Mehreen Zaka

Synthesis and characterisation of metal nanoparticles and their effects on seed germination and seedling growth in commercially important Eruca sativa

The synthesis, characterisation and application of metal nanoparticles have become an important and attractive branch of nanotechnology. In current study, metallic nanoparticles of silver, copper, and gold were synthesised using environment friendly method (polyols process), and applied on medicinally important plant: Eruca sativa. Effects of application of these nanoparticles were evaluated on seed germination frequency and biochemical parameters of plant tissues. Seeds of E. sativa were germinated on Murashige and Skoog (MS) medium incorporated with various combinations of nanoparticles suspension (30 µg/ml). Phytotoxicity study showed that nanoparticles could induce stress in plants by manipulating the endogenous mechanisms. In response to these stresses, plants release various defensive compounds; known as antioxidant secondary metabolites. These plants derived secondary metabolites having a great potential in treating the common human ailments. In the authors study, small-sized nanoparticles showed higher toxicity levels and enhanced secondary metabolites production, total protein content, total flavonoids content and total phenolics content.

Comparative in silico analyses of Cannabis sativa, Prunella vulgaris and Withania somnifera compounds elucidating the medicinal properties against rheumatoid arthritis

From last decade, there has been progressive improvement in computational drug designing. Several diseases are being cured from different plant extracts and products. Rheumatoid Arthritis (RA) is the most shared disease among auto-inflammatory diseases. Tumour necrosis factor (TNF)-α is associated with RA pathway and has adverse effects. Extensive literature review showed that plant species under study (Cannabis sativa, Prunella vulgaris and Withania somnifera) possess anti-inflammatory, anti-arthritic and anti-rheumatic properties. 13 anti-inflammatory compounds were characterised and filtered out from medicinal plant species and analysed for RA by targeting TNF-α through in silico analyses. By using ligand based pharmacophore generation approach and virtual screening against natural products libraries we retrieved twenty unique molecules that displayed utmost binding affinity, least binding energies and effective drug properties. The docking analyses revealed that Ala-22, Glu-23, Ser-65, Gln-67, Tyr-141, Leu-142, Asp-143, Phe-144 and Ala-145 were critical interacting residues for receptor-ligand interactions. It is proposed that the RA patients should use reported compounds for the prescription of RA by targeting TNF-α. This report is opening new dimensions for designing innovative therapeutic targets to cure RA.

Novel Tumor Necrosis Factor-α (TNF-α) Inhibitors from Small Molecule Library Screening for their Therapeutic Activity Profiles against Rheumatoid Arthritis using Target-Driven Approaches and Binary QSAR Models

Abstract Tumor necrosis factor alpha (TNF-α) is a multifunctional cytokine that acts as a central biological mediator for critical immune functions, including inflammation, infection, and antitumor responses. It plays pivotal role in autoimmune diseases like rheumatoid arthritis (RA). The synthetic antibodies etanercept, infliximab, and adalimumab are approved drugs for the treatment of inflammatory diseases bind to TNF-α directly, preventing its association with the tumor necrosis factor receptor (TNFR). These biologics causes serious side effects such as triggering an autoimmune anti-antibody response or the weakening of the bodys immune defenses. Therefore, alternative small-molecule based therapies for TNF-α inhibition is a hot topic both in academia and industry. Most of small-molecule inhibitors reported in the literature target TNF-α, indirectly. In this study, combined in silico approaches have been applied to better understand the important direct interactions between TNF-α and small inhibitors. Our effort executed with the extensive literature review to select the compounds that inhibit TNF-α. High-throughput structure-based and ligand-based virtual screening methods are applied to identify TNF-α inhibitors from 3 different small molecule databases (∼256.000 molecules from Otava drug-like green chemical collection, ∼ 500.000 molecules from Otava Tangible database, ∼2.500.000 Enamine small molecule database) and ∼240.000 molecules from ZINC natural products libraries. Moreover, therapeutic activity prediction, as well as pharmacokinetic and toxicity profiles are also investigated using MetaCore/MetaDrug platform which is based on a manually curated database of molecular interactions, molecular pathways, gene-disease associations, chemical metabolism and toxicity information, uses binary QSAR models. Particular therapeutic activity and toxic effect predictions are based on the ChemTree ability to correlate structural descriptors to that property using recursive partitioning algorithm. Molecular Dynamics (MD) simulations were also performed for selected hits to investigate their detailed structural and dynamical analysis beyond docking studies. As a result, at least one hit from each database were identified as novel TNF-α inhibitors after comprehensive virtual screening, multiple docking, e-Pharmacophore modeling (structure-based pharmacophore modeling), MD simulations, and MetaCore/MetaDrug analysis. Identified hits show predicted promising anti-arthritic activity and no toxicity. Communicated by Ramaswamy H. Sarma

