INTRODUCTION:

Asthma is a chronic, heterogeneous inflammatory airway disease characterized by airway hyperresponsiveness, variable airflow obstruction, and underlying inflammation (Global Initiative for Asthma, 2023). Despite advances in therapeutic strategies, asthma management remains challenging due to significant interindividual variability in drug response and susceptibility to adverse effects.¹

Pharmacogenomics, the study of how genetic variations influence drug response, offers a promising pathway toward personalized medicine in asthma treatment.² By identifying specific genetic markers that predict an individual's response to medications, pharmacogenomics enables the tailoring of therapy to maximize efficacy while minimizing adverse effects. This precision approach is particularly relevant in asthma, where diverse endotypes and phenotypes contribute to heterogeneous treatment outcomes.³ In asthma, biomarkers not only help in predicting treatment response and disease progression but also assist in selecting targeted therapies such as biologics for specific subgroups of patients.⁴This review aims to explore the recent advancements in the identification and clinical application of pharmacogenomic biomarkers in asthma. It discusses key genetic variants associated with differential drug responses, emerging technologies facilitating biomarker discovery, and the future landscape of personalized asthma management.

Asthma Pathophysiology and Genetic Background:

Asthma is a complex, chronic inflammatory disease of the airways characterized by airflow obstruction, bronchial hyperresponsiveness, and airway remodeling. The pathogenesis of asthma involves the interaction of multiple immune cells and mediators. Central to asthma's inflammatory response are type 2 helper T cells (Th2), which secrete cytokines such as interleukin (IL)-4, IL-5, and IL-13. These cytokines orchestrate the hallmark features of asthma, including eosinophilic inflammation, IgE production, mucus hypersecretion, and airway hyperresponsiveness.⁵

IL-4 promotes the class switching of B cells to produce IgE, while IL-5 is crucial for the growth, differentiation, and activation of eosinophils. IL-13 contributes to goblet cell metaplasia, airway hyperreactivity, and subepithelial fibrosis.⁶ In addition to Th2 cells, other immune cells such as innate lymphoid cells type 2 (ILC2s) and regulatory T cells (Tregs) have been implicated in modulating the inflammatory milieu in asthma.⁷

Genetic factors significantly influence asthma susceptibility and heterogeneity. Multiple genome-wide association studies (GWAS) have identified asthma-related loci, such as ORMDL3, IL33, IL1RL1, and TSLP, that regulate immune responses and airway inflammation.⁸ Variants in the 17q21 locus, particularly in ORMDL3 and GSDMB genes, are among the most consistently replicated findings associated with childhood-onset asthma.⁹For instance, exposure to tobacco smoke can modify DNA methylation patterns in genes implicated in immune responses, thereby altering asthma risk.¹⁰

Overall, understanding the genetic and immunological background of asthma has laid the foundation for identifying pharmacogenomic biomarkers, enabling more personalized approaches to asthma management.

Pharmacogenomics: Concept and Application in Asthma:

Pharmacogenomics involves the study of how an individual’s genetic makeup influences their response to drugs. In asthma, pharmacogenomic biomarkers are pivotal in predicting the efficacy and safety of various therapies, offering a path toward personalized treatment strategies. These biomarkers include specific gene variants that modify patient responses to commonly used asthma medications such as inhaled corticosteroids (ICS), beta-agonists, leukotriene modifiers, and biologic therapies targeting inflammatory pathways.¹¹

Inhaled Corticosteroids (ICS): initializing in asthma management. Variants in genes like GLCCI1 have been associated with altered ICS responsiveness, where certain polymorphisms are linked to reduced therapeutic efficacy.^11,12

Beta-Agonist: Polymorphisms in the ADRB2 gene, notably Arg16Gly, have been correlated with differences in bronchodilator response. Patients with the Arg16 variant may exhibit a reduced response to regular beta-agonist therapy, impacting disease control.¹³

Leukotriene Modifiers: Leukotriene receptor antagonists like montelukast show variable efficacy based on ALOX5 promoter polymorphisms, where mutations can affect leukotriene synthesis and thus treatment outcomes.¹⁴

Biologics (Targeted Therapies): Recent advancements have highlighted pharmacogenomic markers in predicting responses to monoclonal antibodies targeting IL-4, IL-5, and IL-13 pathways. For example, variants in IL4RA and IL13 may affect the efficacy of biologics such as dupilumab and mepolizumab.¹⁵

