Five Key Advances Shaping Pharmacogenomics

1 Five Key Advances Shaping Pharmacogenomics Bree Foster, PhD Pharmacogenomics is the study of how genetic variation influences individual responses to medications. This field has gained significant momentum since the completion of the Human Genome Project and the advent of high-throughput sequencing. To date, researchers have catalogued over 69,000 distinct single nucleotide variants (SNVs) and 200 structural variants (SVs) across more than 200 pharmacogenes.1 These advancements have enabled the discovery of numerous drug–gene associations, which now inform pharmacogenetic labeling and clinical guidelines for more than 100 medications.2,3 As pharmacogenomics matures, new technologies are rapidly reshaping how we understand the genetic basis of drug response. From sequencing innovations that decode hard-to-map genes to machine learning models that predict variant effects at scale, these tools are helping researchers pinpoint how genetic variation drives individual responses to therapy. Crucially, these insights are now feeding back into drug discovery pipelines and informing more tailored treatment strategies. This listicle explores five advances – spanning sequencing, AI, real-world data integration and multiomics – that are driving the next generation of pharmacogenomics research and accelerating its clinical impact. 1. Long-read sequencing Long-read sequencing (LRS) is a key technology for pharmacogenomics as it is now revealing previously hidden structural variants, complex haplotypes and pharmacogenetically relevant alleles that were undetectable with short-read technologies. Unlike traditional sequencing, LRS provides contiguous reads that span tens of kilobases, allowing researchers to characterize complex genetic regions in a single assay. This enhanced resolution allows researchers to better characterize genes involved in drug metabolism and improve predictions of individual drug response. For example, the highly polymorphic enzyme cytochrome P450 2D6 (CYP2D6) processes between 20-30% of commonly prescribed drugs but is extremely challenging to genotype accurately due to its complex structural variations, including gene duplications, deletions and hybrid alleles. LRS enables comprehensive mapping of the CYP2D6 locus, capturing these variations in full, thereby improving phenotype prediction and guiding safer, more effective drug dosing.4,5 Several platforms now provide high accuracy reads (up to 99.9%) with improved error correction, making them powerful tools for genome assembly, structural variant discovery and clinical pharmacogenomics applications.6,7 As costs continue to fall and throughput rises, LRS is likely to become a go-to approach for uncovering hidden pharmacovariants and improving personalized medicine. Listicle 5 KEY ADVANCES SHAPING PHARMACOGENOMICS 2 2. Integrating electronic health records and biobanks Electronic health records (EHRs) have become widespread over the past decade as, besides supporting and improving diagnosis, clinical decisions and treatment coordination, they provide new data analytics opportunities.4 EHRs commonly encompass patient demographics, medical history, drug prescriptions and, in some cases, laboratory results, radiological images and wearable device data.8 Modern biobanks, such as the UK Biobank (UKB), All of Us Research Program, FinnGen and the Million Veteran Program, offer access to rich datasets that integrate whole-genome or whole-exome sequencing, longitudinal EHRs, medication records and detailed phenotypic and survey data. These resources allow researchers to move beyond traditional trial-based pharmacogenomics and explore drug response across diverse populations and care settings. The All of Us Research Program has already demonstrated clinical value with pharmacogenomic insights such as DPYD genotyping for fluoropyrimidine toxicity. This has helped to refine dosing guidelines and improve patient safety by distinguishing between variants that truly impact chemotherapy response and those that do not.9 The UKB has also enabled discovery and replication of pharmacogenomic signals at scale, particularly for adverse drug reactions and prescribing behaviors. For example, associations between drug maintenance dose and 9 PGx genes were tested in 200,000 UKB participants by assigning individuals to a metabolizer class (e.g., poor, intermediate and normal) based on their genotype. The study revealed known CYP2C9 and a novel CYP2C19 variant as determinants for warfarin dosage.10 EHR-linked biobanks accelerate the translation of research findings into actionable pharmacogenetic guidelines, guiding more precise prescribing decisions across populations. As integration with genomics becomes more routine, these real-world data ecosystems are helping bridge the gap between genetic discovery and personalized treatment planning. 