AQPs retrieving the amphipod superfamily Gammaroidea as monophyletic and ran phylogenetic analyses to assess their performances. Our results show how most of the AQPs of amphipods are similar to those of other crustaceans, despite the Prip-like displayed different paralogs, and report for the first time a putative Aqp8-like for arthropods. We also found that the candidate genes of Prip-like, Bib-like, Aqp12-like, and Glp-like help solve deeper relationships in phylogenies of amphipods while leaving uncertainties in shallower parts. With our findings, we hope to increase attention to the study of amphipods as models for AQP functioning and evolution.

In silico prediction of the full United Nations Globally Harmonized System eye irritation categories of liquid chemicals by IATA-like bottom-up approach of random forest method

As an alternative to in vivo Draize rabbit eye irritation test, this study aimed to construct an in silico model to predict the complete United Nations (UN) Globally Harmonized System (GHS) for classification and labeling of chemicals for eye irritation category [eye damage (Category 1), irritating to eye (Category 2) and nonirritating (No category)] of liquid chemicals with Integrated approaches to testing and assessment (IATA)-like two-stage random forest approach. Liquid chemicals (n = 219) with 34 physicochemical descriptors and quality in vivo data were collected with no missing values. Seven machine learning algorithms (Naive Bayes, Logistic Regression, First Large Margin, Neural Net, Random Forest (RF), Gradient Boosted Tree, and Support Vector Machine) were examined for the ternary categorization of eye irritation potential at a single run through 10-fold cross-validation. RF, which performed best, was further improved by applying the 'Bottom-up approach' concept of IATA, namely, separating No category first, and discriminating Category 1 from 2, thereafter. The best performing training dataset achieved an overall accuracy of 73% and the correct prediction for Category 1, 2, and No category was 80%, 50%, and 77%, respectively for the test dataset. This prediction model was further validated with an external dataset of 28 chemicals, for which an overall accuracy of 71% was achieved.

Antileishmanial Drug Discovery and Development: Time to Reset the Model?

Leishmaniasis is a vector-borne parasitic disease caused by Leishmania species. The disease affects humans and animals, particularly dogs, provoking cutaneous, mucocutaneous, or visceral processes depending on the Leishmania sp. and the host immune response. No vaccine for humans is available, and the control relies mainly on chemotherapy. However, currently used drugs are old, some are toxic, and the safer presentations are largely unaffordable by the most severely affected human populations. Moreover, its efficacy has shortcomings, and it has been challenged by the growing reports of resistance and therapeutic failure. This manuscript presents an overview of the currently used drugs, the prevailing model to develop new antileishmanial drugs and its low efficiency, and the impact of deconstruction of the drug pipeline on the high failure rate of potential drugs. To improve the predictive value of preclinical research in the chemotherapy of leishmaniasis, several proposals are presented to circumvent critical hurdles-namely, lack of common goals of collaborative research, particularly in public-private partnership; fragmented efforts; use of inadequate surrogate models, especially for in vivo trials; shortcomings of target product profile (TPP) guides.

Scientific Opinion of the Scientific Panel on Plant Protection Products and their Residues (PPR Panel) on testing and interpretation of comparative in vitro metabolism studies

EFSA asked the Panel on Plant Protection Products and their residues to deliver a Scientific Opinion on testing and interpretation of comparative in vitro metabolism studies for both new active substances and existing ones. The main aim of comparative in vitro metabolism studies of pesticide active substances is to evaluate whether all significant metabolites formed in the human in vitro test system, as a surrogate of the in vivo situation, are also present at comparable level in animal species tested in toxicological studies and, therefore, if their potential toxicity has been appropriately covered by animal studies. The studies may also help to decide which animal model, with regard to a particular compound, is the most relevant for humans. In the experimental strategy, primary hepatocytes in suspension or culture are recommended since hepatocytes are considered the most representative in vitro system for prediction of in vivo metabolites. The experimental design of 3 × 3 × 3 (concentrations, time points, technical replicates, on pooled hepatocytes) will maximise the chance to identify unique (UHM) and disproportionate (DHM) human metabolites. When DHM and UHM are being assessed, test item-related radioactivity recovery and metabolite profile are the most important parameters. Subsequently, structural characterisation of the assigned metabolites is performed with appropriate analytical techniques. In toxicological assessment of metabolites, the uncertainty factor approach is the first alternative to testing option, followed by new approach methodologies (QSAR, read-across, in vitro methods), and only if these fail, in vivo animal toxicity studies may be performed. Knowledge of in vitro metabolites in human and animal hepatocytes would enable toxicological evaluation of all metabolites of concern, and, furthermore, add useful pieces of information for detection and evaluation of metabolites in different matrices (crops, livestock, environment), improve biomonitoring efforts via better toxicokinetic understanding, and ultimately, develop regulatory schemes employing physiologically based or physiology-mimicking in silico and/or in vitro test systems to anticipate the exposure of humans to potentially hazardous substances in plant protection products.

