Global pharmacogenomics: Where is the research taking us?
Bibliographic record
Abstract
Pharmacogenomics knowledge and technologies, which couple genomics information with pharmaceutical drug response, have been promised to revolutionise both drug development and prescription. One notable promise of pharmacogenomics is the potential to contribute to some of the Millennium Development Goals (MDGs), namely to increase justice in global health by incentivising public research laboratories and pharmaceutical companies to develop drugs for populations (e.g., in low- and middle-income countries) that have been neglected by the traditional drug development model. To evaluate the credibility of this promise, we examined - both quantitatively and qualitatively - those scientific papers indexed in PubMed and published between 1997 and 2010, with a view to describing the major orientations and tendencies characterising the development of pharmacogenomics research. Our results demonstrate that pharmacogenomics research has focused on three major non-communicable categories of disease: cancer, depression and other psychological disorders and cardiovascular and coronary heart disease. Few publications - and thus, by extension, little scientific interest - concerned orphan diseases, infectious diseases or maternal health, indicating that pharmacogenomics research over the last decade has replicated the well-known 90/10 ratio in drug development. As such, we argue that research in the field of pharmacogenomics has failed in its promise to contribute to the MDGs by reducing global health inequalities.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.018 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.004 | 0.001 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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; both teacher heads agree on what is shown here.
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".