From Genes to Drugs: Pharmacogenomic Strategies for Personalized Therapy in Indian Populations

Abstract

India’s exceptional genetic diversity presents equally and obstacles as well as one prospect towards precision treatment. With over 1.4 billion individuals spanning thousands of ethnolinguistic groups, the country harbours substantial interpatient variability within pharmaceutical biotrasformation as well as pharmacological effect. Despite rapid advances in global pharmacogenomics, translation of genotype-guided therapy in India remains limited by fragmented data, underrepresentation in international studies, and insufficient clinical integration. This narrative review synthesizes current evidence on India’s pharmacogenomic landscape, drawing from national genome initiatives (GenomeIndia, IndiGenomes), clinical implementation studies, and regulatory frameworks. It highlights population-level variation in key pharmacogenes such as CYP2C19, SLCO1B1, TPMT, HLA-B15:02*, and NAT2, and delineates their implications across major therapeutic areas including cardiovascular, oncological, neurological, and infectious diseases. Building on these insights, the review proposes a translational roadmap encompassing prioritization of high-impact gene-drug pairs, integration of genetic information within digital clinical records as well as clinical ecision aid, workforce capacity building, and development of cost-effective, policy-enabled testing models. Emerging frontiers including multi-omics integration, AI-driven predictive analytics, and Ayurgenomics further position India to pioneer a culturally and biologically contextualized model of personalized pharmacotherapy. Collectively, these efforts chart a path toward equitable, population-scale precision medicine that aligns with India’s healthcare transformation agenda and offers a scalable blueprint for other low- and middle-income nations.

Keywords

Introduction

The pharmaceutical sector of India, with an annual production volume worth over USD 50 billion and the third largest in the world, is an important element not only of international drug distribution, but also of national healthcare in terms of innovation (1,2).  The country is home to over 1.4 billion people that include thousands of ethnic, linguistic, and regional groups and makes it one of the most genetically diverse populations globally. Such genetic diversity shows significant influence upon interindividual heterogeneity drug uptake, biotransformation, as well as clinical efficacy (3,4).   The extensive pharmacogenetic investigation demonstrates numerous medical genetic variants within biotransformation enzyme (CYP2C19, CYP2D6, CYP3A4), efflux (SLCO1B1, ABCB1) as well as  therapeutic targets of drugs (HLA-B, TPMT, VKORC1). A notable instance involves non-functional CYP2C19 *2/3 variants, that reduces the biotransformation for PPI as well as platelet inhibition therapies and occur  in approximately 35-45 percent of Indian population compared with 15-20 percent of European population, whereas the allele associated with risk to simvastatin triggered muscle toxicity, SLCO1B1 rs4149056, remain markedly less prevalent (5–7). The impact of such genetic variability impacts the efficacy and safety of the drugs directly, which makes pharmacogenomics the pillar of optimized pharmacotherapy in India. This diversity is clinically expressed as an unpredictable response to treatment and a high number of adverse drug reactions (ADRs), many of which can be avoided using genomic screening, as mentioned by the WHO . According to the global pharmacogenomics studies, it can be stated that almost 30 per cent of ADRs are associated with drugs related to high-evidence (PharmGKB Level 12) clinically annotated variants (8–10). Indian cohort studies also report actionable CYP, TPMT and HLA polymorphisms that have an effect on the response to clopidogrel, thiopurines, carbamazepine and warfarin. It is important to note that in up to 36 per cent of Indian patients, CYP2C19 loss-of-function mutations are found, diminishing the effect of clopidogrel activation, and HLA-B 15:02 causes sensitivity to carbamazepine- associated Severe cutaneous adverse reaction (SCAR) (1,6). Although the evidence on pharmacogenomics is growing, pharmacogenomics has yet to be integrated into drug-development processes and clinical practice in India. Another factor that prevents the use of genetic endpoints in regular prescribing is still the lack of population-specific reference panels, standardized reporting systems, and genomic pharmacovigilance frameworks (11,12). Regulatory reviews have demonstrated that pharmacogenetic biomarkers are only being captured in a few Indian clinical trials and this limits the analysis of genotype-stratified efficacy. Positively, the 2025 amendment to the NDTC rules , issued through the Health Ministry  and Family Welfare under the supervision of CDSCO and DCGI is a historic amendment. These amendments reduce approval time lines and allow the use of genomic endpoints in first phase clinical trials, bringing the regulatory landscape in India into compliance with the standards of precision-medicine in the (13) This review is a synthesis of all pharmacogenomics studies in India, covering the journey of drug discovery through clinical translation to policy. It determines clinically relevant pairs of genes and drugs, assesses the maturity of the Indian pharmaceutical ecosystem to be ready for genomic stratification, and provides a regulatory path to personalized therapy  (14) This review is novel in its industry perspective that is industry-aligned and that provides an overall bridge between genomic science and regulatory translation. It suggests a unified system that connects the genetic diversity in India with accuracy in the development of drugs by linking national genome initiatives such as IndiGen and GenomeIndia with pharmacogenomics knowledgebase and drug-drug interaction repositories. By doing so, it places pharmacogenomics as an alchemy-like bridge between population genomics and personalized therapies (1,6,15)

