Chapter title |
The Power of Zebrafish in Personalised Medicine
|
---|---|
Chapter number | 10 |
Book title |
Personalised Medicine
|
Published in |
Advances in experimental medicine and biology, August 2017
|
DOI | 10.1007/978-3-319-60733-7_10 |
Pubmed ID | |
Book ISBNs |
978-3-31-960731-3, 978-3-31-960733-7
|
Authors |
Baxendale, Sarah, van Eeden, Freek, Wilkinson, Robert, Sarah Baxendale, Freek van Eeden, Robert Wilkinson |
Abstract |
The goal of personalised medicine is to develop tailor-made therapies for patients in whom currently available therapeutics fail. This approach requires correlating individual patient genotype data to specific disease phenotype data and using these stratified data sets to identify bespoke therapeutics. Applications for personalised medicine include common complex diseases which may have multiple targets, as well as rare monogenic disorders, for which the target may be unknown. In both cases, whole genome sequence analysis (WGS) is discovering large numbers of disease associated mutations in new candidate genes and potential modifier genes. Currently, the main limiting factor is the determination of which mutated genes are important for disease progression and therefore represent potential targets for drug discovery. Zebrafish have gained popularity as a model organism for understanding developmental processes, disease mechanisms and more recently for drug discovery and toxicity testing. In this chapter, we will examine the diverse roles that zebrafish can make in the expanding field of personalised medicine, from generating humanised disease models to xenograft screening of different cancer cell lines, through to finding new drugs via in vivo phenotypic screens. We will discuss the tools available for zebrafish research and recent advances in techniques, highlighting the advantages and potential of using zebrafish for high throughput disease modeling and precision drug discovery. |
X Demographics
Geographical breakdown
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Unknown | 3 | 100% |
Demographic breakdown
Type | Count | As % |
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Members of the public | 3 | 100% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
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Unknown | 69 | 100% |
Demographic breakdown
Readers by professional status | Count | As % |
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Researcher | 11 | 16% |
Student > Ph. D. Student | 11 | 16% |
Student > Bachelor | 10 | 14% |
Student > Master | 9 | 13% |
Student > Doctoral Student | 4 | 6% |
Other | 6 | 9% |
Unknown | 18 | 26% |
Readers by discipline | Count | As % |
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Biochemistry, Genetics and Molecular Biology | 16 | 23% |
Agricultural and Biological Sciences | 11 | 16% |
Medicine and Dentistry | 7 | 10% |
Neuroscience | 5 | 7% |
Pharmacology, Toxicology and Pharmaceutical Science | 1 | 1% |
Other | 5 | 7% |
Unknown | 24 | 35% |