Stilbene-pyridazinone hybrids: design, synthesis and in vitro antiplatelet activity screening.

A series of stilbene analogues, in which a phenyl ring was replaced by the pyridazin-3(2H)-one nucleus, was designed and synthesized to be explored as platelet aggregation inhibitors. The proposed stilbene-pyridazinone hybrids were successfully obtained from simple starting materials and by Wittig's reaction. Most of the target compounds displayed improved in vitro activity in comparison with the standard drug, resveratrol, highlighting as the most potent the analogues 10d and 10e, with inhibition percentages of 94.15 % at 100 µM and 100 % at 50 µM, respectively. The pharmacokinetic and toxicity (ADME/T) properties of the novel hybrids were also estimated with the SwissADME and ProTox-II web servers.

Drug repurposing for regenerative medicine and cosmetics: Scientific, technological and economic issues.

Regenerative medicine and cosmetics are currently two outstanding fields for drug discovery. Although many pharmaceutical products for regenerative medicine and cosmetics have received approval by official agencies, several challenges are still needed to overcome, especially financial and time issues. As a result, drug repositioning, which is the usage of previously approved drugs for new treatment, stands out as a promising approach to tackle these problems. Recently, increasing scientific evidence is collected to demonstrate the applicability of this novel method in the field of regenerative medicine and cosmetics. Experts in drug development have also taken advantage of novel technologies to discover new candidates for repositioning purposes following computational approach, one of two main approaches of drug repositioning. Therefore, numerous repurposed candidates have obtained approval to enter the market and have witnessed financial success such as minoxidil and fingolimod. The benefits of drug repositioning are undeniable for regenerative medicine and cosmetics. However, some aspects still need to be carefully considered regarding this method including actual effectiveness during clinical trials, patent regulations, data integration and analysis, publicly unavailable databases as well as environmental concerns and more effort are required to overcome these obstacles.

A computational approach to assessing the prognostic implications of BRAF and RAS mutations in patients with papillary thyroid carcinoma.

Papillary thyroid carcinoma (PTC) is the most common thyroid cancer, posing a growing clinical challenge. PTC exhibits two age-related peaks, with established risk factors including family history and radiation exposure. Managing even low-risk, localized PTC cases remain complex, with growing interest in active surveillance as an alternative to immediate surgery. This study employed single-cell RNA sequencing (scRNA-Seq) to explore the predictive value of BRAF and RAS mutations in PTC, shedding light on their impact on disease progression and outcomes. The analyses emphasized the significance of BRAF and RAS mutations in tumor advancement, particularly the unique BRAF V600E mutation associated with aggressive features. The methodology involved scRNA-Seq analysis of PTC and normal samples, unveiling distinct cell clusters and indicating upregulated BRAF and RAS genes. Pathway enrichment analysis highlighted altered biological processes and immune-related pathways in PTC. The study consolidated previous research showing the prevalence of BRAF and RAS mutations in PTC, subtypes with distinct molecular profiles, and the impact of TERT promoter mutations on disease severity. In summary, this study unveils the complex interplay of genetic mutations and the cellular microenvironment in PTC through scRNA-Seq. The upregulated BRAF and RAS genes suggest their roles as PTC drivers, and pathway enrichment reveals alterations in immune-related processes. This synthesis of prior research enhances our understanding of PTC's molecular foundations, informing better prognosis and personalized treatment approaches. These insights advance the landscape of PTC management and provide directions for further research.

Rationalization of a Streptavidin Based Enantioselective Artificial Suzukiase: An Integrative Computational Approach.

