scholarly journals A Synthetic Strategy for Conjugation of Paromomycin to Cell-Penetrating Tat(48-60) for Delivery and Visualization into Leishmania Parasites

2017 ◽  
Vol 2017 ◽  
pp. 1-7 ◽  
Author(s):  
Sira Defaus ◽  
Maria Gallo ◽  
María A. Abengózar ◽  
Luis Rivas ◽  
David Andreu

A successful approach to deliver paromomycin, a poorly absorbed aminoglycoside antibiotic, to parasite cells is reported, based on selective protection of amino and hydroxyl groups followed by conjugation to a fluorolabeled, PEG-functionalized cell-penetrating Tat(48-60) peptide. The resulting construct is efficiently internalized into Leishmania cells, evidencing the fitness of cell-penetrating peptides as vectors for efficiently transporting low-bioavailability drugs into cells.

2020 ◽  
Vol 16 ◽  
Author(s):  
Ali Ahmadi ◽  
Hadi Esmaeili Gouvarchin Ghaleh ◽  
Ruhollah Dorostkar ◽  
Mahdieh Farzanehpour ◽  
Masoumeh Bolandian

Abstract:: Cancer is a genetic disease triggered by gene mutations, which control cell growth and their functionality inherited from previous generations. The targeted therapy of some tumors was not especially successful. A host of new techniques can be used to treat aptamer-mediated targeting, cancer immunotherapy, cancer stem cell (CSC) therapy, cell-penetrating peptides (CPPs), hormone therapy, intracellular cancer cell targeting, nanoparticles, and viral therapy. These include chemical-analog conjugation, gene delivery, ligand-receptor-based targeting, prodrug therapies, and triggered release strategies. Virotherapy is a biotechnological technique for turning viruses into therapeutic agents by the reprogramming of viruses to cure diseases. In several tumors, including melanoma, multiple myeloma, bladder cancer, and breast cancer, the oncolytic capacity of oncolytic Coxsackievirus has been studied. The present study aims to assess oncolytic Coxsackievirus and its mechanisms of effect on cancer cells.


2019 ◽  
Vol 15 (3) ◽  
pp. 206-211 ◽  
Author(s):  
Jihui Tang ◽  
Jie Ning ◽  
Xiaoyan Liu ◽  
Baoming Wu ◽  
Rongfeng Hu

<P>Introduction: Machine Learning is a useful tool for the prediction of cell-penetration compounds as drug candidates. </P><P> Materials and Methods: In this study, we developed a novel method for predicting Cell-Penetrating Peptides (CPPs) membrane penetrating capability. For this, we used orthogonal encoding to encode amino acid and each amino acid position as one variable. Then a software of IBM spss modeler and a dataset including 533 CPPs, were used for model screening. </P><P> Results: The results indicated that the machine learning model of Support Vector Machine (SVM) was suitable for predicting membrane penetrating capability. For improvement, the three CPPs with the most longer lengths were used to predict CPPs. The penetration capability can be predicted with an accuracy of close to 95%. </P><P> Conclusion: All the results indicated that by using amino acid position as a variable can be a perspective method for predicting CPPs membrane penetrating capability.</P>


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