ReviewThe AAPS journal2026
De-risking Biopharma Asset Acquisition: Towards a Quantitative Framework for Strategic Decision-making.
Review in The AAPS journal, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The impending patent cliff projected between 2028-2030 poses significant commercial and strategic challenges for innovative pharmaceutical and biotechnology companies. To sustain growth and maintain competitive positioning, organizations are increasingly relying on strategic mergers, acquisitions, and partnerships to replenish pipelines. However, systematic quantitative strategies and framework for asset evaluation remain limited. This review outlines how clinical pharmacology and pharmacometrics (CPP) can support asset evaluation and decision-making during the asset due diligence. First, CPP spans the entire drug development continuum, providing a quantitative framework for evaluating external assets, including pharmacological plausibility, dosing feasibility, and overall development risks. Second, Model-informed drug development (MIDD) approaches can be applied to predict human pharmacokinetics, inform dose selection, and estimate the probability of technical and regulatory success. Third, we examine the emerging role of artificial intelligence and machine learning in asset evaluation and portfolio decision-making, by discovering prognostic and predictive factors, and identifying the patient sub-group. We also introduce NewCo as an emerging drug-development and business model, where quantitative strategies may be deployed. Further, we address cognitive biases, such as confirmation bias and sunk cost fallacies that can influence acquisition outcomes. Importantly, we propose the development of a bias-aware, fit-for-purpose corporate template to integrate CPP and MIDD insights, standardize evaluation criteria, and support cross-functional decision-making during asset due diligence. Embedding quantitative and bias-mitigated CPP frameworks into due diligence workflows, can help identify high-value opportunities, de-risk development uncertainties, and accelerate delivery of innovative therapies to patients with unmet medical needs.
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42091774What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.