Pharmacogenomics Beyond Single Common Genetic Variants: The Way Forward

Lauschke, Volker M.; Zhou, Yitian; Ingelman‐Sundberg, Magnus

doi:10.1146/annurev-pharmtox-051921-091209

Cited by 10 publications

(7 citation statements)

References 126 publications

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“…Ref. [6]). However, the functional and clinical validation of these variants in a clinical setting will require an enormous amount of time.…”

Section: Causes Of Inter-individual Variation In Drug Response and Ad...mentioning

confidence: 99%

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Precision medicine in cardiovascular therapeutics: Evaluating the role of pharmacogenetic analysis prior to drug treatment

Ingelman‐Sundberg,

Pirmohamed

2024

J Intern Med

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Pharmacogenomics is the examination of how genetic variation influences drug metabolism and response, in terms of both efficacy and safety. In cardiovascular disease, patient‐specific diplotypes determine phenotypes, thereby influencing the efficacy and safety of drug treatments, including statins, antiarrhythmics, anticoagulants and antiplatelets. Notably, polymorphisms in key genes, such as CYP2C9, CYP2C19, VKORC1 and SLCO1B1, significantly impact the outcomes of treatment with clopidogrel, warfarin and simvastatin. Furthermore, the CYP2C19 polymorphism influences the pharmacokinetics and safety of the novel hypertrophic cardiomyopathy inhibitor, mavacamten. In this review, we critically assess the clinical application of pharmacogenomics in cardiovascular disease and delineate present and future utilization of pharmacogenomics. This includes insights into identifying missing heritability, the integration of whole genome sequencing and the application of polygenic risk scores to enhance the precision of personalized drug therapy. Our discussion encompasses health economic analyses that underscore the cost benefits associated with pre‐emptive genotyping for warfarin and clopidogrel treatments, albeit acknowledging the need for further research in this area. In summary, we contend that cardiovascular pharmacogenomic analyses are underpinned by a wealth of evidence, and implementation is already occurring for some of these gene–drug pairs, but as with any area of medicine, we need to continually gather more information to optimize the use of pharmacogenomics in clinical practice.

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“…Ref. [6]). However, the functional and clinical validation of these variants in a clinical setting will require an enormous amount of time.…”

Section: Causes Of Inter-individual Variation In Drug Response and Ad...mentioning

confidence: 99%

“…Today, more than 69,000 different singlenucleotide variants and 200 structural variants (SVs) have been identified in more than 200 pharmacogenes [4][5][6][7]. There are now many informative websites with functional, regulatory and clinical implications of polymorphic genes and their impact on several hundred drugs [8].…”

Section: Introductionmentioning

confidence: 99%

Precision medicine in cardiovascular therapeutics: Evaluating the role of pharmacogenetic analysis prior to drug treatment

Ingelman‐Sundberg,

Pirmohamed

2024

J Intern Med

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“…Nucleic acid conjugated nanomaterials (NACs) have proven to be a useful tool in cancer research [ 7 , 8 , 9 , 10 , 11 ]. They have the benefit of working as endogenous biomarkers with patient genetic information [ 12 , 13 ]. Graphene oxide/gold hybrid nanostructures (Au@GO NP-NACs) have emerged as an excellent platform for cancer diagnostics, as depicted in ( Figure 1 ).…”

Section: Introductionmentioning

confidence: 99%

Recent Advances in Synergistic Effect of Nanoparticles and Its Biomedical Application

Nanda,

2024

IJMS

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The synergistic impact of nanomaterials is critical for novel intracellular and/or subcellular drug delivery systems of minimal toxicity. This synergism results in a fundamental bio/nano interface interaction, which is discussed in terms of nanoparticle translocation, outer wrapping, embedding, and interior cellular attachment. The morphology, size, surface area, ligand chemistry and charge of nanoparticles all play a role in translocation. In this review, we suggest a generalized mechanism to characterize the bio/nano interface, as we discuss the synergistic interaction between nanoparticles and cells, tissues, and other biological systems. Novel perceptions are reviewed regarding the ability of nanoparticles to improve hybrid nanocarriers with homogeneous structures to enhance multifunctional biomedical applications, such as bioimaging, tissue engineering, immunotherapy, and phototherapy.

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“…Over the last few decades, a multitude of genetic variants significantly associated with drug response have been identified using forward genetics, particularly for cytochrome P450 (CYP) genes. While these variants are promising pharmacogenomic biomarkers to improve the efficacy and safety of some drugs, increased evidence suggests that they are not sufficient to exhaustively explain the genetically encoded variability in drug response [ 11 – 13 ].…”

Section: Introductionmentioning

confidence: 99%

APF2: an improved ensemble method for pharmacogenomic variant effect prediction

Zhou,

Pirmann,

Lauschke

2024

Pharmacogenomics J

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Lack of efficacy or adverse drug response are common phenomena in pharmacological therapy causing considerable morbidity and mortality. It is estimated that 20–30% of this variability in drug response stems from variations in genes encoding drug targets or factors involved in drug disposition. Leveraging such pharmacogenomic information for the preemptive identification of patients who would benefit from dose adjustments or alternative medications thus constitutes an important frontier of precision medicine. Computational methods can be used to predict the functional effects of variant of unknown significance. However, their performance on pharmacogenomic variant data has been lackluster. To overcome this limitation, we previously developed an ensemble classifier, termed APF, specifically designed for pharmacogenomic variant prediction. Here, we aimed to further improve predictions by leveraging recent key advances in the prediction of protein folding based on deep neural networks. Benchmarking of 28 variant effect predictors on 530 pharmacogenetic missense variants revealed that structural predictions using AlphaMissense were most specific, whereas APF exhibited the most balanced performance. We then developed a new tool, APF2, by optimizing algorithm parametrization of the top performing algorithms for pharmacogenomic variations and aggregating their predictions into a unified ensemble score. Importantly, APF2 provides quantitative variant effect estimates that correlate well with experimental results (R2 = 0.91, p = 0.003) and predicts the functional impact of pharmacogenomic variants with higher accuracy than previous methods, particularly for clinically relevant variations with actionable pharmacogenomic guidelines. We furthermore demonstrate better performance (92% accuracy) on an independent test set of 146 variants across 61 pharmacogenes not used for model training or validation. Application of APF2 to population-scale sequencing data from over 800,000 individuals revealed drastic ethnogeographic differences with important implications for pharmacotherapy. We thus think that APF2 holds the potential to improve the translation of genetic information into pharmacogenetic recommendations, thereby facilitating the use of Next-Generation Sequencing data for stratified medicine.

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Pharmacogenomics Beyond Single Common Genetic Variants: The Way Forward

Cited by 10 publications

References 126 publications

Precision medicine in cardiovascular therapeutics: Evaluating the role of pharmacogenetic analysis prior to drug treatment

Precision medicine in cardiovascular therapeutics: Evaluating the role of pharmacogenetic analysis prior to drug treatment

Recent Advances in Synergistic Effect of Nanoparticles and Its Biomedical Application

APF2: an improved ensemble method for pharmacogenomic variant effect prediction

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