A contemporary review on the important role of in silico approaches for managing different aspects of COVID-19 crisis

A Review of the Applications, Benefits, and Challenges of Generative AI for Sustainable Toxicology

Sustainable toxicology is vital for living species and the environment because it guarantees the safety, efficacy, and regulatory compliance of drugs, treatments, vaccines, and chemicals in living organisms and the environment. Conventional toxicological methods often lack sustainability as they are costly, time-consuming, and sometimes inaccurate. It means delays in producing new drugs, vaccines, and treatments and understanding the adverse effects of the chemicals on the environment. To address these challenges, the healthcare sector must leverage the power of the Generative-AI (GenAI) paradigm. This paper aims to help understand how the healthcare field can be revolutionized in multiple ways by using GenAI to facilitate sustainable toxicological developments. This paper first reviews the present literature and identifies the possible classes of GenAI that can be applied to toxicology. A generalized and holistic visualization of various toxicological processes powered by GenAI is presented in tandem. The paper discussed toxicological risk assessment and management, spotlighting how global agencies and organizations are forming policies to standardize and regulate AI-related development, such as GenAI, in these fields. The paper identifies and discusses the advantages and challenges of GenAI in toxicology. Further, the paper outlines how GenAI empowers Conversational-AI, which will be critical for highly tailored toxicological solutions. This review will help to develop a comprehensive understanding of the impacts and future potential of GenAI in the field of toxicology. The knowledge gained can be applied to create sustainable GenAI applications for various problems in toxicology, ultimately benefiting our societies and the environment.

Investigating Therapeutic Potential of Beta-Glucan on Experimental Toxoplasmosis in Immunocompetent and Immunocompromised Mice: In Silico and in Vivo Studies

There is a need to find efficient treatment for toxoplasmosis, a global disease with a public health concern, as the used therapeutics have limited effectiveness. Beta-glucan (β-glucan), a natural polysaccharide, has anti-inflammatory, immunomodulatory, and anti-infective activities against bacteria, viruses, and parasites. We aimed to investigate the therapeutic efficacy of β-glucan on toxoplasmosis using an in silico study and an in vivo mice model. The β-glucan drug-likeness characteristics were evaluated by molecular docking simulations, Lipinski's RO5 filter analysis, and the computational quantitative structural-activity relationships/absorption, distribution, metabolism, excretion, and toxicity predictive characteristics compared to spiramycin, an anti-toxoplasmosis drug. Sixty albino mice were divided into two divisions, immunocompetent and immunosuppressed. Each division contained five groups; healthy, diseased, spiramycin-treated, β-glucan-treated, and both β-glucan and spiramycin-treated groups. Parasitological, histopathological examinations, and immunological analysis were detected. β-glucan docked complexes demonstrated H-bonding and hydrophobic interactions towards their promising anti-toxoplasmosis targets compared to the spiramycin. The treated mice revealed a statistically significant reduction (p < 0.001) in the parasite burden. Regarding immunocompetent and immunosuppressed division, the group that was treated with both β-glucan and spiramycin, demonstrated the greatest outcomes, with 71% and 66% efficacy, respectively. It is concluded that β-glucan shows promising potential as an anti-toxoplasmosis candidate by potentiating the spiramycin effect and modulating the immunological response.

Targeting cell cycle arrest in breast cancer by phytochemicals from Caryto urens L. fruit ethyl acetate fraction: in silico and in vitro validation

BACKGROUND

Caryota urens, also known as Shivjata, has been documented in ancient Indian texts for its therapeutic benefits, addressing conditions from seminal weakness to gastric ulcers. This study aims to investigate its contemporary medicinal potential in treating breast cancer.

OBJECTIVES

The study focuses on exploring the therapeutic potential of Caryota urens fruit against breast cancer, specifically targeting cell cycle genes CDK1, CDC25A, and PLK1 through bioinformatics, network pharmacology, and in vitro validation.

MATERIALS AND METHODS

Using mass spectrometry and nuclear magnetic resonance (NMR), 60 key phytoconstituents from Caryota urens fruit were identified. Bioinformatics analysis, integrating Gene Cards and GEO databases, 15,474 breast cancer-associated genes focusing on the HR+/HER2-subtype were identified. Molecular docking and qPCR validated the interactions of key phytoconstituents, particularly Episesamin, with CDK1, CDC25A, and PLK1. In vitro studies were conducted on the MCF7 cell line, supplemented by ROC and survival analyses to evaluate diagnostic and therapeutic potential.

RESULTS

The bioinformatics analysis identified CDK1, CDC25A, and PLK1 as pivotal genes regulating cell cycle progression and breast cancer tumorigenesis. Network pharmacology and in vitro studies indicated that phytoconstituents, especially Episesamin, downregulated these genes in breast cancer cells. Molecular docking and qPCR confirmed these interactions, and ROC and survival analyses underscored their diagnostic and therapeutic significance.

CONCLUSIONS

This study suggests that Caryota urens fruit extract, particularly Episesamin, may inhibit breast cancer metastasis by downregulating CDK1, CDC25A, and PLK1, offering promising new strategies for targeting the cell cycle in breast cancer and emphasizing the value of integrating bioinformatics with experimental methods in cancer research.

Design, synthesis, and molecular docking studies of thiazole derivatives against phospholipase A2 (Naja oxiana) venom

AIM

Cobra venom phospholipase A2 (PLA2) has been known to induce life threatening effects post-envenomation in the victims. Being the most abundant and noxious component of snake venom, present study was envisaged to investigate new drug candidates against PLA2 enzyme.

METHODS

Amide and sulfonamide thiazole derivatives were synthesized and characterized using FTIR, 1HNMR and 13CNMR followed by docking targeted protein techniques. Furthermore, synthetic analogues were evaluated in vitro for their potentials to neutralize PLA2 activity.

RESULTS

Among the pool of synthetic derivatives, compound (7) (ethyl 2-(2-(4-isobutylphenyl)propanamido)thiazole-4-carboxylate) was found to be completely effective (p > 0.05; IC50 = 1 nM) to mask cent percent PLA2 activity. Moreover, Ramachandran plot further conferred about the location of amino acid residues in the most favored region and, therefore, attributed to confiscate PLA2 activity. Furthermore, ADME profile suggested that compound (7) possesses systemic bioavailability and efficacy with favorable safety profile (high solubility, membrane permeability, metabolic stability, and low potential for off-target results).

CONCLUSION

Present study highlighted compound (7) as a potential PLA2 inhibitor to reverse PLA2-induced snake venom poisoning in future.