METHODOLOGY:

The present article was designed in the form of descriptive , evidence-integrative analysis instead of a structured meta-analysis. Its primary objective was to synthesize recent research on pharmacogenomic diversity, clinically actionable gene–drug interactions, and translational frameworks within the Indian population, with emphasis on public health, pharmaceutical innovation, and regulatory perspectives.A comprehensive literature search was performed across primary digital data sources such as PubMed, ScienceDirect (Elsevier), Scopus, ResearchGate, and Google Scholar. Queries employed group of predetermined keywords and Boolean operators such as “pharmacogenomics in India,” “gene–drug interactions,” “CYP polymorphisms,” “HLA pharmacogenetics,” “IndiGenomes,” “GenomeIndia,” “personalized therapy,” “precision medicine,” and “clinical implementation of pharmacogenomics.” Additional terms including “multi-omics integration India,” “AI in pharmacogenomics,” and “Ayurgenomics” were used to capture emerging technological and cultural frameworks. Google Scholar was included to identify grey literature, policy reports, dissertations, and conference proceedings not indexed elsewhere. Reference lists from seminal reviews, genome initiative reports, and pharmacogenomic guideline papers (e.g., CPIC, PharmGKB, DPWG, ICMR) were also manually reviewed to ensure completeness. This literature survey involved papers issued in English, with no year restrictions. Eligible sources incorporated primary empirical and descriptive analysis, review papers, national project reports, and pharmacogenomic implementation guidelines from Indian or South Asian cohorts. Studies were included if they provided data on allele frequencies, pharmacogenetic associations, clinical response variability, or regulatory and translational initiatives relevant to personalized medicine in India. Standardized acceptance and elimination parameters, PRISMA-based evaluation structures , as well as stastical bias scoring were not applied, consistent with the narrative review design. Instead, conceptual relevance, methodological clarity, and evidence credibility were used as the primary inclusion criteria. Collected literature was analyzed thematically under four major domains: Genetic landscape relevant to drug response, Clinically actionable gene–drug pairs, Roadmap for personalized pharmacotherapy in India, and Future perspectives integrating multi-omics, AI, and Ayurgenomics. Within each domain, findings were cross-compared across multiple datasets and validated against national pharmacogenomic repositories, including GenomeIndia, IndiGenomes, and PharmGKB. Data triangulation from diverse study designs minimized interpretative bias and enhanced robustness of synthesis. This approach facilitated a concept-driven, context-specific analysis, aimed at integrating genomic diversity, pharmacological relevance, and healthcare translation into a cohesive narrative of India’s evolving precision-medicine ecosystem.

