GSK-3 Inhibitors as New Leads to Treat Type-II Diabetes.

In India as well as globally, diabetes is in a state of high risk and next to cardiovascular disease. As per the WHO, the risk of diabetes is expected to rise about 511 million by 2030. In quest of novel targets for type-2 diabetes, many targets were elucidated, such as Glycogen Synthase Kinase-3 (GSK-3), Dipeptidyl Peptidase (DPP-IV), PPAR-γ, α-Glucosidase, α-Amylase, GLP-1, and SGLT. Among the targets, GSK-3 was reported to be an effective target for the treatment of diabetes. In the metabolism of glycogen, GSK is a regulatory enzyme for the biosynthesis of glycogen (glycogenesis). It catalyzes the synthesis of a linear unbranched molecule with 1,4-α-glycosidic linkages. GSK-3 family has two isoenzymes, namely α and ß, which differ in their Nand C- terminal sequences and are semi-conservative multifunctional serine/threonine kinase enzymes. In this chapter, we discuss an overview of general diabetic mechanisms and how GSK-3 modulation may influence these processes. Mutation in the GSK-3 complex causes diabetes. Synthetic and natural scaffolds modulate GSK-3 against diabetes and leading to its optimization for the development of GSK-3 inhibitors. This review mainly focuses on the development of GSK-3 inhibitors and highlights current and potential future therapeutic approaches that support the notion of targeting glucose metabolism with novel antidiabetic agents.

Nanoparticles guided drug delivery and imaging in gastric cancer.

Gastric cancer represents a deadly malignancy worldwide, yet current therapeutic regimens remain ineffective. Nanoparticle (NP) -based solutions could allow the design of novel therapeutic methods to eliminate this fatal disease. NPs typically carry out a significant role in multifunctional, multimodal imaging, and drug delivery carriers. In the recent decade, they have emerged as candidate approaches for the design of novel treatment strategies. Tumor nanotherapeutics characteristically possess various distinct advantages compared to conventional anti-cancer medications, which suffer from nonspecific bio-distribution, low solubility, and poor bioavailability. In this review, we will discuss the application of NPs in diagnosis and controlled drug delivery in gastric cancer (GC). We will focus on various NPs-based strategies employed against GC.

Computational analysis of nuclear factor-κB and resveratrol in colorectal cancer.

Nuclear factor κB (NF-κB), a dimeric transcription factor, is a major regulator and an important determinant of the biological characteristics of tumour cells. Some antioxidants or protease inhibitors have been found to act against NF-κB to suppress colorectal cancer (CRC). In the current investigation, a computational study was performed to investigate the molecular interaction between NF-κB and resveratrol. Molecular docking studies revealed that, resveratrol with NF-κB are predicted to be quite effective. The application of molecular dynamics simulation (MDS) tactics has considerably supported in increasing the prediction precision of the outcomes. Further, this study revealed that NF-κB could be a potential target for various anti-cancerous drugs for cancer therapeutics. Furthermore, animal investigations are necessary to confirm the efficacy and evaluate potency of target and drugs.Communicated by Ramaswamy H. Sarma.

Engineered nanoparticles for imaging and drug delivery in colorectal cancer.

Colorectal cancer (CRC) is one of the deadliest diseases worldwide due to a lack of early detection methods and appropriate drug delivery strategies. Conventional imaging techniques cannot accurately distinguish benign from malignant tissue, leading to frequent misdiagnosis or diagnosis at late stages of the disease. Novel screening tools with improved accuracy and diagnostic precision are thus required to reduce the mortality burden of this malignancy. Additionally, current therapeutic strategies, including radio- and chemotherapies carry adverse side effects and are limited by the development of drug resistance. Recent advances in nanotechnology have rendered it an attractive approach for designing novel clinical solutions for CRC. Nanoparticle-based formulations could assist early tumor detection and help to overcome the limitations of conventional therapies including poor aqueous solubility, nonspecific biodistribution and limited bioavailability. In this review, we shed light on various types of nanoparticles used for diagnosis and drug delivery in CRC. In addition, we will explore how these nanoparticles can improve diagnostic accuracy and promote selective drug targeting to tumor sites with increased efficiency and reduced cytotoxicity against healthy colon tissue.

Targeting STAT proteins via computational analysis in colorectal cancer.

Colorectal cancer (CRC) is the third most common cancer diagnosed worldwide making it a serious global challenge. CRC progression results from dysregulated cytoplasmic transcription factors, including signal transducer and activator of transcription (STAT) proteins that are involved in JAK-STAT pathway. The STAT proteins contain a conserved SH2 domain that facilitates the initiation of STAT activation via binding to tyrosine motifs followed by STAT dimerization. The STAT proteins include STAT1, STAT2 and STAT3 which all facilitate therapeutic targets for many drugs, since they are associated with pathogenesis in various cancers such as CRC. Genistein is an efficient chemopreventive phytochemical drug against CRC. The current investigation presents a computational study performed to investigate the molecular interaction between STAT1, STAT2 and STAT3 proteins with genistein. The molecular dynamic simulation was conducted for STAT2 protein. The studies from molecular docking revealed that the interaction of STAT proteins and genistein is predicted to be effective with better binding energies. Furthermore, targeting STAT3 could be an efficient therapeutic target and understanding the interaction between STAT3 and genistein can help to contribute to a better inhibition process for CRC progression. Treatment with genistein led to significant suppression of cell proliferation and STAT3 protein expression in both CRC (HCT 116 and HT-29) cell lines. This further provides development of efficient STAT inhibitors with better potency and bioavailability.