Synthesis, characterization and bactericidal effect of ZnO nanoparticles via chemical and bio-assisted (Silybum marianum in-vitro plantlets and callus extract) methods: A comparative study

Currently, the evolution of green chemistry in the synthesis of nanoparticles (NPs) with the usage of plants has captivated a great response. In this study, in vitro plantlets and callus of Silybum marianum were exploited as a stabilising agent for the synthesis of zinc oxide (ZnO) NPs using zinc acetate and sodium hydroxide as a substitute for chemical method. The contemporary investigation defines the synthesis of ZnO NPs prepared by chemical and bio-extract-assisted methods. Characterisation techniques such as X-ray diffraction, scanning electron microscopy, Fourier transform infrared spectroscopy and energy dispersive X-ray were used to confirm the synthesis. Although chemical and bio-assisted methods are suitable choices for NPs synthesis, the bio-assisted green assembly is advantageous due to superior stability. Moreover, this report describes the antibacterial activity of the synthesised NPs against standard strains of Klebsiella pneumonia and Bacillus subtilis.

Proposing novel TNFα direct inhibitor Scaffolds using fragment-docking based e-pharmacophore modeling and binary QSAR-based virtual screening protocols pipeline

Tumor necrosis factor alpha (TNFα) is a homotrimer protein that plays a pivotal role for critical immune functions, including infection, inflammation and antitumor responses. It also plays a primary role in autoimmune diseases like rheumatoid arthritis (RA). So far, only biological therapeutics like infliximab, etanercept, and adalimumab are available as treatment of inflammatory diseases. They directly bind to TNFα and interrupt its binding to its receptor protein tumor necrosis factor receptor (TNFR). However, they may also cause serious side effects such as activating an autoimmune anti-antibody response or the weakening of the bodys immune defenses. Thus, small molecule-based therapies can be considered as alternative methods. In this study, a novel method is applied to develop energetically optimized, structure-based pharmacophore models for rapid in silico drug screening. Fragment-based docking results were used in the construction of an universal e-pharmacophore model development. The developed model is then used for screening of small-molecule library Specs-screening compounds (Specs-SC) which includes more than 200.000 drug-like molecules. In another approach, binary QSAR-based models were used to screen Specs-SC, as well as Specs-natural products (NP) which has around 750 compounds, and a library of drugs registered or approved for use in humans NIHs NCGC pharmaceutical collection (NPC) which has around 7500 molecules. The MetaCore/MetaDrug platform was used for binary QSAR models for therapeutic activity prediction as well as pharmacokinetic and toxicity profile predictions of screening molecules. This platform is constructed based on a manually curated database of molecular interactions, molecular pathways, gene-disease associations, chemical metabolism, and toxicity information. Molecular docking and molecular dynamics (MD) simulations were performed for the selected hit molecules. As target protein, both homodimer and homotrimer forms of TNFα were considered. The screening results showed that indinavir and medroxalol from NPC chemical library and a set of compounds (AT-057/43115940, AP-970/42897107, AK-968/41925665, AI-204/31679053, AN-648/41666950, AN-698/42006940) from Specs-SC database were identified as safe and active direct inhibitors of TNFα.