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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