How to Develop Collaboration in Drug Development Process: The Role of Professional Service Firms
Bibliographic record
Abstract
The innovation chain of the pharmaceutical industry is more and more complex. In particular, a new type of players, the start-ups founded by researchers (Academic Start-ups) have proven to be particularly effective in the first steps of exploring new, radically innovative technologies. These small start-ups miss the financial resources and the industrial experience necessary to embark in the later stage of technologies’ development. To overcome these limits, what academic start-ups require the most is a collaborative linkage with large biotech and pharma companies. To such end, Business Development Professionals are offering their services to academic start-ups, to set up a collaborative linkage with potential partners. Our article investigates the process of engagement between Academic Start-ups and Business Development Professionals and in particular, we focus on the factors that influence collaboration between the two actors. In order to investigate the development process of collaboration we conducted an exploratory study trough the submission of a semi-structured questionnaire covering different aspects of the engagement process to a sample of business professionals. The study provide first evidences about the main factors that prevent the development of collaborations and provides some suggestions to overcome the challenges that both parts found in the collaboration process.
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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 | 0.046 | 0.063 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.017 | 0.018 |
| Scholarly communication | 0.026 | 0.017 |
| Open science | 0.002 | 0.017 |
| Research integrity | 0.007 | 0.005 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".