Precision medicine promises to develop diagnoses and treatments that take individual variability into account. According to most specialists, turning this promise into reality will require adapting the established framework of clinical research ethics, and paying more attention to participants’ attitudes towards sharing genotypic, phenotypic, lifestyle data and health records, and ultimately to their desire to be engaged as active partners in medical research.Notions such as participation, engagement and partnership have been introduced in bioethics debates concerning genetics and large-scale biobanking to (...) broaden the focus of discussion beyond individual choice and individuals’ moral interests. The uptake of those concepts in precision medicine is to be welcomed. However, as data and medical information from research participants in precision medicine cohorts will be collected on an individual basis, translating a participatory approach in this emerging area may prove cumbersome. Therefore, drawing on Joseph Raz’s perfectionism, we propose a principle of respect for autonomous agents that, we reckon, can address many of the concerns driving recent scholarship on partnership and public participation, while avoiding some of the limitations these concept have in the context of precision medicine. Our approach offers a normative clarification to how becoming partners in precision is compatible with retaining autonomy.Realigning the value of autonomy with ideals of direct engagement, we show, can provide adequate normative orientation to precision medicine; it can do justice to the idea of moral pluralism by stressing the value of moral self-determination: and, finally, it can reconcile the notion of autonomy with other more communitarian values such as participation and solidarity. (shrink)
Empirical evidence suggests that while people hold the capacity to control their data in high regard, they increasingly experience a loss of control over their data in the online world. The capacity to exert control over the generation and flow of personal information is a fundamental premise to important values such as autonomy, privacy, and trust. In healthcare and clinical research this capacity is generally achieved indirectly, by agreeing to specific conditions of informational exposure. Such conditions can be openly stated (...) in informed consent documents or be implicit in the norms of confidentiality that govern the relationships of patients and healthcare professionals. However, with medicine becoming a data-intense enterprise, informed consent and medical confidentiality, as mechanisms of control, are put under pressure. In this paper we explore emerging models of informational control in data-intense healthcare and clinical research, which can compensate for the limitations of currently available instruments. More specifically, we discuss three approaches that hold promise in increasing individual control: the emergence of data portability rights as means to control data access, new mechanisms of informed consent as tools to control data use, and finally, new participatory governance schemes that allow individuals to control their data through direct involvement in data governance. We conclude by suggesting that, despite the impression that biomedical big data diminish individual control, the synergistic effect of new data management models can in fact improve it. (shrink)
Some patients tolerate a given drug well, without adverse reactions. For others, though, an identical dose of the same medication can have toxic effects. Moreover, while a drug can be effective at relieving symptoms for some patients, it may fail to do the same for others suffering with the same disease. With such variability in treatment responses, tailoring medical interventions to individual patients has long been an aspiration of medicine. Until recently, however, medicine lacked a clear understanding of the biological (...) reasons for human variation in drug response. Attempting to adjust treatment to the individual patient, physicians have relied primarily on direct observation, trial-and-error, and, ultimately... (shrink)
What has been called the new mechanistic philosophy conceives of mechanisms as the main providers of biological explanation. We draw on the characterization of the p53 gene in molecular oncology, to show that explaining a biological phenomenon implies instead a dynamic interaction between the mechanistic level—rendered at the appropriate degree of ontological resolution—and far more general explanatory tools that perform a fundamental epistemic role in the provision of biological explanations. We call such tools “explanatory frameworks”. They are called frameworks to (...) stress their higher level of generality with respect to bare mechanisms; on the other hand, they are called explanatory because, as we show in this paper, their importance in explaining biological phenomena is not secondary with respect to mechanisms. We illustrate how explanatory frameworks establish selective and local criteria of causal relevance that drive the search for, characterisation and usage of biological mechanisms. Furthermore, we show that explanatory frameworks allow for changes of scientific perspective on the causal relevance of mechanisms going beyond the account provided by the new mechanistic philosophy. (shrink)
Massive amounts of data are collected and stored on a routine basis in virtually all domains of human activities. Such data are potentially useful to biomedicine. Yet, access to data for research purposes is hindered by the fact that different kinds of individual-patient data reside in disparate, unlinked silos. We propose that data cooperatives can promote much needed data aggregation and consequently accelerate research and its clinical translation. Data cooperatives enable direct control over personal data, as well as more democratic (...) governance of data pools. This model can realize a specific kind of data economy whereby citizens and communities are empowered to steer data use according to their motivations, preferences, and concerns. Policy makers can promote this model by recognizing citizens’ rights to access and to obtain a copy of their own data, and by funding distributed data infrastructures piloting new data aggregation models. (shrink)