1. 1. Genetic Landscape Relevant to Drug Response

2.1.1 Population Genetics & Drug Metabolism

India’s genomic variability profile, Driven by significant genetic intermixing among Northern ancestral  Indians (NAI) and Southern ancestral Indians (SAI) around 1,900-4,200 years ago, governs considerable pharmacogenetic diversity throughout the demographic group (16,17). The Ancestral Northern Indians subgroup hereditarily linked  to  Mid-asian as well as Euro western ancestry genetic lineages demonstrate unique genetic variant frequency at the genetic loci controlling Biotransformation of foreign compounds . Oppositely  the Southern ancestors lineage, originated from Indigenous Subcontinental ancestry , adds exclusive allelic forms  affecting both drug absorption and metabolism and therapeutic effect related characteristics (17,18). Polymorphisms within CYP 450 isoenzymes (CYPs),  UGT isoforms , and Thiopurine- modifying methyltransferase display significant relationship with  genetic ancestry profile as well as significant  influence on therapeutic outcomes (16). Illustratively this ,  CYP2C19 variant 2, linked to impaired biotransformation ability towards antiplatelet drug clopidogrel, PPIs, as well as some fungicidal agent is observed with a prevalence of around 0.36 among Indians relative to  0.22 globally, indicating an elevated frequency of low function metabolizer  (5). Likewise, this Thiopurine- metabolizing SNPrs1142345 variant, associated with thiopurine triggered myelosuppression , is detected with prevalence of 0.226 among Indians population, nearly twice than recorded in East Asiatic cohorts (0.10 -- 0.14). Polymorphism within NAT2 enzymes, especially slow acetylating NAT2 haplotypes 5B and 6A , are  widely distributed among regional Indians cohorts   and lead to liver toxicity caused by isoniazid and modified phenytoin metabolic processing. Overall, the specified enzyme related biocatalytic genetic variation regulates drug biotransformation  of essential anti-cancer, cardio therapeutic as well as CNS active medications , emphasizing a medical necessity  for pharmacogenomic informed dose strategies within Indian healthcare (5,17,18). This data strengthens the wider perspective that India’s subcontinent's distinctive demographic group configuration forms together a complexity as well as a perspective towards applying ancestry specific pharmacogenetics.

2.1.2 Large-Scale Genomic Initiatives in India

Dual flagship countrywide initiatives IndiGenomes as well as GenomeIndia have reshaped  the profiling of drug related significant genomic polymorphism inside Subcontinental demographic groups. The IndiGenomes resources, obtained out of  exceeding one thousand plus complete genomic assemblies, documented around fifty five million hereditary alleles , such as greater than seven hundred plus drug gene interaction related single base  (SNPs) with anticipated biologically active impact. The GenomeIndia Project, combining twenty  research based universities as well as 2,515 genomically analysed subjects , enhances the current structure through categorising gene variant prevalence dataset throughout the Indian Subcontinent varied language-and - ancestry based along with spital communities (1,19). Evaluation of the mentioned databases shows notable demographic dependent variations . The CYP2C19*2 variants appear with  33–37 % prevalence within the two north Indians as well as south Indians considerably greater compared within the African population (16 %) or European ancestry (13 %) (20). The CYP3A5*3 polymorphism, which affects ??tacrolimus dose optimization among organ transplant patients , is observed within approximately 70 % among Indian Population, resembling with more similarity to Euro ancestry than African ancestry communities. Alternatively, TPMT 3A as well as 3C alleles remain comparatively uncommon (~0.003 and ~0.018, correspondingly) compared with relative to worldwide typical rates, signalling a reduced community level risk of Thiopurine-triggered bone marrow toxicity (5) . Together, the following actions deliver the strong Indigenous of India polymorphic prevalence standard dataset . Its incorporation within healthcare institute based genetic diagnostic services supports the formulation for creation of drug gene detection arrays as well as demographically optimised doses predictive framework, forming the basis towards personalized medication selection inside the Indian healthcare network (1,19).

2.1.3 Regional & Subpopulation Variation

 The interregional variability in pharmacogene distributions between North, South, East and West India also makes the translation of pharmacogenomic data into clinical advice more difficult . In-depth studies show that, CYP2C19 2 allele frequencies of the South Indian cohorts (e.g., the Tamil and Keralite populations) have 38-41% as compared to 30-33 percent in the northern populations. On the other hand, the CYP2C19 17 ultrarapid metabolizer variant is present in almost 19% of Tamil people, which predisposes activities of escalilopram, omeprazole, and voriconazole and requires dose reduction measures accordingly  (22). Equally, NAT 2 slow-acetylator haplotypes (5B, 6A) have north-south gradients with increasing frequencies in the northward regions being associated with increased susceptibility to isoniazid induced hepatotoxicity. There is also enrichment of variants of ABCB1 and UGT1A1; e.g., UGT1A1 28 involved in irinotecan neutropenia is not uncommon in western tribes, but is quite rare in northeastern ones (17,18). This high geographic and ethnic diversity highlights the importance of regional stratified pharmacogenomic reference data. By introducing these lessons into the pharmacovigilance and drug regulation systems of India, one can rationalize dose selection and reduce the occurrence of adverse reactions, which will eventually help the creation of a customized system of prescribing drugs, which considers both genetic heritage and clinical phhenotype (1,20,21).

