Pipeline analysis of a vaccine candidate portfolio for diseases of poverty using the Portfolio-To-Impact modelling tool

Non-commercial pharmaceutical R&D: what do neglected diseases suggest about costs and efficiency?

Background: The past two decades have witnessed significant growth in non-commercial research and development (R&D) initiatives, particularly for neglected diseases, but there is limited understanding of the ways in which they compare with commercial R&D. This study analyses costs, timelines, and attrition rates of non-commercial R&D across multiple initiatives and how they compare to commercial R&D. Methods: This is a mixed-method, observational, descriptive, and analytic study. We contacted 48 non-commercial R&D initiatives and received either quantitative and/or qualitative data from 13 organizations. We used the Portfolio to Impact (P2I) model's estimates of average costs, timelines, and attrition rates for commercial R&D, while noting that P2I cost estimates are far lower than some previous findings in the literature. Results: The quantitative data suggested that the costs and timelines per candidate per phase (from preclinical through Phase 3) of non-commercial R&D for new chemical entities are largely in line with commercial averages. The quantitative data was insufficient to compare attrition rates. The qualitative data identified more reasons why non-commercial R&D costs would be lower than commercial R&D, timelines would be longer, and attrition rates would be equivalent or higher, though the data does not allow for estimating the magnitude of these effects. Conclusions: The quantitative data suggest that costs and timelines per candidate per phase were largely in line with (lower-end estimates of) commercial averages. We were unable to draw conclusions on overall efficiency, however, due to insufficient data on attrition rates. Given that non-commercial R&D is a nascent area of research with limited data available, this study contributes to the literature by generating hypotheses for further testing against a larger sample of quantitative data. It also offers a range of explanatory factors for further exploration regarding how non-commercial and commercial R&D may differ in costs and efficiency.