Unilever Centre for Molecular Sciences Informatics, Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge, CB2 1EW, United Kingdom.
Curr Pharm Des. 2013;19(4):532-77.
Cancer remains a fundamental burden to public health despite substantial efforts aimed at developing effective chemotherapeutics and significant advances in chemotherapeutic regimens. The major challenge in anti-cancer drug design is to selectively target cancer cells with high specificity. Research into treating malignancies by targeting altered metabolism in cancer cells is supported by computational approaches, which can take a leading role in identifying candidate targets for anti-cancer therapy as well as assist in the discovery and optimisation of anti-cancer agents. Natural products appear to have privileged structures for anti-cancer drug development and the bulk of this particularly valuable chemical space still remains to be explored. In this review we aim to provide a comprehensive overview of current strategies for computer-guided anti-cancer drug development. We start with a discussion of state-of-the art bioinformatics methods applied to the identification of novel anti-cancer targets, including machine learning techniques, the Connectivity Map and biological network analysis. This is followed by an extensive survey of molecular modelling and cheminformatics techniques employed to develop agents targeting proteins involved in the glycolytic, lipid, NAD+, mitochondrial (TCA cycle), amino acid and nucleic acid metabolism of cancer cells. A dedicated section highlights the most promising strategies to develop anti-cancer therapeutics from natural products and the role of metabolism and some of the many targets which are under investigation are reviewed. Recent success stories are reported for all the areas covered in this review. We conclude with a brief summary of the most interesting strategies identified and with an outlook on future directions in anti-cancer drug development.
尽管在开发有效化疗药物和化疗方案方面取得了重大进展,但癌症仍然是公共卫生的一个基本负担。在抗癌药物设计方面的主要挑战是,以高特异性选择性地针对癌细胞。通过针对癌细胞代谢改变来治疗恶性肿瘤的研究得到了计算方法的支持,这些方法可以在确定抗癌治疗的候选靶点方面发挥主导作用,并有助于抗癌药物的发现和优化。天然产物似乎具有抗癌药物开发的特权结构,这一特别有价值的化学空间的大部分仍有待探索。在这篇综述中,我们旨在全面概述计算机指导抗癌药物开发的当前策略。我们首先讨论了应用于新型抗癌靶点识别的最先进的生物信息学方法,包括机器学习技术、连接图和生物网络分析。接下来,我们广泛调查了用于开发靶向癌细胞糖酵解、脂质、NAD+、线粒体(TCA 循环)、氨基酸和核酸代谢中涉及的蛋白质的分子建模和化学信息学技术。一个专门的部分突出了从天然产物开发抗癌疗法的最有前途的策略,以及代谢的作用和正在研究的许多目标中的一些进行了综述。报告了所有涵盖领域的最新成功案例。我们在简要总结了所确定的最有趣的策略,并展望了抗癌药物开发的未来方向。