Ginny Beakes-Read
Chair, Regulatory and Scientific Committee, IFPMA
Artificial intelligence; machine learning; real-world data; generative biology. What do these popular phrases mean for clinical trial innovation? They mean the potential for greater understanding of diseases; better and more rapid execution of trials; more breakthrough medicines for patients.
Fast-moving technological innovations can also cause uncertainty. To fully leverage these opportunities, we need all stakeholders to evaluate, learn and adapt so that more innovative clinical trials can deliver on the promise we are beginning to see today.
Digital technologies to analyse patient data
Digital technologies — such as wearable devices and electronic health records — have revolutionised clinical trial execution, offering new opportunities for data collection, patient monitoring and remote participation.
However, tools powered by artificial intelligence (AI) and machine learning (ML) technology go a step further. They can have the potential to analyse patient data, identify drug targets and compounds that may yield large effect sizes, enhance site selection, optimise inclusion/exclusion criteria and aid in the selection of endpoints. These advancements can be an additional tool to empower researchers and promote more informed decision-making.
Researchers can also better understand the feasibility of new medicines by using machine learning models.
AI and ML use in medicine design
Researchers can also better understand the feasibility of new medicines by using machine learning models to design biologics and predict key characteristics, such as viscosity. Using AI/ML models can also help increase diversity in trials by finding the right patients for the right studies at the right time.
Harnessing methodological advancements can enable us to design trials in innovative ways to promote efficiency and outcomes. For example, adaptive trial designs can allow for real-time adjustments based on accumulating data, enabling researchers to better optimize sample sizes, treatment regimens, and patient populations. By embracing adaptive designs, trials can become more agile, faster and more cost-effective.
Multidisciplinary collaboration is the best approach
Despite improved dialogue with regulators, regulatory complexities limit the global adoption of complex trial designs and methodologies. Overcoming these barriers requires collaboration and partnerships among stakeholders in order to foster a multidisciplinary approach to R&D, accelerating the translation of scientific discoveries into tangible patient benefits.
Embracing innovation and fostering collaboration is essential to unlocking the full potential of medical advancements to benefit patients.
