Pharmacogenomic profiling of variants affecting efficacy and toxicity of anti-infective medicines in a south Asian population from Sri Lanka
Anti-infective medicines are a diverse group that are used to treat infections caused by micro-organisms . These medicines are from a diverse class of chemicals that suppress the multiplication and growth or eradicate microorganisms such as bacteria, fungi, or viruses. Data from the World Health Organization (WHO) indicates a significant increase in global antibacterial consumption, which rose by 65% between 2000 and 2015, driven mainly by an increase in consumption in low- and middle-income countries . However, irrational dosing of anti-infectives can cause toxicity in patients and can increase the potential of microorganisms’ evolving antimicrobial resistance . Treatment failure remains a significant issue, particularly in managing global pandemics such as tuberculosis (TB) and HIV/AIDS (Human Immunodeficiency Virus/Acquired Immunodeficiency Syndrome). One of the primary contributors to treatment failure in these diseases is poor adherence to medications, often driven by manifestation of toxicity and adverse effects. These adverse reactions considerably reduce patient adherence, which, in turn, exacerbates the likelihood of treatment failure . Such failures not only heighten the risks of disease progression, transmission, and mortality but also foster the development of drug resistance, thereby perpetuating a cycle of diminished drug efficacy across various infectious diseases. Studies have shown that host’s genetic factors account for about 20–95% of inter-individual variation in response to medicines, and this could have a detrimental influence on the treatment outcomes and lead to emergence of resistance . Pharmacogenomics, the field of genetics that explores genetic variations affecting response to medicines, offers a potential solution to this problem by enabling the design of personalized treatment regimens tailored to individuals and specific populations . Research worldwide has identified pharmacogenomically important variants that have an impact on the efficacy and safety of different anti-infectives medicines . For example, variants in the NAT2 (N-acetyltransferase 2) gene are associated with adverse effects from isoniazide, an anti-tuberculosis medicine , while CYP2C19 (cytochrome P450 family 2 subfamily C member 19) variants influence the metabolism of voriconazole, affecting dosage . Similarly, CYP2B6 (cytochrome P450 family 2 subfamily B member 6) variants impact the metabolism of antiretroviral medicines such as efavirenz and nevirapine . Sri Lanka reported a high rate of antibiotic consumption, amounting to 343.46 million defined daily doses in 2017 . In 2023, the incidence of TB in the country was 41.6 per 100,000 people, while approximately 4,700 adults and children were living with HIV . Similarly, South Asia bears a significant burden of TB and HIV, accounting for about 40% of the global TB burden. India, a South Asian country, has the highest number of people living with HIV outside of Africa . Despite these significant numbers, pharmacogenomic studies focusing on population-specific variants affecting anti-infective medicines, including anti-TB and antiretroviral medicines, are still scarce. To the best of our knowledge, such studies are limited not only in Sri Lanka but across South Asia. A recent Sri Lankan study on the genetic diversity of variants involved in drug metabolism showed an overall high level of similarity among the Sinhalese, Tamils and Moors, the major ethnic groups of the country as well as with the other South Asian populations. However, this study showed a significant difference in genetic diversities between the Sri Lankan population and the European population . As the guidelines for prescription in Sri Lanka are based on recommendations from Western countries that have a population of different genetic diversity from that of South Asia, it is important to understand the pharmacogenomics of our population to develop personalized regimens. Therefore, this study aims to describe the prevalence of pharmacogenomic variants that affect efficacy and safety of anti-infective medicines in the Sri Lankan population and understand its implication on treatment regimes. Given the rising incidence of antimicrobial resistance and treatment failures, the findings from this study could provide a critical step toward optimizing infectious disease management in Sri Lanka, contributing to global efforts.   