The Diversity of CYP2C19 Polymorphisms in the Thai Population: Implications for Precision Medicine
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Bibliographic record
Abstract
Introduction: CYP2C19 plays a major role in the metabolism of various drugs. The most common genetic variants were the CYP2C19*2 and *3 alleles ( rs4244285 and rs4986893 , non-functional variants). In previous studies, we found that genetic polymorphisms in CYP2C19 variants influenced the active metabolites of clopidogrel and caused major adverse cardiovascular and cerebrovascular effects. However, the distribution of CYP2C19 varies among ethnic groups and according to adverse drug reactions. This study aimed to investigate the frequency of CYP2C19 genetic polymorphisms in the Thai population and analyze the differences in the frequency of CYP2C19 genetic polymorphisms between Thai and other populations. Methods: This study enrolled 211 unrelated healthy Thai individuals in total. We performed a real-time polymerase chain reaction to genotype CYP2C19*2 (681G > A) and CYP2C19*3 (636G > A). Results: In the Thai population, the CYP2C19*1 allele was the most prevalent at 70.14%, while the CYP2C19*2 and *3 alleles were found at frequencies of 25.36% and 4.50%, respectively. Conversely, the CYP2C19*3 allele was not detected in Caucasian, Hispanic, African, Italian, Macedonian, Tanzanian, or North Indian populations. The phenotypic profile of this gene revealed that the frequency of intermediate metabolizers (IMs) is nearly equal to that of extensive metabolizers (EMs), at 42.65% and 48.82% respectively, with genotypes *1/*2 (36.02%) and *1/*3 (6.63%). Likewise, poor metabolizers (PMs) with genotypes *2/*2 (6.16%), *2/*3 (2.37%), and *3/*3 (< 1%) are more prevalent in our population as well. Conclusion: The distribution of CYP2C19 genotype and phenotype influenced by non-functional alleles has potential as a pharmacogenomics biomarker for precision medicine and is dependent on an ethnic-specific genetic variation database. Keywords: CYP2C19 gene, genetic diversity, Thai population, interethnic differences
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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 | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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