The application of pharmacogenomics in asthma represents a critical evolution toward precision medicine. By stratifying patients based on genetic biomarkers, clinicians can optimize drug selection, enhance disease control, and reduce the burden of side effects. Recent studies suggest that integrating multi-omics approaches including genomics, transcriptomics, and proteomics will further refine asthma phenotyping and treatment personalization.¹⁶

Key Pharmacogenomic Biomarkers Identified in Asthma:

Beta-2 Adrenergic Receptor (ADR β₂) Polymorphisms:

Beta-agonists, both short-acting (SABAs) and long-acting (LABAs), are the cornerstone of bronchodilation therapy in asthma. However, variability in treatment response has been associated with polymorphisms in the beta-2 adrenergic receptor (ADR β₂) gene. Two common single nucleotide polymorphisms (SNPs), Arg16Gly and Gln27Glu, have been extensively studied.^17,18 The Arg16 variant has been associated with enhanced down regulation of the receptor upon beta-agonist exposure, leading to reduced bronchodilator response.¹⁹ In contrast, individuals carrying the Gly16 allele may experience a more favourable response to beta-agonists.²⁰ Pharmacogenomic profiling of ADRB2 polymorphisms could enable better selection of bronchodilator regimens tailored to patient genotypes.

Glucocorticoid Pathway Genes:

Inhaled corticosteroids (ICS) remain a first-line therapy for asthma control. Variability in corticosteroid responsiveness has been linked to polymorphisms in several genes within the glucocorticoid signaling pathway. Notably, variations in the NR3C1 gene, encoding the glucocorticoid receptor, have been associated with differential anti-inflammatory effects.²¹ Additionally, polymorphisms in CRHR1 (corticotropin-releasing hormone receptor 1) and GLCCI1 (glucocorticoid-induced transcript 1) genes have shown strong associations with steroid responsiveness.²² Particularly, a SNP in GLCCI1 (rs37973) has been linked to decreased ICS efficacy, suggesting a potential genetic basis for steroid resistance in a subset of asthma patients.²³

Leukotriene Pathway Genes:

Leukotriene receptor antagonists (LTRAs), such as montelukast, offer an alternative therapeutic option, particularly for aspirin-exacerbated respiratory disease (AERD) and allergic asthma. Variants in the ALOX5 gene, which encodes 5-lipoxygenase, a key enzyme in leukotriene synthesis, significantly affect response to LTRA therapy²⁵. Polymorphisms in the LTC4S gene (leukotriene C4 synthase) have also been implicated in modulating therapeutic outcomes²⁴. Individuals carrying specific ALOX5 promoter polymorphisms demonstrate reduced gene transcription and, consequently, diminished clinical response to montelukast.²⁵

IL-4, IL-13, and IL-5 Pathway Biomarkers:

Biologic therapies targeting interleukin pathways have revolutionized severe asthma management. Polymorphisms in IL4, IL13, and IL5 genes have been associated with asthma susceptibility and severity, as well as with response to biologics like dupilumab (anti-IL-4/IL-13) and mepolizumab (anti-IL-5).²⁶ For instance, the IL4R Q576R polymorphism has been linked to heightened Th2 inflammatory responses and may predict enhanced responsiveness to IL-4/IL-13 inhibitors.2⁷ Similarly, elevated baseline levels of IL-5 and eosinophils, partially genetically determined, predict better outcomes with anti-IL-5 biologics.²⁶

Other Emerging Genes:

Beyond the classical pathways, emerging research highlights the role of genes such as ORMDL3, IL33, and TSLP in asthma pathogenesis and treatment response. ORMDL3 polymorphisms have been strongly associated with childhood-onset asthma, possibly influencing sphingolipid metabolism and airway hyperresponsiveness.²⁷ IL33 and TSLP, two upstream epithelial-derived cytokines, have emerged as promising targets for novel biologics (e.g., tezepelumab targeting TSLP).²⁸ Variants in these genes not only affect disease susceptibility but also predict responsiveness to specific biologic therapies, suggesting a major step forward in precision medicine for asthma.

TECHNOLOGIES ENABLING PHARMACOGENOMIC DISCOVERY:

The discovery of pharmacogenomic biomarkers in asthma has been significantly accelerated by technological innovations in genomics and systems biology. Several cutting-edge methods have facilitated the identification of genetic variants associated with differential drug responses and disease subtypes.

