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.

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.

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.

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

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.

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.

The authors did not use generative AI or AI-assisted technologies in the development of this manuscript.