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)
• 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.
But the infrastructure of the data is as essential as the quality.
Along with the investments made in the pharma's analytics infrastructure, the companies are also making the same scale investment to manage, ingest, and provide data among their technology stacks. Due to the size of the investment, most organizations have started to pursue common alternatives so that it becomes easy to manage large volumes and various types of data required to accomplish effective drug discovery programs.
The Future of Analytics
The analytics platform is not in the maturity stage, which is necessary to act as a standalone enterprise solution and seen as a competitive asset among the pharmaceutical organizations. A wide range of internal and external resources are responsible for implementing techniques. It can take a long time for analytics to become efficient, like the Enterprise Resource Planning utilizing platforms like SAP ERP.
Data volume has increased because of the new biological, clinical, chemical, process, economic, epidemiological, and other channels. A new era of analytics is developing with more demand for analytics compute services or faster algorithms. The analytics service offers a landscape external to the diverse pharmaceutical organization and will continue to increase until the sector catches up with the other industries.