Designing the angiogenic inhibitor for brain tumor via disruption of VEGF and IL17A expression

Abstract

Glioblastoma multiforme is a highly malignant, heterogenic, and drug resistant tumor. The blood–brain barrier (BBB), systemic cytotoxicity, and limited specificity are the main obstacles in designing brain tumor drugs. In this study a computational approach was used to design brain tumor drugs that could downregulate VEGF and IL17A in glioblastoma multiforme type four. Computational screening tools were used to evaluate potential candidates for antiangiogenic activity, target binding, BBB permeability, and ADME physicochemical properties. Additionally, in vitro cytotoxicity, migration, invasion, tube formation, apoptosis, ROS and ELISA assays were conducted for molecule 6 that was deemed most likely to succeed. The efflux ratio of membrane permeability and calculated docking scores of permeability to glycoproteins (P-gps) were used to determine the BBB permeability of the molecules. The results showed BBB permeation for molecule 6, with the predicted efficiency of 0.55 kcal/mol and binding affinity of − 37 kj/mol corresponding to an experimental efflux ratio of 0.625 and predicted − 15 kj/mol of binding affinity for P-gps. Molecule 6 significantly affected the angiogenesis pathways by 2-fold downregulation of IL17A and VEGF through inactivation of active sites of HSP90 (predicted binding: − 37 kj/mol, predicted efficiency: 0.55 kcal/mol) and p23 (predicted binding: 12 kj/mol, predicted efficiency: 0.17 kcal/mol) chaperon proteins. Additionally, molecule 6 activated the 17.38% relative fold of ROS level at 18.3 μg/mL and upregulated the caspase which lead the potential synergistic apoptosis through the antiangiogenic activity of molecule 6 and thereby the highly efficacious anticancer upshot. The results indicate that the binding of the molecules to the therapeutic target is not essential to produce a lethal effect on cancer cells of the brain and that antiangiogenic efficiency is much more important.

Introduction

Glioblastoma multiforme (GBM) is a highly vascularized primary brain tumor with a moderate life-span of 1 and a half years, even in patients receiving standard chemotherapy, radiotherapy, and surgery (Stupp et al., 2009). It is highly angiogenic and exhibits resistance to chemotherapy drugs, which makes it very difficult to treat. The toxicity of chemo drugs and their inability to permeate the blood–brain-barrier (BBB) are the principal obstacles for the development of brain tumor drugs. The epithelial-like tight junctions within the brain capillary endothelium create the BBB, and to overcome it, researchers seek small compounds suitable for BBB permeation. Creating microbubbles that can entrap a drug and then pass through the BBB is the latest addition to this field (Xiang et al., 2014). This tactic may eventually prove useful, but at first a safe targeted brain tumor drug must be found. Screening for drugs with suitable characteristics can involve the computational study of ADME profiling, toxicity screening, oral central nervous system (CNS) drug scoring, intravenous CNS scoring, half-life characterization models, QSAR analysis and docking binding energy and efficiency analysis prior to in vitro and in vivo experiments to optimize suitable parameters of candidate brain tumor drugs. Despite the development of computational drug discovery tools and a wealth of knowledge, modern chemotherapeutic drugs are still toxic to normal cells and behave heterogeneously in the patient. To overcome this problem, many patients turn into alternative and complementary medicine instead of synthetic modern drugs (De Smet, 2004). However, alternative drugs offer little hope for the treatment of brain tumors because of their weak competency with Lipinski rules of five and diverse polypharmacology (De Smet, 2004). The current understanding of the molecular mechanism by which brain cancer develops combined with in silico-based screening of organic compounds could be utilized fruitfully to develop the brain tumor drugs. In our laboratory, we designed several potential drug compounds based on the angiogenic and inflammatory pathways (Haque et al., 2012). The molecules developed had low molecular weight so that they could penetrate the BBB and were designed to target protein kinases, inflammatory cytokines, and intracellular enzymes. These biological markers may be involved in the mutation, overexpression, and irregular maintenance of biochemical pathways in cancers. Such a rational drug design strategy should lead to the discovery of potential drugs that specifically target brain tumors and are safer than traditional chemotherapy.

