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Genetic polymorphism in drug metabolism is crucial to the inter-individual variability observed in drug responses. Drug metabolism primarily involves the chemical modification of drugs and other xenobiotics to enhance their elimination by increasing their polarity. Two main classes of enzymes mediate this biotransformation process: Phase I enzymes, primarily cytochrome P450s, catalyze oxidation and reduction reactions, while other enzymes, such as esterases, mediate hydrolysis, and Phase II...
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Advances in genomics have profoundly influenced drug discovery by increasing both the speed and accuracy of pharmaceutical development. Pharmacogenomics, which examines how genetic variation influences drug response, facilitates the identification of novel therapeutic targets and enables patient stratification for personalized treatment. These strategies contribute to improved drug efficacy, minimized adverse effects, and more efficient clinical trial design.Mapping genetic differences...
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Pharmacogenetics and pharmacogenomics examine how genetic factors influence an individual's response to drugs. While pharmacogenetics focuses on the impact of specific genetic variants on drug effects, pharmacogenomics takes a broader approach, studying how genetic variation across populations contributes to differences in drug responses. These fields aim to explain why individuals may experience varying levels of efficacy or adverse reactions to the same medication.Variability in drug...
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Genetic variations significantly influence drug response through pharmacokinetics, receptor interactions, and biologic milieu modifications. Pharmacokinetic alterations impact drug metabolism and clearance, affecting efficacy and toxicity. Variants in drug-metabolizing enzymes, such as CYP2C9 and CYP2C19, alter drug activation and elimination. For example, CYP2C9 loss-of-function variants require lower warfarin doses to prevent excessive bleeding, while CYP2C19 variants reduce clopidogrel...
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Cytochrome P450 (CYP450) enzymes are a superfamily of heme-containing monooxygenases that play a pivotal role in Phase I drug metabolism by catalyzing oxidation and reduction reactions.These enzymes transform lipophilic xenobiotics into more hydrophilic metabolites, facilitating subsequent Phase II conjugation and eventual excretion. The CYP450 family is classified into families (e.g., CYP1–CYP3) and subfamilies (e.g., CYP2A, CYP2C), based on amino acid sequence homology.CYP450...
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The human genome is over 99.9% identical between individuals, yet genetic differences exist at millions of bases. The human genome contains approximately 3 million variant positions per individual, many of which are heterozygous, contributing to genetic diversity and individual traits. Genetic variations include single-nucleotide polymorphisms (SNPs), insertions, deletions, and copy number variations (CNVs).SNPs, the most common variation, involve single-base changes in DNA. These can be...
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Using EHR-Linked Biobank Data to Study Metformin Pharmacogenomics.

Matthew K Breitenstein1, Gyorgy Simon2, Euijung Ryu1

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Area of Science:

  • Pharmacogenomics
  • Molecular Biology
  • Diabetes Mellitus Research

Background:

  • Metformin is a widely used type 2 diabetes drug.
  • Its precise mechanism of action remains unclear.
  • Understanding its pharmacogenomics is crucial for personalized treatment.

Purpose of the Study:

  • To investigate the influence of genetic variations on glycemic response to metformin.
  • To identify specific genes and single nucleotide polymorphisms (SNPs) associated with metformin efficacy.
  • To leverage electronic health record (EHR) data for deeper insights into metformin pharmacogenomics.

Main Methods:

  • Utilized electronic health record (EHR)-linked biobank data.
  • Analyzed genomic variation in relation to metformin's effect on glycemic control.
  • Integrated EHR phenotypes to contextualize genetic findings.

Main Results:

  • Identified significant gene-level associations within the beta-2 subunit of the adenosine monophosphate-activated protein kinase (AMPK) complex.
  • Found significant single nucleotide polymorphism (SNP)-level associations within the same complex.
  • Demonstrated a link between specific genetic variations and patient response to metformin.

Conclusions:

  • Genomic variations, particularly within the AMPK beta-2 subunit, significantly impact glycemic response to metformin.
  • The study provides valuable data for the ongoing pharmacogenomic understanding of metformin.
  • EHR-linked data enhances the clarity of genetic associations with drug response.