The convergence of AI and life sciences is becoming an integral component of the North Star vision for more efficient, cost-effective, and personalized treatments. Clinical trials, often likened to a slow-moving pipeline, are responsible for the largest burden of both cost and time in the journey to bring a new drug to market. So, what’s the path forward for biopharmaceutical companies keen to accelerate and streamline this process?
Enter: advanced analytics, machine learning (ML), and AI. By leveraging predictive analytics and cutting-edge algorithms, AI can empower researchers to forecast the probability of a drug's success before it even enters a clinical trial stage, as well as track the probability of clinical milestones and adaptively react. AI is also being used to optimally determine clinical sites and operational cadence to improve design and enrollment to further meet planned protocol milestones. This not only accelerates the pace of drug development but also reduces costs and risks, making it feasible for life sciences companies to invest in treatments for rare diseases and individualized therapies.
If you haven’t yet heard, we recently released Season 2 of the Dataiku web series AI&Us, centered around exploring the hard-hitting questions that life sciences and biopharmaceutical companies are asking, like outlined above. The second episode — “Speed to Market” — specifically dives into the impact of AI on clinical trials, manufacturing, and supply chains. While AI may never replace certain processes like human testing, it is enhancing various aspects of clinical trials, from trial design to data collection and analysis. Moreover, AI continues to streamline quality manufacturing processes in Pharma 4.0, improving productivity, compliance, and efficiency throughout the supply chain. This article is your sneak peek into the episode’s highlights (you can see the full video at the end of the post!).
Episode 2 features Dr. Christopher E. Mason (Professor Genomics, Physiology, and Biophysics), Jonathan Crowther (Head of Predictive Analytics), Norman Azoulay (Director of Scientific Products at Excelra), David West (Co-Founder and CEO at Proscia), Subroto Mukherjee (Global Strategy Lead at Microsoft), Ranjit Kumble (VP of Data Science), Kyle Tretina (Alliance Manager at Insilico Medicine), Harini Gopalakrishnan (Field CTO, Life Sciences at Snowflake), along with many others.
Optimizing All Stops on the Life Sciences Value Chain
Clinical Trials
Transitioning from lab work to testing in living systems represents a crucial juncture in drug development. Preclinical stages involve experimentation with molecules and proteins, followed by animal models such as mice and primates. As drugs progress through the phases of clinical trials, the costs and risks escalate exponentially (90% of drugs fail in Phase I, II, or III). AI-driven insights enable researchers to navigate these challenges more efficiently, identifying optimal trial locations, and enhancing patient recruitment strategies.
Can we speed it up in terms of, can I identify my biomarker ahead of time? Can we get to a point where we know everything about the population, the patients, that I need to be putting into this clinical trial? Yes, we should speed that up. What we shouldn’t speed up is actually doing the right clinical trial and let that take its course.
-Norman Azoulay, Director of Scientific Products at Excelra
Regulatory compliance remains paramount in the pharmaceutical industry, with stringent protocols in place to ensure patient safety. AI augments — rather than replaces — human oversight, providing decision-makers with data-driven insights to support regulatory submissions. By standardizing criteria for patient eligibility and treatment efficacy, AI fosters greater transparency and reproducibility in clinical trials.
Regulation is always going to be a lot of paperwork and proving that whatever result you put out there is traceable, justified, and there is enough evidence to get behind what you have submitted. It’s for the safety of all patients and to make sure that the due diligence has been done before something gets injected into the body en masse. AI will always have a human in the chain.
-Harini Gopalakrishnan, Field CTO, Life Sciences at Snowflake
Manufacturing and Supply Chain Management
Beyond clinical research, AI holds immense potential in revolutionizing manufacturing and supply chain management. The concept of Pharma 4.0 embodies a paradigm shift towards digitized production and distribution practices. By harnessing AI-powered predictive analytics, pharmaceutical companies can optimize production processes, anticipate supply chain disruptions, and enhance inventory management.
AI is helping us step by step optimize our supply chains. It’s helping us better predict the timeframes, it’s helping us identify and pinpoint latencies that can be addressed, it’s helping us balance out imbalances in supply. It’s also helping us anticipate disruptions in a much more timely way.
-Ranjit Kumble, VP of Data Science
Moreover, AI facilitates the development of personalized medicine, particularly in cell and gene therapies. With each patient representing a unique batch, AI-driven supply chain optimization becomes imperative. By leveraging ML techniques, companies can proactively mitigate risks and ensure the timely delivery of life-saving treatments.
The transformative impact of AI extends beyond drug development, offering a promise for patients afflicted by rare diseases and unmet medical needs. By accelerating the pace of innovation and optimizing patient outcomes, AI holds the potential to revolutionize the healthcare landscape, ushering in the era of precision medicine.
Be sure to check out the full episode here for yourself: