Lahoz-Beneytez Julio, Schnizler Katrin, Eissing Thomas
Bayer Technology Services GmbH, Computational Systems Biology, Leverkusen 51368, Germany.
Math Biosci. 2015 Feb;260:2-5. doi: 10.1016/j.mbs.2014.07.006. Epub 2014 Jul 21.
Biological systems are complex and comprehend multiple scales of organisation. Hence, holistic approaches are necessary to capture the behaviour of these entities from the molecular and cellular to the whole organism level. This also applies to the understanding and treatment of different diseases. Traditional systems biology has been successful in describing different biological phenomena at the cellular level, but it still lacks of a holistic description of the multi-scale interactions within the body. The importance of the physiological context is of particular interest in inflammation. Regulatory agencies have urged the scientific community to increase the translational power of bio-medical research and it has been recognised that modelling and simulation could be a path to follow. Interestingly, in pharma R&D, modelling and simulation has been employed since a long time ago. Systems pharmacology, and particularly physiologically based pharmacokinetic/pharmacodynamic models, serve as a suitable framework to integrate the available and emerging knowledge at different levels of the drug development process. Systems medicine and pharmacology of inflammation will potentially benefit from this framework in order to better understand inflammatory diseases and to help to transfer the vast knowledge on the molecular and cellular level into a more physiological context. Ultimately, this may lead to reliable predictions of clinical outcomes such as disease progression or treatment efficacy, contributing thereby to a better care of patients.
生物系统是复杂的,包含多个组织层次。因此,需要采用整体方法来描述这些实体从分子和细胞到整个生物体水平的行为。这也适用于对不同疾病的理解和治疗。传统的系统生物学在描述细胞水平上的不同生物现象方面取得了成功,但它仍然缺乏对体内多尺度相互作用的整体描述。生理背景在炎症中尤为重要。监管机构敦促科学界提高生物医学研究的转化能力,并且人们已经认识到建模和模拟可能是一条可行的途径。有趣的是,在药物研发中,建模和模拟很久以前就已被采用。系统药理学,尤其是基于生理学的药代动力学/药效学模型,为整合药物开发过程不同阶段的现有知识和新出现的知识提供了一个合适的框架。炎症的系统医学和药理学可能会从这个框架中受益,以便更好地理解炎症性疾病,并有助于将分子和细胞水平上的大量知识转化为更符合生理学的背景。最终,这可能会导致对疾病进展或治疗效果等临床结果的可靠预测,从而有助于更好地照顾患者。