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Year : 2022  |  Volume : 54  |  Issue : 2  |  Page : 97--101

Evaluation of genetic polymorphism of CYP3A5 in normal healthy participants from western India - A cross-sectional study

Miteshkumar Rajaram Maurya, Sunanda Gautam, Jeffrey Pradeep Raj, Shruti Saha, Sanchita Ambre, Aishwarya Thakurdesai, Aditya Shah, Urmila Mukund Thatte 
 Department of Clinical Pharmacology, KEM Hospital, Parel, Mumbai, Maharashtra, India

Correspondence Address:
Dr. Urmila Mukund Thatte
Department of Clinical Pharmacology, Seth GS Medical College and KEM Hospital, Parel, Mumbai - 400 012, Maharashtra


BACKGROUND: CYP3A5 enzymes belong to the phase I Group of drug-metabolizing enzymes, which are involved in the metabolism of 50% of the drugs. Participants with CYP3A5 genotype: CYP3A5 *1/*1 are fast metabolizers of drugs and hence will require higher dosing. Whereas those with CYP3A5 * 3/*3 are poor metabolizers of drugs and will require a lower dose to achieve target drug concentration in the blood and those with CYP3A5 * 1/*3 have intermediate drug metabolizing activity. Pharmacogenetic evaluation may improve disease outcomes by maximizing the efficacy and minimizing the toxicity of drugs in patients. MATERIALS AND METHODS: This is a single-center cross-sectional study conducted in the year 2018–2019 to study the population prevalence of genetic polymorphisms of CYP3A5 in healthy participants from western India. Eligible participants willing to give written, informed consent were enrolled in the study. Subsequently, 2 ml venous blood was collected the deoxyribonucleic acid was extracted and then stored at ‒20°C. Genotyping was done by a polymerase chain reaction and restriction fragment length polymorphism. RESULTS: A total of 400 participants with a median age of 22 years (range: 18–58 years) were included. Among them, the genotype prevalence for CYP3A5 * 1/*1 was 17% (n = 67/400); CYP3A5 * 1/*3 was 37% (n = 149/400) and that of CYP3A5 * 3/*3 was 46% (184/400). Out of the total 400 healthy participants analyzed, the allele frequency for CYP3A5 * 1 was 35% (142/400) and that of CYP3A5*3 was 65% (259/400). CONCLUSION: The genotype prevalence for CYP3A5 * 3*3 (46%) and the allele frequency for CYP3A5 * 3 (65%) respectively were the highest among the western Indian population.

How to cite this article:
Maurya MR, Gautam S, Raj JP, Saha S, Ambre S, Thakurdesai A, Shah A, Thatte UM. Evaluation of genetic polymorphism of CYP3A5 in normal healthy participants from western India - A cross-sectional study.Indian J Pharmacol 2022;54:97-101

