Synthesis and antiproliferative evaluation of novel 3,5,8-trisubstituted coumarins against breast cancer.

Aim: This study focused on designing and synthesizing novel derivatives of 3,5,8-trisubstituted coumarin. Results: The synthesized compounds, particularly compound 5, exhibited significant cytotoxic effects on MCF-7 cells, surpassing staurosporine, and reduced toxicity toward MCF-10A cells, highlighting potential pharmacological advantages. Further, compound 5 altered the cell cycle and significantly increased apoptosis in MCF-7 cells, involving both early (41.7-fold) and late stages (33-fold), while moderately affecting necrotic signaling. The antitumor activity was linked to a notable reduction (4.78-fold) in topoisomerase IIß expression. Molecular modeling indicated compound 5's strong affinity for EGFR, human EGF2 and topoisomerase II proteins. Conclusion: These findings highlight compound 5 as a multifaceted antitumor agent for breast cancer.

Computational Exploration of Naturally Occurring Flavonoids as TGF-ß Inhibitors in Breast Cancer: Insights from Docking and Molecular Dynamics Simulations and In-vitro Cytotoxicity Study.

Breast cancer is a global health concern, demanding innovative treatments. Targeting the Transforming Growth Factor-beta (TGF-ß) signaling pathway, pivotal in breast cancer, is a promising approach. TGF-ß inhibits proliferation via G1 phase cell cycle arrest, acting as a suppressor initially, but in later stages, it promotes progression by enhancing motility, invasiveness, and metastasis formation. This study explores naturally occurring flavonoids' interactions with TGF-ß. Using molecular docking against the protein's crystal structure (PDB Id: 1PY5), Gossypin showed the highest docking score and underwent molecular dynamics simulation, revealing complex flexibility and explaining how flavonoids impede TGF-ß signaling in breast cancer. ADMET predictions adhered to Lipinski's rule of Five. Insights into flavonoid-TGF-ß binding offer a novel angle for breast cancer treatment. Flavonoids having a good docking score like gossypin, morin, luteolin and taxifolin shown potent cytotoxic effect on breast cancer cell line, MCF-7. Understanding these interactions could inspire flavonoid-based therapies targeting TGF-ß to halt breast cancer growth. These findings pave the way for personalized, targeted breast cancer therapies, offering hope against this formidable disease.

Synthesis of the chromone-thiosemicarbazone scaffold as promising α-glucosidase inhibitors: An in vitro and in silico approach toward antidiabetic drug design.

Diabetes is a serious metabolic disorder affecting individuals of all age groups and prevails globally due to the failure of previous treatments. This study aims to address the most prevalent form of type 2 diabetes mellitus (T2DM) by reporting on the design, synthesis, and in vitro as well as in silico evaluation of chromone-based thiosemicarbazones as potential α-glucosidase inhibitors. In vitro experiments showed that the tested compounds were significantly more potent than the standard acarbose, with the lead compound 3n exhibiting an IC50 value of 0.40 ± 0.02 µM, ~2183-fold higher than acarbose having an IC50 of 873.34 ± 1.67 µM. A kinetic mechanism analysis demonstrated that compound 3n exhibited reversible inhibition of α-glucosidase. To gain deeper insights, in silico molecular docking, pharmacokinetics, and molecular dynamics simulations were conducted for the investigation of the interactions, orientation, stability, and conformation of the synthesized compounds within the active pocket of α-glucosidase.

Natural compounds for oxidative stress and neuroprotection in schizophrenia: composition, mechanisms, and therapeutic potential.

OBJECTIVE: An imbalance between the generation of reactive oxygen species (ROS) and the body's antioxidant defense mechanisms is believed to be a critical factor in the development of schizophrenia (SCZ) like neurological illnesses. Understanding the roles of ROS in the development of SCZ and the potential activity of natural antioxidants against SCZ could lead to more effective therapeutic options for the prevention and treatment of the illness. METHODS: SCZ is a mental disorder characterised by progressive impairments in working memory, attention, and executive functioning. In present investigation, we summarized the experimental findings for understanding the role of oxidative stress (OS) in the development of SCZ and the potential neuroprotective effects of natural antioxidants in the treatment of SCZ. RESULTS: Current study supports the use of the mentioned antioxidant natural compounds as a potential therapeutic candidates for the treatment of OS mediated neurodegeneration in SCZ. DISCUSSION: Elevated levels of harmful ROS and reduced antioxidant defense mechanisms are indicative of increased oxidative stress (OS), which is associated with SCZ. Previous research has shown that individuals with SCZ, including non-medicated, medicated, first-episode, and chronic patients, exhibit decreased levels of total antioxidants and GSH. Additionally, they have reduced antioxidant enzyme levels such as catalase (CAT), glutathione (GPx), and, superoxide dismutase (SOD) and lower serum levels of brain-derived neurotrophic factor (BDNF) in their brain tissue. The mentioned natural antioxidants may assist in reducing oxidative damage in individuals with SCZ and increasing BDNF expression in the brain, potentially improving cognitive function and learning ability.