An Artificial Metalloenzyme (ArM) built employing the streptavidin-biotin technology has been used for the enantioselective synthesis of binaphthyls by means of asymmetric Suzuki-Miyaura cross-coupling reactions. Despite its success, it remains a challenge to understand how the length of the biotin cofactors or the introduction of mutations to streptavidin leads the preferential synthesis of one atropisomer over the other. In this study, we apply an integrated computational modeling approach, including DFT calculations, protein-ligand dockings and molecular dynamics to rationalize the impact of mutations and length of the biotion cofactor on the enantioselectivities of the biaryl product. The results unravel that the enantiomeric differences found experimentally can be rationalized by the disposition of the first intermediate, coming from the oxidative addition step, and the entrance of the second substrate. The work also showcases the difficulties facing to control the enantioselection when engineering ArM to catalyze enantioselective Suzuki-Miyaura couplings and how the combination of DFT calculations, molecular dockings and MD simulations can be used to rationalize artificial metalloenzymes.

Surrogate indices of insulin resistance using the Matsuda index as reference in adult men-a computational approach.

Background: Overweight and obesity, high blood pressure, hyperglycemia, hyperlipidemia, and insulin resistance (IR) are strongly associated with non-communicable diseases (NCDs), including type 2 diabetes, cardiovascular disease, stroke, and cancer. Different surrogate indices of IR are derived and validated with the euglycemic-hyperinsulinemic clamp (EHC) test. Thus, using a computational approach to predict IR with Matsuda index as reference, this study aimed to determine the optimal cutoff value and diagnosis accuracy for surrogate indices in non-diabetic young adult men. Methods: A cross-sectional descriptive study was carried out with 93 young men (ages 18-31). Serum levels of glucose and insulin were analyzed in the fasting state and during an oral glucose tolerance test (OGTT). Additionally, clinical, biochemical, hormonal, and anthropometric characteristics and body composition (DEXA) were determined. The computational approach to evaluate the IR diagnostic accuracy and cutoff value using difference parameters was examined, as well as other statistical tools to make the output robust. Results: The highest sensitivity and specificity at the optimal cutoff value, respectively, were established for the Homeostasis model assessment of insulin resistance index (HOMA-IR) (0.91; 0.98; 3.40), the Quantitative insulin sensitivity check index (QUICKI) (0.98; 0.96; 0.33), the triglyceride-glucose (TyG)-waist circumference index (TyG-WC) (1.00; 1.00; 427.77), the TyG-body mass index (TyG-BMI) (1.00; 1.00; 132.44), TyG-waist-to-height ratio (TyG-WHtR) (0.98; 1.00; 2.48), waist-to-height ratio (WHtR) (1.00; 1.00; 0.53), waist circumference (WC) (1.00; 1.00; 92.63), body mass index (BMI) (1.00; 1.00; 28.69), total body fat percentage (TFM) (%) (1.00; 1.00; 31.07), android fat (AF) (%) (1.00; 0.98; 40.33), lipid accumulation product (LAP) (0.84; 1.00; 45.49), leptin (0.91; 1.00; 16.08), leptin/adiponectin ratio (LAR) (0.84; 1.00; 1.17), and fasting insulin (0.91; 0.98; 16.01). Conclusions: The computational approach was used to determine the diagnosis accuracy and the optimal cutoff value for IR to be used in preventive healthcare.

Systematic exploration of network pharmacology, in silico modeling and pharmacokinetic profiling for vitamin E in polycystic ovarian syndrome.

Aim: This study seeks to explore the possibility of using vitamin E to alleviate the symptoms of polycystic ovarian syndrome (PCOS). Methods: Various computational methods were employed, including network pharmacology utilizing a compound-target-pathway approach, Swiss ADME, OSIRIS® property explorer, pkCSM, PASS online web resource and MOLINSPIRATION® software. In addition, in silico analysis of vitamin E was performed with ten receptors. Results & discussion: Our findings highlight the diverse potential of vitamin E in alleviating PCOS. The observed influence on hormones is in line with existing PCOS theories regarding cyst development, further enhancing the therapeutic promise of vitamin E. Conclusion: In conclusion, our computational analysis indicates that vitamin E shows potential as a therapeutic agent for alleviating PCOS in adolescents.

