A review of pharmacogenomics in the individualization of pharmacological treatment: present and future

Introduction

Recent advances in genetics and genomics have significantly contributed to the development of personalized and precision medicine. These scientific breakthroughs have enabled the creation of new tools capable of improving disease diagnosis and treatment. Within the pharmacological field, such advances promise to promote individualized pharmacotherapy through pharmacogenomic approaches.

The principle of Primum non nocere, regarded as precursor to modern patient safety, gained considerable significant after the publication of To Err is Human: Building a Safer Health System by the U.S. Institute of Medicine in 2000 (1). This concept is especially relevant in the context of drug therapy, where polypharmacy—commonly used to manage patients with multiple comorbidities—increases the number of prescribed medications and, consequently, the likelihood of adverse events and reduced adherence. Adverse drug reactions (ADRs) represent a major cause of morbidity, mortality, and healthcare expenditure.

A study conducted in the United Kingdom suggests that 1 in 15 hospital admissions is attributable to ADRs, while an analysis in the United States estimates that approximately 2.2 million hospitalized Americans experience ADRs each year, even when medications are correctly prescribed and administered (2). A 2009 meta-analysis of 22 studies from Europe, Asia, and the Americas further raises this incidence to 16% (3). Applying these findings to Spain, a 2012 reported an ADR incidence of 14.7% among hospitalized patients (4). More recent data from 2021 estimate that medication errors in the United States result in 7,000–9,000 deaths annually, with associated costs of about $40 billion per year (5). These figures also include non-fatal adverse drug events. Reducing such numbers would yield substantial clinical and economic benefits including shorter hospital stays and lower costs associated with the managing of adverse events. In terms of efficacy, data indicate that among the ten most prescribed drugs in the United States, for every patient who responds, between three and twenty-four patients fail to exhibit a therapeutic response (6). According to the World Health Organization (WHO), only 25% of diseases are effectively treated, and more than one-third of patients do not respond appropriately to pharmacological therapy. Some patients experience insufficient therapeutic effects, others suffer adverse reactions, and some encounter both simultaneously. In psychiatric disorders such as schizophrenia and depression, therapeutic inefficacy rates reach approximately 40%. These medications are often administered alongside treatments for comorbid conditions, creating scenarios of polypharmacy. Healthcare professionals increasingly recognize polypharmacy as an emerging challenge, especially in elderly and chronically ill populations (7). A U.S. study estimates that about 22% of adults aged 40–79 take five or more medications concurrently (8). Polypharmacy is associated with a heightened risk of ADRs, reduced treatment efficacy, and increased healthcare costs—both direct (e.g., pharmaceutical expenditures) and indirect (e.g., management of adverse events). As long as multiple medications remain necessary, strategies to mitigate the negative consequences of polypharmacy are essential (9).

Individual variability in drug response is a multifactorial process influenced by numerous factors, including genetic variants affecting pharmacokinetic and pharmacodynamic pathways, drug-drug interactions, age-related physiological changes, disease states, gut microbiota composition, lifestyle habits, sex-based differences, and environmental exposures (10). Collectively, these elements contribute to the heterogeneity of treatment responses among patients receiving the same medications.

Differences in drug efficacy and safety can compromise therapeutic outcomes and substantially increase costs in already burdened healthcare systems. Although genetic factors account for an important proportion of this variability, they do not explain it entirely, and their influence varies both across medications and among patients. In this context, pharmacogenomics aims to move beyond the traditional “one-size-fits-all” approach in drug selection and dosing, advancing instead toward more precise and individualized therapeutic strategies tailored to each patient’s biological and clinical characteristics. Pharmacogenetics is presented as a central tool within personalized and precision medicine

The continuous development of pharmacogenomic biomarkers and their gradual integration into healthcare systems represents a crucial tool for reducing ADRs, particularly in the context of polypharmacy (11). The growing capacity for data generation and genomic analysis—enabled by advanced analytical platforms—facilitates a more comprehensive understanding, ultimately enhancing the assessment and management of prescribed medications (12).

The aim of this paper is to examine the current role of pharmacogenomics in the individualization of drug therapy and to assess its potential future applications in clinical practice. It synthesizes evidence on the impact of genetic variability on drug response, evaluates the clinical utility and limitations of existing pharmacogenomic tools, and identifies emerging developments with the capacity to improve treatment efficacy, safety, and personalization. Overall, the paper provides an integrated overview of the field’s present landscape and outlines the main challenges and opportunities that will guide its future incorporation into healthcare.

