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

www.nature.com/npjprecisiononcology PERSPECTIVE OPEN New regulatory thinking is needed for AI-based personalised drug and cell therapies in precision oncology Bouchra Derraz1,2,9, Gabriele Breda1,9, Christoph Kaempf 3, Franziska Baenke 4, Fabienne Cotte5, Kristin Reiche Ulrike Köhl 3,7, Jakob Nikolas Kather4,8, Deborah Eskenazy2 and Stephen Gilbert 4,8 ✉ 3,6,7 , 1234567890():,; 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 AIbased 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. npj Precision Oncology (2024)8:23 ; https://doi.org/10.1038/s41698-024-00517-w INTRODUCTION There is a regulatory approval bottleneck in the translation of the latest advances in precision cancer therapies to patients1. This has led to patients’ perception, perhaps unfairly, that regulators are unnecessarily delaying access to life saving therapies2. It is critical that regulatory frameworks constantly adapt to optimally regulate emerging technologies according to their risks, benefits, and unique properties. The article considers the optimisation of regulatory frameworks relating to the AI-based personalisation of treatment, both for classical cancer drugs and for advanced therapy medicinal products (ATMPs, medicines for human use that are based on genes, tissues, or cells as well as combinations). Most ATMPs are for cancer therapy3,4. ATMPs in clinical trials for both cancer and other diseases currently face substantial waiting times for approval in the US, and even longer timelines in the EU5–7. This situation is likely to exacerbate due to a technological paradigm shift taking place in the development and mode of adaptive use of cell and drug-based therapies. Up until now, true personalisation has been limited to the adaptation of therapeutic protocols by oncologists on a case-by-case basis for their patients, and to the creation of ATMPs from the modified cells of the individual patient. In different medical disciplines, digital approaches, often using AI, are increasingly being used for the genuine personalisation of prevention, diagnosis8, treatment planning and dose adaptation9,10, and this is relevant both for classical drug therapeutics and ATMPs. In the area of precision diagnosis of cancer, examples include CE-marked AI-enabled products for radiological image analysis, of which an increasing number have been demonstrated to improve diagnosis compared to standard care, and some of which have 1 demonstrated positive effect on healthcare system efficiency11. In the area of personalised drug treatment in cancer, digital tools for monitoring patient reported outcomes (ePRO) have been shown to increase quality of life and survival in patients with advanced lung cancer12, and this led in 2020 to the first reimbursed digital therapeutics solution in France13. This automatic monitoring tool automatically triggers alert messages to the treating oncologist, based on an automatic analysis of when patient reported symptoms fall into predefined thresholds for severity and worsening. This rule-based decision making is a simple form of AI (and is defined as AI in the proposal for an EU AI Act14). Through a physician-in-the-loop process, this tool enables the personalised adaptation of drug therapy in cancer. The general trend is that rule-based decision-making is over time substituted for machine/ deep learning-based approaches, as more data is gathered, from on-market use and from future prospective studies15 These approaches can be seen in light of indexes that are already used in clinical settings for risk-adjustment of in-hospital events16. In 2021 alone, more than 100 applications received by the US FDA included some aspects of AI7,17. The potential for the AIguided precision design of non-personalised but disease specific ATMPs is rapidly advancing18. Regulatory bodies have released discussion papers addressing the regulatory approaches needed to meet these challenges7,19. AI approaches applied to the molecular personalisation of cell-based therapies are in development9,20–23. There have been previous high profile but ultimately unsuccessful BigTech excursions into precision oncology, including for drug regime planning, that have delivered some value but have substantially fallen short of their hype (e.g. the ambitious 2012 IBM Watson health project24,25). Is the potential for ProductLife Group, Paris, France. 2Groupe de recherche et d’accueil en droit et économie de la santé (GRADES), Faculty of Pharmacy, University Paris-Saclay, Paris, France. Fraunhofer Institute for Cell Therapy and Immunology, Leipzig, Germany. 4Carl Gustav Carus University Hospital Dresden, Dresden University of Technology, Dresden, Germany. 5 Department of Emergency Medicine, University Clinic Marburg, Philipps-University, Marburg, Germany. 6Center for Scalable Data Analytics and Artificial Intelligence (ScaDS.AI), Dresden/Leipzig, Germany. 7Institute of Clinical Immunology, University Leipzig, Leipzig, Germany. 8Else Kröner Fresenius Center for Digital Health, TUD Dresden University of Technology, Dresden, Germany. 9These authors contributed equally: Bouchra Derraz, Gabriele Breda. ✉email: 3 Published in partnership with The Hormel Institute, University of Minnesota B Derraz et al. 2 Table 1. Digital- and AI-guided/-enabled personalised drug and cell therapy and therapy management in oncology. # Digital- and AI-guided/-enabled personalised drug and cell therapy and Summary of transition to implementation therapy management approach 1 Clinical decision support (CDS) systems that facilitate therapy planning43 1234567890():,; 2 Precision diagnostics (including companion and complementary diagnostics59), and AI-based multi-cancer early detection (MCED) tests10,117 Some approaches are advanced in approval pathways and others are (...truncated)