The Biopharmaceutical industry faces expensive, complex, multi-phase decision-making, punctuated by high technical and regulatory risks as well as uncertain commercial outcomes. Not surprisingly, data gathering and validation become the focal point for much discussion and debate, especially when an analysis of decision alternatives between competing investments results in a marginally dominant policy suggestion. Invariably, decision-makers request more data – not all of which may be decision-relevant – resulting in protracted and inefficient decision-making.

In this vein, the challenge to transparent, defensible decision-making under conditions of risk and uncertainty rests in testing the sensitivity of data ranges to a dominant policy suggestion. In some cases, incrementally small changes to assumptions and data result in a different policy suggestion, while in other cases, a policy suggestion may remain unchanged by large variances in data. In any event, decision trees facilitate good discussion around phase-specific investments and risks as well as uncertain commercial value, and enable decisions to be taken on the basis of phase-specific, risk-adjusted value. The most popularly used metric for such decisions in the Biopharmaceutical industry is risk-adjusted Net Present Value (eNPV).

In the case discussed here, a major Biopharmaceutical company faced a decision to invest $325 million in a lead molecule (ABC) for the treatment of Alzheimer's Disease (AD), or $550 million in a different lead molecule (XYZ) for the treatment of Mild Cognitive Impairment (MCI) and General Anxiety Disorder (GAD). NB: To preserve the confidentiality of the project, the targeted diseases along with their investment profiles have been altered significantly.

Both compounds had clinical and regulatory risks associated with their development and regulatory approval, and because of the rapidly changing competitive environment, there were huge commercial uncertainties associated with their market uptake.

The Biopharmaceutical industry faces expensive, complex, multi-phase decision-making, punctuated by high technical and regulatory risks as well as uncertain commercial outcomes. Not surprisingly, data gathering and validation become the focal point for much discussion and debate, especially when an analysis of decision alternatives between competing investments results in a marginally dominant policy suggestion. Invariably, decision-makers request more data – not all of which may be decision-relevant – resulting in protracted and inefficient decision-making.

In this vein, the challenge to transparent, defensible decision-making under conditions of risk and uncertainty rests in testing the sensitivity of data ranges to a dominant policy suggestion. In some cases, incrementally small changes to assumptions and data result in a different policy suggestion, while in other cases, a policy suggestion may remain unchanged by large variances in data. In any event, decision trees facilitate good discussion around phase-specific investments and risks as well as uncertain commercial value, and enable decisions to be taken on the basis of phase-specific, risk-adjusted value. The most popularly used metric for such decisions in the Biopharmaceutical industry is risk-adjusted Net Present Value (eNPV).

In the case discussed here, a major Biopharmaceutical company faced a decision to invest $325 million in a lead molecule (ABC) for the treatment of Alzheimer's Disease (AD), or $550 million in a different lead molecule (XYZ) for the treatment of Mild Cognitive Impairment (MCI) and General Anxiety Disorder (GAD). NB: To preserve the confidentiality of the project, the targeted diseases along with their investment profiles have been altered significantly.

Both compounds had clinical and regulatory risks associated with their development and regulatory approval, and because of the rapidly changing competitive environment, there were huge commercial uncertainties associated with their market uptake.