Fundamentals of pharmacogenomics

Personalized and precision medicine introduce novel approaches aimed at tailoring treatment to the individual patient. Within this framework, pharmacogenomics plays a pivotal role in advancing strategies that enhance personalization and precision in medical therapy. The first documented association between metabolic factors and genetics was described by Garrod in 1902. It was not until 1957 that Motulsky identified the link between genetics and drug‑related metabolic reactions. Two years later, Vogel coined the term pharmacogenetics to describe this gene-drug relationship. From that point until the post‑genomic era, the development of pharmacogenomics progressed at a relatively slow pace.

Pharmacogenomics is regarded as the first practical application of data generated following the completion of the Human Genome Project around the year 2000. Beginning at that time, pharmacogenomics reemerged, leading to the creation of research consortia dedicated to compiling and systematizing available knowledge in the field. Notable initiatives include the Pharmacogenomic Global Research Network (PGRN) (https://www.pgrn.org/) (13), the CYP Allele Nomenclature Database (http://www.cypalleles.ki.se/) (14) established to standardize CYP450 gene nomenclature, and ClinPGx (https://www.clinpgx.org/) (15), which remains the primary repository of pharmacogenomic information. In parallel, specialized working groups began to develop and publish clinical practice guidelines to facilitate the implementation of pharmacogenomics in clinical settings and to support physicians in interpreting test results. This effort culminated in the establishment of major consortia such as the Clinical Pharmacogenetics Implementation Consortium (CPIC) in the United States (16) and the Dutch Pharmacogenomics Working Group (DPWG) in the Netherlands (17).

In addition, regulatory agencies such as the U.S. Food and Drug Administration (FDA) (https://www.fda.gov/) (18) and the European Medicines Agency (EMA) (https://www.ema.europa.eu/en/homepage) (19) have progressively incorporated pharmacogenomic recommendations into drug labeling. At present, the FDA has identified more than 450 drug-pharmacogenomic associations. Despite its considerable potential, pharmacogenomics remains underutilized. Nevertheless, projections suggest that by 2030 it will be fully integrated into pharmacological management across healthcare systems (20).

U.S. implementation programs have reported that more than 90% of individuals harbor clinically relevant genetic variants in major pharmacogenes, as outlined in clinical practice guidelines (21). When focusing specifically on genes involved in drug metabolism, it has been demonstrated that over 90% of patients carry at least one polymorphism in these genes that influences their response to pharmacological treatments (22). This variability explains why two patients may receive the same drug at the same dosage yet experience markedly different outcomes: one may achieve therapeutic goals, whereas the other may fail to respond adequately.

A notable example is the CYP2D6 enzyme, which is involved in the metabolism of approximately 25% of all prescribed medications. Importantly, about 35% of patients carry a non‑functional polymorphism in CYP2D6, which can significantly alters drug metabolism and therapeutic efficacy.

In summary, pharmacogenomics aims to identify the most appropriate drug and to determine the optimal dosage for each individual patient, thereby enhancing treatment precision and clinical outcomes. Table 1 summarizes the key concepts to be considered in pharmacogenetics, whereas Figure 1 provides an overview of the principal groups of pharmacogenes.

Figure 1 Principal pharmacogene groups. ABC, ATP-binding cassette; ADH, alcohol dehydrogenase; CYP450, cytochrome P450; MAO, monoamine oxidase; OCT, organic cation transporters; UGT, UDP glucuronosyltransferase.

Pharmacogenes present genetic variants that explain the variability in drug response among individuals. These variants include single nucleotide changes (SNPs), insertions or deletions (INDELS), copy number variants (CNVs) (gene duplications or losses), and less common types such as rearrangements or hybrid genes. Cytochrome P450 genes, such as CYP2D6 (25), use a “star allele” nomenclature to identify haplotypes, which combine to form a diplotype in diploid individuals. Haplotypes can share core alleles but differ by additional variants, thereby increasing genetic diversity. These variants can alter enzyme structure and function, resulting in loss of function (haplotypes *4/*5 of CYP2D6), reduced activity (*10 of CYP2D6), altered substrate binding (*3 of CYP2C9), or increased metabolic capacity (*2xN of CYP2D6) (https://www.pharmvar.org/) (26). Table 2 summarizes the drug-pharmacogene relationships with the highest level of scientific evidence.

