Phenytoin (PHT) is one of the most commonly reported aromatic anti-epileptic drugs (AEDs) to cause cutaneous adverse reactions (CADRs), particularly severe cutaneous adverse reactions (SCARs). Although human leukocyte antigen (HLA)-B*15:02 is associated with PHT-induced Steven Johnson syndrome/toxic epidermal necrosis (SJS/TEN) in East Asians, the association is much weaker than it is reported for carbamazepine (CBZ). In this study, we investigated the association of pharmacogenetic variants of the HLA B gene and CYP2C9*3 with PHT-CADRs in South Indian epileptic patients. This prospective case-controlled study included 25 PHT-induced CADRs, 30 phenytoin-tolerant patients, and 463 (HLA-B) and 82 (CYP2C9*3) normal-controls from previous studies included for the case and normal-control comparison. Six SCARs cases and 19 mild-moderate reactions were observed among the 25 cases. Pooled data analysis was performed for the HLA B*51:01 and PHT-CADRs associations. The Fisher exact test and multivariate binary logistic regression analysis were used to identify the susceptible alleles associated with PHT-CADRs. Multivariate analysis showed that CYP2C9*3 was significantly associated with overall PHT-CADRs (OR = 12.00, 95% CI 2.759–84.87, p = 003). In subgroup analysis, CYP2C9*3 and HLA B*55:01 were found to be associated with PHT-SCARs (OR = 12.45, 95% CI 1.138–136.2, p = 0.003) and PHT-maculopapular exanthema (MPE) (OR = 4.041, 95% CI 1.125–15.67, p = 0.035), respectively. Pooled data analysis has confirmed the association between HLA B*51:01/PHT-SCARs (OR = 6.273, 95% CI 2.24–16.69, p = <0.001) and HLA B*51:01/PHT-overall CADRs (OR = 2.323, 95% CI 1.22–5.899, p = 0.037). In this study, neither the case nor the control groups had any patients with HLA B*15:02. The risk variables for PHT-SCARs, PHT-overall CADRs, and PHT-MPE were found to be HLA B*51:01, CYP2C9*3, and HLA B*55:01, respectively. These alleles were identified as the risk factors for the first time in the South Indian Tamil population for PHT-CADRs. Further investigation is warranted to establish the clinical relevance of these alleles in this population with larger sample size.

Introduction: CYP2C19 plays a major role in the metabolism of various drugs. The most common genetic variants were the CYP2C19&ast;2 and &ast;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&ast;2 (681G > A) and CYP2C19&ast;3 (636G > A). Results: In the Thai population, the CYP2C19&ast;1 allele was the most prevalent at 70.14%, while the CYP2C19&ast;2 and &ast;3 alleles were found at frequencies of 25.36% and 4.50%, respectively. Conversely, the CYP2C19&ast;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 &ast;1/&ast;2 (36.02%) and &ast;1/&ast;3 (6.63%). Likewise, poor metabolizers (PMs) with genotypes &ast;2/&ast;2 (6.16%), &ast;2/&ast;3 (2.37%), and &ast;3/&ast;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.

Computational methods analyze genomic data to identify genetic variants linked to drug responses, thereby guiding personalized medicine. This study analyzed 942 whole‐genome sequences from the Electricity Generating Authority of Thailand (EGAT) cohort to establish a population‐specific pharmacogenomic database (TPGxD‐1) in the Thai population. Sentieon (version 201808.08) implemented the GATK best workflow practice for variant calling. We then annotated variant call format (VCF) files using Golden Helix VarSeq 2.5.0 and employed Stargazer v2.0.2 for star allele analysis. The analysis of 63 very important pharmacogenes (VIPGx) reveals 85,566 variants, including 13,532 novel discoveries. Notably, we identified 464 known PGx variants and 275 clinically relevant novel variants. The phenotypic prediction of 15 VIPGx demonstrated a varied metabolic profile for the Thai population. Genes like CYP2C9 (9%), CYP3A5 (45.2%), CYP2B6 (9.4%), NUDT15 (15%), CYP2D6 (47%) and CYP2C19 (43%) showed a high number of intermediate metabolizers; CYP3A5 (41%), and CYP2C19 (9.9%) showed more poor metabolizers. CYP1A2 (52.7%) and CYP2B6 (7.6%) were found to have a higher number of ultra‐metabolizers. The functional prediction of the remaining 10 VIPGx genes reveals a high frequency of decreased functional alleles in SULT1A1 (12%), NAT2 (84%), and G6PD (12%). SLCO1B1 reports 20% poor functional alleles, while PTGIS (42%), SLCO1B1 (4%), and TPMT (5.96%) showed increased functional alleles. This study discovered new variants and alleles in the 63 VIPGx genes among the Thai population, offering insights into advancing clinical pharmacogenomics (PGx). However, further validation is needed using other computational and genotyping methods.