Pharmacogenomic data on anti-infective medicines were obtained from the PharmGKB (Pharmacogenomics Knowledgebase) website ( https://www.pharmgkb.org/ ). PharmGKB is a free online database that provides information on pharmacogenomically significant variants affecting various drugs. The database classifies the clinical significance of these variants based on levels of evidence from 1 to 4, with 1 A representing the highest level of evidence and 4 the lowest. Each drug is given a list of variants categorized into these evidence levels, and a filtering option is available to select the desired level of evidence. Each allele is assigned a function by the Clinical Pharmacogenetics Implementation Consortium (CPIC). Single nucleotide variants (SNVs) of these alleles are also available in the database. For this study, a search was conducted in PharmGKB under the title “Anti-infectives for systemic use” yielding results that include all relevant medicines. Pharmacogenomic variants with evidence levels 1 A (variant-drug combinations that have variant-specific prescribing guidance available in a current clinical guideline annotation or an FDA-approved drug label annotation), 1B (variant-drug combinations with a high level of evidence supporting the association but no variant-specific prescribing guidance in an annotated clinical guideline or FDA drug label), 2 A (variants are found in PharmGKB’s Tier 1 Very Important Pharmacogenes (VIPs), implying causation of drug phenotype is more likely), and 2B (variants not in PharmGKB’s Tier 1 VIPs, however describe variant-drug combinations with a moderate level of evidence supporting the association) were selected using the database filter. Level 3 (variant-drug combinations with a low level of evidence supporting the association) and Level 4 (variant-drug combinations where the total score is negative and the evidence does not support an association between the variant and the drug phenotype) were excluded. Alleles with normal function, which are not associated with any change in the effects of the drug or toxicity, were removed. For the remaining alleles, all SNVs were extracted from PharmGKB. Genetic data for the selected variants were obtained from an anonymized database maintained by the Centre for Genetics and Genomics (CGG), Faculty of Medicine, University of Colombo. This database contains genetic information from 670 Sri Lankan individuals who were referred to the unit from various parts of the country, for whole exome sequencing (WES) between 2015 and 2024, for the investigation of various genetic disorders such as rare disorders and inherited cancers. Informed written consent for genotyping was obtained from all participants, and ethics approval was granted by the Ethics Review Committee (ERC) of the Faculty of Medicine, University of Colombo (EC-22-012). Whole exome sequencing targeting SNVs was performed on the samples, and results were maintained in a database accessible only to authorized personnel. Numerous pharmacogenomics studies in the country have extracted genetic data from this database, including research on the pharmacogenomics of warfarin, statins, CYP2D6 gene variants and others . After extracting the allele data on wild/variant heterozygous and homozygous participants from the selected variants in the CGG database, the minor allele frequencies (MAFs) were calculated. Variants that were not consistent with Hardy-Weinberg equilibrium, those not detected in the population, variants reported only with allele details without specific SNV details, variants located in intronic regions, and HLA associated variants were excluded from the study. The final set of included variants comprised rs1041983 and rs1799931 of the NAT2 gene, rs4148323 of the UGT1A1 (UDP glucuronosyltransferase family 1 member A1) gene, rs3745274, rs34097093, and rs28399499 of the CYP2B6 gene, as well as rs12769205, rs4244285, rs4986893, and rs3758581 of the CYP2C19 gene (Table ). MAFs and genotype frequency percentages are presented with 95% confidence intervals. A N-1 Chi-square test was used to compare MAFs among different Sri Lankan sub-populations and global sub-populations pairwise and no statistical corrections were made. Statistical significance was defined as p < 0.05. The Sri Lankan sub-populations considered include the ethnic groups of Sinhalese, Tamils, and Moors. While these groups primarily differ in their cultural backgrounds, they also have distinct geographical distributions . MAFs of various global populations were obtained from the Genome Aggregation Database (gnomAD v.2.1.1) . The Genome Aggregation Database compiles exome and genome sequencing data and provides summary statistics for use by the broader scientific community. This database provides a larger and more diverse dataset, including over 125,000 exomes and more than 15,000 genomes, which enhances the reliability of allele frequency estimates across various populations compared to other databases like 1000 Genomes or ALFA. This database provides MAFs of African/African American, Latino American, Ashkenazi Jewish, East Asian, Finnish European, Non-Finnish European and South Asian populations. To account for the multiple comparisons conducted in a different genetic variant across different populations, Bonferroni correction was applied, where the significance threshold (0.05) was adjusted by dividing the original threshold of 0.05 by the number of comparisons performed, with corrected p-values being used to determine statistical significance.   