The reduced RMSD values possess capability to predict the experience from the conformational dataset compounds

The reduced RMSD values possess capability to predict the experience from the conformational dataset compounds.(DOCX) pone.0196312.s001.docx (17K) GUID:?396CB4A6-F6F4-4198-A772-126D4168ED0D Data Availability StatementAll relevant data are inside the paper and its own Supporting Information data files. Abstract Insulin-like development factor 1 receptor (IGF-1R) can be an essential therapeutic focus on for breast tumor treatment. features and was additional screened through the use of Lipinski positive compounds. Finally, the most effective drug, fulvestrant, was selected. Fulvestrant is a selective estrogen receptor down regulator (SERD). This inhibitor was further studied by using both and approaches that showed the targeted effect of fulvestrant Chebulinic acid in ER+ MCF-7 cells. Results suggested that fulvestrant has selective cytotoxic effect and a dose dependent response on IRS-1, IGF-1R, Chebulinic acid PDZK1 and ER- in MCF-7 cells. PDZK1 can be an important inhibitory target using fulvestrant because it directly regulates IGF-1R. Introduction Insulin-like growth factor type-1 receptor (IGF-1R), a trans-membrane tyrosine kinase, is involved in normal body growth and development [1]. It has two extracellular ligand binding domains, alpha () and beta () [2, 3]. IGF-1R is regulated by the binding of ligands, insulin-like growth factors such as IGF-1, to process cell proliferation and differentiation [4C6]. Previous and studies have linked higher levels of IGF-1R and its ligands with various types of cancer development and progression including breast cancer [7C10], prostate cancer [11], myeloma [12] and colon cancer [13, 14]. About 50% of the breast tumors have been reported with an over expression of IGF-1R [15]. Although several clinical trials inhibiting this receptor have been completed but unfortunately monoclonal antibodies and tyrosine kinase inhibitors targeting IGF-1R failed in phase III clinical trials Chebulinic acid for several reasons [16C18]. The activation of IGF-1R upon ligand binding induces phosphorylation of an adopter protein insulin receptor substrate-1 (IRS-1) which is also linked to various cancer subtypes [6, 19]. The signaling cascade of IGF-1R begins by the activation of several downstream mediators such as phosphoinositide3 kinase-serine/threonine protein kinases (PI3k-Akt), mitogen activated kinase-extracellular signal regulated kinase (MEK-ERK) and ataxia telangiectasia mutated-ataxia telangiectasia Rad3 related (ATM-ATR) pathways [19C23]. Deregulation of these pathways induce over-expression of estrogen receptor-alpha (ER-) which indirectly stimulates the activation of PDZ domain containing 1 (PDZK1) gene expression [24]. PDZK1 protein, also known as NHERF (Na+/H+ exchange regulatory factor), interacts with phospholipase C- (PLC-) and contributes to the regulation of G-protein coupled receptor (GPCR)-mediated signaling [25]. The increased expression of PDZK1 leads to the subsequent phosphorylation of ERK1/2 and calcium ions (Ca2+) signaling in response to somatostatin (SST) and IGF-1R [25, 26]. The direct molecular interaction between IGF-1R and PDZK1 enhances expression of ER- associated with breast cancer metastasis [26]. The IGF-1R pathway facilitates loss of function mutations of multiple Chebulinic acid tumor suppressor and oncogenes including breast cancer susceptibility genes 1/2 (BRCA1/2), p53 and mouse double minute 2 homolog (Mdm2) which drastically influence resistance to apoptosis [20, 27]. This study focused on the identification of inhibitors against IGF-1R by using well-known approaches, i.e. pharmacophore modeling [28], virtual screening (VS) [29] and continuous hybrid Petri net (PN) [30]. Ligand based pharmacophore modeling is used to Rabbit Polyclonal to Collagen II generate a set of chemical compounds with required pharmacophore features such as hydrophobic (HyD), aromatic (Aro), hydrogen bond acceptors (HBAs) or donors (HBDs), cations, and anions [31C33]. This ligand based modeling defines the supramolecular interactions of the above mentioned features with the desired molecular target to block its biological activity [32]. In order to identify the potential inhibitory drugs that can bind to the target, virtual screening (VS) is performed. VS is a computational drug discovery technique used to screen these chemical structures which are most likely to bind to one or more active ligands [33, 34]. This study was further enriched with continuous PN modeling [35], which allows us to analyze the delay parameters of the involved entities (proteins/genes). PN is a graph theoretical approach, which has been successfully implemented for the models and analysis of homeostatic/pathological response of IGF-1R associated network with breast cancer. The computational modeling provides a new insight to analyze the complex dynamical interactions among genes and proteins related to multifactorial diseases such as cancer. We have deployed a molecular drug screening approach which screened the drugs that bind to the active site of target molecules and inhibit their activity. The purpose of this study is to.