Pipeline Physics

Pipeline Physics produces profit
Gary Summers, PhD	1700 University Blvd, #936
President, Pipeline Physics LLC	Round Rock, TX 78665-8016
gary.summers@PipelinePhysics.com	503-332-4095

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Managing drug development with pipeline physics

Shakespeare wrote, "Uneasy lies the head that wears the crown," and as with kings, so it is with drug development managers. Why? Because of uncertainty.

Suppose the room you are in represents all the factors that affect the outcomes of your decisions and futher suppose you are holding a basketball. The basketball represents your knowledge and the remaining space in the room represents uncertainty - the factors that extend beyond your knowledge, such as forecasting errors, relationships that are only partially understood, organizational issues that affect decision-making and implementation, and unknown-unknowns.

Studying uncertainty, in addition to risk, is an exciting new development in decision theory, with contributions being made from economists, decision theorists, computer scientists, biologists and philosophers. I apply the new theory to drug development to help executives create more new drugs while reducing the rate of phase III failures.

The result is a collection of ideas that explore aspects of pharmaceutical pipelines. Most of the ideas address three issues:

• Dynamic versus static: Dynamic decision situations are ones in which today's decisions affect tomorrow's decision situations. For example, when selecting compounds to advance to phase II one affects the choices subsequently available for phase III. Furthermore, the design of phase II trials affects the probabilities of success used when evaluating compounds for phase III. Current decisions affect future choioces and probabilities.
  
  In contrast, asset selection in finance, which is often used as a metaphor for project portfolio optimization, is a static problem. The financial portfolio one builds today does not affect the set of choices (financial assets) or their returns, risks and correlations in future decisions.
• Source of value: Decision theory posits two sources of value: (1) selecting assets with greater value, such as selecting an optimal portfolio of projects versus a suboptimal one and (2) resolving uncertainty and risk. When developing drugs, resolving uncertainty and risk, which allows one to better distinguish marketable (safe and effective) compounds from unmarketable ones is a significant source of value, perhaps the more significant source. However, many project selection techniques focus on asset selection, which by itself creates less value.
• Uncertainty versus risk: As suggested above, one can define risk as contingencies one can describe via probabilities and uncertainty as those one cannot. Many decision models address risk but not uncertainty. Fortunately, new decision theory is exploring the impact of uncertainty on decision-making and revealing methods for managing both risk and uncertainty.

For an introduction that address these ideas, kindly consider reading my article:

How to make drug development more productive.

The links belore retrieve articles that further explore the paper's ideas.

Uncertainty and project portfolio management

1. Two paradigms for managing uncertainty
2. What modern portfolio theory reveals about PPM.
3. Why some C-level executives are skeptical of PPM.
4. Action flexibility and state flexibility in PPM.
5. The difference between theory and practice: it's disappearing.
6. How to count cards in blackjack.

Drug development

1. Making drug development more productive (mentioned above)
2. Managing drug development pipelines.
3. How discovery impacts development
4. Where's the feedback?
5. How PPM differs from modern portfolio theory.
6. Pipeline diagnostic research project (password required)
7. An introduction to pipeline physics (general phase-gate systems)
8. Pipeline volatility: how to have more boom and less bust (forthcoming).

Scoring models

1. Are scoring models valid?
2. Is my scoring model good enough?
3. Scoring models + optimization = oil and water don't mix.

Decision trees (and capital asset budgeting models)

1. Revenue forecasting errors dominate decision trees.
2. Overconfidence and underestimating risk.
3. Estimating probabilities of success: it's not so successful.
4. Optimistic probability estimates, or "How to unknowingly pick the wrong projects and increase portfolio risk."
5. How erroneous data causes project selection errors.
6. You can't reduce uncertainty by planning.
7. Where's the variation? (forthcoming).
8. To combine or not to combine? That is the question. (forthcoming).
9. Which evaluation method is best? (forthcoming).
10. Four common errors in PPM's Monte Carlo analysis (forthcoming).
11. Modeling risk: probabilities vs. discount rates (forthcoming).
12. What are subjective probabilities? (forthcoming).

Project portfolio optimization

1. What modern portfolio theory reveals about PPM.
2. The optimization tautology.
3. PPM software vendors think risk is value.
4. When is optimization suboptimal?
5. Scoring models + optimization = oil and water don't mix.
6. Project selection: simple or sophisticated? (forthcoming).
7. Optimizer's curse: how PPM overestimates portfolio value (forthcoming).
8. Simulation optimization: is it a panacea or does it merely state the obvious? (forthcoming).

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