Chemical Composition of Piper nigrum L. Cultivar Guajarina Essential Oils and Their Biological Activity.

The essential oils and aroma derived from the leaves (L), stems (St), and spikes (s) of Piper nigrum L. cv. Guajarina were extracted; the essential oils were extracted using hydrodistillation (HD), and steam distillation (SD), and the aroma was obtained by simultaneous distillation and extraction (SDE). Chemical constituents were identified and quantified using GC/MS and GC-FID. Preliminary biological activity was assessed by determining the toxicity against Artemia salina Leach larvae, calculating mortality rates, and determining lethal concentration values (LC50). The predominant compounds in essential oil samples included α-pinene (0-5.6%), ß-pinene (0-22.7%), limonene (0-19.3%), 35 linalool (0-5.3%), δ-elemene (0-10.1%), ß-caryophyllene (0.5-21.9%), γ-elemene (7.5-33.9%), and curzerene (6.9-31.7%). Multivariate analysis, employing principal component analysis (PCA) and hierarchical cluster analysis (HCA), revealed three groups among the identified classes and two groups among individual compounds. The highest antioxidant activity was found for essential oils derived from the leaves (167.9 41 mg TE mL-1). Larvicidal potential against A. salina was observed in essential oils obtained from the leaves (LC50 6.40 µg mL-1) and spikes (LC50 6.44 µg mL-1). The in silico studies demonstrated that the main compounds can interact with acetylcholinesterase, thus showing the potential molecular interaction responsible for the toxicity of the essential oil in A. salina.

Mechanistic QSAR modeling derived virtual screening, drug repurposing, ADMET and in-vitro evaluation to identify anticancer lead as lysine-specific demethylase 5a inhibitor.

A lysine-specific demethylase is an enzyme that selectively eliminates methyl groups from lysine residues. KDM5A, also known as JARID1A or RBP2, belongs to the KDM5 Jumonji histone demethylase subfamily. To identify novel molecules that interact with the LSD5A receptor, we created a quantitative structure-activity relationship (QSAR) model. A group of 435 compounds was used in a study of the quantitative relationship between structure and activity to guess the IC50 values for blocking LASD5A. We used a genetic algorithm-multilinear regression-based quantitative structure-activity connection model to forecast the bioactivity (PIC50) of 1615 food and drug administration pharmaceuticals from the zinc database with the goal of repurposing clinically used medications. We used molecular docking, molecular dynamic simulation modelling, and molecular mechanics generalised surface area analysis to investigate the molecule's binding mechanism. A genetic algorithm and multi-linear regression method were used to make six variable-based quantitative structure-activity relationship models that worked well (R2 = 0.8521, Q2LOO = 0.8438, and Q2LMO = 0.8414). ZINC000000538621 was found to be a new hit against LSD5A after a quantitative structure-activity relationship-based virtual screening of 1615 zinc food and drug administration compounds. The docking analysis revealed that the hit molecule 11 in the KDM5A binding pocket adopted a conformation similar to the pdb-6bh1 ligand (docking score: -8.61 kcal/mol). The results from molecular docking and the quantitative structure-activity relationship were complementary and consistent. The most active lead molecule 11, which has shown encouraging results, has good absorption, distribution, metabolism, and excretion (ADME) properties, and its toxicity has been shown to be minimal. In addition, the MTT assay of ZINC000000538621 with MCF-7 cell lines backs up the in silico studies. We used molecular mechanics generalise borne surface area analysis and a 200-ns molecular dynamics simulation to find structural motifs for KDM5A enzyme interactions. Thus, our strategy will likely expand food and drug administration molecule repurposing research to find better anticancer drugs and therapies.Communicated by Ramaswamy H. Sarma.

