Algorithmic modeling quantifies the complementary contribution of metabolic inhibitions to gemcitabine efficacy.

Gemcitabine (2,2-difluorodeoxycytidine, dFdC) is a prodrug widely used for treating various carcinomas. Gemcitabine exerts its clinical effect by depleting the deoxyribonucleotide pools, and incorporating its triphosphate metabolite (dFdC-TP) into DNA, thereby inhibiting DNA synthesis. This process...

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Autores principales:	Ozan Kahramanoğullari, Gianluca Fantaccini, Paola Lecca, Daniele Morpurgo, Corrado Priami
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2012
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/b33cbb15a1c647dab4e2a072cf1accb3
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spelling	oai:doaj.org-article:b33cbb15a1c647dab4e2a072cf1accb32021-11-18T08:05:39ZAlgorithmic modeling quantifies the complementary contribution of metabolic inhibitions to gemcitabine efficacy.1932-620310.1371/journal.pone.0050176https://doaj.org/article/b33cbb15a1c647dab4e2a072cf1accb32012-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/23239976/pdf/?tool=EBIhttps://doaj.org/toc/1932-6203Gemcitabine (2,2-difluorodeoxycytidine, dFdC) is a prodrug widely used for treating various carcinomas. Gemcitabine exerts its clinical effect by depleting the deoxyribonucleotide pools, and incorporating its triphosphate metabolite (dFdC-TP) into DNA, thereby inhibiting DNA synthesis. This process blocks the cell cycle in the early S phase, eventually resulting in apoptosis. The incorporation of gemcitabine into DNA takes place in competition with the natural nucleoside dCTP. The mechanisms of indirect competition between these cascades for common resources are given with the race for DNA incorporation; in clinical studies dedicated to singling out mechanisms of resistance, ribonucleotide reductase (RR) and deoxycytidine kinase (dCK) and human equilibrative nucleoside transporter1 (hENT1) have been associated to efficacy of gemcitabine with respect to their roles in the synthesis cascades of dFdC-TP and dCTP. However, the direct competition, which manifests itself in terms of inhibitions between these cascades, remains to be quantified. We propose an algorithmic model of gemcitabine mechanism of action, verified with respect to independent experimental data. We performed in silico experiments in different virtual conditions, otherwise difficult in vivo, to evaluate the contribution of the inhibitory mechanisms to gemcitabine efficacy. In agreement with the experimental data, our model indicates that the inhibitions due to the association of dCTP with dCK and the association of gemcitabine diphosphate metabolite (dFdC-DP) with RR play a key role in adjusting the efficacy. While the former tunes the catalysis of the rate-limiting first phosphorylation of dFdC, the latter is responsible for depletion of dCTP pools, thereby contributing to gemcitabine efficacy with a dependency on nucleoside transport efficiency. Our simulations predict the existence of a continuum of non-efficacy to high-efficacy regimes, where the levels of dFdC-TP and dCTP are coupled in a complementary manner, which can explain the resistance to this drug in some patients.Ozan KahramanoğullariGianluca FantacciniPaola LeccaDaniele MorpurgoCorrado PriamiPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 7, Iss 12, p e50176 (2012)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Ozan Kahramanoğullari Gianluca Fantaccini Paola Lecca Daniele Morpurgo Corrado Priami Algorithmic modeling quantifies the complementary contribution of metabolic inhibitions to gemcitabine efficacy.
description	Gemcitabine (2,2-difluorodeoxycytidine, dFdC) is a prodrug widely used for treating various carcinomas. Gemcitabine exerts its clinical effect by depleting the deoxyribonucleotide pools, and incorporating its triphosphate metabolite (dFdC-TP) into DNA, thereby inhibiting DNA synthesis. This process blocks the cell cycle in the early S phase, eventually resulting in apoptosis. The incorporation of gemcitabine into DNA takes place in competition with the natural nucleoside dCTP. The mechanisms of indirect competition between these cascades for common resources are given with the race for DNA incorporation; in clinical studies dedicated to singling out mechanisms of resistance, ribonucleotide reductase (RR) and deoxycytidine kinase (dCK) and human equilibrative nucleoside transporter1 (hENT1) have been associated to efficacy of gemcitabine with respect to their roles in the synthesis cascades of dFdC-TP and dCTP. However, the direct competition, which manifests itself in terms of inhibitions between these cascades, remains to be quantified. We propose an algorithmic model of gemcitabine mechanism of action, verified with respect to independent experimental data. We performed in silico experiments in different virtual conditions, otherwise difficult in vivo, to evaluate the contribution of the inhibitory mechanisms to gemcitabine efficacy. In agreement with the experimental data, our model indicates that the inhibitions due to the association of dCTP with dCK and the association of gemcitabine diphosphate metabolite (dFdC-DP) with RR play a key role in adjusting the efficacy. While the former tunes the catalysis of the rate-limiting first phosphorylation of dFdC, the latter is responsible for depletion of dCTP pools, thereby contributing to gemcitabine efficacy with a dependency on nucleoside transport efficiency. Our simulations predict the existence of a continuum of non-efficacy to high-efficacy regimes, where the levels of dFdC-TP and dCTP are coupled in a complementary manner, which can explain the resistance to this drug in some patients.
format	article
author	Ozan Kahramanoğullari Gianluca Fantaccini Paola Lecca Daniele Morpurgo Corrado Priami
author_facet	Ozan Kahramanoğullari Gianluca Fantaccini Paola Lecca Daniele Morpurgo Corrado Priami
author_sort	Ozan Kahramanoğullari
title	Algorithmic modeling quantifies the complementary contribution of metabolic inhibitions to gemcitabine efficacy.
title_short	Algorithmic modeling quantifies the complementary contribution of metabolic inhibitions to gemcitabine efficacy.
title_full	Algorithmic modeling quantifies the complementary contribution of metabolic inhibitions to gemcitabine efficacy.
title_fullStr	Algorithmic modeling quantifies the complementary contribution of metabolic inhibitions to gemcitabine efficacy.
title_full_unstemmed	Algorithmic modeling quantifies the complementary contribution of metabolic inhibitions to gemcitabine efficacy.
title_sort	algorithmic modeling quantifies the complementary contribution of metabolic inhibitions to gemcitabine efficacy.
publisher	Public Library of Science (PLoS)
publishDate	2012
url	https://doaj.org/article/b33cbb15a1c647dab4e2a072cf1accb3
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Algorithmic modeling quantifies the complementary contribution of metabolic inhibitions to gemcitabine efficacy.

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