The Diversity of CYP2C19 Polymorphisms in the Thai Population: Implications for Precision Medicine
Bibliographic record
Abstract
Vorthunju Nakhonsri,1 Shobana John,2,3 Hathaichanok Panumasmontol,4,5 Manassanan Jantorn,4,5 Pongpipat Chanthot,4,5 Nuntachai Hanpramukkun,6 Supaporn Meelarp,7 Chonlaphat Sukasem,2,3 Sissades Tongsima,1 Sukhontha Hasatsri,4 Abhisit Prawang,8 Thanawat Thaingtamtanha,9,10 Natchaya Vanwong,11,12 Chalirmporn Atasilp,13 Monpat Chamnanphon,14 Pimonpan Jinda,2,3 Patompong Satapornpong4,5 1National Center for Genetic Engineering and Biotechnology, National Science and Technology Development Agency, Pathum Thani, Thailand; 2Division of Pharmacogenomics and Personalized Medicine, Department of Pathology, Faculty of Medicine Ramathibodi Hospital, Mahidol University, Bangkok, Thailand; 3Laboratory for Pharmacogenomics, Somdech Phra Debaratana Medical Center (SDMC), Ramathibodi Hospital, Bangkok, Thailand; 4Division of General Pharmacy Practice, Department of Pharmaceutical Care, College of Pharmacy, Rangsit University, Pathum Thani, Thailand; 5Excellence Pharmacogenomics and Precision Medicine Centre, College of Pharmacy, Rangsit University, Pathum Thani, Thailand; 6Division of Pharmaceutical Technology, Department of Industrial Pharmacy, College of Pharmacy, Rangsit University, Pathum Thani, Thailand; 7Ounjai Medical Clinic, Bangsue, Bangkok, Thailand; 8Division of Pharmacy Practice, Department of Pharmaceutical Care, College of Pharmacy, Rangsit University, Pathum Thani, Thailand; 9Department of Chemistry and Biology, University of Siegen, Siegen, Germany; 10Department of Chemistry and Biomolecular Sciences, University of Ottawa, Ottawa, ON, Canada; 11Department of Clinical Chemistry, Faculty of Allied Health Sciences, Chulalongkorn University, Bangkok, Thailand; 12Department of Clinical Chemistry, SYstems Neuroscience of Autism & PSychiatric Disorders (SYNAPS) Research Unit, Faculty of Allied Health Sciences, Chulalongkorn University, Bangkok, Thailand; 13Chulabhorn International College of Medicine, Thammasat University, Pathumthani, Thailand; 14Department of Pathology, Faculty of Medicine, Srinakharinwirot University, Nakornnayok, ThailandCorrespondence: Patompong Satapornpong, Director of Excellence Pharmacogenomics and Precision Medicine Centre, College of Pharmacy, Rangsit University, Pathum Thani, Thailand, Tel +66- 2-791-6000 1420, Fax +66- 2-791-6000 1403, Email patompong.s@rsu.ac.thIntroduction: 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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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".