Pharmacogenomics: Driving Personalized Medicine

Abstract

Personalized medicine tailors therapies, disease prevention, and health maintenance to the individual, with pharmacogenomics serving as a key tool to improve outcomes and prevent adverse effects. Advances in genomics have transformed pharmacogenetics, traditionally focused on single gene-drug pairs, into pharmacogenomics, encompassing all "-omics" fields (e.g., proteomics, transcriptomics, metabolomics, and metagenomics). This review summarizes basic genomics principles relevant to translation into therapies, assessing pharmacogenomics' central role in converging diverse elements of personalized medicine. We discuss genetic variations in pharmacogenes (drug-metabolizing enzymes, drug transporters, and receptors), their clinical relevance as biomarkers, and the legacy of decades of research in pharmacogenetics. All types of therapies, including proteins, nucleic acids, viruses, cells, genes, and irradiation, can benefit from genomics, expanding the role of pharmacogenomics across medicine. Food and Drug Administration approvals of personalized therapeutics involving biomarkers increase rapidly, demonstrating the growing impact of pharmacogenomics. A beacon for all therapeutic approaches, molecularly targeted cancer therapies highlight trends in drug discovery and clinical applications. To account for human complexity, multicomponent biomarker panels encompassing genetic, personal, and environmental factors can guide diagnosis and therapies, increasingly involving artificial intelligence to cope with extreme data complexities. However, clinical application encounters substantial hurdles, such as unknown validity across ethnic groups, underlying bias in health care, and real-world validation. This review address the underlying science and technologies germane to pharmacogenomics and personalized medicine, integrated with economic, ethical, and regulatory issues, providing insights into the current status and future direction of health care. SIGNIFICANCE STATEMENT: Personalized medicine aims to optimize health care for the individual patients with use of predictive biomarkers to improve outcomes and prevent adverse effects. Pharmacogenomics drives biomarker discovery and guides the development of targeted therapeutics. This review addresses basic principles and current trends in pharmacogenomics, with large-scale data repositories accelerating medical advances. The impact of pharmacogenomics is discussed, along with hurdles impeding broad clinical implementation, in the context of clinical care, ethics, economics, and regulatory affairs.

Fig. 1

Combined pharmacokinetic-pharmacodynamic network of thiopurines (thioguanine, mercaptopurine, azathioprine) displayed in the PharmGKB (https://www.pharmgkb.org/pathway/PA2040). Marked with red arrows are two key genes labeled as “very important pharmacogene” are TPMT (thiopurine methyl transferase), inactivating thiopurines, and NUDT15 (nucleoside diphosphate linked moiety X-type motif 15), mediating dephosphorylation of thioguanine phosphates. When deficient, both genes can cause severe toxicity. Clinical biomarker testing is recommended but not yet broadly implemented. PharmGKB grants use of its data and contents under the Creative Commons Attribution-ShareAlike 4.0 International License. PharmGKB, Pharmacogenomics Knowledge Base.

Fig. 2

CHRNA5, CHNA3, CHNB4 gene cluster. This screenshot of the GTEx IGV Browser displays the alignment of the three nicotinic receptors, with GWAS hits (green bars) and RNA eQTLs (red dots: CHRNA5, gray dots: mostly CHRNB4, some for the noncoding RP11-160C18.2). Top CHRNA5 eQTLS are highly significant (p<e-22), and together with GWAS hits line up over 400,000 bps in several very long overlapping LD blocks. The LD blocks each carry one or more functional variants, including the functionally relevant nsSNP rs16969968. The data used for the analyses described in this manuscript were obtained from the GTEx Portal on January 20, 2023 (https://www.gtexportal.org/home/browseEqtls?location=CHRNA5) (GTEx Consortium, 2020). eQTLs, expression quantitative expression loci; GTEx, Genotype-Tissue Expression.

Fig. 3

Chromatin interactions and histone marks within the CYP3A locus in hepatic tissue or cells. Arrows indicate the orientation of each gene. Blue arced lines represent contact interactions and gray ovals regulatory regions identified in Collins and Wang (2020) and Collins et al. (2022). Gray bars represent histone mark intervals from public databases.

Fig. 4

Use of biomarkers in personalized medicine. Panels include phenotypic and genomic markers. Note that germline mutations can be detected at any time, whereas other markers, including somatic mutations, vary with disease state, age, drug treatment, and other factors.

Fig. 5

Boxed warning in the FDA drug label for Abacavir. HLA-B*-5701carriers are at high risk of fatal hypersensitivity reactions. Drug label boxed warning established in 1998, text box copied from the 11.24.2020 label. (https://www.accessdata.fda.gov/scripts/cder/daf/index.cfm?event=overview.process&varApplNo=020977).