Polycystic ovarian syndrome (PCOS) is a common condition that affects many girls and young women during their reproductive years. PCOS can lead to problems with the menstrual cycle, difficulties with blood sugar control and sometimes infertility. Unfortunately, there's no specific treatment for PCOS yet. This study looked at whether vitamin E might be helpful in treating PCOS in young women. To find out, the researchers used advanced computer-based techniques and analyzed how vitamin E interacts with various parts of the body, including proteins and hormones. Vitamin E appears to influence hormones that play a role in PCOS and cyst formation. It may also have anti-inflammatory properties that could help reduce the symptoms of PCOS. In short, this study suggests that vitamin E might be a valuable option for treating PCOS in adolescents. However, further research and clinical trials are needed to confirm these findings before it can become a standard treatment. This research opens up new possibilities for finding effective solutions for PCOS, which can greatly improve the quality of life for many young women.

Prioritization of genes for translation: a computational approach.

INTRODUCTION: Gene identification for genetic diseases is critical for the development of new diagnostic approaches and personalized treatment options. Prioritization of gene translation is an important consideration in the molecular biology field, allowing researchers to focus on the most promising candidates for further investigation. AREAS COVERED: In this paper, we discussed different approaches to prioritize genes for translation, including the use of computational tools and machine learning algorithms, as well as experimental techniques such as knockdown and overexpression studies. We also explored the potential biases and limitations of these approaches and proposed strategies to improve the accuracy and reliability of gene prioritization methods. Although numerous computational methods have been developed for this purpose, there is a need for computational methods that incorporate tissue-specific information to enable more accurate prioritization of candidate genes. Such methods should provide tissue-specific predictions, insights into underlying disease mechanisms, and more accurate prioritization of genes. EXPERT OPINION: Using advanced computational tools and machine learning algorithms to prioritize genes, we can identify potential targets for therapeutic intervention of complex diseases. This represents an up-and-coming method for drug development and personalized medicine.

Unraveling the complexity of vascular tone regulation: a multiscale computational approach to integrating chemo-mechano-biological pathways with cardiovascular biomechanics.

Vascular tone regulation is a crucial aspect of cardiovascular physiology, with significant implications for overall cardiovascular health. However, the precise physiological mechanisms governing smooth muscle cell contraction and relaxation remain uncertain. The complexity of vascular tone regulation stems from its multiscale and multifactorial nature, involving global hemodynamics, local flow conditions, tissue mechanics, and biochemical pathways. Bridging this knowledge gap and translating it into clinical practice presents a challenge. In this paper, a computational model is presented to integrate chemo-mechano-biological pathways with cardiovascular biomechanics, aiming to unravel the intricacies of vascular tone regulation. The computational framework combines an algebraic description of global hemodynamics with detailed finite element analyses at the scale of vascular segments for describing their passive and active mechanical response, as well as the molecular transport problem linked with chemo-biological pathways triggered by wall shear stresses. Their coupling is accounted for by considering a two-way interaction. Specifically, the focus is on the role of nitric oxide-related molecular pathways, which play a critical role in modulating smooth muscle contraction and relaxation to maintain vascular tone. The computational framework is employed to examine the interplay between localized alterations in the biomechanical response of a specific vessel segment-such as those induced by calcifications or endothelial dysfunction-and the broader global hemodynamic conditions-both under basal and altered states. The proposed approach aims to advance our understanding of vascular tone regulation and its impact on cardiovascular health. By incorporating chemo-mechano-biological mechanisms into in silico models, this study allows us to investigate cardiovascular responses to multifactorial stimuli and incorporate the role of adaptive homeostasis in computational biomechanics frameworks.

Computational and Experimental Approaches Exploring the Role of Hesperetin in Improving Autophagy in Rat Diabetic Retinopathy.

