As a dentist, I’m deeply interested in how technology reshapes patient care, not just in dentistry, but across the entire healthcare industry. One area that truly intrigues me is how new medicines come to life.
Developing a drug is a process that begins with a concept and concludes with a therapy that transforms lives. In order to grasp this journey, we usually refer to three big steps: Discover, Develop, and Deliver. These are the 3Ds of the pharmaceutical industry.
Every step is crucial, and smack in the middle of this process are clinical trials. This critical stage tests promising medicines for safety and effectiveness in real people.
Today, with the increasing application of AI for drug development, clinical trials are revolutionizing. AI is speeding them up, making them more precise and more tailored. Rather than being lengthy, costly, and complicated, trials are becoming more intelligent and streamlined.
How AI Impacts the 3Ds: Discover → Develop → Deliver
AI isn’t merely transforming one aspect of the drug development process, it’s improving every step. Here’s a closer examination of how AI aids each of the 3Ds:
Discover: Identifying What Could Work
- AI can read scientific literature, databases, and genetic data to identify patterns and find new drug targets quicker than any human could.
- Additionally, Technologies such as DeepMind’s AlphaFold assist scientists in forecasting protein structures, allowing it to be simpler to create successful treatments.
- Artificial intelligence is able to model the behavior of new molecules within the body, saving laboratory time.
Develop: Testing and Refining Treatments
- AI helps design clinical trials by choosing the proper patients on the basis of genetics, medical history, and even lifestyle.
- AI can model how a medicine functions through digital twins or virtual patients. This helps minimizing the necessity for some of the initial-phase human trials.
- Firms such as Insilico Medicine have employed AI to create medicines at record speeds. This also helps forecasting their impact before laboratory tests have even begin.
- AI-driven dashboards such as those used at Conduit Pharmaceuticals enable researchers to live monitor trials and react rapidly to any adjustments.
Deliver: How To Get Medicine to People Quickly
- AI assists in predicting demand and scheduling manufacturing so that when a drug is approved, it is available to patients more quickly.
- It also aids the approval process by streamlining clinical data for regulators such as the FDA.
- Pfizer and Moderna utilized AI during the COVID-19 pandemic to accelerate trials and get vaccines to market in record time.
AI for Drug Development using Digital Twin Technology
One of the most thrilling new applications of AI-based drug development is the use of digital twins or virtual patients. These computer-generated models simulate real human patients. They let researchers predict how an individual would react to a new treatment before testing it on an actual patient. And one of their greatest assets? Personalization.
Because researchers can create digital twins with detailed information, such as age, sex, genes, medical history, and lifestyle, they can customize testing in ways unimaginable before. They can simulate how a treatment would work on different kinds of people, even those who are frequently excluded from clinical trials.
Digital Twin in Action:
Sanofi has also utilized AI-driven digital twins to aid their research, particularly in the case of rare diseases where it’s hard to recruit patients. By developing virtual patients, they’re able to simulate testing to determine the safest and most efficacious doses, sometimes skipping early-phase human trials altogether.
In one instance, Sanofi used digital twins to simulate a new asthma medication. Prior to advancing to the next stage of trials, they performed simulations with virtual patients constructed to mimic the biology of asthma. Without reference to data from the first trial, the model accurately forecasted the outcome in real life, giving them greater confidence in the drug’s ability to excel in a crowded market.
The Alan Turing Institute also explains how digital patients might someday be employed to simulate placebo and treatment trials on the same virtual cohort. This would accelerate trials, make them more ethical, and more cost-effective.
What the Rules Say
The application of AI in clinical development is growing rapidly, and regulatory bodies like the U.S. Food and Drug Administration (FDA) are following suit. In February 2025, the FDA revised their comprehensive discussion paper entitled “Using Artificial Intelligence and Machine Learning in the Development of Drug and Biological Products“, which discussed existing considerations and future intentions.
Key points from the paper are:
Clarifying the role of AI in drug development:
The FDA recognizes AI/ML is presently being applied in discovery, clinical trials, manufacturing, and regulatory filings.
Risk-based approach:
The agency suggests analyzing AI tools using the degree of risk they bear for the quality, safety, and efficacy of products.
Transparent communication:
The developers are invited to describe and report the training, validation, and maintenance of the AI models over time.
Lifecycle management:
The FDA endorses a framework under which AI models can be evolved. It also specifies the requirement for controls, particularly on adaptive or continuously learning systems.
Cross-discipline collaboration:
The FDA calls for cooperation between scientific, technical, and regulatory groups. This ensure that AI innovation keeps pace with ethical and safe application.
This paper demonstrates the FDA’s commitment to establishing a secure regulatory system. One that advances innovation while defending public health. With AI assuming a more integral role in the development of drugs, such guidance provides assurance that advance continues in an accountable, open manner.
What’s Next in AI for Drug Development
AI is settling into the background of drug development, guiding choices, streamlining methods, and uncovering knowledge that was tough to obtain not long ago. It’s no longer a tool of the future, but one scientists and doctors are beginning to count on.
As this change grows deeper more focus falls on the groups who will push it ahead. From hospital workers to lab researchers, building the right mix of talent is essential. Many organizations are turning to AI to help with that too.
As artificial intelligence continues to advance, success will rest in how well we harmonize technology with humans, aiding professionals, enhancing systems, and ultimately, creating improved patient outcomes globally.
