It is important with the development of pharmaceuticals that the representation of the population at large, or the intended population for the drug, is reflected in the clinical trials conducted. Yet too many clinical trials are not representative of the general population or of those with a particular disease.
This necessity is sometimes lacking. The concept was featured in an earlier article on Digital Journal where this journalist wrote: “To assess medicines for safety and efficacy that clinical trials need to be representative factors like biological sex and ethnicity. Too often, there is an underrepresentation of one key group or another in clinical trial subject populations. This leads to erroneous data where a societal group differs in presentation, clinical manifestations, and outcomes in comparison to others.”
Despite a U.S. congressional ruling calling on gender and ethnicity to be clear criteria when setting up trials for new medicinal products, progress in the U.S. has been notably slow. This leads Michel Denarié, Senior Principal, Regulatory Affairs and Drug Development Solutions (RADDS), IQVIA, to explain why improving the selection criteria for the assessment of new medicinal products needs an overhaul.
Denarié finds: “In the coming year, clinical trial sponsors will become even more focused on implementing diversity plans and this will continue to push for major shifts across the industry. In fact, within the United States, guidance has been introduced for the creation of diversity plans in clinical trials to push further diversity and inclusion.”
In terms of the guidance, Denarié is enthusiastic: “This is a critical step for drug development as patients must be representative of the population, but it is key to note that it must be done cautiously to be compliant with the expectations of health authorities.”
What does this mean in practice? Drawing on an example of clinical excellence, Denarié cites: “Project optimizations from the FDA, specifically Project Optimus within oncology, is a recent trend that will expand to other therapeutic areas in the coming year.”
The basis of Project Optimus is that a poorly characterized dose and schedule may lead to selection of a dose that provides more toxicity without additional efficacy.
Continuing this, Denarié observes: “Rather than testing only the maximum efficacy, organizations have been pushed to find the therapeutically optimal drug dose. This dose optimization could soon be a de facto requirement, elongating the trial process and increasing overall costs – beyond oncology.”
This should signal changes during 2024. Yet, as to how these changes might disrupt pharmaceutical development there is uncertainty; Denarié predicts: “In terms of indication prioritization, organizations will begin to integrate natural language processing and artificial intelligence for optimization and efficiency.”
And in terms of the main advantages, Denarié finds: “This automation will help reduce the manual time spent on data analysis, allowing senior team members to focus more on operations that require a high level of human expertise. This integration will happen slowly, but steady, as automated technologies must be validated for accuracy.”
