Designing a post-genomics knowledge ecosystem to translate pharmacogenomics into public health action

Abstract

Translation of pharmacogenomics to public health action is at the epicenter of the life sciences agenda. Post-genomics knowledge is simultaneously co-produced at multiple scales and locales by scientists, crowd-sourcing and biological citizens. The latter are entrepreneurial citizens who are autonomous, self-governing and increasingly conceptualizing themselves in biological terms, ostensibly taking responsibility for their own health, and engaging in patient advocacy and health activism. By studying these heterogeneous 'scientific cultures', we can locate innovative parameters of collective action to move pharmacogenomics to practice (personalized therapeutics). To this end, we reconceptualize knowledge-based innovation as a complex ecosystem comprising 'actors' and 'narrators'. For robust knowledge translation, we require a nested post-genomics technology governance system composed of first-order narrators (for example, social scientists, philosophers, bioethicists) situated at arm's length from innovation actors (for example, pharmacogenomics scientists). Yet, second-order narrators (for example, an independent and possibly crowd-funded think-tank of citizen scholars, marginalized groups and knowledge end-users) are crucial to prevent first-order narrators from gaining excessive power that can be misused in the course of steering innovations. To operate such 'self-calibrating' and nested innovation ecosystems, we introduce the concept of 'wiki-governance' to enable mutual and iterative learning among innovation actors and first- and second-order narrators. '[A] scientific expert is someone who knows more and more about less and less, until finally knowing (almost) everything about (almost) nothing.' [1] 'Ubuntu: I am because you are.' [2].

Figure 1

Rethinking knowledge-based innovations as being composed of actors and narrators. In a knowledge ecosystem such as public health pharmacogenomics, innovation actors co-produce knowledge and calibrate their actions and trajectory through open and transparent mutual learning, enabled by recursive practices such as wiki-governance. First-order narrators, who are situated at a crucial but not imperceptible analytical distance from the innovation actors, can examine and steer the innovation ecosystem trajectory, thereby contributing to collective action in the innovation ecosystem. Second-order narrators (for example, innovation observatories represented by citizen scholars, hitherto marginalized groups, and patients) can further keep the first-order narrators in check by making them more accountable, and by rendering visible their actions and situating them in a socio-technical context. Definitions: phase 1 translation (T1) aims to advance a basic genome-based discovery into a candidate health application (for example, a pharmacogenomics test); phase 2 translation (T2) concerns the development of evidence-based guidelines for a pharmacogenomics application; phase 3 translation (T3) aims to connect evidence-based guidelines with health practice, through delivery, dissemination and diffusion research; phase 4 translation (T4) evaluates the real world health outcomes of a pharmacogenomic application. See Khoury et al. [36] for the T1 to T4 translation research continuum and its actors.