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Related Concept Videos

Pharmacogenetics and Pharmacogenomics: Overview01:29

Pharmacogenetics and Pharmacogenomics: Overview

241
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...
241
Pharmacogenomics: Identification of New Drug Targets01:29

Pharmacogenomics: Identification of New Drug Targets

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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...
119
Pharmacogenetic Phenotypes: Alterations in Pharmacokinetics, Drug Targets and Biologic Milieu01:29

Pharmacogenetic Phenotypes: Alterations in Pharmacokinetics, Drug Targets and Biologic Milieu

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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...
156
Pharmacogenetics of Drug Metabolism: Overview01:27

Pharmacogenetics of Drug Metabolism: Overview

191
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...
191
Analysis Methods of Pharmacokinetic Data: Model and Model-Independent Approaches01:14

Analysis Methods of Pharmacokinetic Data: Model and Model-Independent Approaches

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Drug disposition in the body is a complex process and can be studied using two major approaches: the model and the model-independent approaches.
The model approach uses mathematical models to describe changes in drug concentration over time. Pharmacokinetic models help characterize drug behavior in patients, predict drug concentration in the body fluids, calculate optimum dosage regimens, and evaluate the risk of toxicity. However, ensuring that the model fits the experimental data accurately...
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Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

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Pharmacokinetic models are mathematical constructs that represent and predict the time course of drug concentrations in the body, providing meaningful pharmacokinetic parameters. These models are categorized into compartment, physiological, and distributed parameter models.
The distributed parameter models are specifically designed to account for variations and differences in some drug classes. This model is particularly useful for assessing regional concentrations of anticancer or...
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Data science approaches to pharmacogenetics.

N M Penrod, J H Moore1

  • 1Department of Genetics, Institute for Quantitative Biomedical Sciences, Geisel School of Medicine, Dartmouth College, Lebanon, NH 03756, USA. Jason.H.Moore@dartmouth.edu.

Current Molecular Medicine
|August 12, 2014
PubMed
Summary
This summary is machine-generated.

Pharmacogenetic studies use statistics to analyze genetic variations affecting drug responses. Advances enable genome-wide association studies (GWAS) for personalized medicine and optimized drug dosing.

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Area of Science:

  • Genetics and Bioinformatics
  • Pharmacology and Therapeutics
  • Statistical Genetics

Background:

  • Pharmacogenetic studies historically used candidate gene approaches, requiring prior knowledge of specific genes.
  • Focus was on drug metabolizing enzymes, transporters, and targets for efficacy and adverse events.
  • Technological advancements have shifted studies towards hypothesis-free, genome-wide approaches.

Purpose of the Study:

  • To review the evolution of pharmacogenetic study designs.
  • To highlight the application of data science in analyzing pharmacologic outcomes.
  • To discuss the potential for genotype-based dosing algorithms.

Main Methods:

  • Review of candidate gene approaches in early pharmacogenetics.
  • Description of genome-wide association studies (GWAS) for comprehensive genetic analysis.
  • Discussion of data science principles applied to genetic data and drug response.

Main Results:

  • Transition from hypothesis-driven to hypothesis-free genetic analyses.
  • Identification of novel genetic biomarkers and therapeutic targets through GWAS.
  • Potential for discovering gene-gene interactions influencing drug activity.

Conclusions:

  • Genome-wide association studies (GWAS) offer powerful tools for pharmacogenetic discovery.
  • Data science is crucial for integrating genetic data with clinical outcomes.
  • The ultimate goal is to develop personalized dosing algorithms for improved patient safety and efficacy.