An evaluation of stakeholder engagement in comparative effectiveness research: lessons learned from SWOG S1415CD.

Aim: Stakeholder engagement is central to comparative effectiveness research yet there are gaps in definitions of success. We used a framework developed by Lavallee et al. defining effective engagement criteria to evaluate stakeholder engagement during a pragmatic cluster-randomized trial. Methods: Semi-structured interviews were developed from the framework and completed to learn about members' experiences. Interviews were analyzed in a deductive approach for themes related to the effective engagement criteria. Results: Thirteen members participated and described: respect for ideas, time to achieve consensus, access to information and continuous feedback as areas of effective engagement. The primary criticism was lack of diversity. Discussion: Feedback was positive, particularly among themes of respect, trust and competence, and led to development of a list of best practices for engagement. The framework was successful for evaluating engagement. Conclusion: Standardized frameworks allow studies to formally evaluate their stakeholder engagement approach and develop best practices for future research.

What is this article about? This article is about the evaluation of how effective the stakeholder engagement was in a comparative effectiveness research (CER) study funded by the Patient Centered Outcomes Research Institute (PCORI). The research team found a framework (developed by Lavalle et al.) that defined six different criteria for effective stakeholder engagement, and used that criteria to complete semi-structured interviews with the stakeholders involved with our study. These interviews were reviewed to determine what stakeholder engagement processes were successful and helped provide a list of best practices for stakeholder engagement for other researchers doing CER. What were the results? Stakeholders highlighted respect for their ideas, time to achieve consensus, easy access to information and a continuous feedback loop between study team and stakeholders as effective engagement processes. What do the results mean? These results can help other researchers doing CER learn best practices to implement from the outset of a study to best engage stakeholders in their research. The results also show that having a standardized framework to evaluate stakeholder engagement is important and allows for research teams to formally evaluate their engagement approach and learn what was successful and where there are areas for improvement in future studies.

Techno-economic analysis of a transient plant-based platform for monoclonal antibody production.

Plant-based biomanufacturing of therapeutic proteins is a relatively new platform with a small number of commercial-scale facilities, but offers advantages of linear scalability, reduced upstream complexity, reduced time to market, and potentially lower capital and operating costs. In this study we present a detailed process simulation model for a large-scale new "greenfield" biomanufacturing facility that uses transient agroinfiltration of Nicotiana benthamiana plants grown hydroponically indoors under light-emitting diode lighting for the production of a monoclonal antibody. The model was used to evaluate the total capital investment, annual operating cost, and cost of goods sold as a function of mAb expression level in the plant (g mAb/kg fresh weight of the plant) and production capacity (kg mAb/year). For the Base Case design scenario (300 kg mAb/year, 1 g mAb/kg fresh weight, and 65% recovery in downstream processing), the model predicts a total capital investment of $122 million dollars and cost of goods sold of $121/g including depreciation. Compared with traditional biomanufacturing platforms that use mammalian cells grown in bioreactors, the model predicts significant reductions in capital investment and >50% reduction in cost of goods compared with published values at similar production scales. The simulation model can be modified or adapted by others to assess the profitability of alternative designs, implement different process assumptions, and help guide process development and optimization.