Genome-Wide Association Studies (GWAS):

GWAS have been pivotal in uncovering single nucleotide polymorphisms (SNPs) linked to asthma susceptibility and therapeutic outcomes. Studies such as those by identified loci like ORMDL3 and IL1RL1 that not only predict disease risk but also influence corticosteroid responsiveness.⁸ Further GWAS meta-analyses have refined these associations across diverse populations.⁹ Despite their power, GWAS are limited by population stratification and the modest effect size of many variants.

Next-Generation Sequencing (NGS):

NGS technologies, including whole-genome sequencing (WGS) and whole-exome sequencing (WES), have deepened the resolution of pharmacogenomic studies. NGS enables the detection of rare variants that are missed by GWAS.²⁹ For example, variants in genes involved in the IL-33/TSLP axis have been uncovered through deep sequencing approaches, providing new targets for biologic therapies.

Multi-Omics Approaches:

The integration of genomics, transcriptomics, proteomics, and epigenomics — referred to as "multi-omics" — offers a holistic view of asthma heterogeneity.³⁰ Epigenetic modifications, such as DNA methylation patterns identified through epigenomic profiling, have been associated with corticosteroid sensitivity.³¹ Similarly, transcriptomic analyses have identified gene expression signatures predictive of biologic treatment responses.³²

Single-Cell RNA Sequencing (scRNA-seq):

Single-cell RNA sequencing has revolutionized the understanding of cellular heterogeneity in asthma pathophysiology. Recent scRNA-seq studies have revealed rare immune cell subsets and their gene expression profiles associated with severe asthma phenotypes and therapeutic outcomes.³³ Furthermore, scRNA-seq allows the identification of specific gene targets at the cellular level, facilitating the development of highly targeted interventions.³⁴

Collectively, these technologies are transforming the landscape of pharmacogenomics in asthma, bringing personalized therapies closer to clinical practice.

Recent Clinical Trials and Studies:

In recent years, pharmacogenomics has significantly contributed to the personalization of asthma treatment, particularly with the emergence of targeted biologic therapies. Landmark clinical studies have identified key genetic and molecular biomarkers that predict therapeutic response, thereby improving disease management and outcomes.

One of the most promising areas involves predictive biomarkers for anti-IL5 therapies, such as mepolizumab and benralizumab, which are highly effective in patients with severe eosinophilic asthma. Blood eosinophil count has been validated as a predictive biomarker in multiple trials.^35,36 Higher baseline eosinophil levels were associated with better clinical responses, including reduced exacerbations and improved lung function.

Further, genetic polymorphisms in IL5RA (the gene encoding the IL-5 receptor α-chain) have been explored for their potential to predict responsiveness to anti-IL5 monoclonal antibodies. A recent study suggested that specific IL5RA variants may be linked to differential treatment outcomes with benralizumab.³⁷

Personalized use of biologics based on TSLP (thymic stromal lymphopoietin) and IL33 pathway biomarkers has also gained attention. Tezepelumab, an anti-TSLP monoclonal antibody, showed efficacy across a broad range of asthma phenotypes, but patients with elevated TSLP gene expression or upstream inflammatory markers demonstrated superior outcomes.³⁸

Additionally, genome-wide association studies (GWAS) have identified SNPs in genes like GLCCI1 and CRHR1 that correlate with corticosteroid responsiveness, reinforcing the concept of genetically guided asthma therapy.^39,40

The development of multi-biomarker panels, combining blood eosinophils, fractional exhaled nitric oxide (FeNO), and genetic markers, has been proposed to enhance the predictive power for biologic therapy selection.⁴¹ These composite approaches may soon become standard in clinical practice to optimize treatment efficacy. While these advances are promising, large-scale validation studies and real-world data are necessary before widespread implementation. Nevertheless, the integration of pharmacogenomic biomarkers in asthma management marks a transformative shift toward truly personalized therapy.

Challenges and Limitations:

Despite the promising advancements in pharmacogenomic biomarkers for asthma, several challenges hinder their widespread clinical application.