Application of in-silico drug discovery techniques to discover a novel hit for target-specific inhibition of SARS-CoV-2 Mpro's revealed allosteric binding with MAO-B receptor: A theoretical study to find a cure for post-covid neurological disorder.

Several studies have revealed that SARS-CoV-2 damages brain function and produces significant neurological disability. The SARS-CoV-2 coronavirus, which causes COVID-19, may infect the heart, kidneys, and brain. Recent research suggests that monoamine oxidase B (MAO-B) may be involved in metabolomics variations in delirium-prone individuals and severe SARS-CoV-2 infection. In light of this situation, we have employed a variety of computational to develop suitable QSAR model using PyDescriptor and genetic algorithm-multilinear regression (GA-MLR) models (R2 = 0.800-793, Q2LOO = 0.734-0.727, and so on) on the data set of 106 molecules whose anti-SARS-CoV-2 activity was empirically determined. QSAR models generated follow OECD standards and are predictive. QSAR model descriptors were also observed in x-ray-resolved structures. After developing a QSAR model, we did a QSAR-based virtual screening on an in-house database of 200 compounds and found a potential hit molecule. The new hit's docking score (-8.208 kcal/mol) and PIC50 (7.85 M) demonstrated a significant affinity for SARS-CoV-2's main protease. Based on post-covid neurodegenerative episodes in Alzheimer's and Parkinson's-like disorders and MAO-B's role in neurodegeneration, the initially disclosed hit for the SARS-CoV-2 main protease was repurposed against the MAO-B receptor using receptor-based molecular docking, which yielded a docking score of -12.0 kcal/mol. This shows that the compound that inhibits SARS-CoV-2's primary protease may bind allosterically to the MAO-B receptor. We then did molecular dynamic simulations and MMGBSA tests to confirm molecular docking analyses and quantify binding free energy. The drug-receptor complex was stable during the 150-ns MD simulation. The first computational effort to show in-silico inhibition of SARS-CoV-2 Mpro and allosteric interaction of novel inhibitors with MAO-B in post-covid neurodegenerative symptoms and other disorders. The current study seeks a novel compound that inhibits SAR's COV-2 Mpro and perhaps binds MAO-B allosterically. Thus, this study will enable scientists design a new SARS-CoV-2 Mpro that inhibits the MAO-B receptor to treat post-covid neurological illness.

QSAR modeling approaches to identify a novel ACE2 inhibitor that selectively bind with the C and N terminals of the ectodomain.

Due to the high rates of drug development failure and the massive expenses associated with drug discovery, repurposing existing drugs has become more popular. As a result, we have used QSAR modelling on a large and varied dataset of 657 compounds in an effort to discover both explicit and subtle structural features requisite for ACE2 inhibitory activity, with the goal of identifying novel hit molecules. The QSAR modelling yielded a statistically robust QSAR model with high predictivity (R2tr=0.84, R2ex=0.79), previously undisclosed features, and novel mechanistic interpretations. The developed QSAR model predicted the ACE2 inhibitory activity (PIC50) of 1615 ZINC FDA compounds. This led to the detection of a PIC50 of 8.604 M for the hit molecule (ZINC000027990463). The hit molecule's docking score is -9.67 kcal/mol (RMSD 1.4). The hit molecule revealed 25 interactions with the residue ASP40, which defines the N and C termini of the ectodomain of ACE2. The HIT molecule conducted more than thirty contacts with water molecules and exhibited polar interaction with the ARG522 residue coupled with the second chloride ion, which is 10.4 nm away from the zinc ion. Both molecular docking and QSAR produced comparable findings. Moreover, MD simulation and MMGBSA studies verified docking analysis. The MD simulation showed that the hit molecule-ACE2 receptor complex is stable for 400 ns, suggesting that repurposed hit molecule 3 is a viable ACE2 inhibitor.

Carbonylbis(hydrazine-1-carbothioamide) derivatives as a new class of α-glucosidase inhibitors and their mechanistic insights via molecular docking and dynamic simulations.

