Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Pharmacogenomics: Identification of New Drug Targets01:29

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 Targets: β₂-Adrenergic Receptors, Apo E, Thymidylate Synthase01:11

Pharmacogenetics of Drug Targets: β₂-Adrenergic Receptors, Apo E, Thymidylate Synthase

Genetic polymorphisms in drug targets have emerged as critical determinants of interindividual variability in drug response and toxicity. Pharmacogenomic investigations increasingly focus on identifying these variations to personalize and optimize therapeutic interventions. A drug target may be a receptor, enzyme, or signaling protein involved in pharmacologic responses or disease-related pathways. While early pharmacogenetic studies focused primarily on drug metabolism, current research...
Pharmacogenetic Phenotypes: Alterations in Pharmacokinetics, Drug Targets and Biologic Milieu01:29

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...
Drug Discovery: Overview01:26

Drug Discovery: Overview

Drug discovery is a multifaceted process involving extensive screening, testing, and optimization of lead compounds to identify potential new drugs for therapeutic use. It combines several approaches, including screening large numbers of natural products, chemical modification of known active molecules, identification of new drug targets, and rational design based on biological mechanisms and drug-receptor structure. These approaches are carried out in both academic research laboratories and...
Targets for Drug Action: Overview01:26

Targets for Drug Action: Overview

Drugs target macromolecules to modify ongoing cellular processes. Primary drug targets include receptors, ion channels, transporters, and enzymes.
Receptors are either membrane-spanning or intracellular proteins, which upon binding a ligand, get activated and transmit the signal downstream to elicit a response. Drugs bind receptors, either mimicking the action of endogenous ligands or blocking the receptor activity to bring about a modified response. Nearly 35% of approved drugs target the G...
Targeted Cancer Therapies02:57

Targeted Cancer Therapies

The targeted cancer therapies, also known as “molecular targeted therapies,” take advantage of the molecular and genetic differences between the cancer cells and the normal cells. It needs a thorough understanding of the cancer cells to develop drugs that can target specific molecular aspects that drive the growth, progression, and spread of cancer cells without affecting the growth and survival of other normal cells in the body.
There are several types of targeted therapies against specific...

You might also read

Related Articles

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

Sort by
Same author

The howler monkey genome provides new insights into the distinctive howling and folivorous adaptations of New World monkeys.

Genome biology·2026
Same author

AAVC: an automated framework for high-accuracy ACMG-based variant classification.

Genetics in medicine : official journal of the American College of Medical Genetics·2026
Same author

Capturing multi-disease states on a spectrum with machine learning and routine clinical data.

Med (New York, N.Y.)·2026
Same author

When splicing is not all or none: GT>GC 5' splice-site variants as a model for intermediate effects and challenges in variant classification.

HGG advances·2026
Same author

Genome-wide detection of human 5' UTR variants that impact protein translation.

American journal of human genetics·2026
Same author

Ancestry-specific performance of variant effect predictors in clinical variant classification.

bioRxiv : the preprint server for biology·2026

Related Experiment Video

Updated: Jun 13, 2026

Drug Repurposing Hypothesis Generation Using the "RE:fine Drugs" System
05:10

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

Published on: December 11, 2016

Genetically supported drug target prioritization for rare diseases.

Robert Chen1,2,3, Áine Duffy1,2, Matthew Mort4

  • 1The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, 3 East 101st Street, Room 803, New York, NY, USA.

Genome Medicine
|June 12, 2026
PubMed
Summary

RareGPS, a machine-learning tool, identifies promising drug targets for rare diseases by integrating diverse data. It significantly improves predictions for drug development and clinical trial success.

Keywords:
Drug DiscoveryElectronic Health RecordsGenetic Association StudiesMachine LearningOff-Label UseRare Diseases

More Related Videos

Using Human Differentially Expressed Gene Lists to Perform Downstream Pathway Enrichment Analysis and Target Prioritization
03:08

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

Published on: October 3, 2025

Using Human Induced Pluripotent Stem Cell-derived Hepatocyte-like Cells for Drug Discovery
12:40

Using Human Induced Pluripotent Stem Cell-derived Hepatocyte-like Cells for Drug Discovery

Published on: May 19, 2018

Related Experiment Videos

Last Updated: Jun 13, 2026

Drug Repurposing Hypothesis Generation Using the "RE:fine Drugs" System
05:10

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
03:08

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

Published on: October 3, 2025

Using Human Induced Pluripotent Stem Cell-derived Hepatocyte-like Cells for Drug Discovery
12:40

Using Human Induced Pluripotent Stem Cell-derived Hepatocyte-like Cells for Drug Discovery

Published on: May 19, 2018

Area of Science:

  • Genomics
  • Pharmacology
  • Computational Biology

Background:

  • Rare and uncommon diseases often lack targeted therapies due to limited research.
  • Existing drug target prioritization methods struggle with the complexity of rare disease genetics.

Purpose of the Study:

  • To develop and validate a machine-learning framework, RareGPS, for prioritizing drug targets in rare diseases.
  • To improve the prediction of drug indications and clinical trial progression for rare disease therapeutics.

Main Methods:

  • RareGPS integrates 11 sources of genetic, clinical, and experimental evidence.
  • It employs an allelic-series model using the full distribution of genetic associations across allele-frequency bins.
  • The framework was validated using prescriptome analyses in two million patients and the AMELIE literature evaluation tool.

Main Results:

  • RareGPS outperforms existing resources in predicting drug indications and clinical trial progression across 161 phenotypes.
  • Top 1% prioritized targets showed a 58-fold higher likelihood of advancing from nonindicated to Phase IV trials.
  • Targets also demonstrated an 8-fold higher likelihood of advancing from Phase I to Phase IV trials compared to the middle 50%.

Conclusions:

  • RareGPS provides a robust computational approach for identifying high-potential drug targets for rare diseases.
  • The framework's predictions for 3,021,965 gene-phenotype pairs can accelerate rare disease drug discovery.
  • RareGPS enhances the efficiency of drug development pipelines for unmet medical needs in rare conditions.