1. 2. Clinically Actionable Drug–Gene Pairs for Indian Patients

2.2.1 Cardiovascular Drugs

Clopidogrel (CYP2C19):

The communities across India possess a  markedly higher rate regarding reduced function variants of CYP2C19 gene compared with European ancestry, which exert a major influence in metabolic activation of this antiplatelet drug as well as antiplatelet effect. The allele CYP2C19 2 (rs4244285) has a frequency of 0.35-0.42, and almost 40 percent of the Indian patients are poor metabolizers (23,24). Subjects who have these subjects experience approximately 2-3 times greater frequency of a major adverse event in relation to the cardiovascular system post-percutaneous coronary intervention in comparison to the wild type carriers; alternatively, the 17 activating alleles of the CYP2C19 (found in roughly 10% of Indians) has been linked with an increase in inhibition of platlets and therefore an increase in the risk of bleeding with a conventional doses of the medication (25). Genotype guided guidelines in terms of administration of one of prasugrel otherwise ticagrelor are provided based on CPIC recommendation for the benefit of patients carrying a loss-of- function variants; hence it is apparent that there remains requirement for proactive genomic screening  across cardiac healthcare .

Warfarin (CYP2C9, VKORC1):

Genetic variants in CYP2C9 (2, 3), together with VKORC1 (-1639G>A), markedly affect dosage of warfarin needed by patients and their consequent blood thinning effect (anticoagulant response). VKORC1 -1639A occurrence rate within certain Indian group ranges varies from 6.5 % and 70%, through it may be elevated in Tibeto-Burman ethnic group, which is associated with reduced average continued doses of warfarin (i.e., 4.0 mg/day in comparison to 5.2 mg/day) (26). Adding genetic testing to identify the potential for supra-therapeutic levels of INR and subsequent bleeding will reduce the amount of risk related to INR and support the use of CYP2C9-VKORC1 testing as part of patient care based on CPIC and FDA guidelines.

Statins (SLCO1B1):

Genetic diversity inside the genetic locus that  produces the hepatic transporter protein OATP1B1 (SLCO1B1) may affect statin metabolism  and patient susceptibility of experiencing muscle toxicity. An investigation conducted in 2025 of 2,180 participants from a diverse of region in India observed that around  6% of the group  had a reduced function genotype  (5/5 or 15/15) of the SLCO1B1 gene that was linked to  a more than twice the risk of statin related muscle damage (27). The CPIC recommends reducing the dose of simvastatin (or substituting with rosuvastatin) based on the patient's genetic risk for a poor response to simvastatin. These data support the integration of SLCO1B1 and ABCG2 genotyping into lipid clinics as a method to provide individualized statin dosing, and reduce the risk of adverse events.

2.2.2 Anticancer Drugs

Thiopurines (TPMT/6-MP):

The incorporation of genotype-guided therapy has been shown to reduce blood related l toxicity while preserving the remission rate . The relevance of NUDT15 variants for the metabolism of thiopurines have also yet to be explored in Indian cohorts but preliminary data suggest that they may add additional information in this context (28,29).

Irinotecan (UGT1A1):

Genetic variant located within the regulatory region of the UGT1A1 gene (28, 6) are associated with a reduced ability to glucuronidate Irinotecan resulting in increased risk of neutropenia and diarrhea from Irinotecan. The UGT1A1 28 allele is present in approximately 18-25% of the Indian population. This represents an intermediate frequency relative to those found in East Asians and Europeans (30). The homozygote genotype for the TA7/TA7 gene variant (UGT1A1 28/28), can lead to an increased risk of grade IV neutropenia with an increase of up to three fold. The existing Clinical Pharmacogenomics Implementation Consortium (CPIC) guidelines for oncological therapy  to lower doses to 70-80 percent of standard dose regimen in individuals with two variant allele of the TA7 repeat variant; therefore suggesting that UGT1A1 genetic test be conducted in every cancer care centres in India.

2.2.3 Neurological & Psychiatric Drugs

Carbamazepine (HLA-B*15:02):

Standard Pre-therapy Genetic testing for polymorphism That influence valproic acid biotransformation Is advised in compliance with CPIC and DPWG recommendation ; Substitute drug Such As Levetiracetam or Oxcarbazepine Should Be Prioritized in variants Carriers to reduce Serious skin adverse events (31,32).

Valproate (CYP2C9):

Genotype-Guided Dosing and Therapeutic Drug Monitoring Can Greatly Enhance Safety Outcomes in Epilepsy Management (33,33).

2.2.4 Infectious Disease Drugs

Abacavir (HLA-B*57:01):

Follow-up testing before the introduction of abacavir, as is standard of WHO and CPIC, is now highly recommended throughout the Indian programs of HIV treatment (34,35).

