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Optimal selection policies for a sequence of candidate drugs

Part of: Operations research and management science Mathematical economics

Published online by Cambridge University Press:  01 July 2016

C. Charalambous  and
J. C. Gittins
C. Charalambous*
Affiliation:
University of Oxford
J. C. Gittins*
Affiliation:
University of Oxford
*
∗ Postal address: Department of Statistics, University of Oxford, 1 South Parks Road, Oxford OX1 3TG, UK.
∗ Postal address: Department of Statistics, University of Oxford, 1 South Parks Road, Oxford OX1 3TG, UK.
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Abstract

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Pharmaceutical companies have to face huge risks and enormous costs of production before they can produce a drug. Efficient allocation of resources is essential to help in maximizing profits. Yu and Gittins (2007) described a model and associated software for determining efficient allocations for a preclinical research project. This is the starting point for this paper. We provide explicit optimal policies for the selection of successive candidate drugs for two restricted versions of the Yu and Gittins (2007) model. To some extent these policies are likely to be applicable to the unrestricted model.

Keywords

Pharmaceutical researchresource allocationsemi-Markov decision processoptimization

MSC classification

Primary: 90B36: Scheduling theory, stochastic
Secondary: 90C40: Markov and semi-Markov decision processes 91B32: Resource and cost allocation
Type
General Applied Probability
Information
Advances in Applied Probability , Volume 40 , Issue 2 , June 2008 , pp. 359 - 376
Copyright
Copyright © Applied Probability Trust 2008 

References

Bergman, S. W. and Gittins, J. C. (1985). Statistical Methods for Pharmaceutical Research Planning. Marcel Dekker, New York.Google Scholar
Black, F. and Scholes, M. (1973). The pricing of options and corporate liabilities. J. Political Econom. 81, 637–659.CrossRefGoogle Scholar
Brealy, R. A. and Myers, S. C. (2000). Principles of Corporate Finance, 6th edn. McGraw-Hill, New York.Google Scholar
Charalambous, C. (2006). Optimal selection policy for a sequence of candidate drugs. Transfer of Status Report, Department of Statistics, University of Oxford.Google Scholar
Chen, B. P. K. (2004). Prioritization of research projects in the pharmaceutical industry. , Department of Statistics, University of Oxford.Google Scholar
Gittins, J. C. (1996). Quantitative methods in the planning of pharmaceutical research. Drug Inf. J. 30, 479–487.Google Scholar
Gittins, J. C. (1997). Why crash pharmaceutical research? R&D Manag. 27, 79–85.Google Scholar
Gray, N. (2005). PharmExec 50: Our Sixth Annual Report of the World's Top 50 Pharma Companies. Available at http://bio.cocoonworks.com/wp-content/uploads/2006/03/2005_Global_Top_50_Pharamceutical_ Companies.pdf.Google Scholar
Halliday, R. G., Drasdo, A. L., Lumley, C. E. and Walker, S. R. (1997). The allocation of resources for R&D in the World's leading pharmaceutical companies. R&D Manag. 27, 63–77.Google Scholar
Jacob, W. and Kwak, Y. H. (2003). In search of innovative techniques to evaluate pharmaceutical R&D projects. Technovation 23, 291–296.CrossRefGoogle Scholar
Miller, P. (2005). Role of pharmaeconomic analysis in R&D decision making. Pharmacoeconomics 23, 1–12.Google Scholar
Poh, K. L., Ang, B. W. and Bai, F. (2001). A comparative analysis of R&D project evaluation methods. R&D Manag. 31, 63–75.Google Scholar
Ross, S. M. (1970). Applied Probability Models with Optimization Applications. Holden-Day, San Francisco, CA.Google Scholar
White, D. J. (1993). Markov Decision Processes. John Wiley, Chichester.Google Scholar
Yu, J. Y. and Gittins, J. C. (2007). Models and software for improving the profitability of pharmaceutical research. Europ. J. Operat. Res. 189, 459–475.CrossRefGoogle Scholar