Impact of pharmacogenomics

Pharmacogenomics represents a pivotal strategy within personalized medicine, exerting a substantial impact on the optimization of therapeutic interventions. A 2020 review identified pharmacogenomics as one of the principal approaches with the potential to transform healthcare delivery by 2030, within the broader framework of precision medicine, primarily through the enhancement of pharmacological treatment efficacy (20). As a cornerstone of personalized and precision medicine, pharmacogenomics offers several key advantages:

Enhanced efficacy of pharmacological therapy: current evidence indicates that only 50–60% of patients benefit from prescribed treatments. Consequently, physicians are frequently compelled to adjust therapeutic regimens through a trial‑anderror process, during which disease progression may continues and patients remain symptomatic (38).

Reduction of ADRs: epidemiological investigations across diverse healthcare settings consistently demonstrate that pharmacological agents constitute the leading cause of adverse events. Multiple studies estimate that systematic implementation of pharmacogenomic testing could reduce ADRs by 10–25% in the general population (38,39).

Improved adherence and therapeutic compliance: about half of patients follow their prescribed treatment as directed. Nonadherence is particularly prevalent among individuals with chronic conditions or polypharmacy. Evidence shows that patients whose dosing regimens are guided by pharmacogenomic recommendations exhibit significantly higher adherence rates (40).

Favorable cost‑effectiveness and reduced healthcare expenditure: more than 80% of economic evaluations of pharmacogenomic testing report a favorable costbenefit ratio (41). In a randomized study, Elliott et al. (42) compared conventional prescribing protocols with pharmacogenomic‑guided prescriptions based on a panel of six pharmacogenes. Patients in the pharmacogenomic‑guided group demonstrated a 50% reduction in emergency department visits and hospital readmissions. Similarly, Cortejoso et al. (43) focused on the costs associated with fluoropyrimidine‑induced neutropenia, showing that identifying as few as two patients with reduced DPD activity among 1,000 screened renders DPYD polymorphism testing costeffective. Comparable findings were reported by Italian (44) and Dutch (45) research groups. Furthermore, Verbelen et al. (46), in a systematic review of 44 economic evaluations, confirmed the high cost‑effectiveness of pharmacogenomic strategies compared with conventional approaches.

Polypharmacy and pharmacogenomics

Patients in healthcare systems becoming increasingly complex, requiring more treatments and resulting in a rise in prescriptions. This situation can trigger the therapeutic cascade, where one drug causes an adverse effect, another is prescribed to counter it, and this may lead to further prescriptions.

The concept of polypharmacy has two main definitions:

• Quantitative definition: based only on the number of medications, though there is no consensus (2–9 drugs per day) (47).
• Functional definition: refers to the use of more medications than clinically necessary, including inappropriate or duplicate prescriptions (47).

Polypharmacy is a significant health issue. In the U.S., about 22% of adults aged 40–79 take five or more medications simultaneously (8). Studies show that 60% of patients receive suboptimal or unnecessary drugs, and 16% have duplicate prescriptions (48).

Regardless of definition, polypharmacy is considered an iatrogenic condition that harms patients and increases healthcare costs. Efforts should focus on avoiding or reducing it, and when multiple medications are necessary, strategies must be applied to mitigate risks. Pharmacogenomics is highlighted as a valuable tool to optimize treatments, prevent adverse effects, and support deprescribing.

Currently, the available scientific evidence linking polypharmacy and pharmacogenetics is limited compared with the number of studies published on each topic separately. Therefore, it is necessary to carry out more work such as the study of Saldivar et al. (49). In this study involving 177 patients (132 males and 45 females), evaluating the impact of pharmacogenetics in patients exposed to polypharmacy. Patients prescribed five or more medications per month were selected, while those with renal or hepatic dysfunction were excluded. Seventeen polymorphisms in pharmacogenes were analyzed, and treatment modifications were implemented through a proprietary algorithm. As a result, 50% of patients underwent substitution or consolidation of between one and three medications, with most recommendations based on drug-gene relationships.

