A real-world data approach for bridging diversity disparities in clinical trials

[Image courtesy of Pixabay]

The lack of appropriate representation in clinical trials, particularly in terms of ethnicity and race, has been a long-standing issue that directly impacts health equity and treatment efficacy. In a 2020 analysis of the global participation in clinical trials, the Food and Drug Administration (FDA) highlighted the vast difference between enrolled participants and the global population. Of the more than 297,000 participants in clinical trials globally, 76% were white, 11% were Asian, and only 7% were Black. By comparison, 60% of the global population hails from Asia, 16% from Africa, 10% from Europe, 8% from Latin America, and just over 4% from the United States.1

The FDA has focused on addressing this issue, drafting new guidance in April 2022 aimed at increasing clinical trial enrollment from underrepresented racial and ethnic populations. This draft guidance, “Diversity Plans to Im…

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How AI technology can democratize clinical trials in oncology

While drug developers continue to develop promising investigational cancer drugs, conducting clinical research in oncology remains difficult. Here’s how AI-enabled software can help. 

AI image courtesy of Pixabay

The statistics on inadequate trial recruitment and endemic challenges in oncology clinical trials are well known. They have only gotten worse over the past 20 years. While the number of cancer treatments has nearly quadrupled in that time period from 421 to 1,489, cancer drugs take 30–40% longer than other indications to gain approval and 80% of oncology clinical trials fail to meet enrollment timelines. Over this period, trial complexity has also increased due to more comprehensive trial designs (e.g., multi-cohort, basket and umbrella studies), precision medicine studies requiring gene, RNA or protein biomarker assays and the increasing quantity and sophistication of desired endpoints.

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