Pharmaceutical companies regularly review their portfolios to monitor development progress and set priorities in product development. The earliest assets are drug candidates whose efficacy is unknown and whose effects on the human body have not yet been fully explored. For these assets, it is highly uncertain whether they will reach the market and be used in clinical practice. Moreover, not all potential applications are foreseeable, and they can often differ substantially. In the absence of satisfactory methods for deciding how to allocate resources to early development assets, decision-makers focus almost exclusively on assessing an asset’s probability of technical success.

This study proposes a more holistic methodology to support decision-making in the early phase of pharmaceutical development, drawing on value-focused thinking and multi-criteria decision-making. The methodology operates within the decision quality framework and provides a consistent evaluation of different early development assets across a wide range of disease areas. This combination of concepts and methods was implemented at Bayer Pharmaceuticals, where it proved useful, as the company needed a new, more robust decision-making process for early development. This study therefore discusses how concrete trade-offs at the level of corporate objectives can be made in order to align, communicate, and implement the corporate strategy in the portfolio strategy.

In addition, this study presents insights for decision analysts and decision-makers in the pharmaceutical industry on how to develop a set of fundamental objectives, how to construct scales to operationalize these objectives, and how to take steps to unburden an organizational decision-making process.

Methling, Florian; Borden, Steffen A., Veeraraghavan, Deepak; Sommer; Insa, Siebert, Johannes Ulrich; von Nitzsch, Rüdiger; Seidler, Mark „Supporting Innovation in Early-Stage Pharmaceutical Development Decisions “, in Special Issue on Health Decision Analysis: Evolution, Trends, and Emerging Topics by Elisa F. Long, Gilberto Montibeller, Jun Zhuang, Decision Analysis (INFORMS), https://doi.org/10.1287/deca.2022.0452