Isoniazid (NAT2):

Genotype-based dose adjustment might also help increase the therapeutic effect of tuberculosis treatment- especially in DOTS programs- with minimum liver toxicity. These methods portray the possibility of how pharmacogenomics can fortify the Indian public health pharmacotherapy (36).

2.3 Roadmap for Personalized Pharmacotherapy in India

2.3.1 Prioritization of High-Impact Drug–Gene Pairs

These data sets give evidence-based grounds to start implementation by cardiology, oncology, and psychiatric domains where failure rates of treatment and adverse drug reactions (ADRs) have disproportionately negative clinical and economic impact (37). The roadmap suggests that evidence hierarchies provided by the Clinical Pharmacogenetics Implementation Consortium (CPIC) and PharmGKB should be formally adopted that would allow placing gene-drug pairs in a clear ranking in terms of clinical validity and actionability (38).? The prioritization must be based on both allele frequency among the Indian subpopulations and on the impact on public-health and the highest benefit is gained by early national testing panels and also equity in access.

2.3.2 Integration with Digital Healthcare Data and Medical Diagnostic Assistance System

In India, tertiary care facilities such as the AIG Hospitals, Hyderabad, have begun to pilot the implementation of the PGx reports into the EMRs, which issues real-time information about drug-gene interaction during a prescription. The integration to scale nationally should be used to provide opportunities of leveraging the National Digital Health Mission (NDHM) data architecture coupled with secure genomic repositories to map the phenotype at the patient level(40). Connecting CYP2C9 and NAT2 genotypes with HLA-B genotype with the prescribing pathway would enable provision of risk stratification and minimize manual interpretation and real-time provision of genomic decision support to clinicians throughout the health system in India (39).

2.3.3 Capacity Building in Clinical Pharmacogenomics

An effective human-resource and education system will be the secret to the successful adoption of individualized pharmacotherapy. By setting up National Centres of Clinical Pharmacogenomics, e.g. within AIIMS and NIPER could encourage postgraduate education in pharmacogenomic science, biostatistics and bioinformatics. Bachelor in Pharmacy , Masters in Pharm, and MD (Pharmacology) curricula must incorporate modules on pharmacogenomic guide therapy, adverse drug reaction forecasting, and analysis of genetic test results in academic coursework.Healthcare institutions and genetic based enterprises corporations (e.g., MedGenome, MapmyGenome) can create specialist training programs and professional development programs on genomic data analysis, responsible data governance, and incorporation of genetic insights into routine medication processes. Establishment of an interdisciplinary workflow team of physicians, pharmaceutical profession , genomic experts , and clinical informatics experts. This will be crucial in sustaining the execution of personalized medicine across the country (40) .

2.3.4 Policy and Regulatory Strategy for Pharma Companies and Hospitals

It needs a consistent regulatory framework that would integrate the concept of pharmacogenomics with the overall drug-approval system in India. The regulatory blueprint should be set by the Central Drugs Standard Control Organization (CDSCO) and Department of Biotechnology (DBT) in line with the global models. Structured review processes are already covered by the Draft CDSCO Guidelines 2025 on Biologics; similar policies specific to genomics may require pharmacogenomic evidence to be submitted when a new drug is in review and also during post-marketing surveillance. A system that resembles the Japanese “PGx Labeling Rule” could require hospitals to undertake pre-prescription testing on high-risk medications. The tax credits and expedited review of submissions involving the development of  companion diagnostics that are based on the validation of the particularly pharmaceutical companies would incentivize innovation and encourage the international harmonization of the standards related to the genomic labeling (13).

2.3.5 Cost-Effective Testing and Reimbursement Models

Applying tiered study designs such as specialized cardiovascular or psychiatric testing that is less expensive than 10,000 would make it considerably more accessible. Access would also become more democratic by integrating into the national insurance policies, especially the Ayushman Bharat Digital Health Mission. Based on the Taiwanese experience of government-funded high-impact screening HLA-B15:02, India could implement selective reimbursement of high-impact tests by ADR to decrease ADR-related hospitalization and long-term healthcare expenditure (38,41).

