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Related Concept Videos

Protein-Drug Binding: Determination Methods01:22

Protein-Drug Binding: Determination Methods

Determining protein-drug binding can be achieved through indirect and direct methods, each providing valuable insights into the interaction between proteins and drugs.
Indirect methods involve isolating the bound drug from its free form in biological samples such as blood, serum, or plasma. These techniques aim to measure the percentage of drugs bound to proteins. Equilibrium dialysis is a commonly used method where the free drug concentration at equilibrium is measured by separating the bound...
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...
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...
Pharmacokinetic–Pharmacodynamic Relationship: Problems01:24

Pharmacokinetic–Pharmacodynamic Relationship: Problems

The empirical approach to drug therapy optimization relies on correlating pharmacological response with administered dosage. Such an approach can be costly, time-consuming, and often yields poor correlation due to variables like formulation factors and drug elimination characteristics. A more precise approach correlates response with plasma drug concentration or the amount of drug in the body, rather than dosage. This is achieved through pharmacokinetic-pharmacodynamic (PK/PD) modeling, which...
Measurement of Bioavailability: Pharmacodynamic Methods01:20

Measurement of Bioavailability: Pharmacodynamic Methods

Pharmacodynamic methods provide insights into a drug's effects on physiological processes over time and play a crucial role in understanding bioavailability and therapeutic efficacy. These methods can be broadly classified into acute pharmacological and therapeutic response approaches, each with distinct mechanisms and applications.The acute pharmacological response method directly correlates a drug's physiological effects, such as ECG or pupil diameter changes, to its time course in the body.
Impact of Pharmacokinetic–Pharmacodynamic Models: Regulatory Decisions01:15

Impact of Pharmacokinetic–Pharmacodynamic Models: Regulatory Decisions

PK–PD modeling has significantly influenced FDA regulatory decisions, particularly drug approval, dosage optimization, and labeling. These models integrate pharmacokinetics (PK) and pharmacodynamics (PD) to predict drug behavior and effects, aiding in optimizing dosing regimens and enhancing the probability of clinical trial success.One notable example is Nesiritide (Natrecor®), a recombinant human brain natriuretic peptide for treating acute decompensated congestive heart failure (CHF).

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Related Experiment Video

Updated: May 20, 2026

Biosensor-based High Throughput Biopanning and Bioinformatics Analysis Strategy for the Global Validation of Drug-protein Interactions
08:31

Biosensor-based High Throughput Biopanning and Bioinformatics Analysis Strategy for the Global Validation of Drug-protein Interactions

Published on: December 1, 2020

Predicting new indications for approved drugs using a proteochemometric method.

Sivanesan Dakshanamurthy1, Naiem T Issa, Shahin Assefnia

  • 1Department of Oncology, Lombardi Comprehensive Cancer Center, Georgetown University Medical Center , Washington, DC 20057, United States. sd233@georgetown.edu

Journal of Medicinal Chemistry
|July 12, 2012
PubMed
Summary

A novel computational method, train, match, fit, streamline (TMFS), accurately predicts new uses for existing drugs by analyzing drug-target interactions. This drug repurposing approach identifies potential new therapies with high accuracy, accelerating clinical translation.

Related Experiment Videos

Last Updated: May 20, 2026

Biosensor-based High Throughput Biopanning and Bioinformatics Analysis Strategy for the Global Validation of Drug-protein Interactions
08:31

Biosensor-based High Throughput Biopanning and Bioinformatics Analysis Strategy for the Global Validation of Drug-protein Interactions

Published on: December 1, 2020

Area of Science:

  • Computational chemistry
  • Drug discovery
  • Pharmacology

Background:

  • Drug repurposing accelerates clinical translation by identifying new uses for approved drugs.
  • Current methods like screening and docking are often time-consuming.
  • A need exists for rapid, accurate computational methods to predict drug-target interactions.

Purpose of the Study:

  • To introduce a novel computational proteochemometric method, train, match, fit, streamline (TMFS).
  • To map new drug-target interaction space and predict potential drug repurposing opportunities.
  • To validate TMFS predictions experimentally.

Main Methods:

  • Developed the TMFS method combining shape, topology, and chemical signatures.
  • Applied TMFS to 3671 FDA-approved drugs against 2335 human protein crystal structures.
  • Validated predictions for mebendazole and celecoxib derivatives experimentally.

Main Results:

  • TMFS predicts drug-target associations with 91% accuracy.
  • Over 58% of known best ligands were top-ranked; top 40 ranked agents achieved 91% accuracy.
  • Identified mebendazole as a VEGFR2 inhibitor and celecoxib derivatives as cadherin-11 binders.

Conclusions:

  • TMFS is a powerful and accurate tool for drug repurposing.
  • The method successfully identified novel therapeutic potentials for existing drugs.
  • TMFS has the potential to significantly expand the repositioning of clinically active agents for new targets.