Sociodemographic details The study population consisted of 670 Sri Lankans. Most of them were females (54.9%, n = 368) and Sinhalese (82.4%; n = 552). Additionally, there were 27 Tamil participants (4%) and 55 Muslim participants (8.2%). However, the ethnicity of the remaining 36 participants (5.4%) were not available in the database. The age range of the participants ranged from 2 days old to 82 years old at the time of sample collection. Genotype frequencies and minor allele frequencies (MAFs) in the study population The genotype frequencies and MAF are shown in Table . All the observed genotype frequencies were consistent with the Hardy-Weinberg Equilibrium. NAT2 gene Encodes for the enzyme N-acetyltransferase 2, which metabolises isoniazid. Variants in the NAT2 gene affect enzyme activity, which can alter the chemical modification of isoniazid and its metabolites in the liver, potentially leading to hepatic adverse reactions. Almost half of the population were heterozygous for the NAT2 rs1041983 variant allele ( n = 306; 45.7% [95%CI: 41.9–49.5]) and one-fifth were homozygous ( n = 140; 20.9% [95%CI:18.0-24.2]). Whereas for the NAT2 rs1799931 variant more than four-fifths of the population were homozygous for the wild type allele ( n = 577; 86.1% [95%CI: 83.3–88.6]) with only 13.1% (95%CI: 10.7–15.9) and 0.8% (95%CI: 0.2–1.7) being heterozygous and homozygous for the variant allele, respectively. The MAF of NAT2 rs1041983 variant was 43.7% (95%CI: 41.1–46.4) and that of NAT2 rs1799931 variant was 7.3% (95%CI: 6.0-8.8). UGT1A1 gene Encodes for the enzyme uridine diphosphate -glucuronosyltransferase 1A1 that conjugates bilirubin for excretion in bile. Atazanavir inhibits the uridine diphosphate-glucuronosyltransferase 1A1 (UGT1A1) enzyme leading to markedly decreased UGT1A1 activity leading to increased risk of hyperbilirubinemia and variants in this gene can lead to hyperbilirubinemia related discontinuation of atazanavir. A total of 45 individuals (6.7%, 95%CI: 4.9–8.9) in our sample population were heterozygous for the variant UGT1A1 rs4148323 variant, whilst one (0.1%, 95%CI: 0-0.8) was homozygous. Overall, the MAF was 3.5% (95% CI: 2.6–4.6). CYP2B6 gene This gene encodes the enzyme responsible for the metabolism of the drugs efavirenz and nevirapine. Variants in this gene can lead to reduced drug metabolism, resulting in increased plasma levels and a higher likelihood of adverse events. Additionally, variants may also increase the risk of Stevens-Johnson Syndrome/Toxic Epidermal Necrolysis associated with nevirapine. CYP2B6 rs3745274 variant had high numbers of heterozygous individuals ( n = 313; 46.7% [95%CI: 43.0-50.6]) (intermediate metaboliser) and variant homozygous individuals ( n = 109; 16.3% [95%CI: 13.6–19.3]) (poor metaboliser) with a MAF of 39.6% (95%CI: 37.0- 42.3). In contrast, almost all the individuals in the population were homozygous for the wild type allele of the variants CYP2B6 rs34097093 and CYP2B6 rs28399499 except for two (0.3% [95% CI: 0-1.1]) who were heterozygous for the variant allele of CYP2B6 rs34097093 (intermediate metaboliser) and one (0.2% [95% CI: 0-0.8]) who was heterozygous for the variant allele of CYP2B6 rs28399499 (intermediate metaboliser). Overall, their MAFs were 0.2% (95%CI: 0-0.5) and 0.1% (95%CI: 0-0.4), respectively. CYP2C19 Encodes the enzyme responsible for the metabolism of many drugs including voriconazole. CYP2C19 variants rs12769205 and rs4244285 had equal MAF of 41.9%. Almost half of the population were heterozygous for these variants, CYP2C19 rs12769205 ( n = 336; 50.2% [95%CI: 46.3–54.0]) (intermediate metaboliser) and CYP2C19 rs4244285 ( n = 333; 49.7% [95%CI: 45.9–53.6]) (intermediate metaboliser) whilst the variant homozygous count was 113 (16.9%; 95%CI: 14.1–19.9) (poor metaboliser) and 114 (17%; 95%CI: 14.2–20.1) (poor metaboliser), respectively. CYP2C19 rs3758581 variant had a MAF of 9.7% (95%CI: 8.2–11.4), with 114 (17.0%; 95%CI: 14.2–20.1) heterozygous individuals (intermediate metaboliser) and 8 (1.2%; 95%CI: 0.5–2.3) homozygous individuals (poor metaboliser). However, almost all the subjects were homozygous for the wild type allele of the CYP2C19 rs4986893 variant, with only 7 (1.0%; 95%CI: 0.4–2.1) heterozygous individuals (intermediate metaboliser) and no homozygous individuals, resulting in a MAF of 0.5% (95%CI: 