On the other hand, De Vries et al. employed the Rhineland Study cohort to investigate polypharmacy, potentially inappropriate drug prescribing, as well as the impact of pharmacogenetics in this context (50). Approximately 16% of the population was exposed to polypharmacy, defined as the concomitant use of five or more medications. In this study, the prevalence of polypharmacy was higher than that reported in previous investigations (51,52). Of the drugs included in the study, 20.2% were identified as susceptible to pharmacogenetic testing. The most relevant pharmacogenes were SLCO1B1, CYP2C19, and CYP2D6, which together accounted for 95% of drug-gene interactions. For instance, 23% of study participants reported the use of ibuprofen, which is metabolized by CYP2C9. Preventive genotyping of pharmacogenes—particularly SLCO1B1, CYP2C19, and CYP2D6—thus demonstrates substantial potential for reducing ADRs and enhancing therapeutic efficacy in the studied population. In this study, De Vries et al. (50) identified the coexistence of three major general risk factors associated with susceptibility to ADRs in the non-hospitalized general population: polypharmacy, the use of potentially inappropriate medications (PIMs), and exposure to pharmacogenomically relevant drugs (PGX). A high concurrence of these risk factors was observed. Although each factor independently appears to increase risk in different population groups, the simultaneous presence of multiple risk factors may not only add risks but also potentiate them. This situation is particularly critical in vulnerable groups, such as patients with renal dysfunction receiving PIMs or poor metabolizers exposed to PGX drugs, in whom additional polypharmacy could exert an even more detrimental effect.

Meaddough et al. conducted a systematic review to evaluate the impact of pharmacogenetics in the context of polypharmacy (53). Six studies were analyzed, investigating various pharmacogenes (ABCB1, CYP1A2, CYP2B6, CYP2C9, CYP2C19, CYP2D6, CYP3A4, CYP3A5, DPYD, SLCO1B1, TPMT, UGT2B15, VKORC1) with genetic variants related to absorption, distribution, metabolism, and excretion (ADME). Of the six studies reviewed, five reported reductions in ADRs, decreases in the number of medications used, or lower utilization healthcare resources (54-58). The study by van der Wouden et al. (59) was the only trial that failed to demonstrate a benefit of pharmacogenetic-guided polypharmacy management over standard management without pharmacogenetics. Overall, pharmacogenetic studies have demonstrated improved health outcomes in polypharmacy patients, especially those with psychiatric disorders and elderly individuals receiving oncology or cardiology care.

A recent European multicenter study, SafePolyMed (https://www.safepolymed.eu/) (60), has been launched with the aim of enhancing the management of polypharmacy through the application of machine learning and artificial intelligence tools, based on patients’ genetic information.

Although additional studies are still required, the currently available evidence supports the use of pharmacogenetics to minimize unnecessary medication use, lower the risk of adverse events, and improve therapeutic response.

Phenoconversion

Pharmacogenomic information provides added value for the proper management of medications. When this information is integrated with patient-related environmental factors, the characterization of the metabolizer phenotype is more accurate. Thus, the modulation of the metabolizer genotype at the phenotypic level by environmental effects is referred to as phenoconversion (61). Phenoconversion can occur due to concomitant medication use (62,63), the presence of comorbidities (63), or other environmental influences (64,65).

In the group of antidepressant drugs, Gloor et al. (66) studied the discrepancies between the metabolizer genotype and the metabolizer phenotype determined in the six principal hepatic cytochromes (CYP1A2, CYP2B6, CYP2C9, CYP2C19, CYP2D6, and CYP3A4). For all the cytochromes studied, the authors reported a substantial phenoconversion rate, affecting between 33% and 65% of patients, along with a marked overall decrease (P<1E−06) in the unexpected enzymatic activities of CYP2D6, CYP2C9, and CYP2C19 compared with those observed in the general population. This study highlights the assessing the patient’s metabolizer phenotype rather than relying solely on genetic information. A genotype-phenotype mismatch could have significant clinical consequences and may lead to suboptimal treatment, particularly in patients undergoing polypharmacy.

To further evaluate the phenomenon of phenoconversion, Klomp et al. (61) conducted the first systematic review of individual studies addressing this phenomenon. Their work focused on identifying the factors contributing to phenoconversion and assessing its impact on CYP450 enzyme activity. A total of 27 studies were included, linking phenoconversion not only to concomitant drug use but also to other extrinsic patient-related and pathological factors. Ten of the 27 studies reported phenoconversion within specific genotype groups. Concomitant medication emerged as the primary driver of phenoconversion, with inhibitors shifting patients toward a slower metabolizer status and inducers producing the opposite effect (67). An interesting observation was that intermediate metabolizers appeared more susceptible to inhibition than other metabolizer genotypes for CYP2D6, CYP2C9, and CYP2C19 (61).

