Authors
Creative Biogene
Summary
The emergence of CRISPR/Cas9 technology offers virtually unlimited opportunities for genetic editing. Not only can it be used as a therapeutic tool, but genome editing tools can also revolutionize drug discovery. In a comprehensive review in 2017, scientists at the University of California at Berkeley, including the co-discoverers of CRISPR/Cas, Jennifer Doudna, concluded that this type of genetic editing "is ready for the direct impact on real-world drug discovery and development.
Introduction
The process of drug discovery begins with the identification of drug targets (genes, RNA transcripts or proteins) associated with the disease of interest. The ability of CRISPR/Cas to help identify target molecules will have a crucial impact on drug discovery. The CRISPR library can detect living cells with specific conditions, such as drug therapy, positive selection can easily elucidate the resistance mechanism, and negative selection can effectively detect dead or slow-growing cells. By using the system, researchers can identify genes and proteins that cause or prevent disease, thereby identifying potential drug targets. Once the putative targets are determined, further functional information is collected by in vitro and in vivo studies. CRISPR can aid in these processes by promoting gene knockout or protein overexpression in cell lines.
Successful drug development requires appropriate models to aid early drug development decisions, but generate new disease models is a difficult and expensive process in the past. CRISPR/Cas9 has good versatility and has been used in more animal model development. In addition, CRISPR/Cas9 allows multiple genes to be edited at once, which applies to most human diseases that are not single genes. Therefore, CRISPR/Cas can make it easier to create cells that accurately mimic disease and the entire animal model system, which allows scientists to more accurately discovery new drugs and verify the safety and efficacy of the drugs, ensuring that these models better predict what will happen in clinical trials.
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