Pharmacogenomic technologies: a necessary "luxury" for better global public health?
Bibliographic record
Abstract
BACKGROUND: Pharmacogenomic technologies aim to redirect drug development to increase safety and efficacy of individual care. There is much hope that their implementation in the drug development process will help respond to population health needs, particularly in developing countries. However, there is also fear that novel pharmacogenomic drugs will remain too costly, be designed for the needs of the wealthy nations, and so constitute an unnecessary "luxury" for most populations. In this paper, we analyse the promise that pharmacogenomic technologies hold for improving global public health and identify strategies and challenges associated with their implementation. DISCUSSION: This paper evaluates the capacity of pharmacogenomic technologies to meet six criteria described by the University of Toronto Joint Centre for Bioethics group: 1) impact of the technology, 2) technology appropriateness, 3) capacity to address local burdens, 4) feasibility to be implemented in reasonable time, 5) capacity to reduce the knowledge gap, and 6) capacity for indirect benefits. We argue that the implementation of pharmacogenomic technologies in the drug development process can positively impact population health. However, this positive impact depends on how and for which purposes the technologies are used. We discuss the potential of these technologies to stimulate drug discovery in the case of rare (orphan diseases) or neglected diseases, but also to reduce acute adverse drug reactions in infectious disease treatment and prevention, which promises to improve global public health. CONCLUSIONS: The implementation of pharmacogenomic technologies may lead to the development of drugs that appear to be a "luxury" for populations in need of numerous interventions that are known to have a demonstrable impact on population health (e.g., secure access to potable water, reduction of social inequities, health education). However, our analysis shows that pharmacogenomic technologies do have the potential to redirect drug development and distribution so as to improve the health of vulnerable populations. Strategies should thus be developed to better direct their implementation towards meeting the needs and responding to the realities of populations of the developing world (i.e., social, cultural and political acceptability, and local health burdens), making pharmacogenomic technologies a necessary "luxury" for global public health.
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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 | 0.081 | 0.084 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.022 |
| Scholarly communication | 0.019 | 0.023 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.010 | 0.008 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".