Portfolio Optimization of Therapies and Their Predictive Biomarkers

Robert A. Beckman () and Cong Chen ()
Additional contact information
Robert A. Beckman: University of California at San Francisco
Cong Chen: Biostatistics and Decision Research Sciences, Merck Research Laboratories

Chapter Chapter 10 in Optimization of Pharmaceutical R&D Programs and Portfolios, 2015, pp 155-180 from Springer

Abstract: Abstract The future of oncology drug development depends on the identification of predictive biomarkers that can identify subsets of patients who will benefit from the therapy. When predictive biomarkers work, they increase the value of therapies, and decrease the risk and potentially the cost of therapeutic development. However, predictive biomarkers do not always work, and when they do not, they add cost, time, and complexity to development. This chapter describes methods to adaptively integrate predictive biomarkers into oncology clinical development in a data-driven manner that invests in a predictive biomarker in proportion to its proven ability to predict therapeutic efficacy. This not only increases the value of associated therapeutics but also optimizes the investment in predictive biomarkers by managing the uncertainty in their predictive ability. At the end of the chapter, we discuss a global view of portfolios as a group of putative therapies and putative predictive biomarkers competing for limited resources. In modern drug development, portfolio optimization should ultimately be performed across both of these asset categories as an integrated whole.

Keywords: Predictive biomarker; Benefit–cost ratio; Optimization; Decision analysis; Adaptive trials; Portfolio optimization (search for similar items in EconPapers)
Date: 2015
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-319-09075-7_10

Ordering information: This item can be ordered from
http://www.springer.com/9783319090757

DOI: 10.1007/978-3-319-09075-7_10

Access Statistics for this chapter

More chapters in Springer Books from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().