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Developing highER-throughput zebrafish screens for in-vivo CNS drug discovery.

Stewart AM, Gerlai R, Kalueff AV - Front Behav Neurosci (2015)

Bottom Line: 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.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.

View Article: PubMed Central - PubMed

Affiliation: ZENEREI Institute and The International Zebrafish Neuroscience Research Consortium Slidell, LA, USA.

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.

No MeSH data available.


Related in: MedlinePlus

Example of potential decision trees (A,B) that can be used by automated in-vivo drug-screening platforms in adult zebrafish to predict the drugs' pharmacological profiles (see Figures 1B,C for examples of 3D traces). (C) Illustrates the general strategy of drug screening based on machine learning algorithms and 3D trace analyses. Summary of different strategies that can be used to generate high-density biological “big data” from zebrafish in-vivo screens. (D) Illustrated the extensive approach, testing a large number of drugs (D) in multiple animals (N) but recording few endpoints/phenotypes (P). This approach is markedly facilitated by using phenotypic barcoding approaches (Glossary). In contrast, the intensive approach screens few drugs, uses few animals but records many endpoints. The higher-throughput strategy, based on screening many compounds with multiple endpoints in a large number of animals, is empowered by locomotor pattern and behavioral recognition (Glossary) as well as automated slimuli delivery and experimental manipulations. (E) Shows the value of increased drug data “dimensionality” by including pharmacogenetic results (from wild type vs. mutant zebrafish) for providing important mechanistic insights into the drugs action. For example, a hypothetical antagonism of a drug A at a receptor R can be confirmed by screening the reference compound B (with known anti-R activity) and by mutating zebrafish gene R to abolish A/B-like activity in the mutants. Applying bioinformatics-based approaches and combining both lines of such evidence will facilitate the discovery of anti-R compounds (based on A-like pharmacology in zebrafish), followed by subsequent target validation in rodents and clinical studies.
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Figure 2: Example of potential decision trees (A,B) that can be used by automated in-vivo drug-screening platforms in adult zebrafish to predict the drugs' pharmacological profiles (see Figures 1B,C for examples of 3D traces). (C) Illustrates the general strategy of drug screening based on machine learning algorithms and 3D trace analyses. Summary of different strategies that can be used to generate high-density biological “big data” from zebrafish in-vivo screens. (D) Illustrated the extensive approach, testing a large number of drugs (D) in multiple animals (N) but recording few endpoints/phenotypes (P). This approach is markedly facilitated by using phenotypic barcoding approaches (Glossary). In contrast, the intensive approach screens few drugs, uses few animals but records many endpoints. The higher-throughput strategy, based on screening many compounds with multiple endpoints in a large number of animals, is empowered by locomotor pattern and behavioral recognition (Glossary) as well as automated slimuli delivery and experimental manipulations. (E) Shows the value of increased drug data “dimensionality” by including pharmacogenetic results (from wild type vs. mutant zebrafish) for providing important mechanistic insights into the drugs action. For example, a hypothetical antagonism of a drug A at a receptor R can be confirmed by screening the reference compound B (with known anti-R activity) and by mutating zebrafish gene R to abolish A/B-like activity in the mutants. Applying bioinformatics-based approaches and combining both lines of such evidence will facilitate the discovery of anti-R compounds (based on A-like pharmacology in zebrafish), followed by subsequent target validation in rodents and clinical studies.

Mentions: Figures 1, 2 further illustrate how a comprehensive evaluation of individual compounds can foster objective, computer-based prediction of the drugs' pharmacology. For example, phencyclidine (PCP) is a hallucinogenic glutamatergic antagonist that acutely evokes characteristic “top circling” behavior in zebrafish (Figures 1, 2). Assessed by elevated angular velocity and rotation index, this response is similar for anti-glutamatergic hallucinogens, but not other classes of hallucinogenic agents (Kyzar et al., 2012; Neelkantan et al., 2013; Stewart et al., 2013). Analyzing such 3D profiles, it is therefore possible to generate decision trees for predicting the pharmacological profile of different groups of neuroactive compounds, based on zebrafish swimming patterns and their geometry (Figure 2) (Cachat et al., 2010, 2011; Soleymani et al., 2014). Likewise, acute nicotine evokes characteristic top swimming along perimeter of the tank, strikingly differing from top circling (typical for many anti-glutamatergic drugs) or top surfacing without peripheral swimming (typical for serotonergic agents; Figure 1C) (Kyzar et al., 2012; Neelkantan et al., 2013; Stewart et al., 2013). Importantly, such accumulation of large libraries of drugs' behavioral signatures in zebrafish can parallel the application of machine learning algorithms, leading to further refinement and optimization of the prediction of drugs' pharmacological profiles (Soleymani et al., 2014) (Figure 2C).


