Phenotypic Screening in C. elegans as a Tool for the Discovery of New Geroprotective Drugs
Abstract
:1. Introduction
2. Target-Based Versus Phenotypic Screening
2.1. The Pros and Cons of Target-Based Versus Phenotypic Screening
2.2. Phenotypic Screening for Geroprotective Compounds
2.3. Target-Based Screens for Geroprotective Drugs
3. Important Considerations for Geroprotective Drug Screening in C. elegans
3.1. Pros and Cons of Drug Screening in C. elegans
3.2. Genetic Background of C. elegans
3.3. Effect of Bacteria on Administered Drugs
3.4. Drug Administration
3.5. Drug Stability
3.6. Age of First Drug Exposure
3.7. Drug Uptake
3.8. Food Intake
3.9. Abiotic Factors
3.10. Reproducibility and Plate-to-Plate Variability
4. Limitations of Manual Lifespan Assays
4.1. Agar Plate-Based Lifespan Assays
4.2. Liquid Culture-Based Lifespan Assays
4.3. Methods to Increase Throughput of Lifespan Assays
4.4. However, Manual Assays also Have Some Strengths
5. Automated Phenotyping and Lifespan Devices
5.1. Microfluidics-Based Platforms
5.2. Solid Medium-Based Platforms
5.3. Stress-Based Platforms
6. Future Outlook
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
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Target-Based Screening | Phenotypic Screening in Cells | Phenotypic Screening in Small Organisms (e.g., C. elegans) | ||||
---|---|---|---|---|---|---|
Strengths | Weaknesses | Strengths | Weaknesses | Strengths | Weaknesses | |
Target | Known target selected for screen. | Cannot find new targets. | Target agnostic. | Target identification can be cumbersome. | Target agnostic. | Target identification can be cumbersome. |
Human relevance | In vitro study on isolated targets. | In vitro but on whole cells. Cells used can be of human origin. Even patient-derived primary cells or in vitro reprogrammed cells from patient-derived fibroblasts. | Access to disease relevant cell types can be difficult. Diseases cannot always be easily recapitulated in isolated cells because they depend on interactions of various cells and/or systemic factors. | In vivo, small organisms contain multiple cell types and even organ systems thus better capturing disease processes that depend on cell interactions and/or systemic factors. | Small model organisms may not fully capture human biology. | |
False positives | False positives due to nonspecific mechanisms (fluorescence quenching, aggregation). | False positives due to compounds that target generic mechanisms such as protein synthesis which affect the assayed phenotype but are not specific enough to be used as drug leads. | False positives due to compounds that target generic mechanisms such as protein synthesis which affect the assayed phenotype but are not specific enough to be used as drug leads. | |||
Hit identification | Will identify all hits that modify the target of interest. | Hits will include molecules that cannot be used as drug leads (such as cytotoxic compounds). | Initial screen may already inform about toxicity of compounds (cell viability). Hits already have “drug-like” properties. | If the library is screened at high concentrations, low-potency effects could cloud the interpretation of the results. | Initial screen already informs about toxicity of compounds (organism viability). Hits already have “drug-like” properties. | If the library is screened at high concentrations, low-potency effects could cloud the interpretation of the results. However, if too low concentrations are used, then no effect may be seen because drug concentrations in the organism tend to be much smaller than those in the medium. Toxic compounds are eliminated even though they might have pharmacological properties and less toxic variants could possibly be made. |
Lead optimization | Very amendable for lead optimization (SAR). | Exclusion of hits that have poor pharmacokinetic and pharmacodynamic properties but that could still be amendable to medicinal chemistry optimization. In addition, lead optimization (SAR) can be more difficult. | Exclusion of hits that have poor pharmacokinetic and pharmacodynamic properties but that could still be amendable to medicinal chemistry optimization. In addition, lead optimization (SAR) can be more difficult. | |||
Amount of compound required | Low amounts of compound required. | Low amounts of compound required. | Large amounts of compound required. | |||
Throughput | Very high throughput | High throughput | Low throughput |
Manual | Wormbot | Automated Lifespan Machines | WorMotel | Microfluidics | LFASS | |
---|---|---|---|---|---|---|
Culture medium | Liquid or NGM | NGM | Modified version of NGM | NGM | Liquid | See manual, assay in liquid |
FUdR | Optional | Needed | Needed | Needed | Not needed | Generally needed |
Throughput | Very low | High (144 wells) | Moderate (16 Petri plates) | High (240 wells) | Low (depends on used chip) | Very high (96- or 384-well plates) |
Temporal resolution | Very low | High | High | High | High | Very high |
Individual/population | Both are possible | Population | Population | Individual | Both are possible | Population |
Equipment cost (excluding labor cost) | Very low | High | Moderate | Very high | Moderate | Moderate |
Automated data collection and analysis | No | Yes | Yes | Yes | Depends | Yes |
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Bulterijs, S.; Braeckman, B.P. Phenotypic Screening in C. elegans as a Tool for the Discovery of New Geroprotective Drugs. Pharmaceuticals 2020, 13, 164. https://doi.org/10.3390/ph13080164
Bulterijs S, Braeckman BP. Phenotypic Screening in C. elegans as a Tool for the Discovery of New Geroprotective Drugs. Pharmaceuticals. 2020; 13(8):164. https://doi.org/10.3390/ph13080164
Chicago/Turabian StyleBulterijs, Sven, and Bart P. Braeckman. 2020. "Phenotypic Screening in C. elegans as a Tool for the Discovery of New Geroprotective Drugs" Pharmaceuticals 13, no. 8: 164. https://doi.org/10.3390/ph13080164
APA StyleBulterijs, S., & Braeckman, B. P. (2020). Phenotypic Screening in C. elegans as a Tool for the Discovery of New Geroprotective Drugs. Pharmaceuticals, 13(8), 164. https://doi.org/10.3390/ph13080164