Pharmacogenomic Profiling of Frequently Prescribed Medications Among Geriatric Patients in a Tertiary Care Setting in Southwestern India

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Pharmacogenomics, the study of genetic differences affecting drug metabolism and response, can offer hope of more tailored medication regimens and enhanced therapeutic response. Aim The aim of this study is to create pharmacogenomic profiles of elderly patients in order to determine patient-specific genetic variation contributing to drug response, ultimately influencing the efficacy of drug therapy and increasing the potential risk of ADRs. Method A cross-sectional study was conducted involving 50 geriatric patients aged 60 years or older receiving multi-drug therapy. Saliva samples were collected and genotyped using the Infinium™ Global Screening Array v3.0 BeadChip. Single nucleotide polymorphisms (SNPs) associated with drug response were identified using the PharmGKB database. SNPs with significant clinical relevance were analysed, and pharmacogenomic profiles were visualized to assess risks for ADRs and altered drug efficacy. Results Out of 1,243 SNPs analysed per individual, 561 SNPs with two risk alleles were identified. Fifteen SNPs were present in over 90% of participants, with significant variants observed in genes such as CYP2C9 (associated with warfarin metabolism), TPMT (linked to methotrexate dosing), and SLCO1B3 (associated with docetaxel toxicity). More than 50% of participants had two or more risk alleles, highlighting their predisposition to ADRs and altered drug metabolism. Conclusion The findings of this study emphasize the transformative potential of pharmacogenomics for rationalizing drug therapy among elderly patients, especially when they are susceptible to polypharmacy. Through identification of major genetic differences such as CYP2C9, TPMT and other pharmacogenes the study underlines the requirement for individualized pharmacotherapy based on distinct genetic profiles. 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F1000Research 2025, 14 :896 ( https://doi.org/10.12688/f1000research.168453.1 ) NOTE: If applicable, it is important to ensure the information in square brackets after the title is included in all citations of this article. Close Copy Citation Details Export Export Citation Sciwheel EndNote Ref. Manager Bibtex ProCite Sente EXPORT Select a format first Track Share ▬ ✚ Research Article Pharmacogenomic Profiling of Frequently Prescribed Medications Among Geriatric Patients in a Tertiary Care Setting in Southwestern India [version 1; peer review: 1 not approved] Umaima Farhin Khaiser 1 , Rokeya Sultana 1 , Ranajit Das https://orcid.org/0000-0001-5308-3477 2 , [...] Javeriya Farooq https://orcid.org/0000-0002-6981-3689 1 , Haleema Shahin 1 , Mohammed Gulzar Ahmed 3 , Chetan Ashok https://orcid.org/0000-0002-5345-3658 4 , Srikanth Jeyabalan 4 , Ling Shing Wong 5 , Vetriselvan Subramaniyan 6 Umaima Farhin Khaiser 1 , Rokeya Sultana 1 , [...] Ranajit Das https://orcid.org/0000-0001-5308-3477 2 , Javeriya Farooq https://orcid.org/0000-0002-6981-3689 1 , Haleema Shahin 1 , Mohammed Gulzar Ahmed 3 , Chetan Ashok https://orcid.org/0000-0002-5345-3658 4 , Srikanth Jeyabalan 4 , Ling Shing Wong 5 , Vetriselvan Subramaniyan 6 PUBLISHED 10 Sep 2025 Author details Author details 1 Department of Pharmacognosy, Yenepoya Pharmacy College & Research Centre, Yenepoya (Deemed to be University), Mangaluru, Karnataka, 575018, India 2 Department of division of data analytics bioinformatics and structural biology, Yenepoya research center, Yenepoya (Deemed to be University), Mangaluru, Karnataka, 575018, India 3 Department of Pharmaceutics, Yenepoya Pharmacy College & Research Centre, Yenepoya (Deemed to be University), Mangaluru, Karnataka, 575018, India 4 Department of Pharmacology, Faculty of Pharmacy, Sri Ramachandra Institute of Higher Education and Research (Deemed to be University), Chennai, Tamil Nadu, 600116, India 5 Faculty of Health and Life Sciences, INTI International University & Colleges, Nilai, Negeri Sembilan, 71800, Malaysia 6 Department of Biomedical Sciences, Sir Jeffrey Cheah Sunway Medical School, Faculty of Medical and Life Sciences, Sunway University, Bandar Sunway, Selangor, 47500, Malaysia Umaima Farhin Khaiser Roles: Conceptualization, Investigation, Writing – Original Draft Preparation, Writing – Review & Editing Rokeya Sultana Roles: Conceptualization, Investigation, Writing – Review & Editing Ranajit Das Roles: Formal Analysis, Investigation, Writing – Review & Editing Javeriya Farooq Roles: Formal Analysis, Investigation, Writing – Review & Editing Haleema Shahin Roles: Investigation, Methodology, Writing – Review & Editing Mohammed Gulzar Ahmed Roles: Conceptualization, Methodology, Writing – Review & Editing Chetan Ashok Roles: Formal Analysis, Methodology, Writing – Review & Editing Srikanth Jeyabalan Roles: Conceptualization, Supervision, Writing – Review & Editing Ling Shing Wong Roles: Formal Analysis, Resources, Writing – Review & Editing Vetriselvan Subramaniyan Roles: Conceptualization, Methodology, Supervision, Writing – Review & Editing OPEN PEER REVIEW DETAILS REVIEWER STATUS This article is included in the Genomics and Genetics gateway. Abstract Background Polypharmacy, or taking multiple medications simultaneously, is a common occurrence in the elderly population, increasing the risk of adverse drug reactions (ADRs) and drug-drug interactions (DDIs) requiring the intricate management of multiple comorbid conditions. Pharmacogenomics, the study of genetic differences affecting drug metabolism and response, can offer hope of more tailored medication regimens and enhanced therapeutic response. Aim The aim of this study is to create pharmacogenomic profiles of elderly patients in order to determine patient-specific genetic variation contributing to drug response, ultimately influencing the efficacy of drug therapy and increasing the potential risk of ADRs. Method A