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Speeding Innovation with AI and Big Data

10/16/2019

Today, we live in a new era of medicine characterized by unprecedented breakthroughs across many treatment areas, including cancer, infectious diseases, auto-immune conditions and more. Advanced technologies such as artificial intelligence (AI) and machine learning (ML) help grow our understanding of human genetics and disease pathology, which further drives the pace of innovation.

 

Biopharmaceutical researchers are working across the globe to research and develop nearly 7,000 potential new medicines for a wide range of diseases, but research and development can be costly, lengthy and riddled with setbacks. When developing a new medicine, scientists must sort through thousands, if not millions, of potential compounds to find the few that have the right characteristics to become medicines. Biopharmaceutical companies employ teams of researchers for the sole purpose of testing whether these compounds have the potential to treat a specific disease—well before any real-world testing takes place. Due to the industry’s strong commitment to safety, the process is often incredibly time-intensive. That’s where advanced analytics tools like AI/ML can help.

 

Scientists are using AI/ML to sift through complex datasets and model drug-disease interactions to help hone in on the most likely candidates for an effective new treatment. The payoff is speed: AI models can develop, sort and analyze potential new compounds far faster than any human team could achieve through informed trial-and-error. Importantly, AI functions as a guide, helping researchers navigate large volumes of data to focus on the handful of treatments most likely to lead to success. These potential treatments still need to be screened, analyzed and tested through a rigorous clinical trials process to ensure safety and efficacy.

 

AI/ML can also help biopharmaceutical companies design and run clinical trials, develop algorithms that identify patients or help predict the risk of adverse events, and ensure pharmacovigilance.

 

The process of applying tools like AI/ML to biopharmaceutical research and development is often referred to as digital R&D, and it is beginning to positively disrupt biopharmaceutical development. Further large-scale benefits from digital technologies are challenging the ability of the current regulatory and policy landscape to keep pace with the rapidly evolving, scientific and technological aspects of digital R&D. A modern vision and regulatory framework, supported by better tools for assessing how digital technologies can be best used in R&D, are needed to unlock the full potential of tools like AI/ML for the benefit of patients. Such a framework includes addressing validation of technologies and the acceptance by regulators of digitally derived data and lowering or removing present barriers to advancing application of digital technology solutions.

 

Every day, America’s biopharmaceutical researchers go to work with the critical understanding that millions of patients rely on them to push the boundaries of science further as quickly as possible. Advanced technologies are part of that mission. Coupled with the industry’s commitment to advancing pragmatic, pro-consumer solutions that help patients access the medicines they need, the gains realized by AI/ML tools help accelerate the new era of medicine and make a difference in the lives of patients across the world.