2.4 Future Perspectives

In the future , Pharmacogenomics science across the Indian population will be influenced by integration of multi-omics platforms  and Artificial intelligence based predictive framework and  the government led initiatives movement toward advancing personalized healthcare for the whole nation.These factors are synergizing to transform the transition from genomic breakthrough  to their application in clinical use. To better understand complicated disease processes and identify drug targets that are population specific, India is quickly pursuing new research in multi-omics that integrates genomic, transcriptomic, proteomic, and metabolomic data (42,43). Regional efforts, such as the Global Genomics Summit in 2025 and the creation of Multi-Omics Medicine Hubs (e.g., at institutions such as CSIR-IGIB) are promoting a rapid progression of translational research through this incorporation involving broad -scale genomic data with large-scale phenotypic data (44). These integrated approaches better elucidate genotype–phenotype relationships and support the translation of bench discoveries to bedside therapies at scale. The growth of computational biology and high-throughput omics in Indian research settings is catalyzing new diagnostics, real-time patient stratification, and biomarker discovery(42,44). Additionally, these integrated strategies are improving the ability to interpret genotype-phenotype associations, aiding in the discovery of new biomarkers, and facilitating the development of clinical interventions based on bench-top findings relevant to India's highly genetically variable population.  Machine-learning applications and artificial intelligence (AI) are taking shape as a revolution in implementing pharmacogenomic in India. Using a combination of clinical, biochemical, and genetic data, AI-based systems are capable of predicting individual doses, predicting adverse events, and adjusting clinical-trial designs (45) . The leading pharmaceutical firms and hospital chains are starting to pilot machine-learning algorithms that mimic drug-gene interactions and automatically and real-time adjust dosages in order to shorten trials and enhance treatment response. It is expected that predictive analytics will simplify the process of recruiting patients and stratify both responders and non-responders as well as provide dynamic monitoring of safety in the key areas of therapeutic interest, making computational pharmacogenomics a pillar of the preciseness-medicine setting in India (46,47). One such innovation that is inherently Indian is Ayurgenomics which combines traditional concepts of Ayurvedic phenotyping (Prakriti) and new methods of genomic analysis to create culturally and biologically contextualized paradigms of individualized medicine (48) . CSIR, AYUSH, and ICMR collaborative programs are mapping a relationship between Prakriti and pharmacogenomic variants which can then be used to stratify individuals to preventive and therapeutic interventions (49). Pilot studies indicate that it is possible to use Prakriti-genotype correlations to treat metabolic, cardiovascular, and neurological disorders personally. Combined with multi-omics data and national digital-health websites, Ayurgenomics presents a new paradigm in the world, which integrates systems-biology accuracy with traditional wisdom  (43,50). Along with an ever-growing bioinformatics capacity and supported by the government in the form of digital-health infrastructure and unprecedented genetic diversity, India is poised to hold a world leadership in the equitable precision pharmacotherapy. GenomeIndia, IndiGenomes, the Ayushman Bharat Digital Mission and various other initiatives are driving cost effective scalable models of national wide genomic screening and data integration. India can become a model of sharing the latest Indian pharmacogenomic panel, AI analytics, and translational frameworks with other low- and middle-income countries, and then demonstrate how the concept of precision medicine can be provided on population-scale, scientific-robust economically-sustainable, and culturally-inclusive (51,52).

CONCLUSION:

The future of rational therapeutics in India is pharmacogenomics, which will turn the tremendous genetic diversity in the country not into a hindrance, but into a discovery. India has during the last ten years established a solid scientific base- between mapping population-specific pharma-genes in GenomeIndia and IndiGenomes and establishing national systems of clinical implementation. However, to translate those advances into clinical practice, it is necessary to have a concerted effort on genomic research, healthcare delivery, and regulatory ecosystems. The future of pharmacotherapy in India, as made clear because of this review, is the combination of genomic evidence and clinical decision systems, which can be readily trained in the future, with the creation of policy environments conducive to fair access to testing and therapy. Emphasis on high-impact gene-drug interactions, integration of pharmacogenomic information into electronic medical records, and alignment of CDSCO regulatory practices with international standards will make the shift to a predictive rather than a reactive prescribing process. The current frontiers are further extended by emerging technologies, such as multi-omics analytics, AI-driven modeling, and Ayurgenomics, which can be used to offer a context-sensitive approach to individualized therapy. India is in the crossroads now: it has the scientific capability, bioinformatics infrastructure and the governmental vision to be the first to develop precision pharmacotherapy on a population scale. When implemented in a unified manner, the Indian pharmacogenomic roadmap can become a model of precision healthcare on the global plane the one that is inclusive, affordable, and highly sensitive to genetic and cultural diversity. This vision will not only minimize the negative drug reactions and therapeutic failures but will also transform the role of India in drug discovery to the world, such that one day, every genome will have a drug, and every Indian will have a drug.

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