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Maurya MR, Gautam S, Raj JP, Saha S, Ambre S, Thakurdesai A, Shah A, Thatte UM. Evaluation of genetic polymorphism of CYP3A5 in normal healthy participants from western India - A cross-sectional study. Indian J Pharmacol [serial online] 2022 [cited 2023 Sep 29 ];54:97-101
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The CYP3A5 gene of the CYP450 enzyme system in the liver encodes a superfamily of enzymes that are involved in the oxidation and reduction of 50% of xenobiotics, including cancer chemotherapeutic drugs, protease inhibitors, calcium channel blockers, immunosuppressants, and cholesterol-lowering agents.[1] The CYP3A5 gene exhibits wide interindividual variations in expression levels and polymorphisms of this gene results in interindividual variation in drug metabolism and therapeutic response. Individuals with CYP3A5 polymorphisms are classified as CYP3A5 * 1/*1 (Fast Metabolizers), CYP3A5 * 1/*3 (Intermediate Metabolizers), and CYP3A5 * 3/*3 (Poor metabolizers).[2],[3] The relevance of this gene lies in optimizing drug therapy in two major therapeutic areas - Chronic Myeloid Leukemia (CML) and Renal transplantation. Two Indian studies have evaluated polymorphisms in patients with CML. A case–control study has reported that the frequency of CYP3A5 * 1 and * 3 allele to be 34.1% and 65.8% in patients with CML (n = 265) while it was 66.1% and 33.8% respectively in the healthy control group (n = 241).[4] Yet another study showed that the frequency of CYP3A5 * 1 and * 3 allele was 32.5% and 67.4% respectively (n = 183) in patients with CML, while the frequency of CYP3A5 * 1 and * 3 allele was 32.9% and 67.0% respectively (n = 208) in the control group.[5] These genetic polymorphisms play an important role in treatment outcomes. For example, a systematic review and meta-analysis have confirmed that the clinical response rate with imatinib mesylate in CML patients is associated with the CYP3A5 genotypes.[6] Similarly, recent evidence suggests that CYP3A5 genotypes play a role in the dosage requirements of tacrolimus, an immunosuppressant from the class of calcineurin inhibitors class, a cornerstone of maintenance therapy after renal transplantation.[7] The genotype status also is predictive of the impact of the interacting drugs such as fluconazole on tacrolimus pharmacokinetics.[7] Our institute being a tertiary care referral center with approximately 2–3 renal transplants per month, our aim was to estimate the prevalence of CYP3A5 polymorphisms in normal healthy participants. This would enable us to determine if routine CPY3A5 genetic analysis for polymorphisms and personalized medicine be recommended for all patients who would be initiated on tacrolimus postrenal transplantation.

 Materials and Methods


The study was approved by the Institutional Ethics Committee (EC/OA-93/2018) and written informed consent was obtained from all participants. The study was prospectively registered with the Clinical Trials Registry of India (CTRI Registration number: CTRI/2018/10/016104) and was conducted in accordance with the Declaration of Helsinki principles and all other applicable national and international ethical and regulatory rules and guidelines.

Study design and time frame

This was a single-center, cross-sectional study conducted between November 17, 2018, and June 1, 2019.

Eligibility criteria

All consenting healthy participants of any gender between 18 and 60 years of age, with at least three previous generations permanently residing in Western India (Maharashtra, Gujarat, and Rajasthan) were included in the study.[8] Those with a history of gastrointestinal, renal, liver, cardiovascular, respiratory, central nervous system, and endocrine diseases were excluded from the study.

Study procedures

After obtaining written informed consent, a brief medical history and physical examination were conducted to confirm that the participants were healthy. Subsequently, 2 ml of venous blood was collected for each of the participants to evaluate for reference single nucleotide polymorphism Cluster ID: 6986A >G; rs776746). DNA was extracted from the whole blood by using kit method (Cat#NP-61107, DNA Sure® Blood Mini Kit, Genetix Biotech, India) as per the manufacturer's protocol and stored at ‒20°C for subsequent analysis. Genotyping was done by polymerase chain reaction (PCR) and restriction fragment length polymorphism (RFLP). PCR primers for CYP3A5 were used to amplify a 293 bp fragment (forward primer: 5'-CATCAGTTAGTAGACAGATGA-3', reverse primer: 5'GGTCCAAACAGGGAAGAAATA-3'). In each reaction, approximately 100 ng genomic DNA, 1 uM of each forward and reverse primer, and green PCR master mix (Emerald) were used in a final reaction volume of 20 μL. PCR was performed in an Applied Biosystems thermal cycle (Cycling parameters were initial denaturation at 95°C for 30 s, annealing at 51°C for 30 sec, extension at 72°C for 30 s, and a final extension step at 72°C for 7 min and the number of cycles was 35). The PCR product was analyzed on a 2% agarose ethylenediaminetetraacetic acid gel with ethidium bromide staining and bands were analyzed by a gel documentation system. Enzymatic digestion of amplified PCR products of CYP3A5 was performed using “SSP1 endonuclease”, restriction enzyme purified from Sphaerotilus species (Thermo Fisher Scientific). A 15 μL aliquot of PCR product was incubated for 16 h at 37°C and subsequently analyzed on 4% agarose gel with ethidium bromide staining and bands were detected by a ultraviolet transilluminator.[9]

Sample size calculation and sampling technique

The sample size was estimated using the formula n = Z2p(1‒p)/d2 where n is the sample size, Z is the statistic corresponding to the level of confidence, ”p” is the expected prevalence and “d” is precision.[10] Considering an estimated prevalence (p) of CYP3A5 * 3/*3 to be 46%, an alpha error of 5% and power of 80%, the sample size estimated with an absolute precision (d) of 5% was 381.[10] Hence, it was decided to enroll a total of 400 participants to account for inadequate DNA amplification during analysis. Nonprobability [convenience sampling] was used for the study.

Outcome measures

The following were the outcome measures (a) prevalence of CYP3A5*1/*1, *1/*3, *3/*3 polymorphism, (b) prevalence of CYP3A5*1 and *3 alleles, and (c) comparison of allele frequencies of CYP3A5 genotype between men and women.

Statistical analysis

Demographic data were summarized using descriptive statistics. The prevalence of different genotypes and the allele frequencies were expressed in frequencies and percentages with 95% confidence intervals (CI). The allele frequencies were also analyzed for the Hardy–Weinberg equilibrium. Further, the allele frequencies between male and female gender and among different religions (Hinduism, Buddhism, Jainism, Islamism, and Christianity) were compared using the Chi-squared test. All analyses were done at a 5% significance level using SPSS statistical software version 25 (Publisher: IBM Corp., USA, 2017).


Demographic characteristics

A total of 401 participants were screened and 400 were finally enrolled in the study. One participant did not come back for blood withdrawal after giving written consent and hence was excluded. The median age (Interquartile range) was 22 years. Approximately two-thirds of the participants were male (n = 254/400, 63.5%) while the remaining one-thirds were females (146/400, 36.5%). The other demographic characteristics are summarized in [Table 1].{Table 1}

Allele frequency

Among the 400 participants enrolled, the prevalence (95% CI) of CYP3A5 * 1 allele was 35.4% (32.1, 38.8%); n = 283/800 and that of CYP3A5 * 3 allele was 64.6% (61.2, 67.9%); n = 517/800 of the participants. The genotype frequency followed Hardy–Weinberg equilibrium (Chi-squared value [χ2] = 0.034, p = 0.85).

Genotype frequency (N = 400)

Expressors or wild type (CYP3A5 * 1/*1) was found in n = 67/400 (16.8% [13.2, 20.8]) while intermediate or heterozygous type (CYP3A5 * 1/*3) were n = 149/400 (37.2% {32.5, 42.2}) and nonexpressors or mutant type (CYP3A5 * 3/*3) were n = 184/400 (46% {41,51}) with band pattern of CYP3A5 genotype variants visualized on agarose gel electrophoresis shown in [Figure 1]. Among the 146 females, the frequency of CYP3A5 * 1/*1 genotype was 15% n = 22/146 (15.7% [9.7, 21.9]), while for CYP3A5 * 1/*3 genotype was n = 61/146 (41.8% {33.7, 50.2}) and for CYP3A5 * 3/*3 genotype, the frequency was n = 63/146 (43.2% {35.0, 51.6}). Out of n = 254 males, the frequency of CYP3A5 * 1/*1 genotype was n = 45/254 (17.7% [13.2, 23.0]), while for CYP3A5 * 1/*3 genotype was n = 89/254 (35.03% {29.2, 41.3}) and for CYP3A5 * 3/*3 genotype, the frequency was n = 120/254 (47.2% {40.1, 53.6}). There was no association of CYP3A5 genotype with gender (p=0.35) and religion (p=0.96).{Figure 1}


The present study was conducted among normal healthy participants in a tertiary care teaching hospital in Western India. We report that the CYP3A5 * 3/*3 genotype was the predominant genotype (43%) and the CYP3A5 * 3 allele was the predominant allele (64.6%) in the western Indian population. Our findings are similar to those reported in other Indian populations. For instance, Sarasamma et al. have reported that the prevalence of CYP3A5 * 3 was 70% among n = 25 renal transplant recipients in south India.[9] Another study from south India conducted by Krishnakumar et al. among n = 672 unrelated healthy adults has also estimated the prevalence of CYP3A5 * 3 allele to be 63.5%.[11] Similarly, Ashavaid T and et al. in their study on n = 100 normal healthy adults from Mumbai, Maharashtra and Gujarat states of India, found that the prevalence of the CYP3A5 * 3 allele was 70.5%.[12] Another study conducted by Sailaja et al. in North India also found that the prevalence of the CYP3A5 * 3 allele was 67.5% in adult healthy participants.[4] The data from Clinical Pharmacogenetics Implementation Consortium (CPIC) further suggest that the prevalence of CYP3A5 * 3 allele in the Asian population is 74.2% but it is quite different in the African population which stands at 29.8%.[13],[14] We also report that the prevalence of CYP3A5 * 1/*1 genotype is 17% and that of CYP3A5 * 3/*3 is 46% in our study population from western India which was again similar to the findings reported by Krishnakumar et al. from south India (13%, and 40% respectively), Sailaja et al. from North India (19.1% and 51.1% respectively) and Charoenchokthavee et al. from Thailand (13.4% and 38.8% respectively).[4],[11],[15] The importance of estimating the prevalence of different genotypes of CYP3A5 lies in the fact that almost 50% of the current day drugs are metabolized by CYP3A enzymes of which the relative contribution of CYP3A5 may represent >50% of total CYP3A enzymes.[16],[17],[18] Some of the important substrates of CYP3A5 include tacrolimus, everolimus, cyclosporine, vincristine, tamoxifen, midazolam, alprazolam, amlodipine, indinavir, quinine, and verapamil.[19] Among all of these, tacrolimus was the first drug to receive a strong recommendation for pharmacogenetic-guided dosing by the CPIC.[14] Subsequently, a systematic review and meta-analysis of observational studies found a lower dose and weight-adjusted tacrolimus trough levels in the CYP3A5 * 1/*1 group when compared to CYP3A5 * 3/*3 at all-time points in renal transplant recipients on tacrolimus therapy during a 1-year follow-up. Furthermore, there was an increase in the risk of acute rejections and chronic nephrotoxicity in the CYP3A5 * 1/*1 group.[20] Thus, CYP3A5 pharmacogenetic-guided dosing will facilitate clinicians to optimize tacrolimus dosing and minimize the incidence of graft rejections (Acute/chronic) or nephrotoxicity events. Currently, there is a greater impetus to delineate the role of pharmacogenetic-based dosage recommendations for some additional CYP3A5 substrate in specific disease conditions such as imatinib in CML, tamoxifen in breast cancer, cyclosporin in renal transplant recipients, vincristine in malignancies.[4],[11],[20],[21] The relevance of understanding the prevalence of various alleles of CYP3A5 in a population is considered to play a role beyond drug metabolism and personalized drug therapy but also in the pathogenesis of certain diseases. Feng et al. conducted a case-control study among n = 142 Chinese pediatric tuberculosis patients and have proven that the CYP3A5 * 3 allele decreases the susceptibility of children to tuberculosis (p = 0.004, odds ratio = 0.61, 95% CI: 0.43–0.85) as it affects RNA splicing and enzymatic activity.[22] A systematic review meta-analysis including seventeen case–control studies comprising of 7,458 cancer patients and 7,166 healthy controls showed that CYP3A5 * 3 polymorphism may increase the risk of cancers especially acute leukemia, chronic leukemia, and colorectal cancer in Asian and Caucasian populations, but not among African populations. However, no statistically significant risk was found for prostate cancer, liver cancer, and other cancers.[23] Similarly, the prevalence of the CYP3A5 * 3 allele has shown significant associations with certain psychiatric conditions such as schizophrenia and affective disorders due to its crucial role in the disposition of dopamine and serotonin in the central nervous system.[24] Thus, we could anticipate that in the near future, the evaluation of CYP3A5 genetic polymorphisms would form an indispensable part of personalized medicine given its significant role in drug metabolism and pathogenesis of certain illnesses. Our study has a few limitations. We looked at only the two common alleles, CYP3A5 * 1 and * 3, and not all the alleles of CYP3A5. In addition, the RFLP methodology is limited by the fact that it assumes an allele that was either a CYP3A5 * 2 or CYP3A5 * 3 as wild. Although our study had a large sample size (N = 400), the sampling strategy was not probability-based sampling and hence the results may not be generalizable.


The prevalence of CYP3A5 * 3/*3 genotype was 43% and that of CYP3A5 * 3 allele was 64.6% in our study population from western India and these findings were similar to other parts of India. Given the large numbers with CY3A5 polymorphism, performing genotyping in patient groups will help in individual dose optimization thereby preventing toxicity or therapeutic failure. We recommend that future research should focus on how these findings corroborate with clinical care in different patient populations and assess the cost-effectiveness of CYP3A5 pharmacogenetic-based dose individualization.

Financial support and sponsorship

Research Society of Seth GS Medical College and KEM Hospital, Parel, Mumbai-400 012, Maharashtra, India.

Conflicts of interest

There are no conflicts of interest.


1Wilkinson GR. Cytochrome P4503A (CYP3A) metabolism: Prediction of in vivo activity in humans. J Pharmacokinet Biopharm 1996;24:475-90.
2CYP3A5 Cytochrome P450 Family 3 Subfamily a Member 5 [Homo sapiens (Human)]. Available from: [Last accessed on 2020 Apr 19].
3Yamashita T, Fujishima N, Miura M, Niioka T, Abumiya M, Shinohara Y, et al. Effects of CYP3A5 polymorphism on the pharmacokinetics of a once-daily modified-release tacrolimus formulation and acute kidney injury in hematopoietic stem cell transplantation. Cancer Chemother Pharmacol 2016;78:111-8.
4Sailaja K, Rao DN, Rao DR, Vishnupriya S. Analysis of CYP3A5*3 and CYP3A5*6 gene polymorphisms in Indian chronic myeloid leukemia patients. Asian Pac J Cancer Prev 2010;11:781-4.
5Bajpai P, Tripathi AK, Agrawal D. Genetic polymorphism of CYP3A5 in Indian chronic myeloid leukemia patients. Mol Cell Biochem 2010;336:49-54.
6Cargnin S, Ravegnini G, Soverini S, Angelini S, Terrazzino S. Impact of SLC22A1 and CYP3A5 genotypes on imatinib response in chronic myeloid leukemia: A systematic review and meta-analysis. Pharmacol Res 2018;131:244-54.
7Chen L, Prasad GV. CYP3A5 polymorphisms in renal transplant recipients: Influence on tacrolimus treatment. Pharmgenomics Pers Med 2018;11:23-33.
8West Zone Cultural Centre – A Cultural Centre; 2021. Available from: [Last accessed on 2021 Apr 01].
9Hamzah S, Teh LK, Siew JS, Ahmad G, Wong HS, Zakaria ZA, et al. Pharmacogenotyping of CYP3A5 in predicting dose-adjusted trough levels of tacrolimus among Malaysian kidney-transplant patients. Can J Physiol Pharmacol 2014;92:50-7.
10Jones DH. Book Review: Statistical Methods, 8th Edition George W. Snedecor and William G. Cochran Ames: Iowa State University Press, 1989. xix + 491 pp. Journal of Educational Statistics. 1994;19:304-7.
11Krishnakumar D, Gurusamy U, Dhandapani K, Surendiran A, Baghel R, Kukreti R, et al. Genetic polymorphisms of drug-metabolizing phase I enzymes CYP2E1, CYP2A6 and CYP3A5 in South Indian population. Fundam Clin Pharmacol 2012;26:295-306.
12Ashavaid T, Raje H, Shalia K, Shah B. Effect of gene polymorphisms on the levels of calcineurin inhibitors in Indian renal transplant recipients. Indian J Nephrol 2010;20:146-51.
13Lamba J, Hebert JM, Schuetz EG, Klein TE, Altman RB. PharmGKB summary: Very important pharmacogene information for CYP3A5. Pharmacogenet Genomics 2012;22:555-8.
14Birdwell KA, Decker B, Barbarino JM, Peterson JF, Stein CM, Sadee W, et al. Clinical Pharmacogenetics Implementation Consortium (CPIC) guidelines for CYP3A5 genotype and tacrolimus dosing. Clin Pharmacol Ther 2015;98:19-24.
15Charoenchokthavee W, Panomvana D, Sriuranpong V, Areepium N. Prevalence of CYP2D6*2, CYP2D6*4, CYP2D6*10, and CYP3A5*3 in Thai breast cancer patients undergoing tamoxifen treatment. Breast Cancer (Dove Med Press) 2016;8:149-55.
16Evans WE, Relling MV. Pharmacogenomics: Translating functional genomics into rational therapeutics. Science 1999;286:487-91.
17Lu Y, Zhong H, Tang Q, Huang Z, Jing N, Smith J, et al. Construction and verification of CYP3A5 gene polymorphisms using a Saccharomyces cerevisiae expression system to predict drug metabolism. Mol Med Rep 2017;15:1593-600.
18Saiz-Rodríguez M, Almenara S, Navares-Gómez M, Ochoa D, Román M, Zubiaur P, et al. Effect of the most relevant CYP3A4 and CYP3A5 polymorphisms on the pharmacokinetic parameters of 10 CYP3A substrates. Biomedicines 2020;8:94.
19Rojas L, Neumann I, Herrero MJ, Bosó V, Reig J, Poveda JL, et al. Effect of CYP3A5*3 on kidney transplant recipients treated with tacrolimus: A systematic review and meta-analysis of observational studies. Pharmacogenomics J 2015;15:38-48.
20Wegman P, Elingarami S, Carstensen J, Stål O, Nordenskjöld B, Wingren S. Genetic variants of CYP3A5, CYP2D6, SULT1A1, UGT2B15 and tamoxifen response in postmenopausal patients with breast cancer. Breast Cancer Res 2007;9:R7.
21Skiles JL, Chiang C, Li CH, Martin S, Smith EL, Olbara G, et al. CYP3A5 genotype and its impact on vincristine pharmacokinetics and development of neuropathy in Kenyan children with cancer. Pediatr Blood Cancer 2018;65:10.1002/pbc.26854.
22Feng WX, Liu F, Gu Y, Jiao WW, Sun L, Xiao J, et al. Functional polymorphisms in CYP2C19 & CYP3A5 genes associated with decreased susceptibility for paediatric tuberculosis. Indian J Med Res 2012;135:642-9.
23Wang BS, Liu Z, Xu WX, Sun SL. CYP3A5*3 polymorphism and cancer risk: A meta-analysis and meta-regression. Tumour Biol 2013;34:2357-66.
24Du J, Xu Y, Duan S, Zhang A, Xuan J, Wang L, et al. A case-control association study between the CYP3A4 and CYP3A5 genes and schizophrenia in the Chinese Han population. Prog Neuropsychopharmacol Biol Psychiatry 2009;33:1200-4.