The Two Towers: Quest for Drugs from Discovery to Approval
Bibliographic record
Abstract
The introduction of new therapeutic drugs typically involves a long and expensive process that may begin with a relatively simple initial discovery but that includes an extended period of development, which addresses formulation, efficacy, safety, and commercial potential. Many constituencies must be involved and satisfied at each step in this process, while the diverse goals and perspectives that each player brings to the enterprise are dealt with. The two main foundations of this activity are academic research and industry; the latter includes both traditional pharmaceutical companies and newer, usually smaller, biotechnology companies. The recognition of the importance and the differing viewpoints of these "two towers" of the intellectual and commercial undertaking may help to foster more effective working relationships among the parties and, ultimately, may increase the efficiency of bringing new therapies to the consumer. An understanding of the process of discovery and development across the disciplines involved may provide a meaningful answer to patients and families who constantly ask, "Why does it take so long?"
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.016 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.005 | 0.021 |
| Scholarly communication | 0.018 | 0.036 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.012 | 0.020 |
| Insufficient payload (model declined to judge) | 0.019 | 0.006 |
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".