New regulatory thinking is needed for AI-based personalised drug and cell therapies in precision oncology

Abstract

Until recently the application of artificial intelligence (AI) in precision oncology was confined to activities in drug development and had limited impact on the personalisation of therapy. Now, a number of approaches have been proposed for the personalisation of drug and cell therapies with AI applied to therapy design, planning and delivery at the patient's bedside. Some drug and cell-based therapies are already tuneable to the individual to optimise efficacy, to reduce toxicity, to adapt the dosing regime, to design combination therapy approaches and, preclinically, even to personalise the receptor design of cell therapies. Developments in AI-based healthcare are accelerating through the adoption of foundation models, and generalist medical AI models have been proposed. The application of these approaches in therapy design is already being explored and realistic short-term advances include the application to the personalised design and delivery of drugs and cell therapies. With this pace of development, the limiting step to adoption will likely be the capacity and appropriateness of regulatory frameworks. This article explores emerging concepts and new ideas for the regulation of AI-enabled personalised cancer therapies in the context of existing and in development governance frameworks.

Fig. 1

The current approaches for assay- and image-based companion/complementary diagnostics (CDx, TDx, cDx) and the emerging technologies for AI-based and information-integrative approaches. iTDx – image-based TDx.

Fig. 2. A perspective on the US and EU regulatory system readiness for AI-enabled personalised drug and cell-based therapies in precision oncology. The principal difference in regulatory system readiness is that the US FDA’s ‘non-device’ approach for AI-based CDS substantially reduces the gap between new technology and the regulatory framework readiness.

The regulatory system colouring reflects current readiness only and does not reflect the potential for fast adaptability of frameworks (i.e., through greater regulatory science resources, less fragmentation, and smaller backlogs).