In the past, efforts have been made to find a cure for diabetes, mainly evaluating new classes of compounds to explore their potency. In this study, we present the synthesis and evaluation of carbonylbis(hydrazine-1-carbothioamide) derivatives as potential α-glucosidase inhibitors, employing both in vivo and in silico investigations. The in vitro experiments revealed that all tested compounds were significantly potent for α-glucosidase inhibition, with the lead compound 3a displaying approximately 80 times higher activity than acarbose. To delve deeper, in silico induced fit docking, pharmacokinetics, and molecular dynamics studies were conducted. Significantly, compound 3a exhibited a docking score of -7.87 kcal/mol, surpassing acarbose, which had a docking score of -6.59 kcal/mol. The in silico ADMET indicated that most of the synthesized compounds have properties conducive to drug development. Molecular dynamics analysis demonstrated that, when the ligand 3a was coupled with the target 3TOP, Cα-RMSD backbone RMSD values below 2.4 Å and "Lig_fit_Prot" values below 2.7 Å were observed. QSAR analysis demonstrates that the "fOC8A" descriptor positively correlates with α-glucosidase inhibition activity, while "lipoplus_AbSA" positively contributes and "notringC_notringO_8B" negatively contributes to this activity.

In-silico studies to recognize repurposing therapeutics toward arginase-I inhibitors as a potential onco-immunomodulators.

Rudolf Virchow was the first person to point out the important link between immune function and cancer. He did this by noticing that leukocytes were often found in tumors. Overexpression of arginase 1 (ARG1) and inducible nitric oxide synthase (iNOS) in myeloid-derived suppressor cells (MDSCs) and tumour-associated macrophages (TAMs) depletes both intracellular and extracellular arginine. TCR signalling is slowed as a result, and the same types of cells produce reactive oxygen and nitrogen species (ROS and RNS), which aggravates the situation. Human arginase I is a double-stranded manganese metalloenzyme that helps L-arginine break down into L-ornithine and urea. Thus, a quantitative structure-activity relationship (QSAR) analysis was performed to unearth the unrecognised structural aspects crucial for arginase-I inhibition. In this work, a balanced QSAR model with good prediction performance and clear mechanistic interpretation was developed using a dataset of 149 molecules encompassing a broad range of structural scaffolds and compositions. The model was made to meet OECD standards, and all of its validation parameters have values that are higher than the minimum requirements (R2 tr = 0.89, Q2 LMO = 0.86, and R2 ex = 0.85). The present QSAR study linked structural factors to arginase-I inhibitory action, including the proximity of lipophilic atoms to the molecule's centre of mass (within 3A), the position of the donor to the ring nitrogen (exactly 3 bonds away), and the surface area ratio. As OAT-1746 and two others are the only arginase-I inhibitors in development at the time, we have performed a QSAR-based virtual screening with 1650 FDA compounds taken from the zinc database. In this screening, 112 potential hit compounds were found to have a PIC50 value of less than 10 nm against the arginase-I receptor. The created QSAR model's application domain was evaluated in relation to the most active hit molecules identified using QSAR-based virtual screening, utilising a training set of 149 compounds and a prediction set of 112 hit molecules. As shown in the Williams plot, the top hit molecule, ZINC000252286875, has a low leverage value of HAT i/i h* = 0.140, placing it towards the boundary of the usable range. Furthermore, one of 112 hit molecules with a docking score of -10.891 kcal/mol (PIC50 = 10.023 M) was isolated from a study of arginase-I using molecular docking. Protonated ZINC000252286875-linked arginase-1 showed 2.9 RMSD, whereas non-protonated had 1.8. RMSD plots illustrate protein stability in protonated and non-protonated ZINC000252286875-bound states. Protonated-ZINC000252286875-bound proteins contain 25 Rg. The non-protonated protein-ligand combination exhibits a 25.2-Rg, indicating compactness. Protonated and non-protonated ZINC000252286875 stabilised protein targets in binding cavities posthumously. Significant root mean square fluctuations (RMSF) were seen in the arginase-1 protein at a small number of residues for a time function of 500 ns in both the protonated and unprotonated states. Protonated and non-protonated ligands interacted with proteins throughout the simulation. ZINC000252286875 bound Lys64, Asp124, Ala171, Arg222, Asp232, and Gly250. Aspartic acid residue 232 exhibited 200% ionic contact. 500-ns simulations-maintained ions. Salt bridges for ZINC000252286875 aided docking. ZINC000252286875 created six ionic bonds with Lys68, Asp117, His126, Ala171, Lys224, and Asp232 residues. Asp117, His126, and Lys224 showed 200% ionic interactions. In protonated and deprotonated states, GbindvdW, GbindLipo, and GbindCoulomb energies played crucial role. Moreover, ZINC000252286875 meets all of the ADMET standards to serve as a drug. As a result, the current analyses were successful in locating a novel and potent hit molecule that inhibits arginase-I effectively at nanomolar concentrations. The results of this investigation can be used to develop brand-new arginase I inhibitors as an alternative immune-modulating cancer therapy.

