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癌症药物治疗与“纳米马达”的随机建模。

Cancer drug therapy and stochastic modeling of "nano-motors".

机构信息

Department of Chemistry, COMSATS University Islamabad, Lahore 54000, Pakistan.

Department of Mathematics, COMSATS University Islamabad, Wah Cantt, Pakistan.

出版信息

Int J Nanomedicine. 2018 Oct 15;13:6429-6440. doi: 10.2147/IJN.S168780. eCollection 2018.

DOI:10.2147/IJN.S168780
PMID:30410329
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC6198871/
Abstract

BACKGROUND

Controlled inhibition of kinesin motor proteins is highly desired in the field of oncology. Among other interventions, there exists "targeted chemotherapeutic regime/options" of selective Eg5 competitive and allosteric inhibitors, inducing cancer cell apoptosis and tumor regression with improved safety profiles.

RESEARCH QUESTION

Though promising, such studies are still under clinical trials, for the discovery of efficient and least harmful Eg5 inhibitors. The aim of this research was to bridge the computational modeling approach with drug design and therapy of cancer cells.

METHODS

A computational model, interfaced with the clinical data of "Eg5 dynamics" and "inhibitors" via special functions, is presented in this article. Comparisons are made for the drug efficacy, and the threshold values are predicted through numerical simulations.

RESULTS

Results are obtained to depict the dynamics induced by ispinesib, when used as an inhibitor of kinesin Eg5, on cancer cell lines.

摘要

背景

在肿瘤学领域，对驱动蛋白运动蛋白的控制抑制有很高的需求。除其他干预措施外，还存在选择性 Eg5 竞争性和别构抑制剂的“靶向化疗方案/选择”，通过提高安全性来诱导癌细胞凋亡和肿瘤消退。

研究问题

尽管有希望，但此类研究仍处于临床试验阶段，以发现高效且危害最小的 Eg5 抑制剂。本研究旨在将计算建模方法与癌细胞的药物设计和治疗相结合。

方法

本文提出了一种通过特殊功能与“Eg5 动力学”和“抑制剂”的临床数据相连接的计算模型。通过数值模拟对药物功效进行了比较，并预测了阈值。

结果

获得了结果以描述伊匹单抗作为驱动蛋白 Eg5 抑制剂在癌细胞系中诱导的动力学。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2dd3/6198871/b3d08f1d8e99/ijn-13-6429Fig8.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2dd3/6198871/f56b312c2a31/ijn-13-6429Fig1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2dd3/6198871/a88ebdbb6be3/ijn-13-6429Fig2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2dd3/6198871/d8b46f7b5a1f/ijn-13-6429Fig3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2dd3/6198871/e44f4b6eab35/ijn-13-6429Fig4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2dd3/6198871/b39a05e83bfb/ijn-13-6429Fig5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2dd3/6198871/40a437d3cb10/ijn-13-6429Fig6.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2dd3/6198871/66a7b5f4e27a/ijn-13-6429Fig7.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2dd3/6198871/b3d08f1d8e99/ijn-13-6429Fig8.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2dd3/6198871/f56b312c2a31/ijn-13-6429Fig1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2dd3/6198871/a88ebdbb6be3/ijn-13-6429Fig2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2dd3/6198871/d8b46f7b5a1f/ijn-13-6429Fig3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2dd3/6198871/e44f4b6eab35/ijn-13-6429Fig4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2dd3/6198871/b39a05e83bfb/ijn-13-6429Fig5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2dd3/6198871/40a437d3cb10/ijn-13-6429Fig6.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2dd3/6198871/66a7b5f4e27a/ijn-13-6429Fig7.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2dd3/6198871/b3d08f1d8e99/ijn-13-6429Fig8.jpg

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Int J Nanomedicine. 2018 May 25;13:3105-3128. doi: 10.2147/IJN.S152664. eCollection 2018.
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Proc Natl Acad Sci U S A. 2018 May 15;115(20):E4613-E4622. doi: 10.1073/pnas.1801242115. Epub 2018 Apr 27.
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