Generative AI: A new frontier in pharmaceutical drug development and clinical trial analysis
The pharmaceutical industry is synonymous with groundbreaking research, life-changing innovation, and attention to detail regarding the safety and effectiveness of their therapies. Like so many industries, however, pharmaceuticals are facing a world where technology is advancing at a breakneck pace, and with so many advancements filling the headlines, everyone in Life Sciences is trying to cut through the hype and answer questions like:
- Where can we use technology like Large Language Models (LLMs), Generative AI (GenAI), and more to hasten the development, testing, and delivery of new therapies?
- How do we ensure the safety of therapies that humans will use but were created with the assistance of AI?
- What are the advantages of design philosophies when it comes to AI tools?
Generative AI for Drug Discovery and Clinical Trial Analysis
One of the most promising applications of GenAI in pharmaceutical drug development is the generation of new drug candidates. Because these AI models can be trained on large, scientist-curated datasets of existing drug molecules and biological data, they can learn the patterns and relationships that underlie drug discovery and development and use them to hasten the process of getting a safe, effective therapy to market.
For example, a quick query on a well-trained model could generate molecules that have never been synthesized before but have the potential to be more effective and less toxic than existing drugs. This is an application that researchers at Exscientia have put into practice by using a generative AI model to design a new drug candidate for Alzheimer’s disease that is more potent and has fewer side effects than existing drugs.
GenAI can also be used to predict the properties of new drug candidates, which scientists can use to prioritize the most promising candidates for further development. Researchers at Insilico Medicine have used a generative AI model to predict with solid accuracy the likelihood of a new drug candidate succeeding in clinical trials.
So, those are the possibilities, but what are the benefits?
GenAI’s promise in pharma, if realized, offers many potential benefits for pharmaceutical drug development and clinical trial analysis, including:
- Increased speed and efficiency: Automating many time-consuming and labor-intensive tasks involved in drug discovery and clinical trial analysis with GenAI could significantly speed up drug development and reduce costs.
- Improved accuracy: By learning from large datasets of existing data, scientist-led efforts utilizing GenAI could make more accurate predictions about the properties of new drug candidates and the effectiveness and safety of existing drugs.
- New insights: GenAI’s ability to draw connections between vast and separated data sources can generate new hypotheses and insights. This can help researchers to develop more effective drugs and to design clinical trials that are more likely to be successful.
Challenges and future directions
Other industries carry no life-or-death consequences, allowing them to use GenAI in a multitude of experimental applications with minimal concern for failure. When GenAI is used in drug discovery, development, and delivery, however, rules must be put in place to use the expertise of scientists to ensure that AI work is fit for patients. One way to ensure the safety of these drugs is to use a human-in-the-loop design approach, which puts humans in control of GenAI tools and guarantees that they maintain the leading role in the drug design and development process.
There are several ways to implement a human-in-the-loop design approach for GenAI-powered drug development and clinical trial analysis. A few examples:
- Scientists can review the predictions of GenAI models for potential safety concerns. For example, if a GenAI model predicts that a drug candidate is likely toxic, human experts can investigate this further and decide whether to proceed with further development.
- Clinical Trial Managers and Biostatisticians can create clinical trials designed to test the safety of drugs created with GenAI tools or change AI-created trial plans to give them a higher chance of success. For example, clinical trials could be designed to test the drugs in a wider range of patients or to look for specific safety signals.
- Human experts can be used to monitor the safety of drugs created with GenAI tools after they are marketed. This can help to identify any unexpected safety concerns that may arise.
By integrating a human-led, AI-assisted philosophy from the beginning of the design phase and throughout the development process, we can better ensure that drugs created with GenAI tools are safe and effective.
Generative AI is a powerful tool that has the potential to transform pharmaceutical drug development and clinical trial analysis. By automating tasks, improving accuracy, and generating new insights, generative AI can help researchers develop more effective drugs and bring them to market more quickly and efficiently.
But to realize those benefits – and reap the rewards of a smarter drug development process – we must use the expertise of our scientists, researchers, trail designers, statisticians, and more to “keep the human in the loop.”
Sources:
- Generative AI has the potential to revolutionize drug discovery – Pharmaceutical Technology
- What the Growth of Generative AI Means for Drug Discovery and Clinical Trials – HealthTechMagazine
- Generative AI Is a Prescription for Healthcare Success – HealthTechMagazine
- What Is Generative AI and How Can It Be Used in Drug Discovery? – Dataconomy
- Exscientia
- Insilico Medicine
Share