3. Polygenic risk scores While traditional pharmacogenomics often focuses on single gene–drug interactions, many treatment outcomes, such as efficacy, side effects and toxicity, are shaped by a complex interplay of multiple genetic variants. Polygenic risk scores (PGS) offer a way to capture this complexity by aggregating the effects of thousands of variants into a single predictive metric. PGS have already been used to predict response to sulfonylureas in type 2 diabetes, lurasidone efficacy in schizophrenia and to identify heart failure patients most likely to benefit from beta-blockers.11-13 In some cases, PGS rival monogenic mutations in risk prediction while also being more broadly applicable across populations. Clinical translation of PGS is already underway. For example, the Centre for Familial Breast and Ovarian Cancer in Cologne, Germany, offers CanRisk. This CE-certified web tool integrates family history, rare pathogenic variants in cancer susceptibility genes, PGS, lifestyle, hormonal and clinical factors as well as imaging data to predict breast and ovarian cancer risks and estimate the likelihood of carrying pathogenic variants in specific genes. Listicle 5 KEY ADVANCES SHAPING PHARMACOGENOMICS 3 4. Machine learning and AI Pharmacogenomics generates vast, multidimensional datasets from genomic sequences and transcriptomes to clinical outcomes. Extracting actionable insights from this data requires computational approaches that can detect subtle, non-linear patterns. Deep learning algorithms have already been used in various aspects of genetics and pharmacogenomics to improve the understanding of genetic variation and its impact on drug response. For example, a deep learning model called Hubble.2D6 was developed to predict the functional impact of CYP2D6 haplotypes directly from DNA sequence data.14 Similarly, a neural network was trained on a cohort of breast cancer patients to predict CYP2D6-mediated tamoxifen metabolism.15 However, both studies were limited, and further research on a broader set of genes is needed. Future improvements rely on better training datasets, such as those from deep mutational scanning. By combining structural predictions with experimental data from these scans, models like AlphaMissense can refine their predictions based on validated functional impacts, increasing reliability in clinical and research settings.16 Expanding training datasets to include a broader range of variants, particularly those linked to diverse phenotypes and populations, will enhance the model's generalizability. Incorporating multiomics data, like transcriptomics and proteomics, may further improve the ability to predict the pathogenicity of variants in complex biological contexts, advancing personalized medicine and gene therapy strategies. 5. Integrating multiomics data Numerous factors beyond genetics influence how medications are absorbed, distributed, metabolized and eliminated, impacting both efficacy and the risk of adverse effects. Integrating multiple layers of biological information, including genetics, epigenetics, metabolomics, proteomics, nutrition and microbiome data, offers a comprehensive approach to optimizing therapeutic outcomes.17 Key advancements include: • Single-cell multiomics technologies, which reveal how gene expression and chromatin states vary within cell subpopulations in response to treatment. This is crucial for tackling heterogeneity in cancer and immune diseases. • Spatial omics, which helps map where pharmacogenomic effects occur in tissue context, uncovering why drugs may work in some cell niches but not others. • Proteogenomics, enabling researchers to link genetic variants to altered protein networks and downstream drug targets, supporting rational drug design and repurposing. By integrating these data types, scientists can identify regulatory variants, post-translational modifications and context-specific pathways that modulate drug response. For example, one study analyzing tumors from patients resistant to immune checkpoint inhibitors combined transcriptomic, epigenomic and spatial profiling to reveal immune-suppressive states that were undetectable through genomic data alone.18 These findings highlight the importance of incorporating diverse omics approaches to better understand, and ultimately overcome, therapy resistance and adverse effects. Listicle 5 KEY ADVANCES SHAPING PHARMACOGENOMICS 4 Despite their potential, multiomic approaches are still difficult to implement clinically due to high costs, technical complexities in data integration and limited validation through clinical