Pharmacogenomics Tools for Precision Public Health and Lessons for Low- and Middle-Income Countries: A Scoping Review
Bibliographic record
Abstract
Pharmacogenomics is the integration of genomics and pharmacology to optimize drug response and reduce side effects. In terms of personalized or individualized medicine, PGx is defined as the identification and analysis of specific genetic variants associated with particular drug treatments for each patient. Under a precision public health (PPH) approach, population-level data are analyzed to generate public health strategies. The objective of this study was to conduct a scoping review of technological tools, examining their evolution, the predominance of high-income countries in their development, and the gaps and needs for genomic data and advances in low- and middle-income countries (LMICs). This review was conducted in accordance with the ScPRISMA guidelines. A search was conducted in PubMed, Web of Science and Embase until January 2024. A total of 40 documents were selected, which revealed the continuous evolution and progressive development of pharmacogenomic tools. The technological tools developed come from high-income countries, particularly the United States, Canada, China, and several European nations, where international collaboration has been essential to maintain and expand these tools, which have evolved to keep pace with the rapid generation of genomic data. This trend shows a scarce development of technological tools for public health precision in LMICs, which evidences the need to increase investment in genomic research infrastructure in this aspect and in the development of capacities to guarantee global accessibility and boost PPH for all populations.
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 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.020 | 0.019 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".