Azmi Asfar S
Department of Pathology, Wayne State University School of Medicine, 4100 John R, HWCRC Room 732, Detroit, MI 48201, USA.
Curr Drug Discov Technol. 2013 Jun;10(2):95-105. doi: 10.2174/1570163811310020002.
Cancer is a deadly disease and a huge burden to the society. Although the last 60 years has seen improvements in cancer diagnostics, treatment strategies against most of the complex malignancies have not lived up to the mark. In the drug discovery area, the attrition rates have spiraled out of control, indicating that there is certainly something amiss employing the current research approaches against cancer. Advances in computational biology have revealed that cancer is a disease arising from aberrations in complex biological networks and its understanding requires more information than that obtained from the reductionist strategies. Similarly, magic bullet drugs that are designed against a single pathway may not impact these highly intertwined and robust cancer networks. In order to rein in cancer, one has to revamp the concepts in understanding the mechanism of cancer and drastically reform the present approaches to drug discovery. The idea behind this review is to enlighten the readers about the emerging concept of 'Network Pharmacology' in drug discovery. Network technologies have allowed not only in the rational targeting of aberrant signaling in cancer but also helped in understanding secondary drug effects. Concepts in network methods that are helping hit identification, lead selection, optimizing drug efficacy, as well as minimizing side-effects are discussed. Finally, some of the successful network-based drug development strategies are shown through the examples cancer.
癌症是一种致命疾病,给社会带来巨大负担。尽管在过去60年里癌症诊断有了改善,但针对大多数复杂恶性肿瘤的治疗策略仍未达到预期标准。在药物研发领域,淘汰率已失控,这表明采用当前针对癌症的研究方法肯定存在问题。计算生物学的进展表明,癌症是一种由复杂生物网络异常引起的疾病,对其理解需要比从还原论策略中获得的更多信息。同样,针对单一途径设计的“神奇子弹”药物可能无法影响这些高度交织且强大的癌症网络。为了控制癌症,必须彻底改变对癌症机制的理解概念,并大幅改革当前的药物研发方法。本综述的目的是让读者了解药物研发中新兴的“网络药理学”概念。网络技术不仅有助于合理靶向癌症中的异常信号传导,还有助于理解药物的副作用。文中讨论了网络方法在药物靶点确认、先导化合物筛选、优化药物疗效以及最小化副作用等方面的应用。最后,通过癌症相关实例展示了一些基于网络的成功药物研发策略。