Kuepfer Lars, Schuppert Andreas
Computational Systems Biology, Bayer Technology Services GmbH, Leverkusen, Germany.
Institute of Applied Microbiology, RWTH Aachen University, Aachen, Germany.
Methods Mol Biol. 2016;1386:87-104. doi: 10.1007/978-1-4939-3283-2_6.
The development of new drug therapies requires substantial and ever increasing investments from the pharmaceutical company. Ten years ago, the average time from early target identification and optimization until initial market authorization of a new drug compound took more than 10 years and involved costs in the order of one billion US dollars. Recent studies indicate even a significant growth of costs in the meanwhile, mainly driven by the increasing complexity of diseases addressed by pharmaceutical research.Modeling and simulation are proven approaches to handle highly complex systems; hence, systems medicine is expected to control the spiral of complexity of diseases and increasing costs. Today, the main focus of systems medicine applications in industry is on mechanistic modeling. Biological mechanisms are represented by explicit equations enabling insight into the cooperation of all relevant mechanisms. Mechanistic modeling is widely accepted in pharmacokinetics, but prediction from cell behavior to patients is rarely possible due to lacks in our understanding of the controlling mechanisms. Data-driven modeling aims to compensate these lacks by the use of advanced statistical and machine learning methods. Future progress in pharmaceutical research and development will require integrated hybrid modeling technologies allowing realization of the benefits of both mechanistic and data-driven modeling. In this chapter, we sketch typical industrial application areas for both modeling techniques and derive the requirements for future technology development.
新药疗法的开发需要制药公司投入大量且不断增加的资金。十年前，从早期靶点识别与优化到新药化合物首次获得市场授权的平均时间超过10年，成本约为10亿美元。最近的研究表明，与此同时成本甚至大幅增长，主要是由制药研究针对的疾病日益复杂所驱动。建模与模拟是处理高度复杂系统的成熟方法；因此，系统医学有望控制疾病复杂性的螺旋上升以及成本的增加。如今，系统医学在工业中的主要应用重点是机理建模。生物机制由明确的方程式表示，能够洞察所有相关机制的协同作用。机理建模在药代动力学中已被广泛接受，但由于我们对控制机制缺乏了解，从细胞行为预测患者情况很少能够实现。数据驱动建模旨在通过使用先进的统计和机器学习方法来弥补这些不足。药物研发的未来进展将需要集成的混合建模技术，以实现机理建模和数据驱动建模的优势。在本章中，我们概述了这两种建模技术的典型工业应用领域，并推导了未来技术发展的要求。
