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Improvement in the drug discovery or clinical trial process with technologies like big data can speed up the time it takes to bring a potentially life-saving drug to market.
FREMONT, CA: The human body is full of genetic information. The DNA is made up of around three billion genetic bases, and if all the DNA in the human body were laid out, it would extend to twice the diameter of the Solar System, with every cell's DNA being three meters long.
Data, information, and statistics are driving biotechnology ahead, which uses living creatures or biological systems and their derivatives to manufacture products. Bioinformatics has evolved from a tool in the inventory of a biologist or biotechnologist to a discipline.
Business intelligence, data analytics, and technical advancements are critical for the emergence of new technologies and treatments and resolving current issues. Healthcare researchers can find potential drug targets, optimize processes, bring novel drugs to market, and minimize clinical trial errors by making sense of massive data derived from genomics or sensors.
If people consider big data in terms of biotechnology, the first thing that comes to mind is probably genome sequencing. The Human Genome Project was a ground-breaking project that offered access to three billion bases of data, allowing researchers to learn more about mutations, genes, and other topics.
Researchers can now use the data to gain significant insights in fields such as medicine, crime scene investigations, and so on. Data scientists employ frameworks and tools to store, monitor, receive, analyze, and interpret data in order to work successfully with it. Tools to automatically annotate individual genes are currently being developed, and software companies have begun to tackle genome interpretation.
Drug discovery, development and genomics
The process of bringing a novel pharmaceutical product to market is long and complicated, with numerous bottlenecks. Trials frequently fail to reach their objectives, such as enrolment, causing additional delays and increasing the expense of an already costly procedure. Before scientists can start enrolling patients for a clinical trial, they must first discover a drug candidate, which requires a lot of data, experiments, and evaluation of risk/benefit.
Researchers can now screen millions of compounds with automated software to find drug candidates for clinical trials. Pharmaceutical experts might delegate the difficult task of sorting through a vast library of prospective drugs and determining what is likely to succeed based on the trial's unique criteria to artificial intelligence (AI).
Any advancement in the drug discovery or clinical trial process can save millions of dollars in development expenditures and, as a result, minimize the time it takes to bring potentially life-saving treatment to market.