Big data to smart data in Alzheimer's disease: The brain health modeling initiative to foster actionable knowledge

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• 作者：Hugo Geerts^a ; ^{Hugo-Geerts@In-Silico-Biosciences.com" class="auth_mail" title="E-mail the corresponding author} ; Penny A. Dacks^b ; Viswanath Devanarayan^c ; Magali Haas^d ; Zaven S. Khachaturian^e ; Mark Forrest Gordon^f ; Stuart Maudsley^g ; Klaus Romero^h ; Diane Stephenson^h ; on behalf of the Brain Health Modeling Initiative (BHMI)
• 关键词：Brain disorders ; Alzheimer's dementia ; Complexity theory ; Systems pharmacology ; Systems biology ; Drug discovery and development
• 刊名：Alzheimer's & Dementia
• 出版年：2016
• 出版时间：September 2016
• 年：2016
• 卷：12
• 期：9
• 页码：1014-1021
• 全文大小：1666 K

文摘

Massive investment and technological advances in the collection of extensive and longitudinal information on thousands of Alzheimer patients results in large amounts of data. These “big-data” databases can potentially advance CNS research and drug development. However, although necessary, they are not sufficient, and we posit that they must be matched with analytical methods that go beyond retrospective data-driven associations with various clinical phenotypes. Although these empirically derived associations can generate novel and useful hypotheses, they need to be organically integrated in a quantitative understanding of the pathology that can be actionable for drug discovery and development. We argue that mechanism-based modeling and simulation approaches, where existing domain knowledge is formally integrated using complexity science and quantitative systems pharmacology can be combined with data-driven analytics to generate predictive actionable knowledge for drug discovery programs, target validation, and optimization of clinical development.