Phytochemical and antibacterial properties of calyces Hibiscus sabdariffa L.: an in vitro and in silico multitarget-mediated antibacterial study

BACKGROUND

Multidrug-resistant (MDR) bacteria pose a significant threat to human health worldwide by increasing the harmful impact of traditional synthetic antibiotics. Traditional medicinal plants have bioactive metabolites that can significantly modulate the growth rate, cell survival, and pathogenicity of antibiotic-resistant bacteria. Hibiscus sabdariffa L., known as Roselle or Karkade, belongs to the Malvaceae family. It is well-known for its edible aromatic red/purple calyces and is extensively utilized in the food industry and pharmacological applications. H. sabdariffa calyx bioactive phytocompounds have potent therapeutic activities such as antimicrobial, antidiabetic, antiobesity, antioxidant, anti-inflammatory, and anticancer properties.

METHODS

This study utilized gas chromatography-mass spectrometry (GC-MS) analysis to determine the volatile aromatic compounds that found in the hydroethanolic extract of Hibiscus sabdariffa calyces. The purpose was to verify the antibacterial properties of Roselle calyces against selective MDR clinical bacterial isolates, including A. baumanii, E. coli, K. pneumoniae, and P. aeruginosa.

RESULTS

The GC-MS spectrum profile revealed the presence of twenty-seven volatile organic components, including organic fatty acid derivatives, ester compounds, sugar derivatives, and terpene components. The major GC-MS fractionations and the main active chemical compositions of the hydroethanolic extract of H. sabdariffa flowers were (E)-10-Octadecenoic acid methyl ester (59.23%), 8,11-Octadecadienoic acid, methyl ester (11.51%), Butanedioic acid, 3-hydroxy-2,2-dimethyl-, diethyl ester (6.22%), Diethyl succinate/Butanedioic acid, diethyl ester (2.35%), and Heptadecanoic acid, 16-methyl-, methyl ester/Methyl isostearate (2.31%). The hydroethanolic extract of H. sabdariffa dried calyces demonstrated potent antibacterial properties (zones diameter of inhibition growth, MIC, MBC, and MBC/MIC) against selective MDR clinical bacterial isolates, such as A. baumanii, E. coli, K. pneumoniae, and P. aeruginosa, as determined by the phytochemical screening (TAC, TFC, and TPC) and antioxidant activity (DPPH). The surface morphological characteristics of the treated A. baumanii, E. coli, K. pneumoniae, and P. aeruginosa clinical isolates have been affected in comparison to the untreated forms by the hydroethanolic extract of H. sabdariffa calyces, as determined by scanning electron microscopy (SEM). In silico predictive investigation revealed that the volatile aromatic components of the hydroethanolic extract of Roselle calyces exhibited significant scoring functions, binding affinities, and non-covalent intermolecular interactions with the MenB lyase and DNA gyrase targets of E. coli. These interactions significantly enhanced the activities of the volatile aromatic components against the bacterial pathogenicity, cell survival, growth, and differentiation of selective MDR clinical bacterial isolates.

CONCLUSIONS

According to the in vitro and in silico findings, the hydroethanolic extract of H. sabdariffa calyces has shown potentials as an effective antioxidant and antibacterial treatment. It contains volatile aromatic compounds that can modulate selective MDR Gram-negative clinical bacterial isolates.

Improving the binding affinity of plastic degrading cutinase with polyethylene terephthalate (PET) and polyurethane (PU); an in-silico study

Plastic pollution is a worrying problem, and its degradation is a laborious process. Although enzymatic plastic breakdown is a sustainable method, drawbacks such as numerous plastic kinds of waste make the degradation challenging. Therefore, a multi-plastic degrading (MPD) enzyme becomes necessary. In this in-silico study, microorganisms and their enzymes that are known to degrade plastic polymers such as PET, PU, PVC, and PE were identified to assess their MPD capability. The cutinase of Thermobifida fusca was found to degrade both PET and PU polymers. The crystallized structure of cutinase was retrieved from PDB, and PET, PU ligands were docked using Schrodinger. However, the interactions between cutinase and the ligands were not efficient, as evidenced by the docking scores of -4.047 and -4.993 for PET and PU, respectively. Nevertheless, the interaction of the cutinase's active site with the ligands by hydrogen bond formation was promising. In this work, unconserved regions of cutinase were identified as potential mutation sites to enhance binding efficiency. In-silico Alanine Scanning Mutagenesis (ASM) and Site Saturation Mutagenesis (SSM) were performed as screening tests to find variants of cutinase with better docking scores for both ligands, specifically S136D, N28M, and S136Q. Molecular Dynamic Simulation (MDS) was performed for Wild Type (WT) cutinase, variants, and their respective complexes formed with the ligands. This simulation indicated the compactness, stability, and minimal energy of the variant complexes compared to WT complexes. Subsequent in vitro studies can ensure the improved degradation of both PET and PU by the variants.

Targeting cyclin-dependent kinase 11: a computational approach for natural anti-cancer compound discovery

Cancer, a leading global cause of death, presents considerable treatment challenges due to resistance to conventional therapies like chemotherapy and radiotherapy. Cyclin-dependent kinase 11 (CDK11), which plays a pivotal role in cell cycle regulation and transcription, is overexpressed in various cancers and is linked to poor prognosis. This study focused on identifying potential inhibitors of CDK11 using computational drug discovery methods. Techniques such as pharmacophore modeling, virtual screening, molecular docking, ADMET predictions, molecular dynamics simulations, and binding free energy analysis were applied to screen a large natural product database. Three pharmacophore models were validated, leading to the identification of several promising compounds with stronger binding affinities than the reference inhibitor. ADMET profiling indicated favorable drug-like properties, while molecular dynamics simulations confirmed the stability and favorable interactions of top candidates with CDK11. Binding free energy calculations further revealed that UNPD29888 exhibited the strongest binding affinity. In conclusion, the identified compound shows potential as a CDK11 inhibitor based on computational predictions, suggesting their future application in cancer treatment by targeting CDK11. These computational findings encourage further experimental validation as anti-cancer agents.

De novo Designing of the Antimicrobial Peptide as a Curative Agent for Methicillin-Resistant Staphylococcus aureus through a Computational Approach

BACKGROUND

The emergence of resistance to multiple drugs has posed a multitude of difficulties that demand immediate attention and solutions. Multiple drug resistance arises from the accumulation of numerous genes within a single cell, each conferring resistance to a specific drug, and from the heightened expression of genes responsible for multidrug efflux pumps. These pumps effectively expel a diverse array of drugs from the cell.

