publication

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

Authors:
Suerie MOON
Ryan William Robert KIMMITT
2021

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 traditional commercial R&D. This study analyses costs, timeframes, and attrition rates of non-commercial R&D across multiple initiatives and how they compare to commercial R&D using the Portfolio-to-Impact (P2I) model as parameter of comparison. Methods: This is a mixed-method, observational, descriptive and analytic study. We contacted 48 non-commercial R&D initiatives and received quantitative data from 8 organizations on 83 candidate products, and qualitative data through 14 interviews from 12 organizations. Results: The quantitative data suggested that non-commercial R&D for new chemical entities is largely in line with P2I averages regarding total costs and timeframes, with variation by phase. The qualitative data identified more reasons why non-commercial R&D costs would be lower than commercial R&D, timeframes would be longer and attrition rates would be equivalent or higher, though the magnitude of effect is not known. The overall emerging hypothesis is that direct costs of non-commercial R&D are expected to be equivalent or somewhat lower than commercial, timeframes are expected to be equivalent or somewhat longer and attrition rates would be equivalent. Conclusions: The study found that non-commercial R&D differs in many significant ways from commercial R&D. However, it is possible that the sum of these differences cancelled each other out such that total costs, timeframes and attrition rates were largely in line with P2I averages. Given the nascent area, with almost no prior literature focusing on costs, timeframes or attrition rates of non-commercial R&D initiatives, we see the merits of this study as generating hypotheses for further testing against a larger sample of quantitative data, and for understanding reasons underlying any significant differences between non-commercial and commercial initiatives.