Evaluating the Role of Machine Learning and Artificial Intelligence in Oncology Drug Repurposing Efforts

This study aims to investigate how machine learning (ML) contributes to drug repurposing efforts in oncology, considering the pharmaceutical industry's mounting R and D inefficiencies and economic pressures. Through qualitative interviews with experts across artificial intelligence, oncology, and pharmaceutical development, this paper explores the real-world applications of ML in this field, the challenges to its implementation, and its future potential to streamline drug discovery.

This study employed the "research onion" framework (Saunders et al., 2016), adopting an interpretivist philosophy and inductive approach to explore stakeholder perspectives on integrating ML into oncological drug repurposing. A multimethod strategy combined a narrative literature review with 13 semi-structured interviews, selected through purposive and snowball sampling. Data were thematically analyzed using Braun and Clarke's six-step framework, supported by NVivo. Research trustworthiness was ensured via Lincoln and Guba's criteria, and ethical approval was granted by Imperial College London.

Three major thematic domains emerged: The technological, regulatory, and business landscapes. Technological challenges included poor data quality, limited accessibility to real-world datasets, and the need for robust infrastructure to support predictive modeling. Regulatory barriers are centered on ethical concerns in data governance and the difficulty of securing exclusivity and market protection for repurposed drugs. From a business perspective, profitability concerns, generic competition, and fragmented data ownership underscored the need for more collaborative and economically sustainable models.

ML offers potential for oncological drug repurposing, but realizing its benefits requires addressing key technological, regulatory, and economic challenges.