Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Pharmacogenomics: Identification of New Drug Targets

Pharmacogenomics: Identification of New Drug Targets

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...

Pharmacogenetics of Drug Metabolism: Overview

Pharmacogenetics of Drug Metabolism: Overview

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...

Principles of Pharmacogenetics: Types of Genetic Variants

Principles of Pharmacogenetics: Types of Genetic Variants

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...

Pharmacogenetics and Pharmacogenomics: Overview

Pharmacogenetics and Pharmacogenomics: Overview

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...

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

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

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...

Pharmacogenetics of Phase I Enzymes: Cytochrome P450 Isozymes

Pharmacogenetics of Phase I Enzymes: Cytochrome P450 Isozymes

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 isoenzymes,...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Financial Toxicity of Shorter Versus Longer Partial Breast Irradiation: A Randomized Clinical Trial.

International journal of radiation oncology, biology, physics·2026

Same author

Resolving Clinically Indeterminate Findings During Anal Cancer Surveillance with TTMV-HPV DNA.

Cancers·2026

Same author

Current practice on covariate adjustment and stratified analysis -based on survey results by ASA oncology estimand working group conditional and marginal effect task force.

BMC medical research methodology·2025

Same author

Child and adolescent/young adult cancer incidence and survival for Māori in Aotearoa New Zealand.

Cancer epidemiology·2025

Same author

Asian and ethnic minority health research in Aotearoa New Zealand: a scoping review of grey literature (2011-2020).

The New Zealand medical journal·2025

Same author

Balancing Events, Not Patients, Maximizes Power of the Logrank Test: And Other Insights on Unequal Randomization in Survival Trials.

Statistics in medicine·2025

Search research articles

Related Experiment Video

Updated: Jul 2, 2026

Drug Repurposing Hypothesis Generation Using the "RE:fine Drugs" System

Drug Repurposing Hypothesis Generation Using the "RE:fine Drugs" System

Published on: December 11, 2016

Methodologies for extracting functional pharmacogenomic experiments from international repository.

Yi-An Lin¹, Annie Chiang, Ray Lin

¹Stanford University.

AMIA ... Annual Symposium Proceedings. AMIA Symposium

|August 13, 2008

Summary

This study introduces automated methods to identify drug-related microarray experiments using semantic links between PubMed, MeSH, and UMLS. It reveals abundant public gene expression data for novel pharmacogenomic discoveries.

More Related Videos

Using Human Differentially Expressed Gene Lists to Perform Downstream Pathway Enrichment Analysis and Target Prioritization

Using Human Differentially Expressed Gene Lists to Perform Downstream Pathway Enrichment Analysis and Target Prioritization

Published on: October 3, 2025

Implementation of In Vitro Drug Resistance Assays: Maximizing the Potential for Uncovering Clinically Relevant Resistance Mechanisms

Implementation of In Vitro Drug Resistance Assays: Maximizing the Potential for Uncovering Clinically Relevant Resistance Mechanisms

Published on: December 9, 2015

Related Experiment Videos

Last Updated: Jul 2, 2026

Drug Repurposing Hypothesis Generation Using the "RE:fine Drugs" System

Drug Repurposing Hypothesis Generation Using the "RE:fine Drugs" System

Published on: December 11, 2016

Using Human Differentially Expressed Gene Lists to Perform Downstream Pathway Enrichment Analysis and Target Prioritization

Using Human Differentially Expressed Gene Lists to Perform Downstream Pathway Enrichment Analysis and Target Prioritization

Published on: October 3, 2025

Implementation of In Vitro Drug Resistance Assays: Maximizing the Potential for Uncovering Clinically Relevant Resistance Mechanisms

Implementation of In Vitro Drug Resistance Assays: Maximizing the Potential for Uncovering Clinically Relevant Resistance Mechanisms

Published on: December 9, 2015

Area of Science:

Bioinformatics
Genomics
Pharmacology

Background:

Microarray data is rapidly accumulating in public repositories.
Implicit drug information is available in PubMed, MeSH, and UMLS.
Discovering pharmacogenomic relationships from existing data is challenging.

Purpose of the Study:

To develop automatic methods for identifying drug-related microarray experiments from NCBI GEO.
To leverage semantic connections between biomedical data resources.
To facilitate the discovery of novel pharmacogenomic insights.

Main Methods:

Utilized automatic methods to mine NCBI GEO for drug-related microarray experiments.
Employed semantic connections between PubMed, MeSH, and UMLS.
Analyzed the association of microarray experiments with PubMed identifiers and MeSH terms.

Main Results:

51.5% of microarray experiments are linked to at least one PubMed identifier.
22.1% of these experiments include MeSH terms related to UMLS Pharmacologic Substances.
Demonstrated the potential for discovering novel drug indications and classifications.

Conclusions:

Publicly available gene expression data is a rich resource for pharmacogenomic research.
Automated methods can effectively identify relevant datasets for drug discovery.
This approach supports the advancement of personalized medicine and drug development.