Theoretical and Anti-Klebsiella pneumoniae Evaluations of Substituted 2,7-dimethylimidazo[1,2-a]pyridine-3-carboxamide and Imidazopyridine Hydrazide Derivatives.

A series of multistep synthesis protocols was adopted to synthesize substituted imidazopyridines (IMPs) (SM-IMP-01 to SM-IMP-13, and DA-01-05). All substituted IMPs were then characterized using standard spectroscopic techniques such as 1H-NMR, 13C-NMR, elemental analyses, and mass spectrometry. Our both in vitro qualitative and quantitative results for antibacterial analysis, against Klebsiella pneumoniae ATCC 4352 and Bacillus subtilis ATCC 6051 suggested that all compounds essentially exhibited activity against selected strains of bacteria. Our DFT analyses suggested that the compounds of the SM-IMP-01-SM-IMP-13 series have HOMO/LUMO gaps within 4.43-4.69 eV, whereas the compounds of the DA-01-DA-05 series have smaller values of the HOMO/LUMO gaps, 3.24-4.17 eV. The lowest value of the global hardness and the highest value of the global softness, 2.215 and 0.226 eV, respectively, characterize the compound SM-IMP-02; thus, it is the most reactive compound in the imidazopyridine carboxamide series (except hydrazide series). This compound also depicted lesser MIC values against Klebsiella pneumoniae ATCC 4352 and Bacillus subtilis ATCC 6051 as 4.8 µg/mL, each. In terms of another series, hydrazide DA-05 depicted strong antimicrobial actions (MIC: 4.8 µg/mL against both bacterial strains) and also had the lowest energy gap (3.24 eV), higher softness (0.309 eV), and lesser hardness (1.62 eV). Overall, when we compare qualitative and quantitative antimicrobial results, it is been very clear that compounds with dibromo substitutions on imidazopyridine (IMP) rings would act as better antimicrobial agents than those with -H at the eighth position on the IMP ring. Furthermore, substituents of higher electronegativities would tend to enhance the biological activities of dibromo-IMP compounds. DFT properties were also well comparable to this trend and overall, we can say that the electronic behavior of compounds under investigation has key roles in their bioactivities.

Repurposing food molecules as a potential BACE1 inhibitor for Alzheimer's disease.

Alzheimer's disease (AD) is a severe neurodegenerative disorder of the brain that manifests as dementia, disorientation, difficulty in speech, and progressive cognitive and behavioral impairment. The emerging therapeutic approach to AD management is the inhibition of ß-site APP cleaving enzyme-1 (BACE1), known to be one of the two aspartyl proteases that cleave ß-amyloid precursor protein (APP). Studies confirmed the association of high BACE1 activity with the proficiency in the formation of ß-amyloid-containing neurotic plaques, the characteristics of AD. Only a few FDA-approved BACE1 inhibitors are available in the market, but their adverse off-target effects limit their usage. In this paper, we have used both ligand-based and target-based approaches for drug design. The QSAR study entails creating a multivariate GA-MLR (Genetic Algorithm-Multilinear Regression) model using 552 molecules with acceptable statistical performance (R 2 = 0.82, Q 2 loo = 0.81). According to the QSAR study, the activity has a strong link with various atoms such as aromatic carbons and ring Sulfur, acceptor atoms, sp2-hybridized oxygen, etc. Following that, a database of 26,467 food compounds was primarily used for QSAR-based virtual screening accompanied by the application of the Lipinski rule of five; the elimination of duplicates, salts, and metal derivatives resulted in a truncated dataset of 8,453 molecules. The molecular descriptor was calculated and a well-validated 6-parametric version of the QSAR model was used to predict the bioactivity of the 8,453 food compounds. Following this, the food compounds whose predicted activity (pKi) was observed above 7.0 M were further docked into the BACE1 receptor which gave rise to the Identification of 4-(3,4-Dihydroxyphenyl)-2-hydroxy-1H-phenalen-1-one (PubChem I.D: 4468; Food I.D: FDB017657) as a hit molecule (Binding Affinity = -8.9 kcal/mol, pKi = 7.97 nM, Ki = 10.715 M). Furthermore, molecular dynamics simulation for 150 ns and molecular mechanics generalized born and surface area (MMGBSA) study aided in identifying structural motifs involved in interactions with the BACE1 enzyme. Molecular docking and QSAR yielded complementary and congruent results. The validated analyses can be used to improve a drug/lead candidate's inhibitory efficacy against the BACE1. Thus, our approach is expected to widen the field of study of repurposing nutraceuticals into neuroprotective as well as anti-cancer and anti-viral therapeutic interventions.