Comorbidities are not currently considered as sources of phenoconversion, yet pathophysiological processes such as cancer and inflammation have been associated with this phenomenon (68,69). Other extrinsic factors such as tobacco, alcohol, and vitamin D have also been described as contributors to phenoconversion. Alcohol has been associated with CYP2C19 inhibition in the metabolism of mephenytoin (64), tobacco with CYP1A2 induction in the metabolism of olanzapine (70), and vitamin D with CYP3A4 induction in the metabolism of tamoxifen (71).

The interaction between genetic and non-genetic factors contributing to phenoconversion is currently not adequately evaluated. Phenoconversion may help explain some of the contradictory results observed in pharmacogenetic studies and the difficulties in replicating reported findings.

Implementation of pharmacogenomics

To ensure the success of pharmacogenomics, it is necessary not only to expand the available scientific evidence regarding on the relationships between drugs and pharmacogenes, but also to translate this knowledge into clinical application and develop models for its implementation in daily practice. The results of pharmacogenomic tests should be treated as any other analytical result, not interpreted in isolation, but integrated into the overall clinical information of the patient. Currently, there are increasing numbers of projects aiming to implement pharmacogenomics in clinical practice.

The global expansion of pharmacogenetics has led to the development of multiple initiatives aimed at standardizing and promoting its clinical implementation. Among the most influential, the CPIC provides evidence-based guidelines to translate genetic variants into therapeutic recommendations (72), while Pharmacogenomics Knowledge Base (PharmGKB) curates gene-drug associations and levels of evidence (73). These efforts are complemented by Pharmacogene Variation Consortium (PharmVar), which maintains standardized star allele nomenclature for pharmacogenes (74). In Europe, the ubiquitous pharmacogenomics (U-PGx) project promotes pre-emptive genotyping within public healthcare systems (75), whereas in North America, Electronic Medical Records and Genomics Network (eMERGE-PGx) and IGNITE integrate pharmacogenomic results into electronic health records to enhance therapeutic safety and efficacy (76). In genetically diverse regions such as Asia and Latin America, national programs and collaborative networks—including APEC-PGx and RIBEF—enable the characterization of underrepresented population variants and the adaptation of pharmacogenetic recommendations (77) [https://redribef.org/ (78)]. Collectively, these initiatives establish an international framework that is progressively facilitating the integration of pharmacogenetics into precision medicine (79).

Another important project represents the clinical trial Testing for Preventing Adverse Drug Reactions (PREPARE). This is a multicenter, open-label, controlled, and randomized study aimed at investigating the preventive application of pharmacogenomics to avoid the occurrence of ADRs (80). The study uses a panel of 12 genes across 18 hospitals, 9 health centers, and 28 community pharmacies in 7 European countries, involving a total of 7,000 patients. The study concludes that treatment guidance based on this gene panel leads to a significant reduction in drug toxicity, thereby enhancing patient safety.

In Spain, in recent years, the Infrastructure for Precision Medicine associated with Science and Technology (IMPaCT) has been implemented. This project aims to develop a network that enables the implementation of precision medicine in the public health system. It consists of three main areas: Cohort, Data, and Genomics. Within the genomics area, various projects are being developed to select the best drug-pharmacogen combinations for clinical implementation. Furthermore, in 2023, the Commission for Benefits, Assurance, and Financing (Ministry of Health) agreed on the proposal for genetic/genomic tests in the National Health System (NHS), which includes pharmacogenomic tests (81). The pharmacogenes included in the service portfolio are not only related to drugs prescribed in hospitals also with a range of medications commonly used in community settings. To ensure the successful implementation of this strategy, it is essential to develop appropriate training programs at both postgraduate and undergraduate levels. Training in pharmacogenomics for professionals involved in drug management is crucial to fully harness the potential of these tools. As mentioned, pharmacogenomics education should likewise be integrated into health-related undergraduate programs, so that new professionals have sufficient knowledge to manage and integrate this information, which will soon be fully incorporated, for example, into electronic health record systems. Similarly, patients should receive education to understand these tools. In a multidisciplinary manner, the collaborative work of all healthcare professionals is crucial to ensuring the success of the implementation of these processes.

