High-throughput brain activity mapping and machine learning as a foundation for systems neuropharmacology. Issue 1 (December 2018)

• Record Type: Journal Article
• Authors: Lin, Xudong; Duan, Xin; Jacobs, Claire; Ullmann, Jeremy; Chan, Chung-Yuen; Chen, Siya; Cheng, Shuk-Han; Zhao, Wen-Ning; Poduri, Annapurna; Wang, Xin; Haggarty, Stephen; Shi, Peng
• Is Part Of: Nature communications. Volume 9:Issue 1(2018)
• Page Start: 1
• Page End: 12
• Publication Date: 2018-12
• Subjects: Biology -- Periodicals; Physical sciences -- Periodicals; 505
• DOI: 10.1038/s41467-018-07289-5
• Languages: English
• ISSNs: 2041-1723

Abstract

Abstract Technologies for mapping the spatial and temporal patterns of neural activity have advanced our understanding of brain function in both health and disease. An important application of these technologies is the discovery of next-generation neurotherapeutics for neurological and psychiatric disorders. Here, we describe an in vivo drug screening strategy that combines high-throughput technology to generate large-scale brain activity maps (BAMs) with machine learning for predictive analysis. This platform enables evaluation of compounds' mechanisms of action and potential therapeutic uses based on information-rich BAMs derived from drug-treated zebrafish larvae. From a screen of clinically used drugs, we found intrinsically coherent drug clusters that are associated with known therapeutic categories. Using BAM-based clusters as a functional classifier, we identify anti-seizure-like drug leads from non-clinical compounds and validate their therapeutic effects in the pentylenetetrazole zebrafish seizure model. Collectively, this study provides a framework to advance the field of systems neuropharmacology. A major goal in neuropharmacology is to develop new tools to effectively test the therapeutic potential of pharmacological agents to treat neurological and psychiatric conditions. Here, authors present an in vivo drug screening system that generates large-scale brain activity maps to be used with machine learning to predict the therapeutic potential of clinically relevant drug leads.