Population Heterogeneity:

Genetic variations differ significantly across ethnic and racial groups, leading to inconsistent biomarker performance. Most pharmacogenomic studies have been conducted in populations of European ancestry, limiting their generalizability to more diverse global populations. ^42,36

Limited Replication Across Studies:

While numerous candidate biomarkers have been identified, replication in independent cohorts remains limited. Variability in study design, asthma phenotyping, and drug response measures complicate direct comparisons and validation efforts.^22,43

Ethical and Regulatory Issues: The use of genetic data raises ethical concerns related to privacy, consent, and potential discrimination. Furthermore, regulatory pathways for the approval of pharmacogenomic-guided therapies are still underdeveloped, slowing clinical integration.⁴⁴

Cost and Accessibility of Genetic Testing:

High costs associated with genomic testing technologies and data interpretation pose significant barriers, particularly in low- and middle-income countries. Furthermore, the lack of infrastructure to support precision medicine initiatives in routine asthma care remains a major limitation.^45,46

In summary, while pharmacogenomic biomarkers hold immense promise for the personalization of asthma therapy, overcoming these challenges will require coordinated efforts across research, clinical practice, policy-making, and public health sectors.

Future Perspectives:

The integration of pharmacogenomics into clinical practice holds significant promise for improving asthma management. Personalized therapy, driven by genetic biomarkers, has the potential to optimize treatment outcomes, minimize adverse drug reactions, and reduce healthcare costs.⁴⁷ However, real-world application faces several barriers, including limited clinician awareness, regulatory hurdles, and cost-effectiveness considerations.

Artificial intelligence (AI) and machine learning (ML) approaches are increasingly being utilized to accelerate biomarker discovery. AI models can analyze large, complex datasets (e.g., genomics, transcriptomics, and clinical parameters) to predict drug responses and identify novel genetic target.⁴⁸ Deep learning methods have also been used to integrate multi-omics data to uncover complex gene-drug interactions associated with asthma severity and treatment response .⁴⁹

Another critical area for future research is the inclusion of larger, multiethnic cohorts in pharmacogenomic studies. Most current genetic data are derived from populations of European descent, which limits the generalizability of pharmacogenomic findings across diverse populations.⁵⁰ Expanding studies to underrepresented groups will be essential for equitable personalized asthma care.

Finally, developing a comprehensive personalized asthma management roadmap will require not only genetic profiling but also integration with clinical, environmental, and lifestyle factors.⁵¹ The future lies in a multi-dimensional approach that combines pharmacogenomic data with digital health technologies such as wearable devices, real-time symptom monitoring, and adaptive treatment algorithms.⁵² With these advancements, asthma care may shift from reactive symptom control toward proactive, precision-guided management strategies.

CONCLUSION:

Recent advancements in pharmacogenomics have significantly deepened our understanding of asthma heterogeneity and drug response variability. Key discoveries, including genetic variants in ADRB2, GLCCI1, IL4RA, and ALOX5, among others, have paved the way for more personalized and effective asthma treatments. Additionally, the integration of multi-omics technologies and single-cell sequencing has accelerated biomarker discovery and offered new insights into asthma endotypes and phenotypes.

Pharmacogenomic biomarkers hold immense promise for transforming asthma care by enabling tailored therapy, improving drug efficacy, minimizing adverse effects, and optimizing disease control. With growing evidence from clinical trials and real-world studies, these biomarkers are moving closer to clinical implementation. Their application can potentially shift asthma management from a generalized approach to a precision-based strategy, significantly improving patient outcomes.

However, challenges such as population diversity, cost-effectiveness, ethical considerations, and the need for robust clinical validation must be addressed to realize the full potential of pharmacogenomics. Ongoing research, larger multiethnic studies, advancements in digital health technologies, and the integration of artificial intelligence will be critical in overcoming these barriers.

Looking ahead, routine pharmacogenomic testing in asthma management appears increasingly achievable. As the field evolves, combining genetic profiling with clinical, environmental, and lifestyle factors will be essential to deliver truly individualized asthma care. The journey toward precision medicine in asthma is well underway, promising a future of more effective, safer, and patient-centered treatments.

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Received on 02.07.2025 Revised on 19.08.2025

Accepted on 21.09.2025 Published on 12.02.2026

Available online from February 14, 2026

Res.J. Pharmacology and Pharmacodynamics.2026;18(1):1-6.

DOI: 10.52711/2321-5836.2026.00001

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License. Creative Commons License.