Developing highER-throughput zebrafish screens for in-vivo CNS drug discovery.

Stewart AM, Gerlai R, Kalueff AV - Front Behav Neurosci (2015)

Example of potential decision trees (A,B) that can be used by automated in-vivo drug-screening platforms in adult zebrafish to predict the drugs' pharmacological profiles (see Figures 1B,C for examples of 3D traces). (C) Illustrates the general strategy of drug screening based on machine learning algorithms and 3D trace analyses. Summary of different strategies that can be used to generate high-density biological “big data” from zebrafish in-vivo screens. (D) Illustrated the extensive approach, testing a large number of drugs (D) in multiple animals (N) but recording few endpoints/phenotypes (P). This approach is markedly facilitated by using phenotypic barcoding approaches (Glossary). In contrast, the intensive approach screens few drugs, uses few animals but records many endpoints. The higher-throughput strategy, based on screening many compounds with multiple endpoints in a large number of animals, is empowered by locomotor pattern and behavioral recognition (Glossary) as well as automated slimuli delivery and experimental manipulations. (E) Shows the value of increased drug data “dimensionality” by including pharmacogenetic results (from wild type vs. mutant zebrafish) for providing important mechanistic insights into the drugs action. For example, a hypothetical antagonism of a drug A at a receptor R can be confirmed by screening the reference compound B (with known anti-R activity) and by mutating zebrafish gene R to abolish A/B-like activity in the mutants. Applying bioinformatics-based approaches and combining both lines of such evidence will facilitate the discovery of anti-R compounds (based on A-like pharmacology in zebrafish), followed by subsequent target validation in rodents and clinical studies.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4325915&req=5

Figure 2: Example of potential decision trees (A,B) that can be used by automated in-vivo drug-screening platforms in adult zebrafish to predict the drugs' pharmacological profiles (see Figures 1B,C for examples of 3D traces). (C) Illustrates the general strategy of drug screening based on machine learning algorithms and 3D trace analyses. Summary of different strategies that can be used to generate high-density biological “big data” from zebrafish in-vivo screens. (D) Illustrated the extensive approach, testing a large number of drugs (D) in multiple animals (N) but recording few endpoints/phenotypes (P). This approach is markedly facilitated by using phenotypic barcoding approaches (Glossary). In contrast, the intensive approach screens few drugs, uses few animals but records many endpoints. The higher-throughput strategy, based on screening many compounds with multiple endpoints in a large number of animals, is empowered by locomotor pattern and behavioral recognition (Glossary) as well as automated slimuli delivery and experimental manipulations. (E) Shows the value of increased drug data “dimensionality” by including pharmacogenetic results (from wild type vs. mutant zebrafish) for providing important mechanistic insights into the drugs action. For example, a hypothetical antagonism of a drug A at a receptor R can be confirmed by screening the reference compound B (with known anti-R activity) and by mutating zebrafish gene R to abolish A/B-like activity in the mutants. Applying bioinformatics-based approaches and combining both lines of such evidence will facilitate the discovery of anti-R compounds (based on A-like pharmacology in zebrafish), followed by subsequent target validation in rodents and clinical studies.
Mentions: Figures 1, 2 further illustrate how a comprehensive evaluation of individual compounds can foster objective, computer-based prediction of the drugs' pharmacology. For example, phencyclidine (PCP) is a hallucinogenic glutamatergic antagonist that acutely evokes characteristic “top circling” behavior in zebrafish (Figures 1, 2). Assessed by elevated angular velocity and rotation index, this response is similar for anti-glutamatergic hallucinogens, but not other classes of hallucinogenic agents (Kyzar et al., 2012; Neelkantan et al., 2013; Stewart et al., 2013). Analyzing such 3D profiles, it is therefore possible to generate decision trees for predicting the pharmacological profile of different groups of neuroactive compounds, based on zebrafish swimming patterns and their geometry (Figure 2) (Cachat et al., 2010, 2011; Soleymani et al., 2014). Likewise, acute nicotine evokes characteristic top swimming along perimeter of the tank, strikingly differing from top circling (typical for many anti-glutamatergic drugs) or top surfacing without peripheral swimming (typical for serotonergic agents; Figure 1C) (Kyzar et al., 2012; Neelkantan et al., 2013; Stewart et al., 2013). Importantly, such accumulation of large libraries of drugs' behavioral signatures in zebrafish can parallel the application of machine learning algorithms, leading to further refinement and optimization of the prediction of drugs' pharmacological profiles (Soleymani et al., 2014) (Figure 2C).

Bottom Line: 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.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.

View Article: PubMed Central - PubMed

Affiliation: ZENEREI Institute and The International Zebrafish Neuroscience Research Consortium Slidell, LA, USA.

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.

No MeSH data available.


Related in: MedlinePlus