QSAR, Molecular Docking, MD Simulation and MMGBSA Calculations Approaches to Recognize Concealed Pharmacophoric Features Requisite for the Optimization of ALK Tyrosine Kinase Inhibitors as Anticancer Leads.

ALK tyrosine kinase ALK TK is an important target in the development of anticancer drugs. In the present work, we have performed a QSAR analysis on a dataset of 224 molecules in order to quickly predict anticancer activity on query compounds. Double cross validation assigns an upward plunge to the genetic algorithm−multi linear regression (GA-MLR) based on robust univariate and multivariate QSAR models with high statistical performance reflected in various parameters like, fitting parameters; R2 = 0.69−0.87, F = 403.46−292.11, etc., internal validation parameters; Q2LOO = 0.69−0.86, Q2LMO = 0.69−0.86, CCCcv = 0.82−0.93, etc., or external validation parameters Q2F1 = 0.64−0.82, Q2F2 = 0.63−0.82, Q2F3 = 0.65−0.81, R2ext = 0.65−0.83 including RMSEtr < RMSEcv. The present QSAR evaluation successfully identified certain distinct structural features responsible for ALK TK inhibitory potency, such as planar Nitrogen within four bonds from the Nitrogen atom, Fluorine atom within five bonds beside the non-ring Oxygen atom, lipophilic atoms within two bonds from the ring Carbon atoms. Molecular docking, MD simulation, and MMGBSA computation results are in consensus with and complementary to the QSAR evaluations. As a result, the current study assists medicinal chemists in prioritizing compounds for experimental detection of anticancer activity, as well as their optimization towards more potent ALK tyrosine kinase inhibitor.

QSAR Evaluations to Unravel the Structural Features in Lysine-Specific Histone Demethylase 1A Inhibitors for Novel Anticancer Lead Development Supported by Molecular Docking, MD Simulation and MMGBSA.

Using 84 structurally diverse and experimentally validated LSD1/KDM1A inhibitors, quantitative structure-activity relationship (QSAR) models were built by OECD requirements. In the QSAR analysis, certainly significant and understated pharmacophoric features were identified as critical for LSD1 inhibition, such as a ring Carbon atom with exactly six bonds from a Nitrogen atom, partial charges of lipophilic atoms within eight bonds from a ring Sulphur atom, a non-ring Oxygen atom exactly nine bonds from the amide Nitrogen, etc. The genetic algorithm-multi-linear regression (GA-MLR) and double cross-validation criteria were used to create robust QSAR models with high predictability. In this study, two QSAR models were developed, with fitting parameters like R2 = 0.83-0.81, F = 61.22-67.96, internal validation parameters such as Q2LOO = 0.79-0.77, Q2LMO = 0.78-0.76, CCCcv = 0.89-0.88, and external validation parameters such as, R2ext = 0.82 and CCCex = 0.90. In terms of mechanistic interpretation and statistical analysis, both QSAR models are well-balanced. Furthermore, utilizing the pharmacophoric features revealed by QSAR modelling, molecular docking experiments corroborated with the most active compound's binding to the LSD1 receptor. The docking results are then refined using Molecular dynamic simulation and MMGBSA analysis. As a consequence, the findings of the study can be used to produce LSD1/KDM1A inhibitors as anticancer leads.

Identification of potent aldose reductase inhibitors as antidiabetic (Anti-hyperglycemic) agents using QSAR based virtual Screening, molecular Docking, MD simulation and MMGBSA approaches.

