Abstract
Conventional drug screens and treatments often ignore the underlying complexity of brain network dysfunctions, resulting in suboptimal outcomes. Here we ask whether we can correct abnormal functional connectivity of the entire brain by identifying and combining multiple neuromodulators that perturb connectivity in complementary ways. Our approach avoids the combinatorial complexity of screening all drug combinations. We develop a high-speed platform capable of imaging more than 15000 neurons in 50ms to map the entire brain functional connectivity in large numbers of vertebrates under many conditions. Screening a panel of drugs in a zebrafish model of human Dravet syndrome, we show that even drugs with related mechanisms of action can modulate functional connectivity in significantly different ways. By clustering connectivity fingerprints, we algorithmically select small subsets of complementary drugs and rapidly identify combinations that are significantly more effective at correcting abnormal networks and reducing spontaneous seizures than monotherapies, while minimizing behavioral side effects. Even at low concentrations, our polytherapy performs superior to individual drugs even at highest tolerated concentrations.
Highlights
Conventional drug screens and treatments often ignore the underlying complexity of brain network dysfunctions, resulting in suboptimal outcomes
Functional connectivity studies confirm that even focal epilepsies produce widespread changes in connectivity patterns[20], and we report here for the first time the presence of such nontrivial network dysfunctions in epileptic zebrafish brains
To enable large-scale drug screens based on neural activity in zebrafish expressing genetically encoded calcium indicators, we designed and built a high-speed light-sheet microscopy platform paired with peripheral fluidics for rapidly processing large numbers of larvae under multiple treatment conditions (Fig. 1)
Summary
Conventional drug screens and treatments often ignore the underlying complexity of brain network dysfunctions, resulting in suboptimal outcomes. Using a novel clustering algorithm, we classify drugs based on their functional connectivity fingerprints to identify hits that correct complimentary facets of the abnormal brain-wide network in mutant zebrafish This allows us to identify polytherapy combinations that are likely to produce synergistic effects based on their ability to target distinct aspects of the underlying network dysfunction, rather than combining drugs with different suspected MOAs. Having reduced the tremendously large parameter space to a manageable number of options, all top polytherapy combinations can be evaluated over a range of doses and tested in follow-up assays for efficacy and side effects. Our results demonstrate the power of network functional connectivity analysis for the discovery of neuroactive drugs, and in particular for polydrug screening
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