In Silico Biosciences, Lexington, MA, USA; Perelman School of Medicine, Univ. of Pennsylvania, Philadelphia, PA, USA.
In Silico Biosciences, Lexington, MA, USA.
Eur J Pharmacol. 2017 Dec 15;817:38-45. doi: 10.1016/j.ejphar.2017.05.062. Epub 2017 Jun 2.
Development of successful therapeutic interventions in Central Nervous Systems (CNS) disorders is a daunting challenge with a low success rate. Probable reasons include the lack of translation from preclinical animal models, the individual variability of many pathological processes converging upon the same clinical phenotype, the pharmacodynamical interaction of various comedications and last but not least the complexity of the human brain. This paper argues for a re-engineering of the pharmaceutical CNS Research & Development strategy using ideas focused on advanced computer modeling and simulation from adjacent engineering-based industries. We provide examples that such a Quantitative Systems Pharmacology approach based on computer simulation of biological processes and that combines the best of preclinical research with actual clinical outcomes can enhance translation to the clinical situation. We will expand upon (1) the need to go from Big Data to Smart Data and develop predictive and quantitative algorithms that are actionable for the pharma industry, (2) using this platform as a "knowledge machine" that captures community-wide expertise in an active hypothesis-testing approach, (3) learning from failed clinical trials and (4) the need to go beyond simple linear hypotheses and embrace complex non-linear hypotheses. We will propose a strategy for applying these concepts to the substantial individual variability of AD patient subgroups and the treatment of neuropsychiatric problems in AD. Quantitative Systems Pharmacology is a new 'humanized' tool for supporting drug discovery and development in general and CNS disorders in particular.
开发成功的中枢神经系统（CNS）疾病治疗干预措施是一项艰巨的挑战，成功率很低。可能的原因包括缺乏从临床前动物模型的转化、许多病理过程的个体可变性汇聚到相同的临床表型、各种联合用药的药效动力学相互作用，最后但并非最不重要的是人类大脑的复杂性。本文主张使用专注于相邻工程行业的先进计算机建模和模拟的想法，重新设计制药 CNS 研发策略。我们提供了一些示例，证明了这种基于计算机模拟生物过程的定量系统药理学方法，并将临床前研究的最佳成果与实际临床结果相结合，可以提高向临床情况的转化。我们将扩展到：(1) 需要从大数据到智能数据，并开发可用于制药行业的预测和定量算法；(2) 将该平台用作“知识机器”，以主动假设检验方法捕捉社区范围内的专业知识；(3) 从失败的临床试验中学习；以及(4) 需要超越简单的线性假设，接受复杂的非线性假设。我们将提出一种策略，将这些概念应用于 AD 患者亚组的大量个体可变性和 AD 中神经精神问题的治疗。定量系统药理学是一种新的“人性化”工具，可用于支持一般药物发现和开发，特别是中枢神经系统疾病。