Severe cutaneous adverse drug reactions (SCARs), including drug reactions with eosinophilia and systemic symptoms (DRESS), Stevens-Johnson syndrome (SJS) and toxic epidermal necrolysis (TEN), are rare but severe life-threatening adverse drug reactions. Although their incidence is rare, the mortality rates are as high as 10% for DRESS, 15% for SJS and 2550% for TEN. Recent studies have suggested that HLA genes are associated with SCARs during treatment with causative medicines. The HLA gene is located on chromosome 6p21.121.3 and consists of HLA class I, II and III. Interestingly, HLA-pharmacogenomic markers influence these mechanisms of immunopathogenesis in culprit drug-induced SCARs. However, due to genetic differences at the population level, drug-induced SCARs are varied; thus, the specific pharmacogenomic markers for ethnicity might differ among populations. For instance, the HLA-A*31:01 allele is associated with carbamazepine-induced SCARs in Europeans and Japanese individuals, while the HLA-B*15:02 allele is associated with carbamazepine-induced SJS-TEN among Thais, Han Chinese, Taiwanese and Southeast Asians populations. Such differences pose a major challenge to preventing SCARs. Therefore, knowledge of the pharmacogenomics, mechanisms of immunopathogenesis and ethnic-specific genetic variation related to drug-induced SCARs is needed.

Thiopurine drugs are the cornerstone treatment for many diseases such as acute lymphoblastic leukemia (ALL), organ rejection, inflammatory bowel disease (IBD), systemic lupus erythematosus (SLE), rheumatoid arthritis (RA) and other autoimmune diseases. However, their clinical use faces limitations due to the drug‐induced adverse effects, including myelosuppression. Several genetic associations have been evaluated for their association with these adverse drug reactions. TPMT and NUDT15 polymorphisms have emerged as important clinical markers for predicting and optimizing the safety and effectiveness of thiopurine drugs. The bench‐to‐bedside approach of exploring and assessing the genetic associations of TPMT and NUDT15 variants and the new LC‐MS/MS methods for evaluating TPMT is a step forward in the advancement of precision medicine of thiopurine drugs. In Thailand, TPMT and NUDT are routinely genotyped in some hospitals to guide the prescription of thiopurine drugs for optimizing the safety or effectiveness of these drugs. However, the composite effects of these genetic variants remain unexplored at the global scale. Proper large‐scale studies with multi‐ethnic patients can provide a clear understanding of the TPMT/NUDT15 association and would pave the way towards the optimization of thiopurine drugs to achieve precision medicine.

Next‐generation sequencing (NGS) has transformed pharmacogenomics (PGx), enabling thorough profiling of pharmacogenes using computational methods and advancing personalized medicine. The Thai Pharmacogenomic Database‐2 (TPGxD‐2) analyzed 948 whole genome sequences, primarily from the Electricity Generating Authority of Thailand (EGAT) cohort. This study is an extension of the previous Thai Pharmacogenomic Database (TPGxD‐1) and specifically focused on 26 non‐very important pharmacogenes (VIPGx) genes. Variant calling was conducted using Sentieon (version 201808.08) following GATK's best workflow practices. We then annotated variant call format (VCF) files using Golden Helix VarSeq 2.5.0. Star allele analysis was performed with Stargazer v2.0.2, which called star alleles for 22 of 26 non‐VIPGx genes. The variant analysis revealed a total of 14,529 variants in 26 non‐VIPGx genes, with TBXAS1 had the highest number of variants (27%). Among the 14,529 variants, 2328 were novel (without rsID), with 87 identified as clinically relevant. We also found 56 known PGx variants among the known variants (n = 12,201), with UGT2B7 (19.64%), CYP1B1 (8.9%), SLCO2B1 (8.9%), and POR (8.9%) being the most common. We reported a high frequency of intermediate metabolizers (IMs) in CYP2F1 (34.6%) and CYP4A11 (8.6%), and a high frequency of decreased functional alleles in POR (53.9%) and SLCO1B3 (34.9%) genes. This study enhances our understanding of pharmacogenomic profiling of 26 non‐VIPGx genes of notable clinical importance in the Thai population. However, further validation with additional computational and reference genotyping methods is necessary, and novel alleles identified in this study should undergo further orthogonal validation.