Here's How Data Takes Part in Driving Effective R&D for Life Sciences

AI-enabled technologies will reduce the time and expense of bringing new treatments on the market, prevent expensive late-stage delays, and simplify regulatory acceptance and monitoring processes.

FREMONT, CA: Healthcare digitization influences the entire value chain and generates vast volumes of heterogeneous data, but it is not adequately relevant or accessible for harnessing. Life sciences and R&D practitioners spend a substantial amount of time analyzing clinical trial notes, medical data sets, and records to identify critical problems (e.g., unintended drug effects) and provide an overall understanding of the content of the document. Such papers and medical notes are incredibly detailed and unstructured, often leading to an accumulation of information and incomplete or lost data. Oncologists require support in developing perspectives and interpreting trends from unstructured records.

Increased R&D Prices and Lead Times

Drug research is now a costly and time-consuming process for mass-market products. However, the need for specialized and tailored healthcare options (including rare diseases) is growing, with the increasing time and expense of regulatory approval making the situation more acute. Drug development and clinical trials usually have two main phases.

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Usually, phase one may take between three and five years, and phase two may take between six and seven years. It is challenging to shorten this time. There are few financial or human levers available; the amount of analysis work, expertise, and legislation involved means that businesses cannot quickly pour more capital or personnel into the process. The availability of big data resources and data sciences with their power to open the R&D process makes these skills much more important.

AI and Data Science Present a Steady Stream of New Solutions

Artificial Intelligence (AI), computer science, and analytics significantly impact on R&D and drug discovery. These tools and approaches allow researchers to decode associations, behaviors, and genetic factors that drive disease, advance the search and analysis performance of complex chemical spaces. Additionally, they also maximize clinical trials' efficacy by improving the administration, training, patient reporting, and collection of real-world evidence.

AI-enabled technologies will reduce the time and expense of bringing new treatments on the market, prevent expensive late-stage delays, and simplify regulatory acceptance and monitoring processes. They will also offer more customized treatment that has a considerable effect on patients' outcomes and quality of life.