0.2–1.1). Genotype frequencies and MAFs in different Sri Lankan ethnic sub-populations None of the variants showed a significant difference in the MAFs among the ethnic sub-populations of Sri Lanka (Supplementary Table ). Although some differences among Sinhalese, Tamil, and Moor groups in variants such as CYP2C19 (rs12769205) is noted with Sinhalese showing higher frequencies compared to Tamils and Moors, they were not statistically significant. The smaller sample sizes for Tamil ( n = 27) and Moor ( n = 55) groups limit the statistical robustness and generalizability of these observations. Comparison of the MAFs of variants with other populations The comparison of the MAFs from the present study with that of other populations are presented in Table . Variants CYP2C19 rs12769205 and CYP2C19 rs4244285 were distinguished by having higher MAFs than all other populations, which includes, South Asian, East Asian, African/African American, Latino, Ashkenazi Jewish, and European (Finnish and non-Finnish) populations. NAT2 rs1041983 variant also had a significantly higher MAF compared to Latino, and European (Finnish and non-Finnish) populations, while CYP2B6 rs3745274 variant had a significantly higher MAF compared to East Asian, Ashkenazi Jewish, and European (Finnish and non-Finnish) populations. In contrast, NAT2 rs1799931 variant had a lower MAF than the East Asian and Latino American populations but was higher than in African/African American, Ashkenazi Jewish, and European (Finnish and non-Finnish) populations. The UGT1A1 rs4148323 variant showed a lower MAF than the East Asian population but was higher than those in all other populations (except other South Asian populations). Variants CYP2C19 rs4986893 and CYP2C19 rs3758581 were significantly more common than in African/African American, Latino, and European (Finnish) populations.   The study population consisted of 670 Sri Lankans. Most of them were females (54.9%, n = 368) and Sinhalese (82.4%; n = 552). Additionally, there were 27 Tamil participants (4%) and 55 Muslim participants (8.2%). However, the ethnicity of the remaining 36 participants (5.4%) were not available in the database. The age range of the participants ranged from 2 days old to 82 years old at the time of sample collection.   The genotype frequencies and MAF are shown in Table . All the observed genotype frequencies were consistent with the Hardy-Weinberg Equilibrium.   gene Encodes for the enzyme N-acetyltransferase 2, which metabolises isoniazid. Variants in the NAT2 gene affect enzyme activity, which can alter the chemical modification of isoniazid and its metabolites in the liver, potentially leading to hepatic adverse reactions. Almost half of the population were heterozygous for the NAT2 rs1041983 variant allele ( n = 306; 45.7% [95%CI: 41.9–49.5]) and one-fifth were homozygous ( n = 140; 20.9% [95%CI:18.0-24.2]). Whereas for the NAT2 rs1799931 variant more than four-fifths of the population were homozygous for the wild type allele ( n = 577; 86.1% [95%CI: 83.3–88.6]) with only 13.1% (95%CI: 10.7–15.9) and 0.8% (95%CI: 0.2–1.7) being heterozygous and homozygous for the variant allele, respectively. The MAF of NAT2 rs1041983 variant was 43.7% (95%CI: 41.1–46.4) and that of NAT2 rs1799931 variant was 7.3% (95%CI: 6.0-8.8).   Encodes for the enzyme uridine diphosphate -glucuronosyltransferase 1A1 that conjugates bilirubin for excretion in bile. Atazanavir inhibits the uridine diphosphate-glucuronosyltransferase 1A1 (UGT1A1) enzyme leading to markedly decreased UGT1A1 activity leading to increased risk of hyperbilirubinemia and variants in this gene can lead to hyperbilirubinemia related discontinuation of atazanavir. A total of 45 individuals (6.7%, 95%CI: 4.9–8.9) in our sample population were heterozygous for the variant UGT1A1 rs4148323 variant, whilst one (0.1%, 95%CI: 0-0.8) was homozygous. Overall, the MAF was 3.5% (95% CI: 2.6–4.6).   This gene encodes the enzyme responsible for the metabolism of the drugs efavirenz and nevirapine. Variants in this gene can lead to reduced drug metabolism, resulting in increased plasma levels and a higher likelihood of adverse events. Additionally, variants may also increase the risk of Stevens-Johnson Syndrome/Toxic Epidermal Necrolysis associated with nevirapine. CYP2B6 rs3745274 variant had high numbers of heterozygous individuals ( n = 313; 46.7% [95%CI: 43.0-50.6]) (intermediate metaboliser) and variant homozygous individuals ( n = 109; 16.3% [95%CI: 13.6–19.3]) (poor metaboliser) with a MAF of 39.6% (95%CI: 37.0- 42.3). In contrast, almost all the individuals in the population were homozygous for the wild type allele of the variants CYP2B6 rs34097093 and CYP2B6 rs28399499 except for two (0.3% [95% CI: 0-1.1]) who were heterozygous for the variant allele of CYP2B6 rs34097093 (intermediate metaboliser) and one (0.2% [95% CI: 0-0.8]) who was heterozygous for the variant allele of CYP2B6 rs28399499 (intermediate metaboliser). Overall, their MAFs were 0.2% (95%CI: 0-0.5) and 0.1% (95%CI: 0-0.4), respectively.   