cross-sectional study was conducted involving 50 geriatric patients aged 60 years or older receiving multi-drug therapy. Saliva samples were collected and genotyped using the Infinium™ Global Screening Array v3.0 BeadChip. Single nucleotide polymorphisms (SNPs) associated with drug response were identified using the PharmGKB database. SNPs with significant clinical relevance were analysed, and pharmacogenomic profiles were visualized to assess risks for ADRs and altered drug efficacy. Results Out of 1,243 SNPs analysed per individual, 561 SNPs with two risk alleles were identified. Fifteen SNPs were present in over 90% of participants, with significant variants observed in genes such as CYP2C9 (associated with warfarin metabolism), TPMT (linked to methotrexate dosing), and SLCO1B3 (associated with docetaxel toxicity). More than 50% of participants had two or more risk alleles, highlighting their predisposition to ADRs and altered drug metabolism. Conclusion The findings of this study emphasize the transformative potential of pharmacogenomics for rationalizing drug therapy among elderly patients, especially when they are susceptible to polypharmacy. Through identification of major genetic differences such as CYP2C9, TPMT and other pharmacogenes the study underlines the requirement for individualized pharmacotherapy based on distinct genetic profiles. READ ALL READ LESS Keywords Pharmacogenomics, Polypharmacy, Geriatric Pharmacotherapy, Adverse Drug Reactions, Genetic Variations, Personalized Medicine Corresponding Author(s) Rokeya Sultana ( [email protected] ) Close Corresponding author: Rokeya Sultana Competing interests: No competing interests were disclosed. Grant information: The author(s) declared that no grants were involved in supporting this work. Copyright: © 2025 Khaiser UF et al . This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. How to cite: Khaiser UF, Sultana R, Das R et al. Pharmacogenomic Profiling of Frequently Prescribed Medications Among Geriatric Patients in a Tertiary Care Setting in Southwestern India [version 1; peer review: 1 not approved] . F1000Research 2025, 14 :896 ( https://doi.org/10.12688/f1000research.168453.1 ) First published: 10 Sep 2025, 14 :896 ( https://doi.org/10.12688/f1000research.168453.1 ) Latest published: 10 Sep 2025, 14 :896 ( https://doi.org/10.12688/f1000research.168453.1 ) Introduction Polypharmacy, or taking multiple medications simultaneously, is a common occurrence in the elderly population increasing the risk of adverse drug reactions (ADRs) and drug-drug interactions (DDIs) requiring the intricate management of multiple comorbid conditions. This phenomenon will thus be associated with benefits and risks simultaneously. 1 Although it may often be necessary for polypharmacy to address the various health concerns, there are several serious challenges as well, such as the heightened risk of ADRs, drug-drug interactions, and medication non-adherence. 2 The presence of polypharmacy in elderly individuals acts as an indication of an emergent need for better methods of management in their medication regimen. 3 Pharmacogenomics is an up-and-coming methodology that considers individual genetic variation in different responses to drugs. It conveys one of the promising ways to improve the management of polypharmacy. 4 Pharmacogenetic analysis allows for the personalization of drug therapies regarding a patient’s genetic background, in view of enhancing the efficacy of drugs and reducing the risk of side effects. 5 Genetic variations in drug-metabolizing enzymes, which include the majority of the significant ones encoded by the cytochrome P450 genes, serve as one example. Such variants in genes could therefore alter the levels and responses of drugs in individuals and hence are an important guide to pharmacogenomic insights for the optimization of the medication regimen. 6 The integration of pharmacogenomic analysis into clinical practice is a multistep process. First, genetic testing will provide information on specific gene variants that may influence drug metabolism or its response. 7 Pharmacogenomics provides new insights into the metabolism and accurate targeting of both newly created targeted therapies and frequently used medications. By exploiting patients’ genetic information to predict drug responses and reduce ADRs, they have demonstrated favorable outcomes. 8 With its innovative use of genetics in precision medicine, pharmacogenomics-informed pharmacotherapy holds the potential to transform traditional medical practice by promising therapeutic efficacy and individualization through the careful selection of the best medications and dosages. 9 Pharmacogenomic-guided therapy has considerable advantages in polypharmacy management. If a healthcare provider applied pharmacogenomics in the selection and dosing of medications, there would be improved drug efficacy and less incidence of ADRs and DDIs. This personalized medicine would further enhance patient safety and contribute to better use of healthcare resources. For example, genetic information utilized to optimize doses can prevent cases of underdosing and overdosing; this will, in turn, result in better therapeutic outcomes that might reduce the cases of hospitalization attributed to drug errors. 10 On the other hand, pharmacogenomics has also got its own set of challenges in application to geriatric care, including highly specialized clinical knowledge for the interpretation of genetic data and possible cost implications related to genetic testing. 11 Integration of pharmacogenomic data into routine clinical practice also involves ethical and logistic issues, such as ensuring patient consent and data privacy. 12 With all these challenges, the addition of pharmacogenomics into the management of polypharmacy really does mean a quantum leap in personalized medicine, with the potential to optimally drive therapeutic outcomes and quality of life in older adults, thus representing a particularly useful tool in the rapidly developing field of geriatric pharmacotherapy. It goes without saying that as pharmacogenomics continues to advance, its application in polypharmacy will no doubt be integrated further into the personalized healthcare approach for the elderly. 13 The aim of this study is to create pharmacogenomic profiles of elderly patients in order to determine patient-specific genetic variation contributing to drug response, ultimately influencing the efficacy of drug therapy and increasing the potential risk of ADRs. Methodology Study design A cross-sectional study was conducted for patients receiving multiple drug therapy who were older than 60. A practical sampling technique was used to ascertain whether each patient’s genetic profile was associated with the effectiveness of the medication therapy. Study location This cross-sectional study was carried out in the Indian state of Karnataka, at the tertiary care teaching hospital, Yenepoya Medical College and Hospital, Deralakatte. This hospital, which serves a variety of patients from urban, peri-urban and rural areas, was an ideal location to study the effects of poly-pharmacy in the elderly. Study population The study individuals were age 60 years or older and were receiving multi-drug treatment. This population was chosen due to its genetic constitution and vulnerability to polypharmacy-induced issues. Sample size This genetic study included a combination of both in-patients (IP) and out-patients (OP) and resulted in 50 patients in total. Inclusion and exclusion criteria Inclusion criteria: The study included elderly patients of 60 years and older and receiving multi-drug therapy (taking 5 or more and 9 or more drugs for OP and IP, respectively). Exclusion criteria: Patients with critical condition in need of intensive care, patients with mental retardation or cognitive disorders, and patients who refused to be included were all excluded. Research ethics and permission The study was carried out according to the ethical standards of the institutional ethics committee and the Declaration of Helsinki. Ethical approval was obtained from the Yenepoya ethics committee 1 (Approval number: YEC1/2022/041) dated 01-06-22. Written consent form was taken from all the geriatric patients based on inclusion criteria for the study. Medication data collection Medication data were collected from patient records, focusing on those aged 60 years or older who were undergoing multi-drug therapy. The study defined multi-drug therapy as a regimen involving at least five medications for outpatients and nine medications for inpatients. Saliva samples were collected from participants to analyse their genetic profiles and identify associations between pharmacogenomic (PGx) markers and medication response. Pharmacogenomic profiling of geriatric patients Pharmacogenomic profiling was conducted to evaluate whether the recruited geriatric patients could metabolize their prescribed medications or were at risk of ADRs, such as drug toxicity. Pharmacogenetic loci referenced in PharmGKB 14 ( https://www.pharmgkb.org ) were examined for drugs commonly involved in polypharmacy. A comprehensive list of associated SNP markers (N = 648465) linked to drug response was compiled, focusing on medications frequently prescribed in polypharmacy settings. Saliva samples (2 ml) were collected from each participant, stored under appropriate conditions, and later processed for DNA extraction and genotyping using the Infinium™ Global Screening Array-24 v3.0 (GSA v3) BeadChip by Illumina Inc. Quality control (QC) of the genotype data was performed using PLINK v.1.9, excluding Single nucleotide polymorphisms (SNPs) with more than 5% missing data, those that failed the Hardy-Weinberg equilibrium test (P < 0.0001), and SNPs with a minor allele frequency (MAF) below 5% or more than 10% missing genotypes. The curated SNP markers from PharmGKB were compared against the bim file containing SNPs present in the GSA v3 array to identify overlapping markers resulting in a final list of (561) SNPs. The presence of these SNPs, potentially associated with altered drug metabolism or increased toxicity, was evaluated in the patients’ genotype data, with each individual assessed for the presence of such variants. We evaluated antidiabetic, CNS, GI acting, anticancer, hmg COA reductase inhibitors, ARBs, antivirals and antibiotic drugs that are commonly prescribed to the geriatric patients. Pharmacogenomic profiling for each individual was visualized using a bubble plot created in R v4.4.1, where the size of each bubble corresponds to the number of risk alleles present, and a dot represents the complete absence of any risk alleles. Different coloured drugs represented different drug categories. Nomenclature of targets and ligands Drug targets, ligands, and genetic variants related to drug response were the main subjects of the pharmacogenomic analysis. The SNPs for the PharmGKB database was queried. These SNPs are established biomarkers which determine the metabolism, efficacy and toxicity of drugs that are most commonly used in elderly patients. The targeted genes were variants responsible for cytochrome P450 enzymes, which are critical in the metabolism of drugs. PGx drugs The drugs investigated in the study were categorised according to their pharmacogenomic relevance. These drugs that could have varying effects on the metabolism, efficacy, or toxicity on the study participants. Selected drugs were also analysed for particular risk alleles in participants’ genomic data to gain an understanding of their potential pharmacogenomic interactions. Data analysis The pharmacogenomic analysis was performed in multiple steps, aimed at obtaining, accurate and relevant interpretation of the genetic profiles of geriatric patients. Genetic data from the participants was obtained by genotyping saliva samples with Infinium™ Global Screening Array v3. 0 (GSA v3); Illumina Inc., San Diego, CA, USA, which has allowed for the identification of genetic variation that contributes to drug response. Quality assurance steps were taken to ensure the credibility of the data. SNPs with >5% missing data, that deviated from Hardy-Weinberg equilibrium (P < 0.0001), and with minor allele frequency (MAF)<5% were excluded. Using PLINK v1. 9, a package of bioinformatic tools for genome-wide association studies, SNPs from the curated list in PharmGKB was compared to the genotyped data. Cross-mapping the SNP markers with genetic data allowed for a final selection of pharmacogenomic SNPs for the most used drugs in the study population. The pharmacogenomic profiles of the studied cohort were plotted in R software (v4. 4. 1). Bubble plots were generated to illustrate the results with the size of the bubble reflecting the count of the risk alleles for subjects in their genetic profile. Drug categories were color-coded, and dots to the left were shaded signifying absence of risk alleles for a given drug. Results and discussion The pharmacogenomic profiles of 50 elderly polypharmacy patients were analysed in this study. Figure 1 depicts the pictorial representation of the results of the pharmacogenomics study of the commonly prescribed drugs in the Intensive Care Hospital from Southwest India. Genotyping was performed to detect SNPs known to be related to ADRs, altered drug metabolism or toxicity using saliva samples. Less than 50% of the individuals in the 60-year-old and older age group were carriers of at least one risk allele, while more than 50% were carriers of two or more risk alleles for the development of ADRs. A total of 1,243 SNPs was analysed in each participant, and 561 SNPs were determined to have two risk alleles in one or more of the individuals. SNP markers also act as important genetic metrics for evaluation of the individual drug responses. This study identified numerous SNPs that influence enzyme activity, drug metabolism, and toxicity, underscoring their clinical relevance in pharmacogenomics. Figures 2 and 3 illustrate the prevalence of SNPs with two risk alleles across participants. SNPs present in more than 50% of the study population were classified as higher-risk variants, which could significantly impact drug efficacy and safety. Notably, the presence of these SNPs was associated with modified enzyme activity, leading to enhanced or reduced drug metabolism and an increased risk of toxicity. Figure 1. Pharmacogenomics of commonly prescribed drugs in geriatric patients in tertiary care hospital from southwest India. Figure 2. Prevalence of SNPs less than 50%. Figure 3. Prevalence of SNPs more than 50%. The presence of certain SNPs can affect enzyme activity, potentially enhancing or reducing drug efficacy and increasing the risk of toxicity. We identified 15 SNPs that have two risk alleles in >90% recruited patients ( Table 1 ). These SNPs are associated with various drug classes, prominently anti-cancer and immunosuppressive drugs. For instance, individuals with specific SNPs, such as rs1142345 in the TPMT gene, may require a decreased dose of methotrexate, a well-known anti-cancer drug. Similarly, certain SNPs in the CYP2B6 gene are linked to the metabolism of methadone and efavirenz. The SNP rs10455872 has been associated with the efficacy of HMG-CoA reductase inhibitors. Furthermore, SNP rs11045585 is associated with docetaxel toxicity. Table 1. SNPs with two risk allele found among >90% of the recruited patients. Drug class SNV ID Gene Phenotype category Drug(s) Is/Is Not associated Direction of effect PD/PK terms Immunosuppressive rs1142345 TPMT Dosage methotrexate Associated with Decrease Dose of Anti-cancer rs1142345 TPMT Dosage methotrexate Associated with Decrease 0 CNS rs28399499 CYP2B6 Metabolism methadone Associated with Increase Metabolism Antiviral rs28399499 CYP2B6 Metabolism efavirenz Associated with Decrease Metabolism Antimalarial rs28399499 CYP2B6 Efficacy lumefantrine Not Associated 0 Response Hmg co reductase inhibitors rs10455872 LPA Efficacy HMG coa reductase Associated with Decrease Response Statins rs10455872 LPA Efficacy rosuvastatin Associated with Decrease Response Anti-cancer rs11045585 SLCO1B3 Toxicity doxetal Associated with increase 0 Anti-cancer rs121434568 EGFR Efficacy Gefitinib 0 0 NSAID rs72558187 CYP2C9 Metabolism/PK Diclofenac Associated with Decrease Metabolism Antihypertensive rs72558187 CYP2C9 Metabolism/PK losartan Associated with Decrease Metabolism Thrombolytics rs72558187 CYP2C9 Dosage warfarin Associated with Decrease Dose of Respiratory rs72558187 CYP2C9 Clearance zafirlukast Associated with Decrease Clearance Antimalarial rs11572103 CYP2C8 Efficacy, metabolism amodiaquine, artesunate Not Associated 0 Metabolism Anti-cancer rs11572103 CYP2C8 Dosage, metabolism paclitaxel Not Associated 0 Metabolism Thrombolytics rs28371685 CYP2C9 Dosage warfarin Associated with Decrease Dose of CNS rs28371685 CYP2C9 Metabolism/PK hydroxy phenytoin Associated with Decrease Concentration of Antihypertensive rs28371685 CYP2C9 Metabolism/PK losartan Not Associated Decrease Metabolism Antimicrobial/Antibiotic rs28371685 CYP2C9 Efficacy rifampin Associated with Increase Response Thrombolytics rs28399504 CYPC2C19 Metabolism clopidogrel Associated with Decrease Metabolism CNS rs28399504 CYPC2C19 Metabolism mephenytoin Associated with Decrease Metabolism Statins rs17244841 HMGCR Efficacy fluvastatin Not Associated 0 Response CNS rs2687116 CYP3A4 Efficacy Carbamazepine, lamotrigine, phenytoin, primidone, valproic acid Not Associated Increase Response Psychoactive depressant rs3778150 OPRM1 Toxicity Heroin Associated with 0 0 Statins rs17216177 ABCC2 Metabolism/PK Rosuvastatin Associated with Decrease Exposure to Immunosuppressive rs3213619 ABCB1 Metabolism/PK Tacrolimus Associated with 0 0 Anti-cancer rs3213619 ABCB1 Metabolism/PK Cyclosporine/macrolides Not Associated 0 Response Immunosuppressive rs3213619 ABCB1 Efficacy Imatinib Not Associated 0 Response CNS rs9479757 OPRM1 Dosage, Metabolism/PK Methadone Not Associated Increase Dose of SNPs rs72558187 in the CYP2C9 gene are connected to the metabolism of drugs such as diclofenac and losartan, the clearance of zafirlukast, and the dosage of warfarin. Moreover, SNPs in CYP2C9 and OPRM1 related to rs28371685 affect the doses and metabolisms of hydroxy phenytoin, losartan, and warfarin. Lastly, the SNPs rs17216177 and rs3213619 in the ABCB1 gene are correlated with tacrolimus and cyclosporine metabolism as well as imatinib response. Clinically relevant findings were discovered in all individuals, indicating the possibility of personalized health care. The research underscores the importance of reanalysing genomic data as new informatics tools and disease associations develop, which could enhance risk predictions. Many individuals were found to carry high-risk alleles in pharmacogenes, emphasizing the necessity for systematic pharmacogenetic testing. The study also calls for the validation of next-generation sequencing (NGS) platforms for identifying pharmacogenetic variants and suggests conducting economic evaluations for the implementation of pharmacogenomics (PGx). Variants in the TPMT gene (rs1142345) were linked to the need for reduced methotrexate doses in both immunosuppressive and anti-cancer therapies, a finding consistent with earlier studies demonstrating that TPMT polymorphisms can lead to reduced enzyme activity and increased toxicity. 15 Similarly, the SLCO1B3 gene (rs11045585) was associated with elevated toxicity of docetaxel, a chemotherapeutic agent, aligning with prior evidence of SLCO1B3’s role in drug transport and toxicity. 16 SNPs in the CYP2B6 gene (rs28399499) were shown to influence the metabolism of methadone (increased) and efavirenz (decreased). These findings corroborate earlier research indicating that CYP2B6 polymorphisms significantly affect the pharmacokinetics of CNS and antiviral drugs. 17 Variants in CYP2C9 (rs72558187 and rs28371685) were linked to reduced metabolism of diclofenac, losartan, and warfarin, consistent with existing literature that identifies CYP2C9 polymorphisms as major contributors to altered drug metabolism and the risk of adverse drug reactions, particularly for anticoagulants. 18 The LPA gene (rs10455872) was associated with decreased efficacy of HMG-CoA reductase inhibitors, such as rosuvastatin. Previous studies have demonstrated that LPA polymorphisms not only influence statin efficacy but also modulate lipid levels, further supporting the need for pharmacogenomic considerations in managing cardiovascular disease. 19 Other notable findings included SNPs in the CYP3A4 gene (rs2687116), which were linked to the efficacy of anticonvulsants like carbamazepine and valproic acid. These findings are supported by earlier studies showing that CYP3A4 polymorphisms play a critical role in the metabolism of a broad range of medications. 20 Variants in ABCB1 gene (rs3213619) were also observed to modulate the metabolism of tacrolimus and cyclosporine as well as response to imatinib. These findings are consistent with others, where ABCB1 polymorphisms modify drug transport and clinical response, especially of immunosuppressants. 21 Clinical and research implications Our results highlight the crucial application of pharmacogenomics for the optimal treatment of elderly patients. Most noteworthy, was the recognition of 15 SNPs with at least two risk alleles represented in more than 90% of participants, largely predisposing subjects to ARDs and compromised drug efficiency. Variants in genes like CYP2C9 and TPMT illustrate the requirement for dose alteration and close monitoring of the drug treatment to avoid risks. The study also reflects the possibilities of personalized medicine by use of the standardized pharmacogenomic testing. Pharmacogenomics personalized medicine is advocated to increase efficacy and decrease the occurrence of ADRs and subsequently patient safety by individually tuning the drug regimen to the corresponding genetic profile. Incorporating pharmacogenomics into standard clinical care could help decrease healthcare costs by preventing hospitalizations and other adverse outcomes due to medication errors. And lastly, the results to highlight and underscore the importance of more efficient technological platforms (i.e., NGS) to uncover pharmacogenomic variants. These tools may be at the forefront of precision medicine efforts. However, additional investigations are required to confirm these findings in larger and more diverse patient populations. Moreover, establishing the economic viability of routine pharmacogenomic testing is essential to its successful broader adoption into the clinical practice workflows. Conclusion The findings of this study emphasize the transformative potential of pharmacogenomics for rationalizing drug therapy among elderly patients, especially when they are susceptible to polypharmacy. Through identification of major genetic differences such as CYP2C9, TPMT and other pharmacogenes the study underlines the requirement for individualized pharmacotherapy based on distinct genetic profiles. Pharmacogenomic testing can also be applied to reduce the adverse drug reactions, drug effectiveness, and overall patient safety on a systematic basis. Although these results highlight the potential of personalized medicine, additional large-scale validation and cost-effectiveness assessments are needed for wider clinical use of pharmacogenomics targeting elderly patients. CRediT authorship contribution statement Umaima Farheen Khaiser: Conceptualization, Investigation, Data curation, Formal analysis, Resources, Writing – original draft; Rokeya Sultana: Conceptualization, Methodology, Investigation, Data curation, Supervision, Validation, Resources, Writing-review & editing; Ranajit Das: Conceptualization, Investigation, Methodology, Data curation, Resources, Data curation, Supervision, Writing – original draft, Writing-review & editing; Dr Juveriya Farooq: Methodology, Investigation, Formal analysis; Haleema Shahin DH: Data curation, Investigation, Formal analysis, Methodology, Resources, Writing – original draft, Writing-review & editing; Mohammed Gulzar Ahmed: Data curation, Software, Investigation, Formal analysis, Methodology, Resources, Writing – original draft. Chetan Ashok: Methodology, Data curation, Data curation, Writing-review & editing: Srikanth Jeyabalan: Methodology, Investigation, Data curation, Writing-review & editing; Ling Shing Wong: Conceptualization, Data curation, Formal analysis, Writing-review & editing; Vetriselvan Subramaniyan: Conceptualization, Data curation, Formal analysis, Writing-review & editing. AI disclosure The authors did not use generative AI or AI-assisted technologies in the development of this manuscript. Data availability The authors confirm that all data supporting the findings of this study are contained within the manuscript and supplementary materials. No additional datasets were generated or analysed during the current study. Due to ethical and privacy considerations related to patient genetic information, the underlying data cannot be made publicly available. Data can be made available upon reasonable request to the corresponding author ( [email protected] , [email protected] ), subject to institutional and ethical approvals. Acknowledgments Umaima Farhin Khaiser, Rokeya Sultana, Ranajit Das, Juveriya Farooq, Haleema Shahin, and Mohammed Gulzar Ahmed, would like to acknowledge Yenepoya (Deemed to be University) for providing the research facilities to conduct the experiments. The Graphical abstract was drawn by Biorender.com References 1. Nobili A, Garattini S, Mannucci PM: Multiple diseases and polypharmacy in the elderly: challenges for the internist of the third millennium. J. Comorb. 2011; 1 (1): 28–44. PubMed Abstract | Publisher Full Text | Free Full Text 2. Leporini C, De Sarro G, Russo E: Adherence to therapy and adverse drug reactions: is there a link? Expert Opin. Drug Saf. 2014; 13 (sup1): 41–55. Publisher Full Text 3. Kim J, Parish AL: Polypharmacy and medication management in older adults. Nurs. Clin. 2017; 52 (3): 457–468. 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Hopewell JC, et al. : Impact of common genetic variation on response to simvastatin therapy among 18 705 participants in the Heart Protection Study. Eur. Heart J. 2013; 34 (13): 982–992. PubMed Abstract | Publisher Full Text | Free Full Text 20. Daly AK: Pharmacogenetics and human genetic polymorphisms. Biochem. J. 2010; 429 (3): 435–449. Publisher Full Text 21. Hoffmeyer S, et al. : Functional polymorphisms of the human multidrug-resistance gene: multiple sequence variations and correlation of one allele with P-glycoprotein expression and activity in vivo. Proc. Natl. Acad. Sci. 2000; 97 (7): 3473–3478. PubMed Abstract | Publisher Full Text | Free Full Text Comments on this article Comments (0) Version 1 VERSION 1 PUBLISHED 10 Sep 2025 ADD YOUR COMMENT Comment Author details Author details 1 Department of Pharmacognosy, Yenepoya Pharmacy College & Research Centre, Yenepoya (Deemed to be University), Mangaluru, Karnataka, 575018, India 2 Department of division of data analytics bioinformatics and structural biology, Yenepoya research center, Yenepoya (Deemed to be University), Mangaluru, Karnataka, 575018, India 3 Department of Pharmaceutics, Yenepoya Pharmacy College & Research Centre, Yenepoya (Deemed to be University), Mangaluru, Karnataka, 575018, India 4 Department of Pharmacology, Faculty of Pharmacy, Sri Ramachandra Institute of Higher Education and Research (Deemed to be University), Chennai, Tamil Nadu, 600116, India 5 Faculty of Health and Life Sciences, INTI International University & Colleges, Nilai, Negeri Sembilan, 71800, Malaysia 6 Department of Biomedical Sciences, Sir Jeffrey Cheah Sunway Medical School, Faculty of Medical and Life Sciences, Sunway University, Bandar Sunway, Selangor, 47500, Malaysia Umaima Farhin Khaiser Roles: Conceptualization, Investigation, Writing – Original Draft Preparation, Writing – Review & Editing Rokeya Sultana Roles: Conceptualization, Investigation, Writing – Review & Editing Ranajit Das Roles: Formal Analysis, Investigation, Writing – Review & Editing Javeriya Farooq Roles: Formal Analysis, Investigation, Writing – Review & Editing Haleema Shahin Roles: Investigation, Methodology, Writing – Review & Editing Mohammed Gulzar Ahmed Roles: Conceptualization, Methodology, Writing – Review & Editing Chetan Ashok Roles: Formal Analysis, Methodology, Writing – Review & Editing Srikanth Jeyabalan Roles: Conceptualization, Supervision, Writing – Review & Editing Ling Shing Wong Roles: Formal Analysis, Resources, Writing – Review & Editing Vetriselvan Subramaniyan Roles: Conceptualization, Methodology, Supervision, Writing – Review & Editing Competing interests No competing interests were disclosed. Grant information The author(s) declared that no grants were involved in supporting this work. Article Versions (1) version 1 Published: 10 Sep 2025, 14:896 https://doi.org/10.12688/f1000research.168453.1 Copyright © 2025 Khaiser UF et al . This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Download Export To Sciwheel Bibtex EndNote ProCite Ref. Manager (RIS) Sente metrics Views Downloads F1000Research - - PubMed Central info_outline Data from PMC are received and updated monthly. - - Citations open_in_new 0 open_in_new 0 open_in_new SEE MORE DETAILS CITE how to cite this article Khaiser UF, Sultana R, Das R et al. Pharmacogenomic Profiling of Frequently Prescribed Medications Among Geriatric Patients in a Tertiary Care Setting in Southwestern India [version 1; peer review: 1 not approved] . F1000Research 2025, 14 :896 ( https://doi.org/10.12688/f1000research.168453.1 ) NOTE: If applicable, it is important to ensure the information in square brackets after the title is included in all citations of this article. COPY CITATION DETAILS track receive updates on this article Track an article to receive email alerts on any updates to this article. TRACK THIS ARTICLE Share Open Peer Review Current Reviewer Status: ? Key to Reviewer Statuses VIEW HIDE Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions Version 1 VERSION 1 PUBLISHED 10 Sep 2025 Views 0 Cite How to cite this report: Hardi H. Reviewer Report For: Pharmacogenomic Profiling of Frequently Prescribed Medications Among Geriatric Patients in a Tertiary Care Setting in Southwestern India [version 1; peer review: 1 not approved] . F1000Research 2025, 14 :896 ( https://doi.org/10.5256/f1000research.185635.r417176 ) The direct URL for this report is: https://f1000research.com/articles/14-896/v1#referee-response-417176 NOTE: it is important to ensure the information in square brackets after the title is included in this citation. Close Copy Citation Details Reviewer Report 26 Sep 2025 Harri Hardi , Universitas Indonesia, Depok, West Java, Indonesia Not Approved VIEWS 0 https://doi.org/10.5256/f1000research.185635.r417176 The introduction lacks depth and fails to clearly articulate the novelty and gap in the existing literature. Specifically, it should situate the study within the context of existing pharmacogenomic research in India or similar settings. It also should explicitly state ... Continue reading READ ALL The introduction lacks depth and fails to clearly articulate the novelty and gap in the existing literature. Specifically, it should situate the study within the context of existing pharmacogenomic research in India or similar settings. It also should explicitly state what new insights or advancements this study offers compared to previous work. Methodology Sampling Method : The manuscript does not clearly describe the sampling technique (e.g., consecutive, convenience, random). This must be explicitly stated to assess the representativeness of the sample. Sample Size Justification : A sample size of 50 may be insufficient to capture the genetic diversity in pharmacogenomic studies. The authors should provide a power calculation or reference similar studies to justify the sample size. The exclusion criteria was mentioned double, both in Pharmacogenomic profiling of geriatric patients and data analysis section, please remove one of them The discussion lacks depth; the author should engage more thoroughly with CPIC and DPWG guidelines. Additionally, some drugs mentioned, such as zavirlucast and losartan, are not clinically significant for inclusion in the result and discussion. Certain genes were questionable associated with drugs such as HMGCR and LPA. While they may hold statistical significance in a small sample, the absence of their inclusion in CPIC or DPWG guidelines renders this paper potentially misleading. In Table 1, The method for determining phenotype categories (e.g., “Dosage,” “Metabolism,” “Toxicity”) is not described. The authors should clarify in the Methods section how these categories were assigned (e.g., based on PharmGKB evidence levels, functional impact predictions, or prior literature). In conclusion, the author's understanding of pharmacogenetics is insufficient, and further reading and research on the topic are recommended. An example of a study that effectively explains the distribution of pharmacogenetics is provided below. (refer to 1) The discussion part should also address the current state of pharmacogenetic testing implementation in India. Has pharmacogenetic testing been sufficiently utilized in India, and what is the cost of such testing in the country? I provide an example from the study by Hardi et al (refer to 2) in Southeast Asia. The discussion section should address the advancements in pharmacogenetic testing within India. I recommend major revisions before the manuscript can be considered for publication. The authors should thoroughly address the above points, particularly regarding methodological clarity, clinical relevance of findings, and alignment with established pharmacogenomic guidelines. If the concerns related to the authors' understanding of pharmacogenomic principles and clinical applicability are not adequately addressed, the manuscript may not be suitable for indexing. Is the work clearly and accurately presented and does it cite the current literature? No Is the study design appropriate and is the work technically sound? Yes Are sufficient details of methods and analysis provided to allow replication by others? No If applicable, is the statistical analysis and its interpretation appropriate? Partly Are all the source data underlying the results available to ensure full reproducibility? Yes Are the conclusions drawn adequately supported by the results? No References 1. John S, Klumsathian S, Own‐eium P, Eu‐ahsunthornwattana J, et al.: A comprehensive Thai pharmacogenomics database (TPGxD ‐1): Phenotype prediction and variants identification in 942 whole‐genome sequencing data. Clinical and Translational Science . 2024; 17 (6). Publisher Full Text 2. Hardi H, Barinda A, Mahata L, Fitrianti Z: CYP2C19 variability and clinical outcomes of clopidogrel, proton pump inhibitors, and voriconazole in Southeast Asia: a systematic review and meta-analysis. Frontiers in Pharmacology . 2025; 16 . Publisher Full Text Competing Interests: No competing interests were disclosed. Reviewer Expertise: Pharmacogenetic, clinical pharmacology, cardiovascular I confirm that I have read this submission and believe that I have an appropriate level of expertise to state that I do not consider it to be of an acceptable scientific standard, for reasons outlined above. Close READ LESS CITE CITE HOW TO CITE THIS REPORT Hardi H. Reviewer Report For: Pharmacogenomic Profiling of Frequently Prescribed Medications Among Geriatric Patients in a Tertiary Care Setting in Southwestern India [version 1; peer review: 1 not approved] . F1000Research 2025, 14 :896 ( https://doi.org/10.5256/f1000research.185635.r417176 ) The direct URL for this report is: https://f1000research.com/articles/14-896/v1#referee-response-417176 NOTE: it is important to ensure the information in square brackets after the title is included in all citations of this article. COPY CITATION DETAILS Report a concern Respond or Comment COMMENT ON THIS REPORT Comments on this article Comments (0) Version 1 VERSION 1 PUBLISHED 10 Sep 2025 ADD YOUR COMMENT Comment keyboard_arrow_left keyboard_arrow_right Open Peer Review Reviewer Status info_outline Alongside their report, reviewers assign a status to the article: Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions Reviewer Reports Invited Reviewers 1 Version 1 10 Sep 25 read Harri Hardi , Universitas Indonesia, Depok, Indonesia Comments on this article All Comments (0) Add a comment Sign up for content alerts Sign Up You are now signed up to receive this alert Browse by related subjects keyboard_arrow_left Back to all reports Reviewer Report 0 Views copyright © 2025 Hardi H. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 26 Sep 2025 | for Version 1 Harri Hardi , Universitas Indonesia, Depok, West Java, Indonesia 0 Views copyright © 2025 Hardi H. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. format_quote Cite this report speaker_notes Responses (0) Not Approved info_outline Alongside their report, reviewers assign a status to the article: Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions The introduction lacks depth and fails to clearly articulate the novelty and gap in the existing literature. Specifically, it should situate the study within the context of existing pharmacogenomic research in India or similar settings. It also should explicitly state what new insights or advancements this study offers compared to previous work. Methodology Sampling Method : The manuscript does not clearly describe the sampling technique (e.g., consecutive, convenience, random). This must be explicitly stated to assess the representativeness of the sample. Sample Size Justification : A sample size of 50 may be insufficient to capture the genetic diversity in pharmacogenomic studies. The authors should provide a power calculation or reference similar studies to justify the sample size. The exclusion criteria was mentioned double, both in Pharmacogenomic profiling of geriatric patients and data analysis section, please remove one of them The discussion lacks depth; the author should engage more thoroughly with CPIC and DPWG guidelines. Additionally, some drugs mentioned, such as zavirlucast and losartan, are not clinically significant for inclusion in the result and discussion. Certain genes were questionable associated with drugs such as HMGCR and LPA. While they may hold statistical significance in a small sample, the absence of their inclusion in CPIC or DPWG guidelines renders this paper potentially misleading. In Table 1, The method for determining phenotype categories (e.g., “Dosage,” “Metabolism,” “Toxicity”) is not described. The authors should clarify in the Methods section how these categories were assigned (e.g., based on PharmGKB evidence levels, functional impact predictions, or prior literature). In conclusion, the author's understanding of pharmacogenetics is insufficient, and further reading and research on the topic are recommended. An example of a study that effectively explains the distribution of pharmacogenetics is provided below. (refer to 1) The discussion part should also address the current state of pharmacogenetic testing implementation in India. Has pharmacogenetic testing been sufficiently utilized in India, and what is the cost of such testing in the country? I provide an example from the study by Hardi et al (refer to 2) in Southeast Asia. The discussion section should address the advancements in pharmacogenetic testing within India. I recommend major revisions before the manuscript can be considered for publication. The authors should thoroughly address the above points, particularly regarding methodological clarity, clinical relevance of findings, and alignment with established pharmacogenomic guidelines. If the concerns related to the authors' understanding of pharmacogenomic principles and clinical applicability are not adequately addressed, the manuscript may not be suitable for indexing. Is the work clearly and accurately presented and does it cite the current literature? No Is the study design appropriate and is the work technically sound? Yes Are sufficient details of methods and analysis provided to allow replication by others? No If applicable, is the statistical analysis and its interpretation appropriate? Partly Are all the source data underlying the results available to ensure full reproducibility? Yes Are the conclusions drawn adequately supported by the results? No References 1. John S, Klumsathian S, Own‐eium P, Eu‐ahsunthornwattana J, et al.: A comprehensive Thai pharmacogenomics database (TPGxD ‐1): Phenotype prediction and variants identification in 942 whole‐genome sequencing data. Clinical and Translational Science . 2024; 17 (6). Publisher Full Text 2. Hardi H, Barinda A, Mahata L, Fitrianti Z: CYP2C19 variability and clinical outcomes of clopidogrel, proton pump inhibitors, and voriconazole in Southeast Asia: a systematic review and meta-analysis. Frontiers in Pharmacology . 2025; 16 . Publisher Full Text Competing Interests No competing interests were disclosed. Reviewer Expertise Pharmacogenetic, clinical pharmacology, cardiovascular I confirm that I have read this submission and believe that I have an appropriate level of expertise to state that I do not consider it to be of an acceptable scientific standard, for reasons outlined above. reply Respond to this report Responses (0) Hardi H. Peer Review Report For: Pharmacogenomic Profiling of Frequently Prescribed Medications Among Geriatric Patients in a Tertiary Care Setting in Southwestern India [version 1; peer review: 1 not approved] . F1000Research 2025, 14 :896 ( https://doi.org/10.5256/f1000research.185635.r417176) NOTE: it is important to ensure the information in square brackets after the title is included in this citation. 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