Objective: This study aims to investigate how machine learning contributes to drug repurposing efforts in oncology, considering the pharmaceutical industry’s mounting R&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 machine learning in this field, the challenges to its implementation, and its future potential to streamline drug discovery.

Methods: This study employed the “research onion” framework (Saunders et al., 2016), adopting an interpretivist philosophy and inductive approach to explore stakeholder perspectives on integrating machine learning (ML) into oncological drug repurposing. A multi-method strategy combined a narrative literature review with 13 semi-structured interviews, selected through purposive and snowball sampling. Data were thematically analysed 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.

Findings: 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 modelling. Regulatory barriers centred 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.

Conclusion: Machine learning offers potential for oncological drug repurposing, but realising its benefits requires addressing key technological, regulatory, and economic challenges.