Current Trends in Multidrug Optimization.

Q Health Professions
Jala Pub Date : 2016-12-01 DOI:10.1177/2211068216682338
Andrea Weiss, Patrycja Nowak-Sliwinska
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引用次数: 0

Abstract

The identification of effective and long-lasting cancer therapies still remains elusive, partially due to patient and tumor heterogeneity, acquired drug resistance, and single-drug dose-limiting toxicities. The use of drug combinations may help to overcome some limitations of current cancer therapies by challenging the robustness and redundancy of biological processes. However, effective drug combination optimization requires the careful consideration of numerous parameters. The complexity of this optimization problem is clearly nontrivial and likely requires the assistance of advanced heuristic optimization techniques. In the current review, we discuss the application of optimization techniques for the identification of optimal drug combinations. More specifically, we focus on the application of phenotype-based screening approaches in the field of cancer therapy. These methods are divided into three categories: (1) modeling methods, (2) model-free approaches based on biological search algorithms, and (3) merged approaches, particularly phenotypically driven network biology methods and computation network models relying on phenotypic data. In addition to a brief description of each approach, we include a critical discussion of the advantages and disadvantages of each method, with a strong focus on the limitations and considerations needed to successfully apply such methods in biological research.

多药优化的当前趋势。
有效而持久的癌症疗法仍然难以确定,部分原因在于患者和肿瘤的异质性、获得性耐药性和单药剂量限制毒性。通过挑战生物过程的稳健性和冗余性,药物组合的使用可能有助于克服当前癌症疗法的一些局限性。然而,有效的药物组合优化需要仔细考虑众多参数。这一优化问题的复杂性显然非同小可,很可能需要先进的启发式优化技术的帮助。在本综述中,我们讨论了优化技术在确定最佳药物组合方面的应用。更具体地说,我们关注基于表型的筛选方法在癌症治疗领域的应用。这些方法分为三类:(1) 建模方法;(2) 基于生物搜索算法的无模型方法;(3) 合并方法,特别是表型驱动的网络生物学方法和依赖表型数据的计算网络模型。除了对每种方法的简要介绍外,我们还对每种方法的优缺点进行了批判性讨论,重点关注在生物研究中成功应用这些方法所需的限制和注意事项。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Jala
Jala BIOCHEMICAL RESEARCH METHODS-CHEMISTRY, ANALYTICAL
CiteScore
2.11
自引率
0.00%
发文量
0
审稿时长
>12 weeks
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