Resveratrol binds and activates RKIP protein in colorectal cancer.

Raf-1 kinase inhibitory protein (RKIP) acts as a tumor cell metastasis suppressor and prognostic indicator for survival in various cancers. Its use is predicted to improve therapy for various malignancies, including colorectal cancer (CRC). RKIP, frequently denoted as phosphatidylethanolamine-binding protein 1, is expressed in all normal mammalian tissues. RKIP functions as an inhibitor of the Raf-1, PI-3K, and MAP kinase (MAPK) pathways. In this study, we found that resveratrol induced the expression of RKIP at protein levels. To elucidate the structural basis of the interaction between resveratrol and RKIP, we performed computational studies that explore the binding affinity and ligand efficacy of resveratrol against RKIP. This study reveals the prognostic significance of RKIP metastasis suppressor activity against CRC and its structural arrangements during drug-target interactions.

Resveratrol binds and inhibits transcription factor HIF-1α in pancreatic cancer.

Hypoxia-inducible factor-1 alpha (HIF-1α) has been recognized as one of the essential regulators that is expressed in greater levels in pancreatic cancer (PC) and is connected with poor prognosis. Resveratrol was identified as a natural compound with many biological functions, with anti-inflammatory, antioxidant, and anticancer effects that inhibit the proliferation and progression of PC cells caused by HIF-1α. The current investigation explored the binding affinity and ligand efficacy of resveratrol against HIF-1α using an in silico approach, and the execution of molecular dynamics simulation (MDS) increased the prediction precision of these outcomes. This is the first study that provides an in silico characterization of the interaction between resveratrol and HIF-1α and its spatial structural arrangements in pancreatic cancer therapy, providing an in-depth analysis of their drug target interactions.

HIF-1α and RKIP: a computational approach for pancreatic cancer therapy.

Protein-protein interactions (PPIs) are important biochemical processes that represent a major challenge in modern biology. Current approaches, which include high-throughput screening and computer aided ligand design, have limitations regarding the identification of hit matter. This current investigation focuses on computational study for protein-protein docking of hypoxia inducible factor-1α (HIF-1α), a tumor inducible factor, and Raf-1 kinase inhibitory protein (RKIP), a tumor metastasis suppressor. These are individually crystallized structures of interacting proteins, which interact to generate a conformational space. HIF activity in pancreatic tumors is determined by hypoxia and HIF-1α subunit availability. RKIP can be used as a prognostic indicator in a number of tumors. The interaction of RKIP with HIF-1α protects against pancreatic cancer (PC) metastasis by inhibiting its hypoxia function. We have explored the binding affinity between both the proteins with the HADDOCK (high ambiguity driven protein-protein docking) server, which determined that 158 structures in 11 clusters represent 79.0% of water-refined models. Of the best 10 clusters, the structures of cluster 2 were found to be better, as they had the lowest Z-score. Further supporting HIF-1α-RKIP interaction, pulldown assay has shown dissociation of RKIP from HIF-1α after CoCl2 treatment in both PC cell lines.

Pathophysiology, Etiology, Epidemiology of Type 1 Diabetes and Computational Approaches for Immune Targets and Therapy.

Autoimmune diseases occur when the body's natural defense system fails to differentiate its own cells from the foreign cells and mistakenly attacks the healthy cells. Among the autoimmune diseases, the most common serious disease is the type 1 diabetes (T1D). Biomarkers like c-peptide, autoantibodies, and glycated molecules are now widely used for the early diagnosis of diabetes. However, the diverse nature of biomarkers and the available autoantibodies as biomarkers are not enough to differentiate the heterogeneity inherent in T1D. Novel biomarkers have allowed the introduction of bioinformatics for assimilating the new data into clinical tools. Computer-aided drug design contributes to the discovery of novel autoantibodies, and molecular docking promises to enhance it. Moreover, the study of the pathophysiology of diabetes via molecular simulation has been proposed. In this review article, we focus on the characterization of the etiology, epidemiological factors, and mechanisms of hyperglycemia that induce cellular damage due to oxidative stress and proinflammatory responses. We also decribe novel biomarkers used for the detection of ß-cell destruction and diagnosis at early stages. Bioinformatics tools including molecular docking, sequence alignment, and homology modeling are also presented. This report supports researchers in drug design, in disease detection at an early phase, and in therapy development for T1D-associated complications.