Diabetic retinopathy (DR) is a debilitating diabetic disorder of the retinal microvasculature and the main cause of avoidable blindness in old people. Hesperetin is a plant flavanone largely abundant in citrus species with neuroprotective properties in animal models. This study aimed to explore the neuroprotective and autophagy-enhancing effect of hesperetin in rats with DR. Twenty-four male rats were utilized and allocated to groups: (i) the vehicle group, (ii) DR group and (iii-iv) the DR + hesperetin (50 and 100 mg/kg) groups. Treatment with hesperetin continued for 6 weeks. After the rats were euthanized, their eyes were dissected to detect the biochemical and histological changes in the retinas. Quantification of autophagy markers, beclin 1/LC3/p62, and inflammation markers was performed. Histopathologic changes were investigated after staining with hematoxylin and eosin and periodic acid-Schiff (PAS). Results demonstrated that hesperetin decreased the PAS staining in diabetic rats and attenuated histopathological changes and restored retinal organization and thickness of layers in hematoxylin and eosin staining. Moreover, hesperetin reduced the level of mRNA expression for TNF-α (4.9-fold), IL-1ß (4.15-fold), IL-6 (4.6-fold) and NFκB (5.2-fold), as well as the protein level. This was accompanied by induction of autophagy proteins, beclin 1 and LC3-II. Our results afford evidence that hesperetin is effective in alleviating the pathology of DR via suppressing the inflammatory burden and induction of autophagy. After extensive clinical examinations, hesperetin may prove to be a useful option for treatment of DR.

Genomics and Drug Discovery Strategies: The Role of Natural Compounds and Its Receptor in Alzheimer's Disease.

Alzheimer's Disease (AD) is a special class of neurodegenerative diseases demarcated as a progressive disorder affecting especially older adults globally. The AD-infected brain shows declination in cognitive functions, memory loss, and other exhausting symptoms. In this study, we focused on using advanced bioinformatics and next-generation sequencing to explore essential clusters of genes from various diversified Alzheimer's, Parkinson and Frontotemporal Dementia diseased cases. The significant differential expression analysis of genes (p-value ≤ 0.05, log fold change ≤ 0.05) was carried out, followed by meta-analysis, which resulted in the identification of 20 conserved genes across variable case studies. Out of 20 conserved genes, CASP8 and PTPN11 were observed to show essential regulatory mechanisms in AD metabolic pathways and proceeded further for docking analysis. Moreover, the natural compounds were screened for ligand library preparation based on extensive scientific literature and (ADMET (Absorption, Distribution, Metabolism, Excretion, and Toxicity)) property check. Molecular docking was carried out with screened ligands and target receptors, resulting in the identification of Rosmarinic acid (RA) with CASP8 having docked score (∆G = -8.0 kcal/mol); Donepezil (FDA drug) dock score (∆G = -7.3 kcal/mol) (control). PTPN11 receptor with Carnosol ligand resulted in docking score (∆G = -9.1 kcal/mol) w.r.t Tacrine (FDA drug) docked score (∆G = -8.0 kcal/mol) followed by MD simulation. This research will aid in the identification of potential natural compounds that future researchers can use for further validation as a potential candidate drug in combating various neurodegenerative diseases highlighting AD.

Computational Study of Antimicrobial Peptides for Promising Therapeutic Applications Against Methicillin-resistant Staphylococcus Aureus.

BACKGROUND: Methicillin-resistant Staphylococcus aureus (MRSA) is a causative agent for multiple drug-resistant diseases and is a prime health concern. Currently, antibiotics like vancomycin, daptomycin, fluoroquinolones, linezolid, fifth-generation cephalosporin and others are available in the market for the treatment of MRSA infection. METHODS: With the increasing prevalence of drug-resistant cases, researchers are actively investigating alternative strategies to combat MRSA, including the exploration of peptide therapeutics. This study employed computational methods to prospect for potential Antimicrobial Peptides (AMPs). RESULTS: A total of One hundred and fifty antimicrobial peptides were explored based on physicochemical properties. The results showed that Clavanin B was the most appropriate candidate. Molecular Docking and Molecular Dynamics Simulation results showed the protein-peptide interaction of the MRSA target proteins, Penicillin Binding Protein 2a and Panton-Valentine Leukocidin Toxin, with the Antimicrobial Peptide Clavanin B. CONCLUSION: Currently, the antimicrobial peptide database highlights Clavanin B's role as an anti-HIV peptide. Moreover, this investigation proposes Clavanin B as a viable repurposed drug for treating MRSA, underscoring its potential deployment in the management of MRSA infections.

