Another theme at workshops focused on novel trials designs such as “basket,” “umbrella,” and many adaptive designs have been encouraged by regulatory agencies and can exploit emerging AI and ML techniques.
These designs can enroll patients in a trial, profile them (e.g., using DNA sequencing, proteomics, metabolomics, etc.), and then use RWD for matching drugs considered in the trial to the pathologies identified from the profiling. Strategies for matching drugs to patient profiles in these studies can be based on AI and ML analysis of large relevant data sets. AI and ML can further be used to support an electronic version of study data monitoring, thereby ensuring that data are correct and the patients are safe; thus reducing the need for expensive on-site study monitoring. Furthermore, EHR data can be combined with other RWD types, such as genomics and patient-reported concerns, can be mined with AI and ML techniques to create a more comprehensive picture for drug and biomarker discovery.
As methods for each of these tasks are determined and refined, computational solutions, including AI and ML, can be implemented to reliably replicate clinical trial activities at scale. These types of clinical trials—which ultimately test intervention “algorithms” such as drug–patient profile matching schemes—are likely to become more pronounced and prevalent in the future, and could be greatly facilitated by leveraging clinical outcomes monitoring and RWD collection. This interaction could also lead to the development of “Clinical Decision Support” tools that provide insight into optimal ways of treating patients. Experience with these tools could lead to further refinements, ultimately providing continuous feedback on their effectiveness.
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