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Abstract

Pancreatic cancer is a highly lethal disease and little therapeutic progress has been achieved in the last decades. Vaccination against cancer is currently tested in many clinical trials as a new treatment modality. Micro-RNA155 (mir-155) has been shown to play a role in germinal center formation, T cell inflammation, and regulatory T cell development. In other studies, the role of mir-155 in cytotoxic T cell function has further been evaluated. In previous studies it has been reported that mice lacking mir-155 have impaired CD8(+) T cell responses to infections with lymphocytic choriomeningitis virus and the intracellular bacteria Listeria monocytogenes. These data suggested that mir-155 may be a good target for therapies aimed at modulating immune responses.In pancreatic cancer, few molecularly characterised antigens have so far been available for use in vaccines. However, the catalytic subunit of telomerase, hTERT, expressed in 85–90% of human cancer tissues (Vasef et al, 1999) and an attractive ‘universal’tumour antigen (Autexier, 1999), is also expressed in pancreatic cancer where it has been used diagnostically (Suehara et al, 1998; Uehara et al, 1999). By turning on hTERT and telomerase activity, cancer cells are enabled to maintain functional telomeres at the end of chromosomes, and are prevented from going into senescence. Telomerase is consequently a key enzyme in the process of immortalisation of cancer cells and has a pivotal role in carcinogenesis. A 100-mer MUC1 peptide consisting of the extracellular tandem repeat domain and incomplete Freund’s adjuvant were subcutaneously administered to 6 pancreatic and 3 bile duct cancer patients at weeks 1, 3 and 5 and doses ranging from 300 to 3000 microg. Extensive analyses demonstrate how these algorithms can be part of an iterative combinatorial chemistry procedure to speed up the discovery and the validation of peptide mimotopic novel leads. Moreover, the proposed approach introduce the use of known ligands for our recent multi-target machine learning predictors in Recent multi-target machine learning predictors assessing Intra-Metastasis MicroRNA-155 trainig data sets on MUC1- LLDILDTAGHEEYSAMRDQ targeted domains by a telomerase GV1001 peptide mimetic chemo-pharmacophores for the future induction of CTL responses.

Keywords

Pilot Research; Scientific Project;Assessing; Intra-Metastasis; Administration;Autologous; Tumor Lysate-pulsed;MicroRNA-155 loaded; Dendritic Cells;immunogenic; pre-conditioned; MUC1;telomerase peptide; GV1001mimetic; polytargeted;computer-aided; predicted;chemopharmacophore;CTL responses;

Article Type

Research Article – Abstract

Publication history

Received: Sep 20, 2017
Accepted: Sep 25, 2017
Published: Oct 01, 2017

Citation

Grigoriadis Ioannis, Grigoriadis George, Grigoriadis Nikolaos, George Galazios (2017) Recent multi-target machine learning predictors assessing Intra-Metastasis MicroRNA-155 trainig data sets on MUC1- LLDILDTAGHEEYSAMRDQ targeted domains by a telomerase GV1001 peptide mimetic chemo-pharmacophores for the future induction of CTL responses.

Authors Info

Grigoriadis Nikolaos
Department of IT Computer Aided Personalized Myoncotherapy, Cartigenea-Cardiogenea, Neurogenea-Cellgenea, Cordigenea-HyperoligandorolTM,
Biogenea Pharmaceuticals Ltd,
Thessaloniki, Greece;

Grigoriadis Ioannis
Department of Computer Drug Discovery Science, BiogenetoligandorolTM,
Biogenea Pharmaceuticals Ltd,
Thessaloniki, Greece;

Grigoriadis George
Department of Stem Cell Bank and ViroGeneaTM,
Biogenea Pharmaceuticals Ltd,
Thessaloniki, Greece;

George Galazios
Professor of Obstetrics and Gynecology,
Democritus University of Thrace,
Komotini, Greece;

E-mail: biogeneadrug@gmail.com

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