Structure-based drug discovery to identify SARS-CoV2 spike protein-ACE2 interaction inhibitors.

After the emergence of the COVID-19 pandemic in late 2019, the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) has undergone a dynamic evolution driven by the acquisition of genetic modifications, resulting in several variants that are further classified as variants of interest (VOIs), variants under monitoring (VUM) and variants of concern (VOC) by World Health Organization (WHO). Currently, there are five SARS-CoV-2 VOCs (Alpha, Beta, Delta, Gamma and Omicron), two VOIs (Lambda and Mu) and several other VOIs that have been reported globally. In this study, we report a natural compound, Curcumin, as the potential inhibitor to the interactions between receptor binding domain (RBD(S1)) and human angiotensin-converting enzyme 2 (hACE2) domains and showcased its inhibitory potential for the Delta and Omicron variants through a computational approach by implementing state of the art methods. The study for the first time revealed a higher efficiency of Curcumin, especially for hindering the interaction between RBD(S1) and hACE-2 domains of Delta and Omicron variants as compared to other lead compounds. We investigated that the mutations in the RBD(S1) of VOC especially Delta and Omicron variants affect its structure compared to that of the wild type and other variants and therefore altered its binding to the hACE2 receptor. Molecular docking and molecular dynamics (MD) simulation analyses substantially supported the findings in terms of the stability of the docked complexes. This study offers compelling evidence, warranting a more in-depth exploration into the impact of these alterations on the binding of identified drug molecules with the Spike protein. Further investigation into their potential therapeutic effects in vivo is highly recommended.Communicated by Ramaswamy H. Sarma.

PanDelos-frags: A methodology for discovering pangenomic content of incomplete microbial assemblies.

Pangenomics was originally defined as the problem of comparing the composition of genes into gene families within a set of bacterial isolates belonging to the same species. The problem requires the calculation of sequence homology among such genes. When combined with metagenomics, namely for human microbiome composition analysis, gene-oriented pangenome detection becomes a promising method to decipher ecosystem functions and population-level evolution. Established computational tools are able to investigate the genetic content of isolates for which a complete genomic sequence is available. However, there is a plethora of incomplete genomes that are available on public resources, which only a few tools may analyze. Incomplete means that the process for reconstructing their genomic sequence is not complete, and only fragments of their sequence are currently available. However, the information contained in these fragments may play an essential role in the analyses. Here, we present PanDelos-frags, a computational tool which exploits and extends previous results in analyzing complete genomes. It provides a new methodology for inferring missing genetic information and thus for managing incomplete genomes. PanDelos-frags outperforms state-of-the-art approaches in reconstructing gene families in synthetic benchmarks and in a real use case of metagenomics. PanDelos-frags is publicly available at https://github.com/InfOmics/PanDelos-frags.

Antiviral Peptides Derived from Plants: Their Designs and Functions.

In recent years, plant-derived bioactive compounds have been developed as antiviral agents. Plants synthesize a variety of compounds, especially peptides, which possess antimicrobial activity. Current studies have shown that some antimicrobial peptides have antiviral activity against a wide range of human DNA and RNA viruses and play an effective role in the treatment of human viral diseases. These peptides act through different mechanisms. They can integrate into the envelope of the target virus or cell membrane of the host, resulting in an unstable membrane. For instance, some peptides prevent the attachment of viral spike proteins to host cells. On the other hand, some peptides may alter the cellular pathways, including DNA replication or protein synthesis, leading to the suppression of viral infection. However, the antiviral activity of peptides can be affected by their chemical and structural properties. In several studies, the properties of antimicrobial (antiviral) peptides were altered by minor modifications, but these changes require tools to predict. Recently, computational approaches have been introduced to analyze the effects of structural modifications on the physicochemical properties, mechanism of action, stability, and activity of peptides. In this mini-review, we will describe the design and function of antiviral peptides derived from plants.