Encodes the enzyme responsible for the metabolism of many drugs including voriconazole. CYP2C19 variants rs12769205 and rs4244285 had equal MAF of 41.9%. Almost half of the population were heterozygous for these variants, CYP2C19 rs12769205 ( n = 336; 50.2% [95%CI: 46.3–54.0]) (intermediate metaboliser) and CYP2C19 rs4244285 ( n = 333; 49.7% [95%CI: 45.9–53.6]) (intermediate metaboliser) whilst the variant homozygous count was 113 (16.9%; 95%CI: 14.1–19.9) (poor metaboliser) and 114 (17%; 95%CI: 14.2–20.1) (poor metaboliser), respectively. CYP2C19 rs3758581 variant had a MAF of 9.7% (95%CI: 8.2–11.4), with 114 (17.0%; 95%CI: 14.2–20.1) heterozygous individuals (intermediate metaboliser) and 8 (1.2%; 95%CI: 0.5–2.3) homozygous individuals (poor metaboliser). However, almost all the subjects were homozygous for the wild type allele of the CYP2C19 rs4986893 variant, with only 7 (1.0%; 95%CI: 0.4–2.1) heterozygous individuals (intermediate metaboliser) and no homozygous individuals, resulting in a MAF of 0.5% (95%CI: 0.2–1.1).   None of the variants showed a significant difference in the MAFs among the ethnic sub-populations of Sri Lanka (Supplementary Table ). Although some differences among Sinhalese, Tamil, and Moor groups in variants such as CYP2C19 (rs12769205) is noted with Sinhalese showing higher frequencies compared to Tamils and Moors, they were not statistically significant. The smaller sample sizes for Tamil ( n = 27) and Moor ( n = 55) groups limit the statistical robustness and generalizability of these observations.   The comparison of the MAFs from the present study with that of other populations are presented in Table . Variants CYP2C19 rs12769205 and CYP2C19 rs4244285 were distinguished by having higher MAFs than all other populations, which includes, South Asian, East Asian, African/African American, Latino, Ashkenazi Jewish, and European (Finnish and non-Finnish) populations. NAT2 rs1041983 variant also had a significantly higher MAF compared to Latino, and European (Finnish and non-Finnish) populations, while CYP2B6 rs3745274 variant had a significantly higher MAF compared to East Asian, Ashkenazi Jewish, and European (Finnish and non-Finnish) populations. In contrast, NAT2 rs1799931 variant had a lower MAF than the East Asian and Latino American populations but was higher than in African/African American, Ashkenazi Jewish, and European (Finnish and non-Finnish) populations. The UGT1A1 rs4148323 variant showed a lower MAF than the East Asian population but was higher than those in all other populations (except other South Asian populations). Variants CYP2C19 rs4986893 and CYP2C19 rs3758581 were significantly more common than in African/African American, Latino, and European (Finnish) populations.   Antimicrobials have been revolutionized the treatment of infectious diseases over the past few decades . However, inter-individual variations in drug response continues to pose challenges, contributing to treatment failure and the rise of antimicrobial resistance. Understanding the impact of pharmacogenomic variants, and incorporating pharmacogenetic testing, could help to individualize therapy, enhance treatment outcomes, and reduce adverse effects . Although numerous studies on pharmacogenomic variants affecting the safety and efficacy of different classes of anti-infective medicines have been conducted globally, this study is the first to investigate pharmacogenomic variants influencing their metabolism in the Sri Lankan population. In this study, we have explored the NAT2 variants affecting isoniazid, UGT1A1 and CYP2B6 variants affecting anti-retroviral metabolism and CYP2C29 impacting voriconazole metabolism. ITPA rs1127354 variant that increases inosine triphosphate pyrophophohydrolase activity and reduces the risk of haemolyic anemia induced by peginterferon alfa-2b, is not discussed in this study, as it has already been covered in another paper of ours on pharmacogenomic variants affecting the safety and efficacy of immunomodulators . In addition, recent studies have highlighted the role of pharmacogenomics in optimizing the response to anti-COVID-19 therapies. For example, the variability in response to drugs like remdesivir and dexamethasone has been linked to genetic polymorphisms affecting drug metabolism and efficacy . In the context of this study, the observed polymorphisms in genes such as CYP2C19 and CYP2B6 could also influence the metabolism of antiviral agents or immune-modulatory treatments used in COVID-19. Our study sample included 54.9% females and 