Identifying and optimizing compounds that target angiogenic factors of VEGF i.e. vascular endothelial growth factor and its receptors VEGFRs could be a milestone to develop safer drugs. VEGF and VEGFR are essential molecules in physiological and pathological angiogenesis (Ferrara, 2002, Ivy et al., 2009). Briefly, cancer cells send signals via VEGF, which binds to the VEGFR2 (transmembrane) on endothelial cells, which in turn leads the phosphorylation of various downstream signal transduction proteins, including mitogen-activated protein kinase (MAPK), signal transducer and activator of transcription-3 (STAT3), protein kinase B (AKT), focal adhesion kinase (FAK), and Src kinase (Wiesmann et al., 1997, Lu et al., 2000, Van der Meel et al., 2011). Among the three subtypes of VEGFR, VEGFR2 stimulated by VEGF can support cancer cell proliferation, vascular permeation, migration, and cell survival in the angiogenesis pathway. Usually, VEGR is not activated, but activation occurs in embryonic and tumor angiogenesis via VEGF autophosphorylation due to oncogenic mutation (Rak et al., 1995) and metabolic factors (oxygen, glucose) (Shweiki et al., 1995, Wedge et al., 2005). VEGF also, is upregulated in the presence of interleukin 17 (IL17). In glioblastoma, elevated IL17A levels are observed due to the dysfunction of T helper cells (Andaloussi et al., 2008) and infiltration of immunosuppressive microglia and macrophage cells (Watters et al., 2005). IL17A also activates platelet endothelial cell adhesion molecule (PECAM-1), also known as cluster of differentiation 31 (CD31) cells, and the IL-6–STAT3 signaling pathway and suppresses cytotoxic T lymphocytes, which reduces their cytotoxic effect via the support of CD8 (Nam et al., 2008, Toh et al., 2009, Wang et al., 2009, Stewart et al., 2006).

In the inflammatory and tumor microenvironment, an induced level of IL17A is observed through heat-shock protein 90 (Hsp90) and Act1-interacting protein. In the IL17A dependent phosphorylation, IL-17 induced signaling and gene expression are abolished due to inhibition of Hsp90 chaperone function by the loss of interaction between Hsp90 and IL17A (Toh et al., 2009). IL17A promoted the upregulation of pro-inflammatory and neutrophil-mobilizing cytokines and chemokines including JAK2/STAT3, MAPK, NF-κB, IL-6, IL-8, MMP2, VEGF, GM-CSF, GCSF, TNF-α, TGF-β and IL-1β. The interaction between Hsp90 and Akt is reported by Sato et al. (2000) and Basso et al. (2002). Basso et al. (2002) has reported Cdc37 as one of the chaperon proteins for Hsp90 which can protect the Akt in the Akt–Hsp90 complex, from proteasome-mediated degradation. Wang et al. (2002) has found Hsp90 and Cdc37 in the IKK complex (Wang et al., 2012). In the Hsp90 chaperoning system, p23s act as a co-chaperone that is specific for steroid receptors and fibrillization of the protein. The molecular targets for the inhibition of the IKK complex or inactivation Cdc37 and p23 proteins that are co-chaperones of HSP90 might suppress the IL-17, IL-6 and VEGF activity.

Several VEGFR2 inhibitors have been developed, such as FDA approved drugs (sunitinib, sorafenib, and vandetanib). Besides, several drugs are under clinical trial. However, the compound binding with the Hsp90 chaperoning system could downregulate VEGF and IL17A as a dual activity for the inhibition of angiogenesis which is an exciting new approach to drug discovery and development. The focus of this study was to target VEGF and IL17A specifically for glioblastoma in our drug discovery process. We applied a sequential in silico approach to determine ADME, cytotoxicity, and brain penetration for candidate antiglioblastoma compounds before conducting the in vitro and in vivo study. Our objective is that the rules-motivated algorithmic-driven computational approach and side by side in vitro experiments will help to characterize, standardize, and identify the activity of the molecules to optimize their multiple biological parameters. This approach can provide guidelines for cost-effective wet lab synthesis of new drugs.