The aldose reductase (AR) enzyme is an important target enzyme in the development of therapeutics against hyperglycaemia induced health complications such as retinopathy, etc. In the present study, a quantitative structure activity relationship (QSAR) evaluation of a dataset of 226 reported AR inhibitor (ARi) molecules is performed using a genetic algorithm - multi linear regression (GA-MLR) technique. Multi-criteria decision making (MCDM) analysis furnished two five variables based QSAR models with acceptably high performance reflected in various statistical parameters such as, R2 = 0.79-0.80, Q2 LOO = 0.78-0.79, Q2 LMO = 0.78-0.79. The QSAR model analysis revealed some of the molecular features that play crucial role in deciding inhibitory potency of the molecule against AR such as; hydrophobic Nitrogen within 2 Å of the center of mass of the molecule, non-ring Carbon separated by three and four bonds from hydrogen bond donor atoms, number of sp2 hybridized Oxygen separated by four bonds from sp2 hybridized Carbon atoms, etc. 14 in silico generated hits, using a compound 18 (a most potent ARi from present dataset with pIC50 = 8.04 M) as a template, on QSAR based virtual screening (QSAR-VS) furnished a scaffold 5 with better ARi activity (pIC50 = 8.05 M) than template compound 18. Furthermore, molecular docking of compound 18 (Docking Score = -7.91 kcal/mol) and scaffold 5 (Docking Score = -8.08 kcal/mol) against AR, divulged that they both occupy the specific pocket(s) in AR receptor binding sites through hydrogen bonding and hydrophobic interactions. Molecular dynamic simulation (MDS) and MMGBSA studies right back the docking results by revealing the fact that binding site residues interact with scaffold 5 and compound 18 to produce a stable complex similar to co-crystallized ligand's conformation. The QSAR analysis, molecular docking, and MDS results are all in agreement and complementary. QSAR-VS successfully identified a more potent novel ARi and can be used in the development of therapeutic agents to treat diabetes.

Perceiving the Concealed and Unreported Pharmacophoric Features of the 5-Hydroxytryptamine Receptor Using Balanced QSAR Analysis.

The 5-hydroxytryptamine receptor 6 (5-HT6) has gained attention as a target for developing therapeutics for Alzheimer's disease, schizophrenia, cognitive dysfunctions, anxiety, and depression, to list a few. In the present analysis, a larger and diverse dataset of 1278 molecules covering a broad chemical and activity space was used to identify visual and concealed structural features associated with binding affinity for 5-HT6. For this, quantitative structure-activity relationships (QSAR) and molecular docking analyses were executed. This led to the development of a statistically robust QSAR model with a balance of excellent predictivity (R2tr = 0.78, R2ex = 0.77), the identification of unreported aspects of known features, and also novel mechanistic interpretations. Molecular docking and QSAR provided similar as well as complementary results. The present analysis indicates that the partial charges on ring carbons present within four bonds from a sulfur atom, the occurrence of sp3-hybridized carbon atoms bonded with donor atoms, and a conditional occurrence of lipophilic atoms/groups from nitrogen atoms, which are prominent but unreported pharmacophores that should be considered while optimizing a molecule for 5-HT6. Thus, the present analysis led to identification of some novel unreported structural features that govern the binding affinity of a molecule. The results could be beneficial in optimizing the molecules for 5-HT6.

Mechanistic Analysis of Chemically Diverse Bromodomain-4 Inhibitors Using Balanced QSAR Analysis and Supported by X-ray Resolved Crystal Structures.

Bromodomain-4 (BRD-4) is a key enzyme in post-translational modifications, transcriptional activation, and many other cellular processes. Its inhibitors find their therapeutic usage in cancer, acute heart failure, and inflammation to name a few. In the present study, a dataset of 980 molecules with a significant diversity of structural scaffolds and composition was selected to develop a balanced QSAR model possessing high predictive capability and mechanistic interpretation. The model was built as per the OECD (Organisation for Economic Co-operation and Development) guidelines and fulfills the endorsed threshold values for different validation parameters (R2tr = 0.76, Q2LMO = 0.76, and R2ex = 0.76). The present QSAR analysis identified that anti-BRD-4 activity is associated with structural characters such as the presence of saturated carbocyclic rings, the occurrence of carbon atoms near the center of mass of a molecule, and a specific combination of planer or aromatic nitrogen with ring carbon, donor, and acceptor atoms. The outcomes of the present analysis are also supported by X-ray-resolved crystal structures of compounds with BRD-4. Thus, the QSAR model effectively captured salient as well as unreported hidden pharmacophoric features. Therefore, the present study successfully identified valuable novel pharmacophoric features, which could be beneficial for the future optimization of lead/hit compounds for anti-BRD-4 activity.

Challenges and measures during management of mounting biomedical waste in COVID-19 pandemic: an Indian approach.