Adaptive Modelling of Mutated FMO3 Enzyme Could Unveil Unexplored Scenarios Linking Variant Haplotypes to TMAU Phenotypes

Background: Trimethylaminuria (TMAU) is a rare genetic disease characterized by the accumulation of trimethylamine (TMA) and its subsequent excretion trough main body fluids, determining the characteristic fish odour in affected patients. We realized an experimental study to investigate the role of several coding variants in the causative gene FMO3, that were only considered as polymorphic or benign, even if the available literature on them did not functionally explain their ineffectiveness on the encoded enzyme. Methods: Mutational analysis of 26 TMAU patients was realized by Sanger sequencing. Detected variants were, subsequently, deeply statistically and in silico characterized to determine their possible effects on the enzyme activity. To achieve this goal, a docking prediction for TMA/FMO3 and an unbinding pathway study were performed. Finally, a TMAO/TMA urine quantification by 1H-NMR spectroscopy was performed to support modelling results. Results: The FMO3 screening of all patients highlighted the presence of 17 variants distributed in 26 different haplotypes. Both non-sense and missense considered variants might impair the enzymatic kinetics of FMO3, probably reducing the interaction time between the protein catalytic site and TMA, or losing the wild-type binding site. Conclusions: Even if further functional assays will confirm our predictive results, considering the possible role of FMO3 variants with still uncertain effects, might be a relevant step towards the detection of novel scenarios in TMAU etiopathogenesis.

Potential Papain-like Protease Inhibitors against COVID-19: A Comprehensive In Silico Based Review

The entire world has been in a battle against the COVID-19 pandemic since its first appearance in December 2019. Thus researchers are desperately working to find an effective and safe therapeutic agent for its treatment. The multifunctional coronavirus enzyme papain-like protease (PLpro) is a potential target for drug discovery to combat the ongoing pandemic responsible for cleavage of the polypeptide, deISGylation, and suppression of host immune response. The present review collates the in silico studies performed on various FDA-approved drugs, chemical compounds, and phytochemicals from various drug databases and represents the compounds possessing the potential to inhibit PLpro. Thus this review can provide quick access to a potential candidate to medicinal chemists to perform in vitro and in vivo experiments who are thriving to find the effective agents for the treatment of COVID-19.

Chemical Exposures Affect Innate Immune Response to SARS-CoV-2

Severe outcomes of COVID-19 are associated with pathological response of the immune system to the SARS-CoV-2 infection. Emerging evidence suggests that an interaction may exist between COVID-19 pathogenesis and a broad range of xenobiotics, resulting in significant increases in death rates in highly exposed populations. Therefore, a better understanding of the molecular basis of the interaction between SARS-CoV-2 infection and chemical exposures may open opportunities for better preventive and therapeutic interventions. We attempted to gain mechanistic knowledge on the interaction between SARS-CoV-2 infection and chemical exposures using an in silico approach, where we identified genes and molecular pathways affected by both chemical exposures and SARS-CoV-2 in human immune cells (T-cells, B-cells, NK-cells, dendritic, and monocyte cells). Our findings demonstrate for the first time that overlapping molecular mechanisms affected by a broad range of chemical exposures and COVID-19 are linked to IFN type I/II signaling pathways and the process of antigen presentation. Based on our data, we also predict that exposures to various chemical compounds will predominantly impact the population of monocytes during the response against COVID-19.

