Pharmacogenomics in Asian Subpopulations and Impacts on Commonly Prescribed Medications
Why this work is in the frame
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Bibliographic record
Abstract
Asians as a group comprise > 60% the world's population. There is an incredible amount of diversity in Asian and admixed populations that has not been addressed in a pharmacogenetic context. The known pharmacogenetic differences in Asian subgroups generally represent previously known variants that are present at much lower or higher frequencies in Asians compared with other populations. In this review we summarize the main drugs and known genes that appear to have differences in their pharmacogenetic properties in certain Asian populations. Evidence-based guidelines and summary statistics from the US Food and Drug Administration and the Clinical Pharmacogenetics Implementation Consortium were analyzed for ethnic differences in outcomes. Implicated drugs included commonly prescribed drugs such as warfarin, clopidogrel, carbamazepine, and allopurinol. The majority of these associations are due to Asians more commonly being poor metabolizers of cytochrome P450 (CYP) 2C19 and carriers of the human leukocyte antigen (HLA)-B*15:02 allele. The relative risk increase was shown to vary between genes and drugs, but could be > 100-fold higher in Asians. Specifically, there was a 172-fold increased risk of Stevens-Johnson syndrome and toxic epidermal necrolysis with carbamazepine use among HLA-B*15:02 carriers. The effects ranged from relatively benign reactions such as reduced drug efficacy to severe cutaneous skin reactions. These reactions are severe and prevalent enough to warrant pharmacogenetic testing and appropriate changes in dose and medication choice for at-risk populations. Further studies should be done on Asian cohorts to more fully understand pharmacogenetic variants in these populations and to clarify how such differences may influence drug response.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it