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Portfolio Management in New Drug Development

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Innovation and Marketing in the Pharmaceutical Industry

Part of the book series: International Series in Quantitative Marketing ((ISQM,volume 20))

Abstract

The pharmaceutical industry leads all industries in terms of R&D spend. Portfolio management in new drug development is extremely challenging due to long drug development cycles and high probabilities of failure. In 2010, a pharmaceutical company like GlaxoSmithKline (GSK) spent over USD 6 billion in R&D expenditure and managed a total of 147 R&D projects across 13 therapeutic areas in different stages of development. There are a lot of challenges in deciding on how to allocate resources to these projects in order to achieve the maximum returns. For example, how to evaluate the value and risk of each project, how to choose new projects for both short-term cash flow and long-term development, how to decide which projects to prioritize and which projects to remove from the portfolio, how to design drug development unit and incentive schemes to maximize the likelihood of success, and so forth.

This chapter reviews both practice and the state-of-the-art research and summarizes the latest insights from both industry and academia. For a manager, it provides a guide to the tools they need in portfolio management in the new drug development context. For an academic, it provides a quick overview of the extant research and points out some promising research directions.

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Notes

  1. 1.

     A new molecular entity (NME) is a medication containing an active pharmaceutical ingredient (API) that has not previously been approved for marketing in any form (Munos 2009). This usually excludes biologic drugs.

  2. 2.

    The same compound could target multiple indications. Each compound-indication combination is a separate project that follows the pharmaceutical regulatory approval process. In other words, a compound that is approved by a body such as the FDA for one indication can only be marketed for that indication.

  3. 3.

    Note that pharmaceutical companies typically report their projects starting from Phase I and do not provide details about preclinical/discovery projects, since these are still in the early stage of development. This is the reason for the discrepancy between the 289 total compounds in GSK’s portfolio versus the 147 projects spanning Phase I through launch.

  4. 4.

    Another relevant example of a drug development decision tree is found in Loch and Bode-Greuel (2001).

  5. 5.

    The mean-variance approach to evaluate projects or portfolios is popular due to its ease of computation and interpretation (Ruefli et al. 1999).

  6. 6.

    Note that the term “pipeline” is sometimes used interchangeably with the term “portfolio” in the business press. Our definitions for each of these terms are distinct and not synonymous with one another.

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Authors and Affiliations

  1. Smeal College of Business, Pennsylvania State University, University Park, PA, USA

    Min Ding

  2. School of Management, Fudan University, Shanghai, China

    Min Ding

  3. College of Business and Economics, Australian National University, Canberra, ACT, Australia

    Songting Dong

  4. The Wharton School, University of Pennsylvania, Philadelphia, PA, USA

    Jehoshua Eliashberg & Arun Gopalakrishnan

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  1. Min Ding
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  2. Songting Dong
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  3. Jehoshua Eliashberg
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  4. Arun Gopalakrishnan
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Correspondence to Min Ding .

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Editors and Affiliations

  1. Smeal College of Business, The Pennsylvania State University, University Park, Pennsylvania, USA

    Min Ding

  2. The Wharton School, University of Pennsylvania, Philadelphia, Pennsylvania, USA

    Jehoshua Eliashberg

  3. Department of Business Economics, Erasmus School of Economics, Erasmus University Rotterdam, Rotterdam, The Netherlands

    Stefan Stremersch

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Ding, M., Dong, S., Eliashberg, J., Gopalakrishnan, A. (2014). Portfolio Management in New Drug Development. In: Ding, M., Eliashberg, J., Stremersch, S. (eds) Innovation and Marketing in the Pharmaceutical Industry. International Series in Quantitative Marketing, vol 20. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-7801-0_3

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