outcomes. However, as analytical tools improve and more outcome-linked datasets become available, these approaches are poised to become foundational in next-generation precision pharmacotherapy. Looking Ahead Seventy years of methodological development have transformed pharmacogenomics from an emerging science into an interdisciplinary area of research that is key to the implementation of personalized medicine. As we move towards a future where pharmacogenomics becomes an integral part of clinical decision- making, advances – such as LRS, multiomics and machine learning – are setting the stage for more personalized, precise and effective healthcare. The next generation of pharmacogenomics promises to deliver treatments that are not only informed by genetic data but also by the complex interactions within the multiscale biological network, offering the potential to minimize adverse drug reactions and maximize therapeutic efficacy. References: 1. Ingelman-Sundberg M, Nebert DW, Lauschke VM. Emerging trends in pharmacogenomics: from common variant associations toward comprehensive genomic profiling. Hum Genom. 2023;17(1):105. doi: 10.1186/s40246-023-00554-9 2. Weinshilboum RM, Wang L. Pharmacogenomics: Precision medicine and drug response. Mayo Clin Proc. 2017;92(11):1711- 1722. doi: 10.1016/j.mayocp.2017.09.001 3. Table of pharmacogenomic biomarkers in drug labeling. FDA. September 23, 2024. Accessed July 22, 2025. https://www. fda.gov/drugs/science-and-research-drugs/table-pharmacogenomic-biomarkers-drug-labeling 4. Ingelman-Sundberg M. Pharmacogenetics of cytochrome P450 and its applications in drug therapy: the past, present and future. Trends Pharmacol Sci. 2004;25(4):193-200. doi: 10.1016/j.tips.2004.02.007 5. Ammar R, Paton TA, Torti D, Shlien A, Bader GD. Long read nanopore sequencing for detection of HLA and CYP2D6 variants and haplotypes. Published online May 20, 2015. doi: 10.12688/f1000research.6037.2 6. Mantere T, Kersten S, Hoischen A. Long-Read sequencing emerging in medical genetics. Front Genet. 2019;10. doi: 10.3389/fgene.2019.00426 7. Pollard MO, Gurdasani D, Mentzer AJ, Porter T, Sandhu MS. Long reads: their purpose and place. Hum Mol Genet. 2018;27(R2):R234-R241. doi:10.1093/hmg/ddy177 8. Dinh-Le C, Chuang R, Chokshi S, Mann D. Wearable health technology and electronic health record integration: Scoping review and future directions. JMIR mHealth uHealth. 2019;7(9):e12861. doi: 10.2196/12861 9. Turner AJ, Haidar CE, Yang W, et al. Updated DPYD HapB3 haplotype structure and implications for pharmacogenomic testing. Clin Transl Sci. 2024;17(1):e13699. doi: 10.1111/cts.13699 10. Auwerx C, Sadler MC, Reymond A, Kutalik Z. From pharmacogenetics to pharmaco-omics: Milestones and future directions. HGG Adv. 2022;3(2):100100. doi: 10.1016/j.xhgg.2022.100100 11. Li JH, Szczerbinski L, Dawed AY, et al. A polygenic score for type 2 diabetes risk is associated with both the acute and sustained response to sulfonylureas. Diabetes. 2020;70(1):293-300. doi: 10.2337/db20-0530 12. Li J, Yoshikawa A, Brennan MD, Ramsey TL, Meltzer HY. Genetic predictors of antipsychotic response to lurasidone identified in a genome wide association study and by schizophrenia risk genes. Schizophr Res. 2018;192:194-204. doi: 10.1016/j. schres.2017.04.009 13. Lanfear DE, Luzum JA, She R, et al. Polygenic score for β-blocker survival benefit in European ancestry patients with reduced ejection fraction heart failure. Circ Heart Fail. 2020;13(12):e007012. doi:10.1161/CIRCHEARTFAILURE.119.007012 14. McInnes G, Dalton R, Sangkuhl K, et al. Transfer learning enables prediction of CYP2D6 haplotype function. PLoS Comput Biol. 2020;16(11):e1008399. doi: 10.1371/journal.pcbi.1008399 15. van der Lee M, Allard WG, Vossen RHAM, et al. Toward predicting CYP2D6-mediated variable drug response from CYP2D6 gene sequencing data. Sci Transl Med. 2021;13(603):eabf3637. doi: 10.1126/scitranslmed.abf3637 Listicle 5 KEY ADVANCES SHAPING PHARMACOGENOMICS 5 16. Cheng J, Novati G, Pan J, et al. Accurate proteome-wide missense variant effect prediction with AlphaMissense. Science. 2023;381(6664):eadg7492. doi: 10.1126/science.adg7492 17. Shaman JA. The future of pharmacogenomics: Integrating epigenetics, nutrigenomics, and beyond. J Pers Med. 2024;14(12):1121. doi: 10.3390/jpm14121121 18. Wen J, Wang Y, Wang S, et al. Genetic and transcriptional insights into immune checkpoint blockade response and survival: lessons from melanoma and beyond. J Transl Med. 2025;23(1):467. doi: 10.1186/s12967-025-06467-6 About the author: Bree Foster is a science writer at Drug Discovery News. She holds a PhD in comparative and functional genomics from the University of Liverpool and enjoys crafting compelling stories for science. Listicle