Purification, Identification and Characterization of Antioxidant Peptides from Corn Silk Tryptic Hydrolysate: An Integrated In Vitro-In Silico Approach

Corn silk (CS) is an agro-by-product from corn cultivation. It is used in folk medicines in some countries, besides being commercialized as health-promoting supplements and beverages. Unlike CS-derived natural products, their bioactive peptides, particularly antioxidant peptides, are understudied. This study aimed to purify, identify and characterize antioxidant peptides from trypsin-hydrolyzed CS proteins. Purification was accomplished by membrane ultrafiltration, gel filtration chromatography, and strong-cation-exchange solid-phase extraction, guided by 2,2′-azino-bis(3-ethylbenzothiazoline-6-sulfonic acid) diammonium salt radical cation (ABTS^•+) scavenging, hydrogen peroxide scavenging, and lipid peroxidation inhibition assays. De novo sequencing identified 29 peptides (6–14 residues; 633–1518 Da). The peptides consisted of 33–86% hydrophobic and 10–67% basic residues. Molecular docking found MCFHHHFHK, VHFNKGKKR, and PVVWAAKR having the strongest affinity (−4.7 to −4.8 kcal/mol) to ABTS^•+, via hydrogen bonds and hydrophobic interactions. Potential cellular mechanisms of the peptides were supported by their interactions with modulators of intracellular oxidant status: Kelch-like ECH-associated protein 1, myeloperoxidase, and xanthine oxidase. NDGPSR (Asn-Asp-Gly-Pro-Ser-Arg), the most promising peptide, showed stable binding to all three cellular targets, besides exhibiting low toxicity, low allergenicity, and cell-penetrating potential. Overall, CS peptides have potential application as natural antioxidant additives and functional food ingredients.

A Comparative Study between Spanish and British SARS-CoV-2 Variants

The study of the interaction between the SARS-CoV-2 spike protein and the angiotensin-converting enzyme 2 (ACE2) receptor is key to understanding binding affinity and stability. In the present report, we sought to investigate the differences between two already sequenced genome variants (Spanish and British) of SARS-CoV-2. Methods: In silico model evaluating the homology, identity and similarity in the genome sequence and the structure and alignment of the predictive spike by computational docking methods. Results: The identity results between the Spanish and British variants of the Spike protein were 28.67%. This close correspondence in the results between the Spanish and British SARS-CoV-2 variants shows that they are very similar (99.99%). The alignment obtained results in four deletions. There were 23 nucleotide substitutions also predicted which could affect the functionality of the proteins produced from this sequence. The interaction between the binding receptor domain from the spike protein and the ACE2 receptor produces some of the mutations found and, therefore, the energy of this ligand varies. However, the estimated antigenicity of the British variant is higher than its Spanish counterpart. Conclusions: Our results indicate that minimal mutations could interfere in the infectivity of the virus due to changes in the fitness between host cell recognition and interaction proteins. In particular, the N501Y substitution, situated in the RBD of the spike of the British variant, might be the reason for its extraordinary infective potential.

Inherited and Acquired Rhythm Disturbances in Sick Sinus Syndrome, Brugada Syndrome, and Atrial Fibrillation: Lessons from Preclinical Modeling

Rhythm disturbances are life-threatening cardiovascular diseases, accounting for many deaths annually worldwide. Abnormal electrical activity might arise in a structurally normal heart in response to specific triggers or as a consequence of cardiac tissue alterations, in both cases with catastrophic consequences on heart global functioning. Preclinical modeling by recapitulating human pathophysiology of rhythm disturbances is fundamental to increase the comprehension of these diseases and propose effective strategies for their prevention, diagnosis, and clinical management. In silico, in vivo, and in vitro models found variable application to dissect many congenital and acquired rhythm disturbances. In the copious list of rhythm disturbances, diseases of the conduction system, as sick sinus syndrome, Brugada syndrome, and atrial fibrillation, have found extensive preclinical modeling. In addition, the electrical remodeling as a result of other cardiovascular diseases has also been investigated in models of hypertrophic cardiomyopathy, cardiac fibrosis, as well as arrhythmias induced by other non-cardiac pathologies, stress, and drug cardiotoxicity. This review aims to offer a critical overview on the effective ability of in silico bioinformatic tools, in vivo animal studies, in vitro models to provide insights on human heart rhythm pathophysiology in case of sick sinus syndrome, Brugada syndrome, and atrial fibrillation and advance their safe and successful translation into the cardiology arena.