Drugmakers deploy AI to accelerate clinical trials and slash regulatory submission timelines

Reviewed byNidhi Govil

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Major pharmaceutical companies including Novartis, GSK, and Pfizer are using artificial intelligence to streamline clinical trials and regulatory submissions, shaving weeks off labor-intensive processes. While AI hasn't yet delivered breakthrough drug discoveries, it's transforming the administrative backbone of drug development—from identifying clinical trial participants to drafting regulatory documents—with McKinsey predicting 35-45% productivity increases over the next five years.

Artificial Intelligence Transforms the Administrative Side of Drug Development

Artificial intelligence is reshaping how pharmaceutical companies approach drug development, though not in the way many expected. While AI hasn't yet cracked the code on discovering new molecules that lead to major medical advances, it's proving invaluable in streamlining the less glamorous but equally critical administrative tasks that have long plagued the pharmaceutical industry

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. Seven large drugmakers and six smaller biotech companies revealed at the recent JP Morgan Healthcare Conference that AI is helping them find participants and sites for clinical trials while drafting documents for regulators, shaving weeks off these labor-intensive processes

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Source: ET

Source: ET

The stakes are high in an industry where bringing a new drug to market can take a decade and cost $2 billion

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. Companies like Eli Lilly, which has partnered with chipmaker Nvidia, are betting that AI can improve not just efficiency but also the success rate of new drugs. Consultancy McKinsey predicted that agentic AI—autonomous systems requiring minimal human intervention—could increase clinical development productivity by 35% to 45% over the next five years

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How Leading Pharmaceutical Companies Are Deploying AI to Speed Trials

Teva Pharmaceutical Industries exemplifies the industry's pragmatic approach to AI adoption. CEO Richard Francis explained that the company is utilizing AI in multiple ways to focus on successfully bringing new drugs to market while making everything else "as efficient and as small as possible"

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. He emphasized that AI digitization and process improvement—"all the unsexy stuff"—is what makes a tangible difference in accelerating the drug development pipeline.

Novartis provides perhaps the most striking example of AI's impact on clinical trials. When the Swiss drugmaker launched a 14,000-person late-stage cardiovascular outcomes trial for its cholesterol drug Leqvio in 2023, AI transformed site selection from a typical four- to six-week process into a two-hour meeting. The technology helped identify higher-performing sites and allowed Novartis to close participant enrollment with only 13 patients above its trial target. Chief Medical Officer Shreeram Aradhye noted that these time savings can add up to months over a complete drug-development program, coining the phrase "AI becomes augmenting intelligence, not artificial intelligence"

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Streamlining Regulatory Submissions and Reducing Costs

The challenge of drafting regulatory submission documents has long been a bottleneck in drug development. Executives from AstraZeneca, Roche, and Pfizer, along with smaller biotechs like Spyre and Nuvalent, described the burden of tracking thousands of pages of documents for regulators, including clinical, safety, and manufacturing records

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. These documents must be compiled, cross-checked, and kept consistent across geographies, often requiring costly outside contractors, as AstraZeneca Chief Financial Officer Aradhana Sarin explained.

Source: Reuters

Source: Reuters

GSK is using a mix of digital and AI tools to reduce manual data collection and aggregation, aiming to speed up all clinical trials by 15%

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. This approach helped save about 8 million pounds ($10.87 million) in costs for late-stage studies of asthma drug Exdensur last year, with the drug winning U.S. approval last month. German radiopharmaceuticals firm ITM has figured out how to use AI to convert long trial reports into FDA template formats, potentially saving weeks of effort requiring several staff, though it has yet to deploy the system

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Addressing the Challenge of Identifying Clinical Trial Participants

Patient recruitment remains one of drug development's most persistent challenges. Jorge Conde, a general partner at venture capital firm Andreessen Horowitz, described trial enrollment as a "leaky funnel" where participants drop out along the way

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. He's investing in fixes to what he calls drug development's "messy middle," including putting $4.3 million into startup Alleviate Health, which uses AI technology to help with patient outreach, education, screening, and scheduling.

Genmab, a Danish antibody developer, plans to deploy Anthropic's Claude chatbot-powered agentic AI to support clinical development priorities

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. Hisham Hamadeh, Genmab's head of AI, said the goal is to automate post-trial work, including analysis of data and its transformation into graphs, tables, figures, and clinical study reports. TD Cowen analyst Brendan Smith noted that the use of AI, including large language models like Microsoft Copilot, for administrative tasks has become fairly common in the pharmaceutical industry.

What This Means for the Future of Drug Discovery and Market Timelines

While AI is delivering measurable benefits in streamlining labor-intensive processes and helping to enhance productivity, the technology's full impact on the drug-to-market timeline remains to be seen. Smith cautioned that it may take another one to three years before investors can accurately measure how AI has begun speeding up drug development, as quantifying savings depends heavily on how and where the tools are deployed

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Amgen research chief Jay Bradner suggested that the industry is closer to AI-discovered drugs than many realize, stating: "What everybody's waiting for is the AI drug. When do I get the AI drug? I actually think those molecules are in pipelines right now"

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. This hints at a future where AI's role expands beyond administrative efficiency to fundamentally changing drug discovery itself. For now, the technology is proving its worth by helping companies reduce costs, improve site selection for trials, and accelerate patient recruitment—incremental gains that collectively could reshape the economics of pharmaceutical development and potentially bring life-saving treatments to patients faster.

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