OBJECTIVE

The multi-drug-resistant organisms, including methicillin-resistant Staphylococcus aureus, are the hub of numerous diseases, from minute ailments to fatal diseases, like catheter infections. Nowadays, a combination of many antibiotics is given together as a multimodality therapy to cure MRSA infections. However, researchers are exploring novel approaches to find better solutions.

METHODS

De novo designing of the peptide sequences has been done through an in silico tool. The peptides were further screened using different computational methods. Following this, the selection was conducted utilizing physicochemical properties as criteria. Molecular docking of the selected peptide sequence was carried out. Based on the highest docking score, the model complex was chosen for validation purposes by conducting studies through molecular dynamics simulations.

RESULTS

A total of fifty-two novel antimicrobial peptides were designed and evaluated based on various parameters, targeting MRSA-specific proteins PBP2a and PVL toxin. Among these designed peptides, the peptide sequence VILRMFYHWAVKTNGP emerged as the optimal candidate, satisfying all the necessary parameters to be an effective antimicrobial peptide.

CONCLUSION

Molecular docking and MD simulation results showed that the designed peptide sequence could be the possible solution for MRSA treatment.

In silico exploration of bioactive secondary metabolites with anesthetic effects on sodium channels Nav 1.7, 1.8, and 1.9 in painful human dental pulp

AIM

To investigate the efficacy of medicinal plant bioactive secondary metabolites as inhibitors of voltage-gated sodium channels (Nav1.7, Nav1.8, and Nav1.9) in managing painful states of dental pulps.

METHODOLOGY

Molecular docking, ADME prediction, toxicity profiling, and pharmacophore modeling were used to assess the binding affinities, pharmacokinetic properties, toxicological profiles, and active pharmacophores of the selected bioactive compounds.

RESULTS

Three compounds (Sepaconitine, Lappaconitine, and Ranaconitine) showed binding affinities (ΔG = -8.95 kcal/mol, -7.77 kcal/mol, and -7.44 kcal/mol, respectively) with all three Nav1.7, Nav1.8, and Nav1.9 sodium channels. The sepaconitine amine group formed hydrophobic interactions with key residues. The Lappaconitine benzene ring contributed to hydrophobic interactions and hydrogen bond acceptor interactions. The hydrophobic interactions of the ranaconitine amine group play a critical role with specific residues on Nav1.8 and Nav1.9.

CONCLUSION

The natural fusicoccane diterpenoid derivatives Sepaconitine, Lappaconitine, and Ranaconitine are potential lead compounds for the development of novel analgesics as selective antihyperalgesic drugs, which will provide a new dental pharmacological intervention for managing painful dental pulp conditions. Further experimental validation and clinical studies that confirm the efficacy and safety of these compounds will strengthen their applicability in dental practice.

Phytoconstituents as modulator of inflammatory pathways for COVID-19: A comprehensive review and recommendations

SARS-CoV-2 infection causes disruptions in inflammatory pathways, which fundamentally contribute to COVID-19 pathophysiology. The present review critically evaluates the gaps in scientific literature and presents the current status regarding the inflammatory signaling pathways in COVID-19. We propose that phytoconstituents can be used to treat COVID-19 associated inflammation, several already formulated in traditional medications. For this purpose, extensive literature analysis was conducted in the PubMed database to collect relevant in vitro, in vivo, and human patient studies where inflammation pathways were shown to be upregulated in COVID-19. Parallelly, scientific literature was screened for phytoconstituents with known cellular mechanisms implicated for inflammation or COVID-19 associated inflammation. Studies with insufficient evidence on cellular pathways for autophagy and mitophagy were considered out of scope and excluded from the study. The final analysis was visualized in figures and evaluated for accuracy. Our findings demonstrate the frequent participation of NF-κB, a transcription factor, in inflammatory signaling pathways linked to COVID-19. Moreover, the MAPK signaling pathway is also implicated in producing inflammatory molecules. Furthermore, it was also analyzed that the phytoconstituents with flavonoid and phenolic backbones could inhibit either the TLR4 receptor or its consecutive signaling molecules, thereby, decreasing NF-κB activity and suppressing cytokine production. Although, allopathy has treated the early phase of COVID-19, anti-inflammatory phytoconstituents and existing ayurvedic formulations may act on the COVID-19 associated inflammatory pathways and provide an additional treatment strategy. Therefore, we recommend the usage of flavonoids and phenolic phytoconstituents for the treatment of inflammation associated with COVID-19 infection and similar viral ailments.

Biomarkers From Discovery to Clinical Application: In Silico Pre-Clinical Validation Approach in the Face of Lung Cancer

Background

Clinical biomarkers, allow better classification of patients according to their disease risk, prognosis, and/or response to treatment. Although affordable omics-based approaches have paved the way for quicker identification of putative biomarkers, validation of biomarkers is necessary for translation of discoveries into clinical application.

Objective

Accordingly, in this study, we emphasize the potential of in silico approaches and have proposed and applied 3 novel sequential in silico pre-clinical validation steps to better identify the biomarkers that are truly desirable for clinical investment.

Design

As protein biomarkers are becoming increasingly important in the clinic alongside other molecular biomarkers and lung cancer is the most common cause of cancer-related deaths, we used protein biomarkers for lung cancer as an illustrative example to apply our in silico pre-clinical validation approach.

Methods

We collected the reported protein biomarkers for 3 cases (lung adenocarcinoma-LUAD, squamous cell carcinoma-LUSC, and unspecified lung cancer) and evaluated whether the protein biomarkers have cancer altering properties (i.e., act as tumor suppressors or oncoproteins and represent cancer hallmarks), are expressed in body fluids, and can be targeted by FDA-approved drugs.

Results

We collected 3008 protein biomarkers for lung cancer, 1189 for LUAD, and 182 for LUSC. Of these protein biomarkers for lung cancer, LUAD, and LUSC, only 28, 25, and 6 protein biomarkers passed the 3 in silico pre-clinical validation steps examined, and of these, only 5 and 2 biomarkers were specific for lung cancer and LUAD, respectively.

Conclusion

In this study, we applied our in silico pre-clinical validation approach the protein biomarkers for lung cancer cases. However, this approach can be applied and adapted to all cancer biomarkers. We believe that this approach will greatly facilitate the transition of cancer biomarkers into the clinical phase and offers great potential for future biomarker research.

Predicting host species susceptibility to influenza viruses and coronaviruses using genome data and machine learning: a scoping review

Introduction

Predicting which species are susceptible to viruses (i.e., host range) is important for understanding and developing effective strategies to control viral outbreaks in both humans and animals. The use of machine learning and bioinformatic approaches to predict viral hosts has been expanded with advancements in in-silico techniques. We conducted a scoping review to identify the breadth of machine learning methods applied to influenza and coronavirus genome data for the identification of susceptible host species.

Methods

The protocol for this scoping review is available at https://hdl.handle.net/10214/26112. Five online databases were searched, and 1,217 citations, published between January 2000 and May 2022, were obtained, and screened in duplicate for English language and in-silico research, covering the use of machine learning to identify susceptible species to viruses.

Results

Fifty-three relevant publications were identified for data charting. The breadth of research was extensive including 32 different machine learning algorithms used in combination with 29 different feature selection methods and 43 different genome data input formats. There were 20 different methods used by authors to assess accuracy. Authors mostly used influenza viruses (n = 31/53 publications, 58.5%), however, more recent publications focused on coronaviruses and other viruses in combination with influenza viruses (n = 22/53, 41.5%). The susceptible animal groups authors most used were humans (n = 57/77 analyses, 74.0%), avian (n = 35/77 45.4%), and swine (n = 28/77, 36.4%). In total, 53 different hosts were used and, in most publications, data from multiple hosts was used.

Discussion

The main gaps in research were a lack of standardized reporting of methodology and the use of broad host categories for classification. Overall, approaches to viral host identification using machine learning were diverse and extensive.

Integrating Blood Biomarkers and Marine Brown Algae-Derived Inhibitors in Parkinson's Disease: A Multi-scale Approach from Interactomics to Quantum Mechanics

Parkinson's disease (PD) involves alpha-synuclein accumulation according to Braak's pattern, with diverse clinical progressions that complicate diagnosis and treatment. We aimed to correlate Braak's pattern with rapid progressive PD to identify blood-based biomarkers and therapeutic targets exploiting brown algae-derived bioactives for potential treatment. We implemented a systematic workflow of transcriptomic profiling, co-expression networks, cluster profiling, transcriptional regulator identification, molecular docking, quantum calculations, and dynamic simulations. The transcriptomic analyses exhibited highly expressed genes at each Braak's stage and in rapidly progressive PD. Co-expression networks for Braak's stages were built, and the top five clusters from each stage displayed significant overlap with differentially expressed genes in rapidly progressive PD, indicating shared biomarkers between the blood and the PD brain. Further investigation showed, NF-kappa-B p105 as the master transcriptional regulator of these biomarkers. Molecular docking screened phlorethopentafuhalol-A from brown algae, exhibiting a superior inhibitory effect with p105 (- 7.51 kcal/mol) by outperforming PD drugs and anti-inflammatory compounds (- 5.73 to - 4.38 kcal/mol). Quantum mechanics and molecular mechanics (QM/MM) calculations and dynamic simulations have confirmed the interactive stability of phlorethopentafuhalol-A with p105. Overall, our combined computational study shows that phlorethopentafuhalol-A derived from brown algae, may have healing properties that could help treat PD.

Multioviz: an interactive platform for in silico perturbation and interrogation of gene regulatory networks

In this paper, we aim to build a platform that will help bridge the gap between high-dimensional computation and wet-lab experimentation by allowing users to interrogate genomic signatures at multiple molecular levels and identify best next actionable steps for downstream decision making. We introduce Multioviz: a publicly accessible R package and web application platform to easily perform in silico hypothesis testing of generated gene regulatory networks. We demonstrate the utility of Multioviz by conducting an end-to-end analysis in a statistical genetics application focused on measuring the effect of in silico perturbations of complex trait architecture. By using a real dataset from the Wellcome Trust Centre for Human Genetics, we both recapitulate previous findings and propose hypotheses about the genes involved in the percentage of immune CD8+ cells found in heterogeneous stocks of mice. Source code for the Multioviz R package is available at https://github.com/lcrawlab/multio-viz and an interactive version of the platform is available at https://multioviz.ccv.brown.edu/ .

Bioinformatics and molecular biology tools for diagnosis, prevention, treatment and prognosis of COVID-19

Since December 2019, a new form of Severe Acute Respiratory Syndrome (SARS) has emerged worldwide, caused by SARS coronavirus 2 (SARS-CoV-2). This disease was called COVID-19 and was declared a pandemic by the World Health Organization in March 2020. Symptoms can vary from a common cold to severe pneumonia, hypoxemia, respiratory distress, and death. During this period of world stress, the medical and scientific community were able to acquire information and generate scientific data at unprecedented speed, to better understand the disease and facilitate vaccines and therapeutics development. Notably, bioinformatics tools were instrumental in decoding the viral genome and identifying critical targets for COVID-19 diagnosis and therapeutics. Through the integration of omics data, bioinformatics has also improved our understanding of disease pathogenesis and virus-host interactions, facilitating the development of targeted treatments and vaccines. Furthermore, molecular biology techniques have accelerated the design of sensitive diagnostic tests and the characterization of immune responses, paving the way for precision medicine approaches in treating COVID-19. Our analysis highlights the indispensable contributions of bioinformatics and molecular biology to the global effort against COVID-19. In this review, we aim to revise the COVID-19 features, diagnostic, prevention, treatment options, and how molecular biology, modern bioinformatic tools, and collaborations have helped combat this pandemic. An integrative literature review was performed, searching articles on several sites, including PUBMED and Google Scholar indexed in referenced databases, prioritizing articles from the last 3 years. The lessons learned from this COVID-19 pandemic will place the world in a much better position to respond to future pandemics.

Toward Personalized Salbutamol Therapy: Validating Virtual Patient-Derived Population Pharmacokinetic Model with Real-World Data

Interindividual variability, influenced by patient-specific factors including age, weight, gender, race, and genetics, among others, contributes to variations in therapeutic response. Population pharmacokinetic (popPK) modeling is an essential tool for pinpointing measurable factors affecting dose-concentration relationships and tailoring dosage regimens to individual patients. Herein, we developed a popPK model for salbutamol, a short-acting β2-agonist (SABA) used in asthma treatment, to identify key patient characteristics that influence treatment response. To do so, synthetic data from physiologically-based pharmacokinetic (PBPK) models was employed, followed by an external validation using real patient data derived from an equivalent study. Thirty-two virtual patients were included in this study. A two-compartment model, with first-order absorption (no delay), and linear elimination best fitted our data, according to diagnostic plots and selection criteria. External validation demonstrated a strong agreement between individual predicted and observed values. The incorporation of covariates into the basic structural model identified a significant impact of age on clearance (Cl) and intercompartmental clearance (Q); gender on Cl and the constant rate of absorption (ka); race on Cl; and weight on Cl in the volume of distribution of the peripheral compartment (V2). This study addresses critical challenges in popPK modeling, particularly data scarcity, incompleteness, and homogeneity, in traditional clinical trials, by leveraging synthetic data from PBPK modeling. Significant associations between individual characteristics and salbutamol's PK parameters, here uncovered, highlight the importance of personalized therapeutic regimens for optimal treatment outcomes.

