Improving the predictive value of interventional animal models data

• Published in: Drug discovery today 2015-04, Vol.20 (4), p.475-482
• Main Author: Zeiss, Caroline J.
• Format: Article
• Language: English
• Subjects: Alzheimer Disease - therapy; Animals; Cholinesterase Inhibitors - pharmacology; Cholinesterase Inhibitors - therapeutic use; Data Mining; Databases, Factual; Disease Models, Animal; Drug Evaluation, Preclinical; Exercise Therapy; Humans; Indans - pharmacology; Indans - therapeutic use; Memantine - pharmacology; Memantine - therapeutic use; Piperidines - pharmacology; Piperidines - therapeutic use; Species Specificity; Systems Biology; Systems Integration; Translational Medical Research
• ISSN: 1359-6446; 1878-5832
• DOI: 10.1016/j.drudis.2014.10.015

•448 interventions across 752 human and animal studies for Alzheimer's disease were compared.•A networks-based systems biology approach was used to assess repeatability of outcomes across species by intervention and mechanism.•This approach supports in silico reduction of positive outcomes bias in animal studies. For many chronic diseases, translational success using the animal model paradigm has reached an impasse. Using Alzheimer's disease as an example, this review employs a networks-based method to assess repeatability of outcomes across species, by intervention and mechanism. Over 75% of animal studies reported an improved outcome. Strain background was a significant potential confounder. Five percent of interventions had been tested across animals and humans, or examined across three or more animal models. Positive outcomes across species emerged for donepezil, memantine and exercise. Repeatable positive outcomes in animals were identified for the amyloid hypothesis and three additional mechanisms. This approach supports in silico reduction of positive outcomes bias in animal studies.