Experimental models for anxiolytic drug discovery in the era of omes and omics.
BACKGROUND
Animal behavioral models have become an indispensable tool for studying anxiety disorders and testing anxiety-modulating drugs. However, significant methodological and conceptual challenges affect the translational validity and accurate behavioral dissection in such models. They are also often limited to individual behavioral domains and fail to target the disorder's real clinical picture (its spectrum or overlap with other disorders), which hinder screening and development of novel anxiolytic drugs.


METHODS
In this article, the authors discuss and emphasize the importance of high-throughput multi-domain neurophenotyping based on the latest developments in video-tracking and bioinformatics. Additionally, the authors also explain how bioinformatics can provide new insight into the neural substrates of brain disorders and its benefit for drug discovery.


CONCLUSIONS
The throughput and utility of animal models of anxiety and other brain disorders can be markedly increased by a number of ways: i) analyzing systems of several domains and their interplay in a wider spectrum of model species; ii) using a larger number of end points generated by video-tracking tools; iii) correlating behavioral data with genomic, proteomic and other physiologically relevant markers using online databases and iv) creating molecular network-based models of anxiety to identify new targets for drug design and discovery. Experimental models utilizing bioinformatics tools and online databases will not only improve our understanding of both gene-behavior interactions and complex trait interconnectivity but also highlight new targets for novel anxiolytic drugs.
Tulane University Medical School, Department of Pharmacology and Neuroscience Program ,  Tulane Neurophenotyping Platform, SL-83, 1430 Tulane Ave, New Orleans, LA 70112 ,  USA   +1 504 988 3354 ;  avkalueff@gmail.com.
Adam
A
Stewart
Siddharth
S
Gaikwad
Peter
P
Hart
Evan
E
Kyzar
Andrew
A
Roth
Allan V
AV
Kalueff