Abstract

The high prevalence of brain disorders and the lack of their efficient treatments necessitate improved in-vivo pre-clinical models and tests. The zebrafish (Danio rerio), a vertebrate species with high genetic and physiological homology to humans, is an excellent organism for innovative central nervous system (CNS) drug discovery and small molecule screening. Here, we outline new strategies for developing higher-throughput zebrafish screens to test neuroactive drugs and predict their pharmacological mechanisms. With the growing application of automated 3D phenotyping, machine learning algorithms, movement pattern- and behavior recognition, and multi-animal video-tracking, zebrafish screens are expected to markedly improve CNS drug discovery.

Highlights

  • ZEBRAFISH SCREENS FOR central nervous system (CNS) DRUG DISCOVERY Brain disorders are complex multifaceted illnesses with poorly understood causes and frequently ineffective therapies (Duman et al, 1994; Nestler, 2013)

  • Despite the growing public health impact of these disorders (Mitchell, 2011), the central nervous system (CNS) drugs have not improved in decades (WHO, 2008; Griebel and Holmes, 2013), necessitating novel pre-clinical invivo models for drug discovery (Markou et al, 2009; Stewart and Kalueff, 2013, 2014)

  • Research in this field is determined by the complex nature of CNS syndromes, the importance of targeting their biological mechanisms, and the need in high-throughput screens (HTS) for drug targets and potential therapies (Kokel and Peterson, 2008; Rihel et al, 2010; Laggner et al, 2012; Stewart and Kalueff, 2014)

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Summary

Introduction

ZEBRAFISH SCREENS FOR CNS DRUG DISCOVERY Brain disorders are complex multifaceted illnesses with poorly understood causes and frequently ineffective therapies (Duman et al, 1994; Nestler, 2013). Research in this field is determined by the complex nature of CNS syndromes, the importance of targeting their biological mechanisms, and the need in high-throughput screens (HTS) for drug targets and potential therapies (Kokel and Peterson, 2008; Rihel et al, 2010; Laggner et al, 2012; Stewart and Kalueff, 2014).

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