How Analytics is Beneficial in the Life Science Industry
Analytics helps the life science sector collect more reliable, clean, and valid data so that they can achieve an efficient drug discovery program.
FREMONT, CA: Analytics needs a different learning curve and set of tools to gain the most ground in terms of research and development. Today, the analytics methods are being used across every part of the drug discovery process that can be roughly grouped under manufacturing, commercial, and research and development. These stages can be divided into certain long-standing analytics events, like,
• Analysis of sizeable high throughput screening (biological testing) datasets for identifying new potential drugs (R&D)
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• Simulation of the clinical efficiency and safety of a potential new drug (R&D)
• Determining when, where, to whom, and at what price to sell a drug, etc. (Commercial)
• Data mining for new potential targets (R&D)
• Optimization of the biological activity of a potential new drug through statistical/mechanical model-driven changes to the structure or formulation (R&D)
• Optimization of the manufacturing process through simulations (Manufacturing)
Importance of the Data
Analytics plays a vital role in every stage of drug discovery. The effectiveness of applying predictive and other types of analytics methods depends on the quality of the data, which means it has to be reliable, clean, and valid to provide an actionable outcome.
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