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一种用于对有效对抗脑肿瘤的抗癌药物进行临床前筛选的算法。

An Algorithm for the Preclinical Screening of Anticancer Drugs Effective against Brain Tumors.

作者信息

Yakisich Juan Sebastian

机构信息

Department of Clinical Neuroscience, Karolinska University Hospital, Karolinska Institute, SE-141 86 Stockholm, Sweden.

出版信息

ISRN Pharmacol. 2012;2012:513580. doi: 10.5402/2012/513580. Epub 2012 Jul 3.

Abstract

The anticancer drugs screening program is a long and expensive process. It is estimated that only 5% of drugs entering clinical trials are approved by the FDA. Moreover, many of the drugs that enter clinical trials are often of limited use in clinical practice, and most cancers remain untreatable. Brain tumors are particularly difficult to treat due to the presence of the blood brain barrier that limits the penetration of anticancer drugs. Additionally the isolation from most brain tumors of putative cancer stem cells and novel models of cancer stem cell biology suggest that anticancer drugs should be delivered for prolonged time and at higher concentrations to deplete any potential tumorigenic cell. In this paper, current concepts of cancer stem cell biology and novel concepts of anticancer drugs screening are integrated to develop a seven-steps algorithm as a guideline for the preclinical evaluation of active compounds for the treatment of brain tumors. The flexibility of the algorithm allows the inclusion of alternative studies to exhaustively investigate anticancer drugs and creates multiple opportunities where decisions to engage or not in early clinical trials can be made providing a useful tool for translational research in neurooncology.

摘要

抗癌药物筛选计划是一个漫长且昂贵的过程。据估计,进入临床试验的药物中只有5%能获得美国食品药品监督管理局(FDA)的批准。此外,许多进入临床试验的药物在临床实践中的用途往往有限,大多数癌症仍然无法治愈。由于血脑屏障的存在限制了抗癌药物的渗透,脑肿瘤尤其难以治疗。此外,从大多数脑肿瘤中分离出假定的癌症干细胞以及癌症干细胞生物学的新模型表明,抗癌药物应以更高的浓度长时间给药,以耗尽任何潜在的致瘤细胞。在本文中,癌症干细胞生物学的当前概念与抗癌药物筛选的新概念相结合,开发出一种七步算法,作为治疗脑肿瘤活性化合物临床前评估的指导方针。该算法的灵活性允许纳入替代研究,以详尽地研究抗癌药物,并创造多个机会,以便做出是否参与早期临床试验的决定,为神经肿瘤学的转化研究提供了一个有用的工具。

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