Berberine modulates cardiovascular diseases as a multitarget-mediated alkaloid with insights into its downstream signals using in silico prospective screening approaches

Atherosclerosis is potentially correlated with several cardiac disorders that are greatly associated with cellular oxidative stress generation, inflammation, endothelial cells dysfunction, and many cardiovascular complications. Berberine is a natural isoquinoline alkaloid compound that widely modulates pathogenesis of atherosclerosis through its different curative potentials. This in silico screening study was designed to confirm the potent restorative properties of berberine chloride as a multitarget-mediated alkaloid against the CVDs and their complications through screening, identifying, visualizing, and evaluating its binding models, affinities, and interactions toward several CVDs-related targets as direct and/or indirect-mediated signals via inhibiting cellular ER stress and apoptotic signals and activating autophagy pathway. The drug-likeness properties of berberine were predicted using the computational QSAR/ADMET and Lipinski's RO5 analyses as well as in silico molecular docking simulations. The potent berberine-binding modes, residues-interaction patterns, and free energies of binding scores towards several CVDs-related targets were estimated using molecular docking tools. Furthermore, the pharmacokinetic properties and toxicological features of berberine were clearly determined. According to this in silico virtual screening study, berberine chloride could restore cardiac function and improve pathogenic features of atherosclerotic CVDs through alleviating ER stress and apoptotic signals, activating autophagy, improving insulin sensitivity, decreasing hyperglycemia and dyslipidemia, increasing intracellular RCT signaling, attenuating oxidative stress and vascular inflammation, and upregulating cellular antioxidant defenses in many cardiovascular tissues. In this in silico study, berberine chloride greatly modulated several potent CVDs-related targets, including SIGMAR1, GRP78, CASP3, BECN1, PIK3C3, SQSTM1/p62, LC3B, GLUT3, INSR, LDLR, LXRα, PPARγ, IL1β, IFNγ, iNOS, COX-2, MCP-1, IL10, GPx1, and SOD3.

In silico and in vivo evaluation of the anti-cryptosporidial activity of eugenol

Background

Cryptosporidiosis is an opportunistic parasitic disease widely distributed worldwide. Although Cryptosporidium sp. causes asymptomatic infection in healthy people, it may lead to severe illness in immunocompromised individuals. Limited effective therapeutic alternatives are available against cryptosporidiosis in this category of patients. So, there is an urgent need for therapeutic alternatives for cryptosporidiosis. Recently, the potential uses of Eugenol (EUG) have been considered a promising novel treatment for bacterial and parasitic infections. Consequently, it is suggested to investigate the effect of EUG as an option for the treatment of cryptosporidiosis.

Materials and methods

The in silico bioinformatics analysis was used to predict and determine the binding affinities and intermolecular interactions of EUG and Nitazoxanide (NTZ) toward several Cryptosporidium parvum (C. parvum) lowa II target proteins. For animal study, five groups of immunosuppressed Swiss albino mice (10 mice each) were used. Group I was left uninfected (control), and four groups were infected with 1,000 oocysts of Cryptosporidium sp. The first infected group was left untreated. The remaining three infected groups received NTZ, EUG, and EUG + NTZ, respectively, on the 6th day post-infection (dpi). All mice were sacrificed 30 dpi. The efficacy of the used formulas was assessed by counting the number of C. parvum oocysts excreted in stool of infected mice, histopathological examination of the ileum and liver tissues and determination of the expression of iNOS in the ileum of mice in different animal groups.

Results

treatment with EUG resulted in a significant reduction in the number of oocysts secreted in stool when compared to infected untreated mice. In addition, oocyst excretion was significantly reduced in mice received a combination therapy of EUG and NTZ when compared with those received NTZ alone. EUG succeeded in reverting the histopathological alterations induced by Cryptosporidium infection either alone or in combination with NTZ. Moreover, mice received EUG showed marked reduction of the expression of iNOS in ileal tissues.

Conclusion

Based on the results, the present study signified a basis for utilizing EUG as an affordable, safe, and alternative therapy combined with NTZ in the management of cryptosporidiosis.

Antifibrotic effect of silymarin on arecoline-induced fibrosis in primary human buccal fibroblasts: an in silico and in vitro analysis

BACKGROUND

This study aimed to assess silymarin's anticancer and antifibrotic potential through in silico analysis and investigate its impact on in vitro arecoline-induced fibrosis in primary human buccal fibroblasts (HBF).

METHODS & RESULTS

The study utilized iGEMDOCK for molecular docking, evaluating nine bioflavonoids, and identified silymarin and baicalein as the top two compounds with the highest target affinity, followed by subsequent validation through a 100ns Molecular Dynamic Simulation demonstrating silymarin's stable behavior with Transforming Growth Factor Beta. HBF cell lines were developed from tissue samples obtained from patients undergoing third molar extraction. Arecoline, a known etiological factor in oral submucous fibrosis (OSMF), was employed to induce fibrogenesis in these HBFs. The inhibitory concentration (IC50) of arecoline was determined using the MTT assay, revealing dose-dependent cytotoxicity of HBFs to arecoline, with notable cytotoxicity observed at concentrations exceeding 50µM. Subsequently, the cytotoxicity of silymarin was assessed at 24 and 72 h, spanning concentrations from 5µM to 200µM, and an IC50 value of 143µM was determined. Real-time polymerase chain reaction (qPCR) was used to analyze the significant downregulation of key markers including collagen, epithelial-mesenchymal transition (EMT), stem cell, hypoxia, angiogenesis and stress markers in silymarin-treated arecoline-induced primary buccal fibroblast cells.

CONCLUSION

Silymarin effectively inhibited fibroblast proliferation and downregulated genes associated with cancer progression and EMT pathway, both of which are implicated in malignant transformation. To our knowledge, this study represents the first exploration of silymarin's potential as a novel therapeutic agent in an in vitro model of OSMF.

