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Abstract

Candida albicans is one of the most common opportunistic human fungal pathogens. In healthy human populations, it is a member of the normal flora of the skin, genital, and intestinal mucosa. However, C. albicansas well as other Candida species (e.g., C. parapsilosis or C. krusei) may lead to morbidity and mortality in immunocompromised patients as a consequence of fungal overgrowth and severe cutaneous or systemic infections. For the treatment of invasive candidiasis, amphotericin B-based preparations, azoles, and echinocandins are used. In the therapy of mucocutaneous infections (e.g., vaginal infections), azoles are the dominant agents. The synthetic peptides TKCFQWQRNMRKVRGPPVSCIKR Lfpep and FFSASCVPGADKGQFPNLCRLCAGTGENKCA kaliocin-1 include the sequences from positions 18 to 40 and 153 to 183 of human lactoferrin, respectively. In this research we will give the lead in a new drug discovery paradigm that focuses on mechanisms of action. Furthermore, the technologies developed in this project will offer new pharmaceutical chemico-scaffolds to facilitate basic scientific research. Within the next three years, Biogenea SA will contribute proprietary resources to take the new drug compounds through clinical trials and ultimately to market. Pathogenic microbes can recruit to their cell surface human proteins that are components of important proteolytic cascades involved in coagulation, fibrinolysis and innate immune response. Once located at the bacterial or fungal surface, such deployed proteins might be utilized by pathogens to facilitate invasion and dissemination within the host organism by interfering with functionality of these systems or by exploiting specific activity of the bound enzymes. Aim of the study presented here is to perform Ultimate target-ligand based Quantum learning Drug Discovery without quantum memory approaches using predicted binding affinity matrices as a Chemogenomics-Driven NCR, Lfpep, Brevinin-1Sa and kaliocin-1 peptidomimetic Neo-agent against Candida albicans antimicrobial CXG motif signatures. was to characterize this phenomenon in Candida parapsilosis (Ashford) Langeron et Talice – an important causative agent of systemic fungal infections (candidiases and candidemias) in humans.

Keywords

Ultimate target-ligand; experimental; predicted binding affinity; matrices;Quantum learning; quantum memory; Chemogenomics-Driven; NCR, Lfpep, Brevinin-1Sa; kaliocin-1 peptidomimetic; Drug Discovery; Neo-agent; Candida albicans; antimicrobial; CXG motif; signatures; Chemogenomics-Driven;peptidomimetic; Drug Discovery;Human Fungal Pathogen;Candida albicans; plasminogen; high-molecular-mass; kininogen; cell surface-exposed;

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) Ultimate target-ligand based Quantum learning Drug Discovery without quantum memory approaches using predicted binding affinity matrices as a Chemogenomics-Driven NCR, Lfpep, Brevinin-1Sa and kaliocin-1 peptidomimetic Neo-agent against Candida albicans antimicrobial CXG motif signatures.

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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