Pharmacogenomics

Pharmacogenomics integrates genomic science with pharmacology to understand how inherited genetic variation influences an individual’s response to therapeutic agents. By mapping variants in genes encoding drug‐metabolising enzymes, transporters and molecular targets, it aims to shift prescribing from a trial‐and‐error approach towards precision medicine. Key enzyme families such as the cytochrome P450s (notably CYP2D6, CYP2C9, CYP2C19 and CYP3A4/5) display polymorphisms that can categorise patients as poor, intermediate, extensive or ultra‐rapid metabolisers, directly affecting drug concentrations, efficacy and risk of toxicity. Transport proteins including P‐glycoprotein (ABCB1), organic anion transporting polypeptide 1B1 (SLCO1B1) and others further modulate absorption and distribution. On the pharmacodynamic side, variation in drug targets such as VKORC1 for warfarin or the serotonin transporter (SLC6A4) for antidepressants underpins interindividual differences in therapeutic response and adverse effects. By integrating genomic data with clinical variables, pharmacogenomics offers the prospect of optimised drug selection and dosing across diverse therapeutic areas—ranging from oncology and psychiatry to cardiology and infectious diseases—and has the potential to reduce hospitalisation rates, improve therapeutic indices and lower healthcare costs on a global scale.

Research from Nature Portfolio

Implementing a community‐engaged model of pharmacogenomic research has demonstrated both scientific and social value. An international collaboration worked alongside Indigenous communities to co‐design pharmacogenomic screening strategies that respect cultural priorities. This work uncovered population‐specific allele frequencies, improved annotation of regional variants and established best practice frameworks for consent and data governance. These efforts not only enriched the global database of drug‐metabolising and transporter polymorphisms, but also provided a blueprint for equitable implementation of precision therapeutics.

Earlier seminal work established the clinical translation of pharmacogenomics by identifying actionable germline variants in approximately 20 genes that affect over 80 medications. These variations in metabolism enzymes and drug targets have been standardised into clinical guidelines, enabling genotype-guided dose adjustment to achieve therapeutic concentrations while minimising adverse events. This foundational research paved the way for routine pharmacogenomic testing in drug labels and clinical decision-support systems.

Research from all publishers

A comprehensive review has highlighted the evolution of pharmacogenomics beyond single common variants towards polygenic and multi-omics models. Advances in high-throughput genotyping, transcriptomics and metabolomics are being integrated with machine-learning algorithms to parse complex interactions and address missing heritability. This approach supports the development of novel predictive scores for drug selection and dosing and emphasises the need for standardised translation of genotype to phenotype.

Machine-learning methods applied to real-world electronic health record data have demonstrated robust predictions of antidepressant class response in over 17 000 patients. By combining structured and unstructured clinical data with prescription records, these algorithms achieved area under the curve values above 0.70 for selective serotonin reuptake inhibitors, SNRIs, bupropion and mirtazapine, highlighting the potential of data-driven models to guide class-specific treatment decisions.

In psychiatric care, large-scale pharmacogenomic testing using next-generation sequencing in 15 000 patients revealed that 65% had actionable CYP2D6 or CYP2C19 phenotypes. Inclusion of allele-specific copy-number variants and drug-induced phenoconversion further refined predictions, underscoring the clinical utility of early pharmacogenomic screening to optimise medication selection and tolerability.

Pharmacogenomics publication trend

The graph below shows the total number of articles in pharmacogenomics across all publications each year (not limited to Nature Index journals).

Technical terms

Pharmacogenomics: The study of how genomic variation affects drug response, integrating pharmacology with genomics to personalise therapy.

Cytochrome P450 enzymes: A superfamily of hepatic enzymes that metabolise a large proportion of prescription drugs; key polymorphic isoforms include CYP2D6, CYP2C9, CYP2C19 and CYP3A4/5.

Ultra-rapid/poor metaboliser: Phenotypic categories reflecting high or low metabolic activity for a specific enzyme, determined by genotype and affecting drug clearance rates.

P-glycoprotein (P-gp): An ATP-binding cassette efflux transporter (encoded by ABCB1) that limits intracellular drug accumulation and influences oral bioavailability and tissue penetration.

Phenoconversion: A change in predicted drug-metaboliser status due to non-genetic factors, such as concomitant inhibitors or inducers of metabolic enzymes.

Machine learning: Computational methods that identify patterns in large datasets to make predictions or stratify patients based on complex multimodal data.

References

1. Pharmacogenetics, Pharmacogenomics, and Personalized Medicine.
2. Standardizing CYP2D6 Genotype to Phenotype Translation: Consensus Recommendations from the Clinical Pharmacogenetics Implementation Consortium and Dutch Pharmacogenetics Working Group. Clinical and Translational Science (2019).
3. Implementing community-engaged pharmacogenomics in Indigenous communities. Nature Communications (2024).
4. Pharmacogenomics in the clinic. Nature (2015).
5. Pharmacogenomics Beyond Single Common Genetic Variants: The Way Forward. The Annual Review of Pharmacology and Toxicology (2023).
6. AI-assisted prediction of differential response to antidepressant classes using electronic health records. npj Digital Medicine (2023).
7. Pharmacogenomic insights in psychiatric care: uncovering novel actionability, allele-specific CYP2D6 copy number variation, and phenoconversion in 15,000 patients. Molecular Psychiatry (2024).