82.4% Sinhalese, with Tamil representation at 4% and Moors at 8.2%. Whereas, the national statistics show that Sri Lanka’s population consists of 74.9% Sinhalese, 11.2% Tamils, and 9.3% Moors, with females accounting for 51.9% . Additionally, the national gender distribution shows females represent 51.9% of the population . In our study, we investigated the prevalence of the NAT2 rs1041983 and NAT2 rs1799931 variants. The NAT2 gene encodes enzymes responsible for the activation and deactivation of various drugs, including isoniazid, a first-line anti–tuberculosis (TB) medication. Variants in this gene lead to fast, medium, or slow acetylator phenotypes, with slow NAT2 acetylators being strongly associated with anti-TB drug-induced hepatotoxicity . Both these variants of the NAT2 gene have been associated with an increased risk of developing Anti-TB drug-induced hepatotoxicity in populations from Beijing and India. Homozygous variants of these SNVs are also linked to elevated Cmax (maximum concentration) of isoniazid . In the present study, the MAF of NAT2 rs1799931 variant was 7.3% (95% CI: 6.0%-8.8%) while that of NAT2 rs1041983 variant was 43.7% (95% CI: 41.1%-46.4%). To our knowledge, the MAFs of these variants in Sri Lanka have not been previously reported. However, these frequencies did not exhibit statistically significant differences when compared to those found in other South Asian populations, suggesting compatibility. Notably, both variants showed significant differences when compared to various other populations, with frequencies significantly higher than those in European populations. This may partially explain the higher pooled incidence of anti-TB drug-induced liver injury observed in Asians (12.6%) compared to Europeans (6.8%) from 1990–2022 . Although data on anti-TB drug-induced liver injury (AT-DILI) is lacking for the Sri Lankan population, a Sri Lankan study reported an incidence of anti-TB drug-induced hepatitis, a subset of AT-DILI, at 9.5% for the period 2001–2002 . This suggests that the true incidence of AT-DILI might be higher, and more aligned with the Asian prevalence, as AT-DILI encompasses a broader clinical spectrum and appears to be increasing over time . The high prevalence of these variants supports this observation. Many current studies recommend genotyping multiple SNVs ( NAT2 rs1041983, NAT2 rs1801280, NAT2 rs1799930, NAT2 rs1799931) to determine acetylator status . This approach can address variability in drug response and reduce the risk of adverse effects. In Sri Lanka, the high prevalence of these NAT2 variants, along with a significant incidence of AT-DILI, indicates a higher risk of isoniazid-induced hepatotoxicity. Therefore, a ‘one-size-fits-all’ treatment approach is not advisable. Instead, implementing pharmacogenetic testing to personalize isoniazid dosing could improve treatment safety and effectiveness. The introduction of highly active antiretroviral therapy (HAART) has significantly reduced mortality associated with HIV infection. However, variability in efficacy and toxicity remains a challenge . In the current study we investigated the UGT1A1 rs4148323 variant that affects Atazanavir/Ritonavir and CYP2B6 variants rs3745274, rs34097093 and rs28399499 that affect Efavirenz and Nevirapine metabolism. Several UGT1A1 variants have been identified with clinical significance and are strongly associated with jaundice in patients treated with atazanavir, which can lead to treatment withdrawal . The UGT1A1 rs4148323 variant, in particular, has also been identified as an independent predictor of serum bilirubin concentration in Asian populations (Chinese and Korean) . Hence, many physicians advocate for genetic testing for this variant before starting atazanavir  In the present study, MAF of rs4148323 was 3.5% (95% CI: 2.6-4.6%) and exhibited a significant difference in prevalence compared to various other populations. Notably, it had a significantly higher prevalence than in other South Asian populations. Although pharmacogenomic variants are generally considered to have similar prevalences within South Asian sub-populations, a previous study has shown that some variants differ between Sri Lanka and other South Asian populations . Thus, this variant stands out as one that may require specific attention in Sri Lankan guidelines. Further investigation into the UGT1A1 rs4148323 variant and the development of cost-effective pharmacogenomic testing tailored for the Sri Lankan population could facilitate more personalized treatment regimens and help minimize treatment withdrawal rates. CYP2B6 variants rs28399499 and rs3745274 are associated with higher plasma levels of nevirapine and efavirenz, as well as increased risk of neurological adverse effects and virological failure with efavirenz . However, CYP2B6 rs34097093 has been less extensively studied, though it is also associated with elevated plasma levels of nevirapine and efavirenz . In our study, CYP2B6 rs34097093 exhibited a low MAF of 0.2% (95% CI: 0-0.5%) and did not show significant inter-population differences within the limited available global data. Similarly, CYP2B6 rs28399499 had a low MAF but was significantly lower than in the African/African-American population, which is known for a high prevalence of this variant . Conversely, CYP2B6 rs3745274 demonstrated a relatively higher MAF of 39.6% (95% CI: 37.0-42.3%) and was significantly more frequent than in other populations including the European population. Despite this, none of the three variants showed a significant difference compared to the South Asian population. Notably, studies recommend CYP2B6 -guided efavirenz therapy, particularly in the presence of CYP2B6 rs3745274 variant, as it is associated with better treatment outcomes . Given the high frequency of the CYP2B6 rs3745274 variant in Sri Lankans, dose adjustments of efavirenz may be necessary to avoid adverse effects and optimize therapy. Voriconazole is an effective treatment for invasive fungal infections, including aspergillosis and candidiasis. Variants in the CYP2C19 gene are associated with variable voriconazole plasma concentrations, which in turn correlate with the clinical outcome and efficacy of the drug. Most of these variants are loss of function variants such as CYP2C19 rs4244285 and CYP2C19 rs4986893 . These loss-of-function variants lead to decreased enzyme activity and are associated with an increased risk of hepatotoxicity, visual hallucinations, and encephalopathy with voriconazole . The current study highlights a high prevalence of no-function SNVs, CYP2C19 rs4244285 and CYP2C19 rs12769205, which are significantly more common in Sri Lankans compared to all other populations, including European and South Asian populations. This suggests that Sri Lankans may be at a higher risk of toxicity and treatment failure at standard recommended doses which are based on western populations and could benefit from genotype-guided dosing adjustments. Although CYP2C19 rs4986893 exhibited the lowest MAF among the variants studied, it was still significantly more prevalent in Sri Lankans compared to the European population. This underscores the need for tailored guidelines rather than directly adapting European guidelines to our population. However, this study has certain limitations that must be acknowledged. While the sample size of 670 is, to the best of our knowledge, the largest cohort studied in Sri Lanka, and the sample characteristics align with national statistics, it consists of patients referred for genetic testing due to various genetic diseases. A larger sample comprising healthy individuals would provide a more representative cross-section of the general population, thus yielding more generalizable results. This would also increase the likelihood of detecting additional variants that had to be excluded from this study because they were not found in the current sample. Furthermore, the nature of the sample used in this study limits the ability to explore clinical correlations with the genotypes, which would have offered deeper insights into the pharmacogenomic relevance of the findings. Another limitation is that whole exome sequencing was used to genotype the samples, thereby excluding data on intronic variants, which may also play a significant role in drug response. A further noteworthy limitation is the lack of technical facilities to genotype alleles, meaning genotyping was restricted to single nucleotide variants (SNVs) without assessing allele combinations. Genotyping at the allele level is crucial for reliably identifying phenotypes, particularly in the context of drug metabolism in clinical practice. For example, dosing recommendations for drugs such as voriconazole are often based on alleles rather than individual SNVs . Since SNVs within a single allele exhibited different frequencies, a single SNV was common to multiple alleles, and many SNVs lacked a designated clinical function in PharmGKB, deriving allele frequencies from this data was not feasible. As a result, we were unable to report on phenotypes/protein function. However, in emerging research, many SNVs are being identified as linked to various phenotypes and are increasingly considered as pharmacogenomic markers. In addition, although, none of the variants showed a significant difference in the MAFs among the ethnic sub-populations of Sri Lanka the smaller sample sizes for Tamil ( n = 27) and Moor ( n = 55) groups limit the statistical robustness and generalizability of these observations, warranting further studies with larger cohorts to validate these