Medicinal Chemistry & Computer Aided Drug Designing

Biography

Biography: I. Grigoriadis

Abstract

Drug discovery and development is an interdisciplinary, expensive and time-consuming process. Scientific advancements during the past two decades have changed the way pharmaceutical research generate novel bioactive molecules. Advances in computational techniques and in parallel hardware support have enabled in silico methods, and in particular structure-based drug design method, to speed up new target selection through the identification of hits to the optimization of lead compounds in the drug discovery process. Glioblastoma multiforme (GBM) is the most aggressive central nervous system (CNS) tumor because of its fast development, poor prognosis, difficult control and terrible mortality. Poor penetration and retention in the glioblastoma parenchyma were crucial challenges in GBM nanomedicine therapy. The peptide, YIGSR, decreases tumor growth and experimental metastasis via a 32/67 kD receptor. The CREKA-modified PAMAM could penetrate the GBM tissue deeply and enhance the retention effect, which was a promising strategy for brain tumor therapy Researchers have also designed and synthesized novel peptides that target a deadly brain cancer, glioblastoma multiforme, by binding to the IL-13Rα2 receptor, which is highly expressed by these cancer cells. The peptides cross the blood brain barrier and strongly bind specifically to IL-13Rα2. The peptides are conjugated to one or more drugs that are toxic to cells. Upon binding specifically to brain tumor cells, the peptide-drug conjugates are internalized and then kill the tumor cells without targeting normal brain cells. Alternatively, the peptides can deliver imaging molecules that can be used to precisely diagnose brain tumors. Identification of such sites will have use in defining strategies to develop therapeutics for cancer. Protein complexes involving IDPs are short-lived and typically involve short amino acid stretches bearing few "hot spots", thus the identification of molecules able to modulate them can produce important lead compounds: in this scenario peptides and/or peptidomimetics, deriving from structure-based, combinatorial or protein dissection approaches, can play a key role as hit compounds. Multivalency is a design principle that can convert inhibitors with low affinity to ones with high avidity and/or biological "activity" gauged by some relevant parameter: (for example, values of IC50 the concentration of free ligand, often approximated as the total ligand, that reduces the experimental signal to 50% of its initial value). In addition, multivalent approaches can be effective in generating high-avidity ligands for proteins with multiple binding sites from low-affinity ligands. Multivalent ligands (primarily polyvalent ones) are especially well suited for inhibiting or augmenting interactions at biological surfaces (e. g., surfaces of bacteria, viruses, cells they can prevent adhesion of these surfaces to other surfaces by grafting polymers to the surfaces of viruses to prevent adhesion to cells). Computational docking, colchicine-tubulin competitive binding, and tubulin polymerization studies demonstrated that these compounds bind at the colchicine-binding site on tubulin and inhibit the formation of microtubules. The mode of action of the VDAC-1 peptides involves dysfunction of mitochondria energy production and apoptosis induction. In this study, we confine attention to the so called ligand-based target prediction machine learning peptide mimetic drug discovery approaches, commonly referred to as drug target fishing. Here, in Biogenea we have for the first time discovered an in silico high binding free energy affinity value predicted Novel Hyper-Multi-Targeted computer-aided Inhibitor against tumor growth and experimental metastasis related Glioblastoma conserved motif-like peptide domains. These results demonstrate that the VDAC1 treating CLL peptides may assist target-fishing approaches that are currently ubiquitous in cheminformatics and can be essentially viewed as single-label peptidomimetic drug discovery schemes. Here, we have for the first time in silico Development of a novel class of hyper-multi-targeted computer-aided CREKA/YIGSR-peptide mimotopic dual Inhibitor against tumor growth, metastasis related glioblastoma conserved motif-like peptide domains VDAC1-peptide mimetic tubulin targeted HA14-1-based multivalent chemical inhibitorory promising anticancer activities as novel in silico high binding free energy affinity value pro-apoptotic annotated agent for B-cell chronic lymphocytic leukemia.