The Inherent Imprecision in Precision Medicine: How Do We Deal With the Resulting Uncertainties?

ISSUE: Described as 'The right treatment to the right patient at the right time', precision medicine is conceptually deceivingly simple but challenges the way we generate, evaluate and act upon evidence as broad implementation becomes the pre-requisite rather than the consequence of successful evidence-generation. With its reliance on molecular diagnostics to identify the appropriate treatment, precision medicine represents an interesting challenge for determining the value at different points in its lifecycle due to interdependencies. This panel will look at the multiple sources of uncertainty and discuss pragmatic mitigation strategies. With the upcoming EU partnership for Personalised Medicine, the topic is very timely.

OVERVIEW: 10' BR will provide a patient perspective on PCM, provide relevant European context and introduce the session. 15' KT will report on the current phenomenon of national DRUP-like clinical trials, DLCTs, as pragmatic European multi-stakeholder precision medicine implementation initiatives, partially including pragmatic outcome-based risk sharing agreements between payers and manufacturers, followed by an HTA assessment once sufficient evidence has accumulated. 15' WO will introduce the HTAi-DIA working group's uncertainty framework for drug regulatory and HTA decision making and reflect on how to apply it to the context of a real-world precision medicine implementation initiative such as the DLCTs. 10' Panel discussion reflecting on the different types of uncertainties that arise for different stakeholder groups in precision medicine, their impact on the respective as well as other stakeholder group(s)- biological tumour heterogeneity e.g. limits the true validity of any diagnostic analysis - as well as potential mitigation strategies. 10' Q&A This session is relevant for anyone with interest in precision medicine: HTA bodies, payers, regulators, HEOR specialists, patient advocates and clinicians, data scientists.