findings. However, despite these limitations, our data on SNVs remains valuable and could contribute to future advancements in pharmacogenomics. Moreover, this study provides a valuable contribution to pharmacogenomics, being the first to focus on pharmacogenomic variants affecting anti-infectives in Sri Lankans. It highlights that Sri Lankans have a distinct pharmacogenomic profile compared to other populations, including South Asian population, underscoring the need for personalized, population-based drug regimens exclusive for Sri Lankans. By integrating pharmacogenomic testing into clinical practice, high-risk groups (those at extremes of age and impaired organ function) can benefit from tailored therapies, reducing the burden of adverse effects and enhancing treatment efficacy . The observed similarities in MAFs with other South Asian populations, emphasize the importance of regional collaboration in building shared databases and standardizing pharmacogenomic testing across South Asia, paving the way for regional guidelines regarding dosing. Such collaborative strategies can enhance equity in healthcare and minimize gaps in access to personalized medicine, whilst imporing efficacy and safety of medicines in the region. Sri Lanka needs to focus on expanding pharmacogenomic research, emphasizing on clinical correlation studies and clinical trials, exploring the clinical significance of the identified variants. Implementing more allele-level genotyping will help generate clinically important data on pharmacogenomics. With this data, we can tailor dosing to individual patients, improving efficacy while anticipating and preventing adverse reactions. This approach may also help reduce the development of antimicrobial resistance within the population. However, we must acknowledge the logistical challenges in implementing such tests across the public health sector. Access may be limited to only a few centers, particularly in urban areas like Colombo, which could hinder services for individuals in more remote regions. Additionally, the cost of these tests may be prohibitive, making it difficult to offer them free of charge in the public sector. In this regards, WES provides comprehensive genetic data, enabling novel discoveries, but its high cost and resource demands limit feasibility for routine use in resource limited settings such as Sri Lanka . In contrast, SNV target analysis focuses on well-known variants, such as CYP2C19 , at a lower cost and with fewer infrastructure requirements, making it more practical for clinical application . Thus, while WES may suit research contexts, targeted SNV analysis is the more cost-effective choice for implementation in Sri Lanka. When commonly used antibiotics cannot be administered due to pharmacogenetic factors, ensuring access to alternative medications will also be a significant challenge in a low-resource setting like Sri Lanka. Moreover, healthcare providers will require comprehensive training to modify dosing regimens based on pharmacogenomic test results. This training could begin now to prepare them for the future implementation of personalized medicine. Despite these challenges, collaboration with international health organizations and increased research in this field can pave the way toward personalized medicine in Sri Lanka, ultimately enhancing patient care and health outcomes. With commitment and strategic planning, we can navigate these obstacles and make significant strides in pharmacogenomics.   This pioneering study reveals distinct pharmacogenomic profiles affecting anti-infective medicines among Sri Lankans, differing from those in other populations, including South Asians and Europeans. The high prevalence of specific genetic variants increases the risk of drug toxicity and treatment failure, highlighting the need for pharmacogenetic testing. This underscores the need for pharmacogenetic testing and personalized therapy regimens. Implementing genotype-guided dosing strategies could significantly enhance treatment outcomes and minimize adverse effects, leading to more effective and safer drug therapies for the Sri Lankan population. However, further research with larger sample sizes, with clinical correlation, is needed to validate these findings and refine recommendations for personalized treatment approaches.   Below is the link to the electronic supplementary material. Supplementary Material 1: Supplementary table 1: Genotype and minor allele frequencies of pharmacogenomic variants associated with anti-infective drug metabolism among ethnic sub-populations. This table illustrates the genotypes and Minor Allele Frequencies of pharmacogenomic variants affecting the safety and efficacy of anti-infective medicines among the Sinhalese, Tamil and Moors ethnic sub-populations of Sri Lanka.   