Quantum biochemical analysis of the binding interactions between a potential inhibitory drug and the Ebola viral glycoprotein

Ebola virus disease (EVD) causes outbreaks and epidemics in West Africa that persist until today. The envelope glycoprotein of Ebola virus (GP) consists of two subunits, GP1 and GP2, and plays a key role in anchoring or fusing the virus to the host cell in its active form on the virion surface. Toremifene (TOR) is a ligand that mainly acts as an estrogen receptor antagonist; however, a recent study showed a strong and efficient interaction with GP. In this context, we aimed to evaluate the energetic affinity features involved in the interaction between GP and toremifene by computer simulation techniques using the Molecular Fractionation Method with Conjugate Caps (MFCC) scheme and quantum-mechanical (QM) calculations, as well as missense mutations to assess protein stability. We identified ASP522, GLU100, TYR517, THR519, LEU186, LEU515 as the most attractive residues in the EBOV glycoprotein structure that form the binding pocket. We divided toremifene into three regions and evaluated that region i was more important than region iii and region ii for the formation of the TOR-GP1/GP2 complex, which might control the molecular remodeling process of TOR. The mutations that caused more destabilization were ARG134, LEU515, TYR517 and ARG559, while those that caused stabilization were GLU523 and ASP522. TYR517 is a critical residue for the binding of TOR, and is highly conserved among EBOV species. Our results may help to elucidate the mechanism of drug action on the GP protein of the Ebola virus and subsequently develop new pharmacological approaches against EVD.Communicated by Ramaswamy H. Sarma.

Identification of starvation-mimetic bioactive phytocomponent from Withania somnifera using in-silico molecular modelling and flow cytometry-based analysis for the management of malaria

Multidrug resistance episodes in malaria increased from 3.9% to 20% from 2015 to 2019. Synchronizing the clinical manifestation in chronological sequence led to a unique impression on glucose demand (increased up to 100-fold) by the parasite-infected RBCs. Hence, restriction in the glucose uptake to parasite-infected RBCs could be an alternative approach to conquer the global burden of malaria to a greater extent. A C28 steroidal lactone Withaferin A (WS-3) isolated from Withania somnifera leave extract shows better thermodynamically stable interactions with the glucose transporters (GLUT-1 and PfHT) to standard drugs metformin and lopinavir. MD simulations for a trajectory period of 100 ns reflect stable interactions with the interactive amino acid residues such as Pro141, Gln161, Gln282, Gln283, Trp388, Phe389, and Phe40, Asn48, Phe85, His168, Gln169, Asn311 which potentiating inhibitory activity of WS-3 against GLUT-1 and PfHT respectively. WS-3 was non-hemotoxic (%hemolysis <5%) for a high concentration of up to 1 mg/ml in the physiological milieu. However, the %hemolysis significantly increased up to 30.55 ± 0.929% in a parasitophorous simulated environment (pH 5.0). Increased hemolysis of WS-3 could be due to the production of ROS in an acidic environment. Further, the inhibitory activity of WS-3 against both glucose transporters was supported with flow cytometry-based analysis of parasite-infected RBCs. Results show that WS-3 has low mean fluorescence intensities for both target proteins compared to conventional drugs, suggesting a potential sugar transporter inhibitor against GLUT-1 and PfHT for managing malaria. Communicated by Ramaswamy H. Sarma.

An Efficient Synthesis of 1-(1,3-Dioxoisoindolin-2-yl)-3-aryl Urea Analogs as Anticancer and Antioxidant Agents: An Insight into Experimental and In Silico Studies

The present investigation reports the efficient multistep synthesis of 1-(1,3-dioxoisoindolin-2-yl)-3-aryl urea analogs (7a-f) in good yields. All the 1-(1,3-dioxoisoindolin-2-yl)-3-aryl urea analogs (7a-f) were characterized by spectroscopic techniques. Five among the six compounds were tested against 56 cancer cell lines at 10 µM as per the standard protocol. 1-(4-Bromophenyl)-3-(1,3-dioxoisoindolin-2-yl)urea (7c) exhibited moderate but significant anticancer activity against EKVX, CAKI-1, UACC-62, MCF7, LOX IMVI, and ACHN with percentage growth inhibitions (PGIs) of 75.46, 78.52, 80.81, 83.48, 84.52, and 89.61, respectively. Compound 7c was found to exhibit better anticancer activity than thalidomide against non-small cell lung, CNS, melanoma, renal, prostate, and breast cancer cell lines. It was also found to exhibit superior anticancer activity against melanoma cancer compared to imatinib. Among the tested compounds, the 4-bromosubstitution (7c) on the phenyl ring demonstrated good anticancer activity. Docking scores ranging from -6.363 to -7.565 kcal/mol were observed in the docking studies against the molecular target EGFR. The ligand 7c displayed an efficient binding against the EGFR with a docking score of -7.558 kcal/mol and displayed an H-bond interaction with Lys745 and the carbonyl functional group. Compound 7c demonstrated a moderate inhibition of EGFR with an IC50 of 42.91 ± 0.80 nM, in comparison to erlotinib (IC50 = 26.85 ± 0.72 nM), the standard drug. The antioxidant potential was also calculated for the compounds (7a-f), which exhibited good to low activity. 1-(2-Methoxyphenyl)-3-(1,3-dioxoisoindolin-2-yl)urea (7f) and 1-(4-Methoxyphenyl)-3-(1,3-dioxoisoindolin-2-yl)urea (7d) demonstrated significant antioxidant activity with IC50 values of 15.99 ± 0.10 and 16.05 ± 0.15 µM, respectively. The 2- and 4-methoxysubstitutions on the N-phenyl ring showed good antioxidant activity among the series of compounds (7a-f). An in silico ADMET prediction studies showed the compounds' adherence to Lipinski's rule of five: they were free from toxicities, including mutagenicity, cytotoxicity, and immunotoxicity, but not for hepatotoxicity. The toxicity prediction demonstrated LD50 values between 1000 and 5000 mg/Kg, putting the compounds either in class IV or class V toxicity classes. Our findings might create opportunities for more advancements in cancer therapeutics.

A review on in silico prediction of the environmental risks posed by pharmaceutical emerging contaminants

Computer-aided (in silico) prediction has shown good potential to support the environmental risk assessment (ERA) of pharmaceutical emerging contaminants (PECs), allowing low-cost, animal-free, high-throughput screening of multiple potential risks posed by a wide variety of pharmaceuticals in the environment based on insufficient toxicity data. This review provided recent insights regarding the application of in silico approaches in prediction for environmental risks of PECs. Based on the review of 20 included articles from 8 countries published since 2018, we found that the researchers' interest and concern in this research topic were sharply aroused since 2021. Recently, in silico approaches have been widely used for the prediction of bioaccumulation and biodegradability, lethal endpoints, developmental toxicity, mutagenicity, other eco-toxicological effects such as ototoxicity and hematological toxicity, and human health hazards of exposure to PECs. Particular attention has been given to the simultaneous discernment of multiple environmental risks and health effects of PECs based on mechanistic data of pharmaceuticals using advanced bioinformatic methods such as transcriptomic analysis and network pharmacology prediction. In silico software platforms and databases used in the included studies were diversified, and there is currently no standardized and accepted in silico model for ERA of PECs. Date suggested that in silico prediction of the environmental risks posed by PECs is still in its infancy. Considerable critical challenges need to be addressed, including consideration of environmental exposure concentration for PECs, interactions among mixtures of PECs and other contaminants coexisting in environments, and development of in silico models specific to ERA of PECs.

