Portfolio Management in New Drug Development - Innovation and Marketing in the Pharmaceutical Industry - page 106

We define a pipeline 6 as a series of new drug developments targeting one
business opportunity (a single indication). The key question revolves around the
number of projects/products a firm should keep in the pipeline.
Dahan and Mendelson ( 2001 ) examine a setting in which there is only one stage
of product development and multiple potential projects can be tested in parallel.
They investigate the trade-off between the benefits and costs by assuming that the
profits follow extreme-value probability distributions. The key result is that optimal
number of projects for a pipeline is the ratio of the scale parameter of profit uncer-
tainty to the cost per project. In other words, greater profit uncertainty or lower cost
per project drive a fatter pipeline.
Ding and Eliashberg ( 2002 ) take a further step and study the optimal number of
projects to be funded at each stage in a multiple stage development setting. They
find the optimal structure of the pipeline (i.e., the pipeline with optimal number of
projects at each stage) is determined by the cost of developing a project, its success
probability, and its expected reward. Comparing their normative results with empir-
ical practice data, they find that firms tend to have fewer projects in their pipelines
than the optimal structure. Hence, pharmaceutical firms may be better off increasing
the investment for a given pipeline. However, even if the optimal number of projects
in the pipeline is determined, a sequencing of funding these projects may be needed
if resources are scarce (which is usually the case).
Childs and Triantis ( 1999 ) conduct a simulation scenario analysis which accom-
modates multiple characteristics of R&D projects, including learning-by-doing,
collateral learning between different projects in the program, interaction between
project cash flows, periodic reevaluations of the program, different intensities of
investment, capital rationing constraints, and competition. Their model considers
complex interactions of multiple factors and is therefore much more realistic.
However, they do not obtain analytical optimal policies. Childs and Triantis ( 1999 )
demonstrate that it may be profitable for a firm to fund multiple projects even if only
one can be launched, and during the development procedure, it is possible that the
firm may alter its prioritization policy significantly at different stages. The findings
from the simulation model appear to fit the reality of pharmaceutical innovation
fairly well.
Blau et al. ( 2004 ) propose a simulation-based approach to selecting sequences
of projects in a portfolio, which maximizes the expected economic returns for a
given level of risk and budget. They do not obtain closed form optimal solutions,
but demonstrate an improvement of 28 % in expected return using the simulation
approach as compared to a traditional bubble chart approach. The approach takes
into account interdependencies among projects which is otherwise difficult to
quantify in closed form.
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.