A major unresolved issue for the pharmaceutical industry in the 21st century is that few, if any, optimization techniques
have found their way into the research portfolio arena. That is surprising, for several reasons:
- The industry has been very open to using sophisticated mathematical optimization techniques in the sales and marketing arenas.
- Research project management increasingly includes MBAs who are familiar with these techniques.
- Our preliminary research strongly suggests that the top pharmaceutical companies are pursuing too many therapeutic categories,
and that by reducing the number of categories they could have more backup candidates in the therapeutic categories remaining
as research areas.
The industry is consolidating; there are fewer and fewer companies. Those that remain are increasing their R&D spending at
double-digit annual percentage growth. This pattern suggests that pharma is becoming mature and, like all mature industries,
more commodity oriented. As "commoditization" occurs, there is a need for increased quality through the use of systematic
planning and benchmarking metrics.
This article discusses some of the current issues in the use of optimization techniques to manage an R&D portfolio, particularly
some recent trends that make optimization techniques a more practical choice for companies."How to Improve It"
Early in the development of total quality management concepts the quality-improvement pioneer W. Edwards Deming offered:
"Competent men [sic] in every position, from top management to the humblest worker, if they are doing their best, know all
there is to know about their work except how to improve it. Help toward improvement can come only from some other kind of
knowledge. Help may come from outside the company, or from better use of knowledge and skills already within the company,
or both."
In the arena of the R&D portfolio, the pharmaceutical industry actually has a poor performance record. The need for improvement
in portfolio management exists because "scientists traditionally have been reluctant to try new R&D management approaches."
(See "In the Eye of the Storm," Pharm Exec, April 2003.)
The need for improved portfolio management was expressed by Pfizer’s research chief, Dr. Peter B. Corr, who "is trying to
reduce the number of projects that fail later in development, when costs are escalating. And Pfizer is forging earlier and
stronger links between researchers and marketing executives" (New York Times, September 8, 2002).
Objectives and Basic Approach
The essential elements of effective portfolio management are well documented. They include working as a team with marketing,
sales, and finance to:
1. Quantitatively determine acceptable ranges for the key input factors of timing, cost, risk, and expected return (e.g., NPV)
for each project under consideration.
2. Select projects based on management specified criteria aimed at balancing risk and return.
Tiggemann et al (in the Drug Information Journal, vol. 32, 1998) state, "the key is to focus on the best projects in terms of NPV, taking advantage of experience curve effects
and then balance the portfolio accordingly to insure both short- and long-term success."
This is a bottom-up approach in the sense that, first, various graphical displays and spreadsheets are used to assess the
value of the current portfolio. Then comparative results are determined for "trial" alternative portfolios to identify and
exploit opportunities for improvement. This type of fine-tuning will lead to improvements in the strategic portfolio, but
could provide results that fall short of a portfolio that is "optimal" with respect to minimizing risk and/or maximizing ROI.
In addition, seeking improvements by adjusting the current portfolio may overlook opportunities that might be of significant
long-term value to the firm.
New Directions
Recent advances in portfolio management go beyond the current commonly used practice of identifying which projects to continue
or drop based on consideration of a set of reasonable scenarios. Optimization modeling as applied to portfolio management
is not new. For example, mutual funds use optimization techniques (e.g., "Markowitz" models) to select stocks that will maximize
overall return subject to a desired overall risk as measured by volatility. However, approaches applied successfully to other
disciplines are not directly applicable to pharmaceutical R&D portfolio optimization for a variety of reasons, including differences
in risk measurement and the impact of multiple clinical phases.
Two Approaches to Optimization
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The two charts pictured here illustrate two approaches to the problem of optimizing an R&D portfolio. The chart on top
("Structuring the Pipeline"), drawn from the research of Min Ding of Penn State University and John Eliashberg of the Wharton
School, provides a good example of using modeling techniques to optimally structure the pipeline for a given therapeutic class.
Ding and Eliashberg determine the number of projects at each clinical phase that maximize expected NPV, given information
on the cost of development, probability of surviving each clinical phase and the magnitude of the business opportunity. Using
historically based assumptions on required model input data, they show that achieving high NPV would require much greater
levels of spending on more projects than in the past.
While these results are interesting and significant, they probably raise more questions than they answer. Can the number of
projects actually be increased to levels anywhere near those suggested, given budget constraints and the current state of
research? And if the number of projects can’t be raised to the levels suggested by the models, what is the best practical
strategy? In particular, what number of projects should be in each therapeutic class to maximize portfolio NPV? These questions,
and others, have been the subject of continued research and implementation of software to provide effective decision support
to strategic R&D portfolio management.
The bottom chart ("Optimizing the R&D Portfolio") shows one response to these questions. Here the focus is not on maximizing
NPV, but rather on getting the best possible NPV within a fixed budget. Where a pure NPV approach yielded results that couldn’t
really be acted upon, these results (based on the actual research portfolio of a major pharma company) are fairly practical:
Drop projects for immune systems, blood, and cardiovascular, and increase projects for cancer and infectious diseases. This
result is the "top" of the top-down approach. Good decision-support software would allow R&D project management to evaluate
changes in NPV for various "what if" scenarios relative to the initial optimal model results. These scenarios may be based
on considerations of the firm’s own research, partnering, joint ventures, support of contract sales organizations (CSOs) and
other avenues to augment their own acquisition/divestiture needs.
Implementation Needs
Is the pharmaceutical industry prepared to effectively utilize optimization models? The answer is based on key considerations
of data, software availability and support staff.
Data. Since the problem of strategic portfolio management is not new, methods are commonly available to adequately satisfy data
input needs. Firms have developed their own internal procedures (often with the help of suppliers and management-science consultants)
to determine likely values and optimistic-pessimistic ranges for project timing, cost, chance of surviving clinical phases
(i.e., risk measures), and NPV. For example, risk measures may be based on internal/industry benchmarks combined with management
input (e.g., using "Delphi" methods). With regard to NPV, continual improvement in forecasting accuracy has been realized
through ongoing development of new methods and models that can accurately measure the impact of anticipated product positioning
and alternative marketing mix strategies.
Software availability. There is definitely a need for user-friendly optimization software. Good commercial and academic modules exist for many optimization
methods, but they have to be tied to the specific needs of R&D portfolio management. Minimal criteria for software are that
it be PC based, provide a meaningful initial optimal strategy through easy-to-read tables and graphs, and allow efficient
interactive evaluation of "what if" adjustments to the initial strategy (known technically as "sensitivity analysis").
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