Title: 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
comprising VDAC1-peptide mimetic tubulin targeted HA141-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.
Name: I. Grigoriadis
Biogenea Pharmaceuticals Ltd, Thessaloniki, Greece, PC54627,
Abstract: Drug discovery and development is an interdisciplinary, expensive and timeconsuming 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.
Keywords: novel class, hyper-multi-targeted, computer-aided, CREKA/YIGSR-peptide,
mimotopic, dual Inhibitor, tumor growth, metastasis related glioblastoma, conserved motiflike peptide domains, VDAC1-peptide mimetic tubulin targeted, HA14-1-based multivalent.
Biography Ioannis Grigoriadis has completed his PharmacistD at the age of 24 years from
Aristotle University of Thessaloniki and doctoral studies from University of Ioannina Medical
School. He is the scientific director of Biogenea Pharmaceuticals Ltd, a premier biotechnology
personalized cancer vaccination service organization. He has published more than 25 papers
in reputed journals and has been serving as an editorial board member of repute.