No abstract

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.