A pilot study to evaluate the feasibility, usability, and perceived satisfaction with eCO (eCediranib-Olaparib), a mobile application for side effect monitoring and reporting, in women with recurrent ovarian cancer

Liu, J.; Whalen, Christin; Morrissey, Stephanie; Komatsu, Eiji; Lee, Jang-Ming; Houston, Nicole; O’Malley, David M.; Strock, Ellie S.; Phillips, Rob; Mari, K.; Ivy, S. Percy; Killam, Bill

doi:10.1093/annonc/mdx388.020

Cited by 1 publication

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“…In the US, some have a non-device status not requiring specific regulatory approval 43 2 Precision diagnostics (including companion and complementary diagnostics 59 ), and AI-based multi-cancer early detection (MCED) tests 10 , 117 Some approaches are advanced in approval pathways and others are in early implementation. In the US some are available under clinical laboratory waiver (US 118 ) 3 Drug companion apps that personalise therapy regime management and adaptation 53 , 72 , 73 Some approaches are in late development and clinical validation 4 personalised ATMP design 9 , 20 – 23 Some approaches are in late development and clinical validation 5 Digital twins as an emerging concept in diagnosis and therapy - integrate near-real-time patient data management with simulation-/model-based diagnosis and therapy design and monitoring 91 , 93 and generalist medical AI approaches of broad purpose medical AI 32 , 68 largely theoretical/ research concepts not yet in as generalised approaches in approval pathways …”

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confidence: 99%

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

Derraz,

Breda,

Kaempf

et al. 2024

npj Precis. Onc.

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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.

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