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De-risking Biopharma Asset Acquisition: Towards a Quantitative Framework for Strategic Decision-making.
Xiaotang Ma1, Zheng Lu2,3, Yating Zhao4
1Sanofi, Morristown, New Jersey, USA.
The AAPS Journal
|May 6, 2026
Summary
Pharmaceutical companies face patent cliffs by using quantitative strategies like clinical pharmacology and pharmacometrics (CPP) to evaluate assets for mergers and acquisitions. This approach aids in de-risking development and accelerating therapies.
Area of Science:
- Drug development
- Pharmaceutical business strategy
- Quantitative pharmacology
Background:
- The pharmaceutical industry faces significant challenges due to the upcoming patent cliff (2028-2030).
- Mergers, acquisitions, and partnerships are key strategies for pipeline replenishment.
- Limited quantitative frameworks exist for systematic asset evaluation in due diligence.
Purpose of the Study:
- To outline how clinical pharmacology and pharmacometrics (CPP) can enhance asset evaluation during due diligence.
- To explore the application of Model-Informed Drug Development (MIDD) and AI/ML in asset assessment.
- To propose a bias-aware framework for integrating quantitative insights into corporate decision-making.
Main Methods:
- Review of CPP applications across the drug development continuum.
- Application of MIDD for pharmacokinetic prediction and success probability estimation.
- Examination of AI/ML for identifying predictive factors and patient subgroups.
- Discussion of cognitive biases and proposed mitigation strategies.
Main Results:
- CPP provides a quantitative framework for evaluating pharmacological plausibility, dosing, and development risks.
- MIDD enables prediction of human pharmacokinetics and estimation of technical/regulatory success.
- AI/ML can uncover prognostic factors and define patient subgroups for targeted development.
- Addressing cognitive biases is crucial for successful acquisition outcomes.
Conclusions:
- Integrating quantitative CPP and MIDD frameworks into due diligence can identify high-value assets.
- This approach helps mitigate development uncertainties and accelerates the delivery of innovative medicines.
- A standardized, bias-aware corporate template is proposed to support cross-functional decision-making.
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