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Background: It takes lot more studies to evaluate the molecular interaction of nanoparticles with the drug, their drug delivery potential and release kinetics. Thus, we have taken in silico and in vitro approaches into the account for the evaluation of drug delivery ability of the chitosan nanoparticles. Objective: The present work was aimed to develop the interaction of chitosan nanoparticles with appropriate aromatase inhibitors using in-silico tools. Further, synthesis and characterization of chitosan nanoparticles having optimal binding energy and affinity between drug and polymer in terms of size, encapsulation efficiency was carried out. Methods: In current study, molecular docking was used to map the molecular interactions and estimation of binding energy involved between the nanoparticles and the drug molecules in silico. Letrozole is used as a model cytotoxic agent currently being used clinically, hence Letrozole loaded chitosan nanoparticles were formulated and characterized using photomicroscope, particle size analyzer, scanning electron microscope and fourier transform infra-red spectroscopy. Results: Letrozole had the second highest binding affinity within the core of chitosan with MolDock (-102.470) and Rerank (-81.084) scores. Further, it was investigated that formulated nanoparticles were having superior drug loading capacity and high encapsulation efficiency. In vitro drug release study exhibited prolonged release of the drug from chitosan nanoparticles. Conclusion: Results obtained from the in silico and in vitro studies suggest that Letrozole loaded nanoparticles are ideal for breast cancer treatment.

Malaria is a mosquito-borne infectious disease caused by eukaryotic protist of the genus Plasmodium. It is

AVAPRO® (irbesartan) is an angiotensin II receptor (AT1 subtype) antagonist. Irbesartan is a non-peptide compound, chemically described as a 2-butyl-3-[p-(o-1Htetrazol-5ylphenyl)benzyl]-1,3-diazaspiro[4.4]non-1-en-4-one.

Background: In spite of the availability of various treatment approaches including surgery, radiotherapy, and hormonal therapy, the steroidal aromatase inhibitors (SAIs) play a significant role as chemotherapeutic agents for the treatment of estrogen-dependent breast cancer with the benefit of reduced risk of recurrence. However, due to greater toxicity and side effects associated with currently available anti-breast cancer agents, there is emergent requirement to develop target-specific AIs with safer anti-breast cancer profile. Methods: It is challenging task to design target-specific and less toxic SAIs, though the molecular modeling tools viz. molecular docking simulations and QSAR have been continuing for more than two decades for the fast and efficient designing of novel, selective, potent and safe molecules against various biological targets to fight the number of dreaded diseases/disorders. In order to design novel and selective SAIs, structure guided molecular docking assisted alignment dependent 3D-QSAR studies was performed on a data set comprises of 22 molecules bearing steroidal scaffold with wide range of aromatase inhibitory activity. Results: 3D-QSAR model developed using molecular weighted (MW) extent alignment approach showed good statistical quality and predictive ability when compared to model developed using moments of inertia (MI) alignment approach. Conclusion: The explored binding interactions and generated pharmacophoric features (steric and electrostatic) of steroidal molecules could be exploited for further design, direct synthesis and development of new potential safer SAIs, that can be effective to reduce the mortality and morbidity associated with breast cancer.

Aromatase is a crucial enzyme for the catalysis of aromatization reaction at the last and rate‐limiting step involved in the conversion of androgenic substrates to an estrogenic substrate. A hormone‐dependent breast cancer in postmenopausal woman can be cured by inhibition of estrogen biosynthesis by the help of aromatase inhibitors (AIs). The mode of interactions of flavonones with the active site of aromatase has been studied in search of potent and selective AIs as a substitute of the natural steroidal ligand. Structure‐based computational approach namely, molecular docking simulations were performed to investigate the structural features of the docked complex of aromatase and flavonoid ligands. A nonsteroidal flavonoid pharmacophore showing electrostatic and steric features for selective binding within the main pocket of the catalytic active site of aromatase has been identified as an outcome of the study. The binding affinity of quercetin and isoflavone were predicted within aromatase. Isoflavone was used as a negative control to compare its binding affinities with the selected dataset. The predicted binding affinity of negative control isoflavone was in accordance with its in vitro AI efficacy. Isoflavone showed poor binding affinity and ranked last in terms of MolDock score (−86.309 kcal/molÅ) compared to dataset molecules. The generated pharmacophoric information will be helpful for the synthetic chemist to design and synthesize selective AIs with comparable binding affinity to the natural steroidal ligand.

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World is facing a new pandemic called covid-19SARS-CoV-2) since a year ago. Unfortunately there is no treatment for Covid 19 nowadays as well as no potential therapies has been developed to overcome from coronavirus pandemic. Some potential drug molecules with combination have ability to respond for covid19 virus. From the research it was found that the reduction of viral load can be treated with hydroxychloroquine and azithromycin combination. We evaluate the mode of interactions of hydroxychloroquine and azithromycin with the dynamic site of SARS-CoV-2 coronavirus main protease. Molecular Structure-based computational approach viz. molecular docking simulations were performed to scale up their affinity and binding fitness of the docked complex of novel SARS-CoV-2 coronavirus protease and hydroxychloroquine and azithromycin. The natural inhibitor N3 of novel SARS-CoV-2 coronavirus protease were exhibited highest affinity in terms of MolDock score (-167.203Kcal/mol), and hydroxychloroquine was found with lowest target affinity (-55.917 Kcal/mol).The amino acid residue cysteine 145 and histidine 41 is bound covalently and formed hydrogen bond interaction with SARS-CoV-2 inhibitor known as inhibitor N3 as such, hydroxychloroquine and azithromycin also formed hydrogen bond interaction. The binding patterns of the inhibitor N3 of SARS-CoV-2 coronavirus main protease could be used as a guideline for medicinal chemist to explore their SARS-CoV-2 inhibitory potential.