Table 3 includes both national and international projects aimed at promoting the implementation of pharmacogenetics.

New sources of variation

In addition to the genetic variation introduced by the well-characterized variants in pharmacogenes, these alone are insufficient to explain the full extent of variability in drug management. Accordingly, we describe additional sources of variability, whose more detailed understanding will contribute to improved comprehension of therapeutic management. Some of these sources of variability require further investigation, as they remain insufficiently characterized.

Genetic rare variants

It is important to identify all genetic variation present in pharmacogenes in order to obtain a comprehensive view of each individual’s metabolization and transport capacity. A large portion of the heritable component underlying drugresponse phenotypes is still unresolved, with one study reporting estimates that span from 0.05 to 0.59 (83). Given that most pharmacogenomic research has concentrated on common variants, a portion of the unexplained heritability may stem from rare variants (84). Schärfe et al. focus on the study of both more common variants and those that are less studied (85). Based on more of sixty thousand (60,706) human exomes from the Exome Aggregation Consortium (ExAC) dataset they conducted a comprehensive computational assessment of the prevalence of functional variants across 806 drug‑related genes, including 628 established drug targets, and 1,236 FDA-approved drugs. Approximately 70% of the FDA‑approved drugs examined (962 compounds) lack any pharmacogenomic information in publicly available repositories. The authors demonstrate that numerous functional variants are present in their target genes. Of all the variation in drug response, it is estimated that approximately 70–80% is attributable to environmental and pathophysiological effects, while the remaining 20–30% is due to genetic factors. Of the genetic factors, 50% are known variants whereas the other 50% consist minor elements or unknown variants. Ingelman-Sundberg et al. (86) investigated the role of these less frequent variants in pharmacokinetics pathways. The authors analyzed the genetic variability in 208 genes relevant to drug pharmacokinetic using exome sequencing data. In total, they identified 69,923 variants distributed across transporter genes, genes encoding Phase I and Phase II enzymes, and nuclear receptors. Notably, 83% of the variants they identified were novel.

To evaluate the role of different variants, both polymorphic and rare variants, the authors added the data of the different variants identified in each gene. They observed that both the pattern and distribution of genetic variation varied markedly across the 208 pharmacogenes examined. In some genes, functionally relevant genetic variation was largely driven by a small number of high‑frequency variants. In contrast, the functionality of most pharmacogenes is largely shaped by rare genetic variants. Notably, these rare pharmacogenetic variants were highly enriched for mutations predicted to have functional consequences.

To assess the impact of the variants, whether polymorphic or rare, the researchers used the pharmacogenomic study of olanzapine serum levels as an example (86). The therapeutic benefits of olanzapine in patients with schizophrenia or bipolar disorder are limited by extensive inter-individual variability in olanzapine serum concentrations, which may result in exposure levels outside the therapeutic interval. Because olanzapine serum levels are directly linked to both therapeutic success and the risk of ADRs, individualized dosing regimens hold promise for improving to enhance treatment outcomes. Although the metabolic pathways of olanzapine are well established, the contribution of genetic factors remains more debated. There are pharmacogenes that present a high number of frequent variants but whose role in olanzapine metabolism is not clear, such as CYP2D6, or that play an important role in its metabolism, such as CYP1A2 (86). The number of rare variants in these genes is low (6.3% and 1.6% respectively). On the other hand, there are genes such as the ABCB1 transporter in which 100% of its variants are rare and notably participate in the maintenance of olanzapine serum levels. The percentage of rare variants involved in tolanzapine metabolism is close to 10%.

While polymorphisms in genes involved in pharmacokinetic have been extensively studied, pharmacodynamic variability remains less studied. Zhou et al., (87) characterize human drug target variability. In total, 606 proteins targeted by 1,155 drugs result in 3,346 distinct drug-target pairs. Most human drug targets are enzymes, ion channels, and membrane receptors. As with variants in genes related to the pharmacokinetic pathway, ethnicity has a significant impact on the distribution of variants. Interethnic variation may play a substantial role in shaping differences in pharmacological response. Genetic variability was greatest among individuals of African ancestry, with one in four carrying at least one bindingpocket variant, and lowest in East Asian populations, where such variants occurred in roughly one out of every fourteen individuals (87). Among the most common variants, pronounced ethnogeographic differences were observed. Overall, the Finnish population exhibited the highest number of individuals homozygous for binding‑site variants. These findings indicate that binding‑site variability patterns differ substantially among populations and may help explain ethnogeographic disparities in drug response.