Background: During coronavirus pandemic, an unpredictable pile of biomedical waste (BMW) gathers at the top. India produces 710 tonnes of biomedical waste daily. The contribution of COVID-19 related biomedical waste was 126 tonnes per day in first wave of the pandemic. BMW's rapid growth is putting a strain on current waste management facilities, especially in developing countries. A sudden boost in biomedical waste needs rapid and proper segregation and disposal methods to avoid future consequences. Main body of the abstract: From literatures and statistical data available on Central Pollution Control Board (CPCB) it shows that India lags behind in large-scale sorting, collection, careful storage, transfer and disposal of bio waste. India has its own guidelines set by the CPCB to ensure the safe disposal of biomedical waste during diagnosis, treatment and quarantine of COVID-19 patients. Although there are strict guidelines for bio-waste management, many hospitals in the process of implementing them often dispose of waste in inappropriate, chaotic and indiscriminate ways due to negligence or laziness. Often, due to poor separation practices, hospital waste is mixed with general waste, resulting in harmful overall waste flow. Waste disposal handlers are not safe due to their exposure to various health risks and inadequate training in waste management. The present review sheds light on guidelines, measures, and challenges related to biomedical waste management. Short conclusion: Improper waste separation leads to improper waste disposal. Waste generation and management issues are causing daily problems as they have a profound impact on the dramatically changing global environment, including air, water and soil pollution. In addition, BMW's daily production and its processing are inversely proportional. This situation suggests that India will soon be drowning in its own garbage. The focus of this review is on the generation and disposal of biomedical waste. Based on a review of the literature, this evaluation provides a comparative picture of the current status of waste generation, national waste management strategies, and some measures to contribute to waste management and avoid future disasters.

Identification of concealed structural alerts using QSTR modeling for Pseudokirchneriella subcapitata.

In the present work, QSTR modeling was conducted for microalga Pseudokirchneriella subcapitata using a data set of 271 molecules belonging to different types of chemical classes for the prediction of EC50 for 72 hr based assays. The balanced QSTR model encompasses seven easily interpretable molecular descriptors and possesses statistical robustness with high predictive ability. This Genetic Algorithm Multi-linear regression (GA-MLR) model was subjected to internal validation, Y-randomization test, applicability domain analysis, and external validation as per the recommended OECD guidelines. The newly developed model fulfilled the threshold values for more than 20 recommended validation parameters including R2 = 0.72, Q2LOO = 0.70, etc. The developed QSTR model was successful in identifying the type of hybridization or specific type of atoms of previously reported and newer structural alerts. Thus, the model could be useful for data gap filling and expanding mechanistic interpretation of toxicity for different chemicals.

Target Specific Inhibition of Protein Tyrosine Kinase in Conjunction With Cancer and SARS-COV-2 by Olive Nutraceuticals.

The fact that viruses cause human cancer dates back to the early 1980s. By reprogramming cellular signaling pathways, viruses encoded protein that can regulate altered control of cell cycle events. Viruses can interact with a superfamily of membrane bound protein, receptor tyrosine kinase to modulate their activity in order to increase virus entrance into cells and promotion of viral replication within the host. Therefore, our study aimed at screening of inhibitors of tyrosine kinase using natural compounds from olive. Protein tyrosine kinase (PTK) is an important factor for cancer progression and can be linked to coronavirus. It is evident that over expression of Protein tyrosine kinase (PTK) enhance viral endocytosis and proliferation and the use of tyrosine kinase inhibitors reduced the period of infection period. Functional network studies were carried out using two major PTKs viz. Anaplastic lymphoma kinase (ALK) and B-lymphocytic kinase (BTK). They are associated with coronavirus in regulation of cell signaling proteins for cellular processes. We virtually screened for 161 library of natural compounds from olive found overexpressed in ALK and BTK in metastatic as well as virus host cells. We have employed both ligand and target-based approach for drug designing by high throughput screening using Multilinear regression model based QSAR and docking. The QSAR based virtual screening of 161 olive nutraceutical compounds has successfully identified certain new hit; Wedelosin, in which, the descriptor rsa (ratio of molecular surface area to the solvent accessible surface area) plays crucial role in deciding Wedelosin's inhibitory potency. The best-docked olive nutraceuticals further investigated for the stability and effectivity of the BTK and ALK during in 150 ns molecular dynamics and simulation. Post simulation analysis and binding energy estimation in MMGBSA further revealed the intensive potential of the olive nutraceuticals in PTK inhibition. This study is therefore expected to widen the use of nutraceuticals from olive in cancer as well as SARS-CoV2 alternative therapy.