Exploring the Risk of Biotech–Pharma Alliance Breakups Following Drug Launches
Bibliographic record
Abstract
Biotech-pharma alliances are essential to drug development but often involve asymmetrical dependencies, with biotech firms more reliant on their pharma partners than vice-versa. This imbalance can pose an overlooked vulnerability to biotech firms: an increased likelihood of alliance termination following a pharmaceutical partner’s drug launch. Using a dataset of more than 1,600 terminated biotech-pharma alliances spanning 20 years, we find that the risk of alliance termination increases post-launch. Using machine learning models and explainable artificial intelligence (AI) methods, we show that this risk is predictable at the onset of an alliance and demonstrate the importance of considering complex interactions and nonlinear effects. Our findings suggest that these terminations are not simply unpromising alliances strategically delayed to offset the negative impact of a termination announcement with the positive news of a drug launch. Instead, they reflect an unexplored cost of open innovation in asymmetric alliances and provide insights for biotech firms to effectively evaluate prospective partners against the risk of an alliance termination after a drug launch. Methodologically, we show the value of machine learning and explainable AI in management research to explore previously unexamined phenomena through a data-driven approach.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.079 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".