The increasingly widespread use of next-generation sequencing (NGS) techniques is facilitating the identification of an ever-growing number of rare variants in pharmacogenes; however, it remains necessary to establish their role in relation to pharmacogene variability. To address this, machine learning and artificial intelligence tools are being developed to prioritize these variants, along with functional studies aimed at elucidating the specific contribution of each variant (83). These tools enable the precise characterization of the impact of rare genetic variants on pharmacological response phenotypes and, subsequently, the translation of this knowledge into evidencebased clinical implementation strategies (88). The implementation of innovative study designs, including N‑of1 trials (89), will be essential to overcome current limitations in pharmacogenomics. A critical issue concerning rare genetic variants is the characterization of their functionality, as most are classified as variants of uncertain significance. To address this challenge, in silico approaches for functional prediction have been developed, including artificial intelligence-based models specifically tailored to pharmacogenomic variants (90).

Ethnicity

It is well established that individuals with different geographic ancestries possess genetic variants with varying frequencies, which is reflected in population-specific data. In this context, the six populations represented in the ExAC database are differentiated as African, South Asian, East Asian, Finnish, Non-Finnish European, and Admixed American (Latino) ancestries (91).

Analysis of existing Genome-Wide Association Study (GWAS) data indicates that 97% of participants were of European ancestry, with only 2.2% of Asian, 0.02% of African, and 0.02% of African American or Afro-Caribbean ancestry (92). On the other hand, the same issue has been observed in pharmacogenes. Approximately, about half of all functional variants in drug related genes are unique to a single one of the six populations and only 0.1% of functional variants occurs with an allele frequency greater than or equal to 0.1% across all populations. As a result, drugrelated genes exhibit a high likelihood of functional variants that varies according to geographic ancestry (92). For instance, the authors found that 231 drug-related genes have functional variants in the cohort of European ancestry compared to 298 genes with functional variants for the cohort in the African ancestry cohort. In this work, the authors demonstrate the importance of drug-related genes show geographic differences in genetic variation.

There are clinically relevant pharmacogenes for which drug dosing is primarily based on polymorphisms characterized in European populations. For instance, CYP2C9 (*2 and *3) must be assessed to guide dosing of siponimod (used for the treatment of multiple sclerosis) (https://cima.aemps.es/cima/dochtml/ft/1191414003/FT_1191414003.html, 2025) (37), as patients with partially reduced enzyme function require a 50% dose reduction, and the drug should be avoided when activity is reduced to 10%. In contrast, CYP2C9 polymorphisms more prevalent in African populations (*5, *6, and *11) have not been evaluated for siponimod pharmacokinetics, and no specific dosing recommendations are currently available (https://cima.aemps.es/cima/dochtml/ft/1191414003/FT_1191414003.html, 2025) (37).

Other example of the impact of ethnicity on pharmacogenomic study can be described in the drug-pharmacogene relationship between tacrolimus and CYP3A5 (93). In the pharmacogenes, haplotypes *3, *6 and *7 generate a loss of enzymatic function in the protein. Of the haplotypes described, haplotype *3 is the most important for the metabolism of tacrolimus, and is the most prevalent in the Caucasian population with an allelic frequency of 92% in Caucasians, 74% in Asians, lowering the percentage to 18% in Africans. Since clinical trials have been carried out mainly in Caucasians where the *3 haplotype is the most prevalent, the standard treatment dose is adjusted for this poor metabolizing group. In the case of efficient metabolizers, the tacrolimus dose must be increased up to 2 times at the beginning of the transplant.

Currently, several international initiatives are underway to enhance understanding of population genomic diversity, including H3Africa (https://h3africa.org/) (94), the Qatar Genome Programme (https://www.ga4gh.org/driver_project/qatar-genome-program/) (95), the GenomeAsia 100K Project (96), and the China Kadoorie Biobank (ckbiobank.org) (97).