Computational Approaches: A New Frontier in Cancer Research.

Cancer is a broad category of disease that can start in virtually any organ or tissue of the body when aberrant cells assault surrounding organs and proliferate uncontrollably. According to the most recent statistics, cancer will be the cause of 10 million deaths worldwide in 2020, accounting for one death out of every six worldwide. The typical approach used in anti-cancer research is highly time-consuming and expensive, and the outcomes are not particularly encouraging. Computational techniques have been employed in anti-cancer research to advance our understanding. Recent years have seen a significant and exceptional impact on anticancer research due to the rapid development of computational tools for novel drug discovery, drug design, genetic studies, genome characterization, cancer imaging and detection, radiotherapy, cancer metabolomics, and novel therapeutic approaches. In this paper, we examined the various subfields of contemporary computational techniques, including molecular docking, artificial intelligence, bioinformatics, virtual screening, and QSAR, and their applications in the study of cancer.

A review of SARS-CoV-2 drug repurposing: databases and machine learning models.

The emergence of Severe Acute Respiratory Syndrome Corona Virus 2 (SARS-CoV-2) posed a serious worldwide threat and emphasized the urgency to find efficient solutions to combat the spread of the virus. Drug repurposing has attracted more attention than traditional approaches due to its potential for a time- and cost-effective discovery of new applications for the existing FDA-approved drugs. Given the reported success of machine learning (ML) in virtual drug screening, it is warranted as a promising approach to identify potential SARS-CoV-2 inhibitors. The implementation of ML in drug repurposing requires the presence of reliable digital databases for the extraction of the data of interest. Numerous databases archive research data from studies so that it can be used for different purposes. This article reviews two aspects: the frequently used databases in ML-based drug repurposing studies for SARS-CoV-2, and the recent ML models that have been developed for the prospective prediction of potential inhibitors against the new virus. Both types of ML models, Deep Learning models and conventional ML models, are reviewed in terms of introduction, methodology, and its recent applications in the prospective predictions of SARS-CoV-2 inhibitors. Furthermore, the features and limitations of the databases are provided to guide researchers in choosing suitable databases according to their research interests.

Corrigendum: Identification of circRNA biomarker for gastric cancer through integrated analysis.

[This corrects the article DOI: 10.3389/fmolb.2022.857320.].

Predictions of the Biological Effects of Several Acyclic Monoterpenes as Chemical Constituents of Essential Oils Extracted from Plants.

Acyclic terpenes are biologically active natural products having applicability in medicine, pharmacy, cosmetics and other practices. Consequently, humans are exposed to these chemicals, and it is necessary to assess their pharmacokinetics profiles and possible toxicity. The present study considers a computational approach to predict both the biological and toxicological effects of nine acyclic monoterpenes: beta-myrcene, beta-ocimene, citronellal, citrolellol, citronellyl acetate, geranial, geraniol, linalool and linalyl acetate. The outcomes of the study emphasize that the investigated compounds are usually safe for humans, they do not lead to hepatotoxicity, cardiotoxicity, mutagenicity, carcinogenicity and endocrine disruption, and usually do not have an inhibitory potential against the cytochromes involved in the metabolism of xenobiotics, excepting CYP2B6. The inhibition of CYP2B6 should be further analyzed as this enzyme is involved in both the metabolism of several common drugs and in the activation of some procarcinogens. Skin and eye irritation, toxicity through respiration and skin-sensitization potential are the possible harmful effects revealed by the investigated compounds. These outcomes underline the necessity of in vivo studies regarding the pharmacokinetics and toxicological properties of acyclic monoterpenes so as to better establish the clinical relevance of their use.