The Multifaceted Future of Pharma – Chapter 3: A World of AI, Data, and Automation
From advanced analytics and screening to setting new standards in quality and productivity, pharmaceutical manufacturing experts share their thoughts on how the use of AI, ML and other digital and automated technologies will transform drug development and manufacture.
What we asked: “Looking ahead to the next 5–10 years, what will be the key disruptors and/or what can be improved upon in the pharma industry?”
Accelerated Approval – with Robert Hughes, Research Fellow, Grace:
“One of the primary reasons that many of us – myself included – embarked on a career in the pharma industry was to help people. Whether for a mild ailment or a debilitating disease, medicines are one tangible way that we can help relieve a patient’s suffering. However, in my 25 plus years in this industry, we have not seen a significant change in the time that it takes to get a new drug to market; the timeline from discovery through commercial launch is still in the 10-15 year range. While some medicines receive accelerated approval via fast-track, orphan, or breakthrough status to serve unmet medical needs, in general, patients in need must wait entirely too long for them to come to market.
“In the next ten years, I hope to see a reduction in the time it takes to get a new medicine to market, and I believe that the application of AI and ML in all areas of drug development represents our best hope for shortening the average to under 10 years. A few of the ways AI/ML tools can make a material impact on the time it takes to get a new drug to market include:
- Analyzing large biological datasets to identify potential drug targets more quickly and accurately than traditional methods.
- Generating and screening millions of potential drug compounds; predicting their properties and efficacy before entering the lab testing phase; and predicting a drug's toxicity, side effects, and interactions, potentially reducing the need for extensive animal testing.
- Analyzing patient data to identify ideal candidates for clinical trials; optimizing clinical trial protocols and analyzing results more efficiently.
- Identifying new uses for existing drugs by analyzing mechanisms of action and potential effects on different diseases.
- Using data to improve drug manufacturing processes, potentially reducing costs and increasing efficiency
“We’ve seen how ML can significantly reduce the time it takes to realize a scalable manufacturing process. While AI is unlikely to reduce the lengthy clinical trial and regulatory review phase in the near term – and as tools improve and become more integrated into all stages of drug development – the cumulative impact could become more significant over time.
“We have only scratched the surface when it comes to AI and ML applications in the drug development and manufacturing process. I’m energized by the possibilities that we can uncover for accelerating change in the next decade and beyond.”
Productivity, Pipelines, and Patients – with Will Lewis, Chair and CEO, Insmed:
“The continued advancement of AI has the potential to dramatically change the biopharma industry. Though still in its nascent phase, AI not only offers the opportunity to augment some of the most exciting breakthrough science, but also introduces significant productivity enhancements, which our industry desperately needs.
“Many companies are exploring ways to harness the power of generative AI to potentially reduce the time and increase the efficiency of developing and delivering much-needed medicines. Within Insmed, some initial applications include expediting the identification of potential therapeutic candidates for early-stage pipelines, producing initial drafts of clinical study reports and other labor-intensive regulatory documents, and helping to sift through large quantities of scientific literature, as well as internal enterprise data.
“There are countless ways to potentially leverage AI to deliver tangible improvements in our industry. Companies are starting to explore this space, and we’re all rooting for each other to succeed because of the tremendous implications it could have for the entire life sciences ecosystem – and for patients!”
