A novel metabolism-based phenotypic drug discovery platform in zebrafish uncovers HDACs 1 and 3 as a potential combined anti-seizure drug target

Ibhazehiebo, Kingsley; Gavrilovici, Cezar; Hoz, Cristiane L de la; Ma, Shun-Chieh; Rehak, Renata; Kaushik, Gaurav; Santoscoy, Paola L. Meza; Scott, Lucas; Nath, Nandan; Kim, Doyoung; Rho, Jong M.; Kurrasch, Deborah M.

doi:10.1093/brain/awx364

Cited by 59 publications

(64 citation statements)

References 90 publications

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“…In our efforts to validate the prediction results using the PTZ-seizure model, seven of the 14 compounds tested (50%) decreased seizure frequency even at concentrations where they did not significantly impact the larvae locomotive activity. Given that PTZ is a noncompetitive GABA(A) antagonist, the fact that the other seven compounds tested were negative in the PTZ model may reflect limited sensitivity of the PTZ model to detect anti-seizure activity mediated through a pathway other than GABA rather than true lack of efficacy as potential anti-seizure agents 48 .…”

Section: Discussionmentioning

confidence: 99%

High-throughput brain activity mapping and machine learning as a foundation for systems neuropharmacology

et al. 2018

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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.

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Section: Discussionmentioning

confidence: 99%

High-throughput brain activity mapping and machine learning as a foundation for systems neuropharmacology

et al. 2018

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“…Final DMSO concentration of drug dilutions used for testing was~1%. To facilitate large-scale primary screening (Figure 1), single drug concentrations (10 and 250 µM) were selected based on previously published high-throughput screens in larval zebrafish (Baraban et al, 2013;Robertson et al, 2014;Dinday and Baraban, 2015;Lin et al, 2018;Ibhazehiebo et al, 2018;Eimon et al, 2018); additional concentrations (1, 10, and 100 µM) were selected for secondary concentration-dependent screening assays (Fig. 2).…”

Section: Drugsmentioning

confidence: 99%

Phenotype-Based Screening of Synthetic Cannabinoids in a Dravet Syndrome Zebrafish Model

et al. 2020

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Dravet syndrome is a catastrophic epilepsy of childhood, characterized by cognitive impairment, severe seizures, and increased risk for sudden unexplained death in epilepsy (SUDEP). Although refractory to conventional antiepileptic drugs, emerging preclinical and clinical evidence suggests that modulation of the endocannabinoid system could be therapeutic in these patients. Preclinical research on this topic is limited as cannabis, delta-9-tetrahydrocannabinol (THC) and cannabidiol (CBD), are designated by United States Drug Enforcement Agency (DEA) as illegal substances. In this study, we used a validated zebrafish model of Dravet syndrome, scn1lab homozygous mutants, to screen for anti-seizure activity in a commercially available library containing 370 synthetic cannabinoid (SC) compounds. SCs are intended for experimental use and not restricted by DEA designations. Primary phenotype-based screening was performed using a locomotion-based assay in 96-well plates, and a secondary local field potential recording assay was then used to confirm suppression of electrographic epileptiform events. Identified SCs with anti-seizure activity, in both assays, included five SCs structurally classified as indole-based cannabinoids JWH 018 N-(5-chloropentyl) analog, JWH 018 N-(2-methylbutyl) isomer, 5-fluoro PB-22 5-hydroxyisoquinoline isomer, 5-fluoro ADBICA, and AB-FUBINACA 3-fluorobenzyl isomer. Our approach demonstrates that two-stage phenotype-based screening in a zebrafish model of Dravet syndrome successfully identifies SCs with anti-seizure activity.

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“…90 Based on these features, forward and reverse genetic strategies have been used to discover and validate genes involved in CNS processes and later associated to neurological disorders. The majority of these studies have used morpholinos (MOs), 91,92 which are modified antisense oligonucleotides that cause a transient "knockdown" of target gene expression by blocking messenger RNA (mRNA) splicing or translation. 93 The popularity of MOs has decreased, however, since the development of gene-editing tools and especially CRISPR/Cas9 technology.…”

Section: Neurological Disordersmentioning

confidence: 99%

CRISPR Meets Zebrafish: Accelerating the Discovery of New Therapeutic Targets

et al. 2020

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Bringing a new drug to the market costs an average of US$2.6 billion and takes more than 10 years from discovery to regulatory approval. Despite the need to reduce cost and time to increase productivity, pharma companies tend to crowd their efforts in the same indications and drug targets. This results in the commercialization of drugs that share the same mechanism of action (MoA) and, in many cases, equivalent efficacies among them—an outcome that helps neither patients nor the balance sheet of the companies trying to bring therapeutics to the same patient population. Indeed, the discovery of new therapeutic targets, based on a deeper understanding of the disease biology, would likely provide more innovative MoAs and potentially greater drug efficacies. It would also bring better chances for identifying appropriate treatments according to the patient’s genetic stratification. Nowadays, we count with an enormous amount of unprocessed information on potential disease targets that could be extracted from omics data obtained from patient samples. In addition, hundreds of pharmacological and genetic screenings have been performed to identify innovative drug targets. Traditionally, rodents have been the animal models of choice to perform functional genomic studies. The high experimental cost, combined with the low throughput provided by those models, however, is a bottleneck for discovering and validating novel genetic disease associations. To overcome these limitations, we propose that zebrafish, in conjunction with the use of CRISPR/Cas9 genome-editing tools, could streamline functional genomic processes to bring biologically relevant knowledge on innovative disease targets in a shorter time frame.

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A novel metabolism-based phenotypic drug discovery platform in zebrafish uncovers HDACs 1 and 3 as a potential combined anti-seizure drug target

Cited by 59 publications

References 90 publications

High-throughput brain activity mapping and machine learning as a foundation for systems neuropharmacology

High-throughput brain activity mapping and machine learning as a foundation for systems neuropharmacology

Phenotype-Based Screening of Synthetic Cannabinoids in a Dravet Syndrome Zebrafish Model

CRISPR Meets Zebrafish: Accelerating the Discovery of New Therapeutic Targets

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