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Related Concept Videos

Pharmacokinetic–Pharmacodynamic Relationship: Exposure, Response and Effect01:26

Pharmacokinetic–Pharmacodynamic Relationship: Exposure, Response and Effect

The pharmacokinetic-pharmacodynamic (PK-PD) relationship describes the intricate link between drug exposure, efficacy, and toxicity, forming the foundation for optimal dosing regimens. This relationship uses mathematical modeling to characterize drug concentration-effect dynamics, ensuring precise therapeutic outcomes.Exposure represents the pharmacokinetic aspect of the PK-PD relationship, denoting the drug amount that elicits a biological response. It is typically quantified by administered...
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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...
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Pharmacokinetic-pharmacodynamic (PK–PD) modeling is essential in drug development and clinical pharmacology. It provides a quantitative framework to predict drug behavior and response over time. This approach integrates pharmacokinetics (PK), which describes the drug's absorption, distribution, metabolism, and excretion, with pharmacodynamics (PD), which characterizes the drug’s biological effects and mechanisms of action.The disposition kinetics of a drug determine its plasma...
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Diagonal Method to Measure Synergy Among Any Number of Drugs
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Published on: June 21, 2018

Drug effect prediction by polypharmacology-based interaction profiling.

Zoltán Simon1, Agnes Peragovics, Margit Vigh-Smeller

  • 1Department of Biochemistry, Institute of Biology, Eötvös Loránd University, Pázmány Péter sétány 1/C, H-1117 Budapest, Hungary.

Journal of Chemical Information and Modeling
|November 22, 2011
PubMed
Summary
This summary is machine-generated.

This study introduces a new method to link drug-protein interactions with drug effects, revealing and predicting drug actions. The approach successfully maps complex polypharmacology to clinical outcomes for many medications.

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Area of Science:

  • Pharmacology
  • Computational Biology
  • Drug Discovery

Background:

  • Most drugs interact with multiple protein targets, a concept known as polypharmacology.
  • Understanding these complex drug-target interactions is crucial for explaining drug efficacy and side effects.
  • Current methods struggle to fully elucidate the intricate relationships between drug targets and observed effects.

Purpose of the Study:

  • To develop a novel approach for relating complex drug-protein interaction profiles with drug effect profiles.
  • To systematically uncover and predict hitherto unknown drug effects based on interaction data.
  • To analyze the relationship between drug targets and 177 major effect categories for FDA-approved small-molecule drugs.

Main Methods:

  • Collected structural data and registered effect profiles for FDA-approved small-molecule drugs.
  • Calculated drug interactions with a series of nontarget protein binding sites.
  • Employed statistical analyses to establish relationships between interaction profiles and effect categories.

Main Results:

  • Confirmed a strong correlation between drug-protein interaction profiles and 177 major effect categories.
  • Successfully revealed the complete effect profiles for approximately 1200 small-molecule drugs.
  • Demonstrated the ability to systematically predict previously unknown drug effects.
  • Found that prediction power is independent of the specific protein set used for analysis.

Conclusions:

  • The developed approach effectively links drug-protein interactions to clinical effects, advancing the understanding of polypharmacology.
  • This method enables comprehensive elucidation and prediction of drug effects, aiding in drug discovery and development.
  • The findings highlight the power of computational analysis in unraveling complex drug-action mechanisms.