The Microsoft Research-University of Trento Centre for Computational and Systems Biology, Rovereto (Trento), Italy.
PLoS One. 2012;7(12):e50176. doi: 10.1371/journal.pone.0050176. Epub 2012 Dec 11.
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.
吉西他滨(2,2-二氟脱氧胞苷,dFdC)是一种广泛用于治疗各种癌症的前体药物。吉西他滨通过耗尽脱氧核苷酸池并将其三磷酸代谢物(dFdC-TP)掺入 DNA 来发挥其临床作用,从而抑制 DNA 合成。该过程将细胞周期阻断在早期 S 期,最终导致细胞凋亡。吉西他滨掺入 DNA 与天然核苷 dCTP 竞争发生。这些级联之间为 DNA 掺入而进行的间接竞争的机制是通过竞争来实现的;在专门用于确定耐药机制的临床研究中,核糖核苷酸还原酶(RR)和脱氧胞苷激酶(dCK)和人平衡核苷转运蛋白 1(hENT1)与吉西他滨的疗效相关,因为它们在 dFdC-TP 和 dCTP 的合成级联中发挥作用。然而,直接竞争,即这些级联之间的抑制作用,仍有待量化。我们提出了一种吉西他滨作用机制的算法模型,该模型经过独立实验数据验证。我们在不同的虚拟条件下进行了计算机模拟实验,这些条件在体内很难实现,以评估抑制机制对吉西他滨疗效的贡献。与实验数据一致,我们的模型表明,由于 dCTP 与 dCK 的结合以及吉西他滨二磷酸代谢物(dFdC-DP)与 RR 的结合引起的抑制作用在调节疗效方面起着关键作用。前者调节 dFdC 的限速第一磷酸化的催化,后者负责耗尽 dCTP 池,从而通过核苷转运效率的依赖性对吉西他滨的疗效做出贡献。我们的模拟预测了从低效到高效的连续非疗效状态的存在,其中 dFdC-TP 和 dCTP 的水平以互补的方式耦合,这可以解释某些患者对该药物的耐药性。