Non-exonic variants and haplotype phasing

In addition to the sources of variability described, other types should be considered in the future and are likely to be incorporated into pharmacogenomic studies. One important category is variants located in introns, which may impact drug metabolism. Indeed, several haplotypes have been described within intronic regions. As more intronic variants are discovered, the concept of suballele has emerged in pharmacogenomics. For example, starting from a core allele, such as *41 of CYP2D6, constituted by 3 single nucleotide polymorphism variants, it presents up to 4 suballeles with different combinations of intronic variants between them. When the role of these intron variants is known, new haplotypes may be generated that derive from this *41 (98). It is necessary to continue investigating variants located in intronic regions and their role in the generation of novel suballeles as well as future haplotypes. Beyond identifying these variants, it is also essential to elucidate their functional impact on enzymes, a field in which current knowledge remains limited for this type of genetic variation.

Another important concept is haplotype phasing. It is essencial to determine on which DNA strand each haplotype variants are located and what potential consequences this may have. For CYP2B6, 4 different haplotypes have been described depending on the DNA strand on which the variants are located. Thus, if the two variants are on the same strain (cis conformation), haplotype *6 is defined. On the other hand, if each variant is in a different strain (trans conformation) the haplotype is redefined to *9 and *4. In this example the diplotype *1/*6 and *9/*4 present the same metabolizing activity, but in other case this change may generate a change in the metabolizing activity (99). New sequencing techniques make it possible to identify the specific strain each haplotype is located on.

Another gene in which phasing plays a particularly important role is CYP2D6. This pharmacogene is characterized by being highly polymorphic. Phasing determination is essential in the presence of duplications/multiplications, deletions, and hybrid tandems, genetic variants frequently observed in CYP2D6 (100). In this gene, when genotyping identifies the haplotypes CYP2D6*1 and CYP2D6*4 with more than one copy, it becomes necessary to determine on which chromosome these haplotypes are located (phasing). The one haplotype exhibits normal enzymatic activity (activity score 1), whereas the four haplotype produces a non‑functional enzyme (activity score 0). Based on this, the diplotype CYP2D6*1x2/*4 (two copies of *1) corresponds to efficient metabolism (activity score 2), while the diplotype CYP2D6*1/*4x2 (two copies of *4) corresponds to intermediate metabolic activity (activity score 1) (101) CYP2D6 is a crucial gene for the metabolism of multiple drugs, including psychoactive agents; therefore, accurately determining the metabolizer genotype with high precision would significantly impact the management of therapeutic treatments. The determination of phasing in the presence of multiple genetic variants is only achievable when these variants are located in close proximity, specifically within a range of 100–150 base pairs (12). Thus, accurate phasing resolution requires the use of longread sequencing to disentangle complex and highly homologous genomic regions and to properly determine haplotypes (102).

Polygenic risk scores (PRS)

The concept of the PRS enables the assessment of the relative contribution of each genetic variant to the patient’s overall metabolizing capacity. In this way, not only the most frequent variants but also rare variants previously described can be incorporated (103). Currently, PRSs are being developed to predict risk, stratify disease, and establish prognosis and screening strategies for various pathologies (104).

With respect to pharmacogenetics, PRSs have already been developed in specific contexts, such as the prevention of ischemic cardiac events and treatment with clopidogrel (105), or the use of βblockers in heart failure (106).

Nevertheless, significant biases currently hinder the development of PRSs in pharmacogenetics, endering conventional pharmacogenetic markers preferable (107). Among the main limitations are the lack of large genetic datasets with detailed pharmacological information, and the fact that the few available datasets often include conditions of polypharmacy that complicate interpretation (12). Another critical issue is the bias toward populations of European ancestry in currently available pharmacogenomic data (107).

Despite these limitations, continued efforts to develop of PRSs in pharmacogenetics are warranted, given their potential future applications.

Conclusions

Pharmacogenomics emerges as a tool with high potential, enabling more precise adjustment of medications and thereby increasing their efficacy and safety. It is increasingly supported by scientific evidence and implementation, both through studies aimed at translating accumulated knowledge and through public initiatives that incorporate these studies into the service portfolios of health systems. Despite its advantages, pharmacogenomics still faces barriers that need be overcome to achieve broader implementation. Continued investigation of all possible sources of variability is necessary to generate more precise metabolizing phenotypes, integrating different -omics sciences such as proteomics, microbiomics, metabolomics, and epigenomics.

Acknowledgments

None.

Footnote

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