Computational Protein Design for COVID-19 Research and Emerging Therapeutics

As the world struggles with the ongoing COVID-19 pandemic, unprecedented obstacles have continuously been traversed as new SARS-CoV-2 variants continually emerge. Infectious disease outbreaks are unavoidable, but the knowledge gained from the successes and failures will help create a robust health management system to deal with such pandemics. Previously, scientists required years to develop diagnostics, therapeutics, or vaccines; however, we have seen that, with the rapid deployment of high-throughput technologies and unprecedented scientific collaboration worldwide, breakthrough discoveries can be accelerated and insights broadened. Computational protein design (CPD) is a game-changing new technology that has provided alternative therapeutic strategies for pandemic management. In addition to the development of peptide-based inhibitors, miniprotein binders, decoys, biosensors, nanobodies, and monoclonal antibodies, CPD has also been used to redesign native SARS-CoV-2 proteins and human ACE2 receptors. We discuss how novel CPD strategies have been exploited to develop rationally designed and robust COVID-19 treatment strategies.

Structure-Activity Relationship of Methyl 4-Aminobenzoate Derivatives as Being Drug Candidate Targeting Glutathione Related Enzymes: in Vitro and in Silico Approaches

A thiol compound, glutathione, is essential for healthy cell defence against xenobiotics and oxidative stress. Glutathione reductase (GR) and glutathione S-transferase (GST) are two glutathione-related enzymes that function in the antioxidant and the detoxification systems. In this study, potential inhibitory effects of methyl 4-aminobenzoate derivatives on GR and GST were examined in vitro. GR and GST were isolated from human erythrocytes with 7.63 EU/mg protein and 5.66 EU/mg protein specific activity, respectively. It was found that compound 1 (methyl 4-amino-3-bromo-5-fluorobenzoate with K_i value of 0.325±0.012 μM) and compound 5 (methyl 4-amino-2-nitrobenzoate with K_i value of 92.41±22.26 μM) inhibited GR and GST stronger than other derivatives. Furthermore, a computer-aided method was used to predict the binding affinities of derivatives, ADME characteristics, and toxicities. Derivatives 4 (methyl 4-amino-2-bromobenzoate) and 6 (methyl 4-amino-2-chlorobenzoate) were estimated to have the lowest binding energies into GR and GST receptors, respectively according to results of in silico studies.

In-silico targeting TMPK from monkey pox virus: Molecular docking analysis, density functional theory studies and molecular dynamic simulation analysis

The World Health Organization (WHO) proclaimed the monkeypox epidemic a "public health emergency of worldwide significance" recently. The monkeypox virus is a member of the same Orthopoxvirus genus as the smallpox virus. Although smallpox medications are advised against monkeypox, no monkeypox-specific drugs are currently available. In the event of such an outbreak, in-silico medication identification is a practical and efficient strategy. As a result, we report a computational drug repurposing analysis to discover medicines that may be potential inhibitors of thymidylate kinase, a critical monkeypox viral enzyme. The target protein structure of the monkeypox virus was modeled using the vaccinia virus's homologous protein structure. Using molecular docking and density functional theory, we found 11 possible inhibitors of the monkeypox virus from an Asinex library of 261120 chemicals. The primary purpose of this in silico work is to find possible inhibitors of monkeypox viral proteins that can then be experimentally tested in order to develop innovative therapeutic medicines for monkeypox infection.Communicated by Ramaswamy H. Sarma.

Short Antimicrobial Peptides: Therapeutic Potential and Recent Advancements

There has been a lot of interest in antimicrobial peptides (AMPs) as potential next-generation antibiotics. They are components of the innate immune system. AMPs have broad-spectrum action and are less prone to resistance development. They show potential applications in various fields, including medicine, agriculture, and the food industry. However, despite the good activity and safety profiles, AMPs have had difficulty finding success in the clinic due to their various limitations, such as production cost, proteolytic susceptibility, and oral bioavailability. To overcome these flaws, a number of solutions have been devised, one of which is developing short antimicrobial peptides. Short antimicrobial peptides do have an advantage over longer peptides as they are more stable and do not collapse during absorption. They have generated a lot of interest because of their evolutionary success and advantageous properties, such as low molecular weight, selective targets, cell or organelles with minimal toxicity, and enormous therapeutic potential. This article provides an overview of the development of short antimicrobial peptides with an emphasis on those with ≤ 30 amino acid residues as a potential therapeutic agent to fight drug-resistant microorganisms. It also emphasizes their applications in many fields and discusses their current state in clinical trials.

Virtual screening based on the structure of more than 105 compounds against four key proteins of SARS-CoV-2: MPro, SRBD, RdRp, and PLpro

Background

SARS-CoV-2 initially originated in Wuhan (China) around December 2019, and spread all over the world. Currently, WHO (Word Health Organization) has licensed several vaccines for this viral infection. However, not everyone can be vaccinated. People with underlying health conditions that weaken their immune systems or those with severe allergies to some vaccine components, may not be able to be vaccinated. Moreover, no vaccination is 100% safe, and the emergence of new SARS-CoV-2 mutations may reduce the efficacy of immunizations. Therefore, it is urgent to develop effective drugs to protect people against this virus.

Material and method

We performed structure-based virtual screening (SBVS) of a library that was built from ChemDiv and PubChem databases against four SARS-CoV-2 target proteins: S-protein (spike), main protease (M_Pro), RNA-dependent RNA polymerase, and PL_pro. A virtual screening study was performed using PyRx and AutoDock tools.

Results

Our results suggest that twenty-five top-ranked drugs with the highest energy binding as the potential inhibitors against four SARS-CoV-2 targets, relative to the reference molecules. Based on the energy binding, we suggest that these compounds could be used to produce effective anti-viral drugs against SARS-CoV-2.

Conclusion

The discovery of novel compounds for COVID-19 using computer-aided drug discovery tools requires knowledge of the structure of coronavirus and various target proteins of the virus. These compounds should be further assessed in experimental assays and clinical trials to validate their actual activity against the disease. These findings may contribute to the drug design studies against COVID-19.

