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Review
. 2019 May;197:122-152.
doi: 10.1016/j.pharmthera.2019.01.002. Epub 2019 Jan 22.

Novel Genetic and Epigenetic Factors of Importance for Inter-Individual Differences in Drug Disposition, Response and Toxicity

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Free PMC article
Review

Novel Genetic and Epigenetic Factors of Importance for Inter-Individual Differences in Drug Disposition, Response and Toxicity

Volker M Lauschke et al. Pharmacol Ther. .
Free PMC article

Abstract

Individuals differ substantially in their response to pharmacological treatment. Personalized medicine aspires to embrace these inter-individual differences and customize therapy by taking a wealth of patient-specific data into account. Pharmacogenomic constitutes a cornerstone of personalized medicine that provides therapeutic guidance based on the genomic profile of a given patient. Pharmacogenomics already has applications in the clinics, particularly in oncology, whereas future development in this area is needed in order to establish pharmacogenomic biomarkers as useful clinical tools. In this review we present an updated overview of current and emerging pharmacogenomic biomarkers in different therapeutic areas and critically discuss their potential to transform clinical care. Furthermore, we discuss opportunities of technological, methodological and institutional advances to improve biomarker discovery. We also summarize recent progress in our understanding of epigenetic effects on drug disposition and response, including a discussion of the only few pharmacogenomic biomarkers implemented into routine care. We anticipate, in part due to exciting rapid developments in Next Generation Sequencing technologies, machine learning methods and national biobanks, that the field will make great advances in the upcoming years towards unlocking the full potential of genomic data.

Figures

Fig. 1
Fig. 1
Overview of drug labels and pharmacogenetic expert guidelines. a, Overview of the number of drug labels by EMA and FDA and recommendations by CPIC and DPWG, respectively. Note that some labels and guidelines contain references to more than one biomarker. b, The majority of EMA labels refer to pharmacokinetic germline variants, whereas FDA approved labels primarily pertain to variations in the somatic genome. Only the indication, contraindication and posology sections were considered. c-e, Overview of the number of drug labels and pharmacogenetic recommendations, stratified into germline variations that impact drug pharmacokinetics (c), somatic mutations in tumors (d) and other germline variants (e). f, Venn diagram depicting the overlap of pharmacogenetic guidance from EMA (blue) and FDA (red) approved drug labels and recommendations by CPIC (green) and DPWG (purple). EMA label information was reviewed in Ehmann et al. (Ehmann et al., 2015) and only encompasses drugs registered after the foundation of EMA in 1995, which creates some lack of coherence in the comparison. FDA labels were extracted from https://www.fda.gov/Drugs/ScienceResearch/ResearchAreas/Pharmacogenetics/ucm083378.htm. CPIC and DPWG guidelines were obtained from https://cpicpgx.org/guidelines and https://www.pharmgkb.org/guidelines, respectively. All sources were accessed Nov 1st 2018.
Fig. 2
Fig. 2
Overview of the utility of HLA biomarkers for the prediction of hypersensitivity reactions to different medicines. The abscissa (predictive power) refers to the strength of association between a HLA variant alleles and adverse drug reactions. We refer to Table 2, Table 3, Table 4 for details about the specific variant alleles of importance for the listed medications. The ordinate estimates the usefulness of a test that considers various practical aspects, including cost-effectiveness, availability of alternative treatments and severity of the adverse event. The box shaded in light red highlights the space that supports clinical implementation of the companion diagnostic.
Fig. 3
Fig. 3
Individualization of treatment based on comprehensive NGS-based genotyping data. In conventional care for most indications, treatment is based on clinical parameters without consideration of the patient's genotype (left track in the figure). While these regimens are efficacious and safe in most individuals, some patients do not respond to the prescribed medication or might experience adverse reactions. The utilization of Next Generation Sequencing (NGS) aims to leverage genomic data to predict those outlier patients and pre-emptively provide advice regarding alternative treatments or to flag patients for follow-up monitoring (right track in the figure). To achieve this goal, variations in genes encoding proteins involved in drug absorption, distribution, metabolism and excretion (ADME) and drug targets, as well as their regulatory regions are identified in the NGS data of the given patient. The effects of these variants are interpreted based on available characterization data collected in dedicated databases or the scientific literature. For novel variants, functional effects will be predicted using quantitative computational algorithms specifically developed for pharmacogenomic predictions. Effects of target variations on drug binding are predicted using available structural information. Subsequently, effects of all identified variants are collated and translated into activity scores for all pharmacogenes. Integration of gene activity scores with information about the pharmacology of medications available for the given therapeutic indication, allows to predict their efficacies and risks to cause adverse reactions. These results can provide guidance to the responsible physician regarding choice of drug and its dose, as well as incentivize the scheduling of more frequent follow-ups in at-risk patients, resulting in increased treatment efficacy and safety also for outlier patients. Figure modified with permission from the publisher and authors (Lauschke & Ingelman-Sundberg, 2018).

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