PhRMA Survey of Pharmacogenomic and Pharmacodynamic Evaluations: What Next?
Bibliographic record
Abstract
Interindividual variation in pharmacodynamic (PD) response to drugs is an ongoing area of research for drugs in clinical development, pre- and postapproval. To characterize how pharmacogenomic (PG ) variations can serves a predictor of differences in PD outcomes, the pharmaceutical industry has incorporated PG /PD analysis into clinical drug development. The Pharmaceutical Research and Manufacturers of America (PhRMA ) and the Industry Pharmacogenomics Working Group (I-PWG) conducted a survey of 16 pharmaceutical companies to ascertain to what extent PG/PD research is being incorporated into drug development. The survey results showed that, while the industry has made some attempt to incorporate PG/PD studies into drug development, application has been inconsistent. Nevertheless, several valid PG/PD markers have since emerged in drug labels. The I-PWG considers PG/PD research an important approach to improving success rates in drug development. This article reports the results of the survey and proposes steps toward increasing the use of PG/PD research by the industry.
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 | 0.064 | 0.114 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".