Comprehensive Highlights of the Universal Efforts towards the Development of COVID-19 Vaccine

The world has taken proactive measures to combat the pandemic since the coronavirus disease 2019 (COVID-19) outbreak, which was caused by severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2). These measures range from increasing the production of personal protective equipment (PPE) and highlighting the value of social distancing to the emergency use authorization (EUA) of therapeutic drugs or antibodies and their appropriate use; nonetheless, the disease is still spreading quickly and is ruining people's social lives, the economy, and public health. As a result, effective vaccines are critical for bringing the pandemic to an end and restoring normalcy in society. Several potential COVID-19 vaccines are now being researched, developed, tested, and reviewed. Since the end of June 2022, several vaccines have been provisionally approved, whereas others are about to be approved. In the upcoming years, a large number of new medications that are presently undergoing clinical testing are anticipated to hit the market. To illustrate the advantages and disadvantages of their technique, to emphasize the additives and delivery methods used in their creation, and to project potential future growth, this study explores these vaccines and the related research endeavors, including conventional and prospective approaches.

A quick scoping review of the first year of vaccination against the COVID-19 pandemic: Do we need more shots or time?

BACKGROUND

The emergence of new severe acute respiratory syndrome coronavirus 2 variants, along with the waning of vaccine-induced immunity, has increased breakthrough infections and urged booster jabs and debates. In the short term, the administration of booster doses has been reported to be safe and enhance severe acute respiratory syndrome coronavirus 2-specific neutralizing antibody levels. However, the effects of these doses on the pandemic trajectory and herd immunity are unclear. There is insufficient evidence that a third booster shot of the coronavirus disease 2019 (COVID-19) vaccine maintains longer immunity and covers new viral variants. The lack of sufficient evidence, combined with the fact that millions of people have not yet received 1 or 2 jabs of the COVID-19 vaccine, has raised concerns regarding the call for booster vaccinations.

METHODS

We conducted a quick scoping review to explore the literature on the need for a booster COVID-19 vaccination from January 1, 2021, to April 30, 2022.

RESULTS

Sixty-one relevant publications were identified, of which 17 were related to waning immunity after 2 doses of the vaccine among the general population or healthcare workers, 19 were related to the third or booster dose of vaccination after the second dose among the general population or healthcare workers, and 25 were related to booster dose among immunocompromised patient.

CONCLUSIONS

Initially, the need for a booster dose was equivocal; however, several studies demonstrated the benefit of the booster dose over time. Adequate scientific information is required regarding the administration of booster doses to the general population as well as the high-risk individuals.

In silico approaches for drug repurposing in oncology: Protocol for a scoping review of existing evidence

Drug repurposing has been applied in the biomedical field to optimize the use of existing drugs, leading to a more efficient allocation of research resources. In oncology, this approach is particularly interesting, considering the high cost related to the discovery of new drugs with therapeutic potential. Computational methods have been applied to predict associations between drugs and their targets. However, drug repurposing has not always been promising and its efficiency has yet to be proven. Therefore, the present scoping review protocol was developed to screen the literature on how in silico strategies can be implemented in drug repurposing in oncology. The scoping review will be conducted according to the Arksey and O'Malley framework (2005) and the Joanna Briggs Institute recommendations. We will search the PubMed/MEDLINE, Embase, Scopus, and Web of Science databases, as well as the grey literature. We will include peer-reviewed research articles involving in silico strategies applied to drug repurposing in oncology, published between January 1, 2003, and December 31, 2021. Data will be charted and findings described according to review questions. We will report the scoping review using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Review guidelines (PRISMA-ScR).

In Silico Studies on Zinc Oxide Based Nanostructured Oil Carriers with Seed Extracts of Nigella sativa and Pimpinella anisum as Potential Inhibitors of 3CL Protease of SARS-CoV-2

Coming into the second year of the pandemic, the acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and its variants continue to be a serious health hazard globally. A surge in the omicron wave, despite the discovery of the vaccines, has shifted the attention of research towards the discovery and use of bioactive compounds, being potential inhibitors of the viral structural proteins. The present study aimed at the green synthesis of zinc oxide (ZnO) nanoparticles with seed extracts of Nigella sativa and Pimpinella anisum—loaded nanostructured oil carriers (NLC)—using a mixture of olive and black seed essential oils. The synthesized ZnO NLC were extensively characterized. In addition, the constituent compounds in ZnO NLC were investigated as a potential inhibitor for the SARS-CoV-2 main protease (3CLpro or Mpro) where 27 bioactive constituents, along with ZnO in the nanostructure, were subjected to molecular docking studies. The resultant high-score compounds were further validated by molecular dynamics simulation. The study optimized the compounds dithymoquinone, δ-hederin, oleuropein, and zinc oxide with high docking energy scores (ranging from −7.9 to −9.9 kcal/mol). The RMSD and RMSF data that ensued also mirrored these results for the stability of proteins and ligands. RMSD and RMSF data showed no conformational change in the protein during the MD simulation. Histograms of every simulation trajectory explained the ligand properties and ligand–protein contacts. Nevertheless, further experimental investigations and validation of the selected candidates are imperative to take forward the applicability of the nanostructure as a potent inhibitor of COVID-19 (Coronavirus Disease 2019) for clinical trials.

Current Artificial Intelligence (AI) Techniques, Challenges, and Approaches in Controlling and Fighting COVID-19: A Review

SARS-CoV-2 (COVID-19) has been one of the worst global health crises in the 21st century. The currently available rollout vaccines are not 100% effective for COVID-19 due to the evolving nature of the virus. There is a real need for a concerted effort to fight the virus, and research from diverse fields must contribute. Artificial intelligence-based approaches have proven to be significantly effective in every branch of our daily lives, including healthcare and medical domains. During the early days of this pandemic, artificial intelligence (AI) was utilized in the fight against this virus outbreak and it has played a major role in containing the spread of the virus. It provided innovative opportunities to speed up the development of disease interventions. Several methods, models, AI-based devices, robotics, and technologies have been proposed and utilized for diverse tasks such as surveillance, spread prediction, peak time prediction, classification, hospitalization, healthcare management, heath system capacity, etc. This paper attempts to provide a quick, concise, and precise survey of the state-of-the-art AI-based techniques, technologies, and datasets used in fighting COVID-19. Several domains, including forecasting, surveillance, dynamic times series forecasting, spread prediction, genomics, compute vision, peak time prediction, the classification of medical imaging-including CT and X-ray and how they can be processed-and biological data (genome and protein sequences) have been investigated. An overview of the open-access computational resources and platforms is given and their useful tools are pointed out. The paper presents the potential research areas in AI and will thus encourage researchers to contribute to fighting against the virus and aid global health by slowing down the spread of the virus. This will be a significant contribution to help minimize the high death rate across the globe.