The process by which new candidate medications are discovered
CRISPR is used systematically during the target identification and validation process to knockout, inhibit, and alter the expression levels of certain genes. It helps researchers understand how genes and their expression can improve or diminish disease states, and identify and help better predict how drug candidates may produce beneficial or harmful patient outcomes. Both in vivo and in vitro studies are done using CRISPR technology to facilitate gene knockout and expression level studies in the drug discovery process.
Clustered regularly interspaced short palindromic repeats (CRISPR) technology gives drug discovery researchers the ability to alter sequences and expression levels of genes to help them identify therapeutic drug targets and test the therapeutic efficiency of drug candidates. CRISPR has become more useful in the drug discovery process as the cost of using CRISPR and DNA sequencing technologies has decreased due to technological and economies of scale improvements. CRISPR can play a role in several stages of the drug discovery process as outlined in the following diagram:
How CRISPR Is Accelerating Drug Discovery
Brittany L. Enzmann, PhD, Ania Wronski, PhD
January 11, 2019
How CRISPR is transforming drug discovery
March 7, 2018
Selecting a drug candidate for clinical trials typically need to demonstrate desirable properties in several areas before clinical trials begin. The drug candidate should show evidence of having the following favourable properties:
Machine learning, artificial intelligence (AI), and other software developments have made it possible to increase the speed, cost, and overall effectiveness of the drug discovery process through enhancing pattern recognition. Thomas Chittenden, a team leader at the drug discovery company Wuxi NextCODE, commented on the role AI is playing in the drug discovery process in an interview with the scientific journal Nature in 2018 by saying:
The drug discovery process begins with the identification of unmet medical needs through market analysis and input from patients, medical practitioners, therapeutic researchers, scientific conferences. The medical need must have a level of unsatisfactory treatment options to justify initiating the drug discovery process, or the potential of a novel drug may offer substantial advantages compared to existing treatments such as improved therapeutic efficiency, less adverse side-effects, better patient compliance, fewer drug interactions, and improvements in the overall patient quality of life.
After an unmet medical need is established the drug discovery process moves onto identifying potential drug targets to solve the unmet medical need through techniques such as phenotypic screening, genetic association, transgenic organisms, and medical imaging. Identifying unmet medical needs is an ongoing process of understanding available therapeutic treatment options, disease etiology, and epidemiology. This constant identification process produces an always changing analysis gap of the perceived value and potential of potential drug candidates leading to certain drug candidates taking priority over others to better address particular medical needs.
Drug discovery is the process by which new candidate medications are discovered. It combines the fields of medicine, biotechnology, chemistry, and pharmacology to create new safe and effective medications and treatments. Traditional approaches to drug discovery involve identifying medicinal properties and compounds in found in plants and other traditional medical remedies. Technological developments in software, artificial intelligence, biotechnology, medicinal chemistry, and manufacturing have widened the possibilities of drug discovery beyond traditional drug discovery methods.
Machine learning, artificial intelligence, and other software developments have made it possible to increase the speed, cost, and overall effectiveness of the drug discovery process through enhancing pattern recognition. Thomas Chittenden, a team leader at the drug discovery company Wuxi NextCODE, commented on the role AI is playing in the drug discovery process in an interview with the scientific journal Nature by saying:
AI is going to lead to the full understanding of human biology and give us the means to fully address human disease. The way we develop drugs and assess them in clinical trials will all come down to very sophisticated pattern recognition.