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Related Concept Videos

Protein-Drug Binding: Mechanism and Kinetics01:16

Protein-Drug Binding: Mechanism and Kinetics

488
Protein-drug binding refers to the interaction between drugs and proteins within the body. This binding process can occur intracellularly, involving drug interactions with enzymes or receptors within cells, or extracellularly, involving plasma proteins in the blood.
Various forces drive these interactions, including hydrogen bonds, hydrophobic interactions, ionic bonds, electrostatic interactions, and van der Waals forces. These bonds enable drugs to bind to specific sites on proteins,...
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Factors Affecting Protein-Drug Binding: Protein-Related Factors01:20

Factors Affecting Protein-Drug Binding: Protein-Related Factors

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Drug binding to proteins is a key aspect of pharmacokinetics and can influence a drug's distribution, absorption, and elimination in the body. Several factors, including the drug's physiochemical properties, protein concentration, disease states, and the number of binding sites on the protein, influence this process.
The physicochemical properties of a drug play a significant role in its ability to bind to proteins. Lipophilic drugs, which dissolve in fats, oils, and lipids, can be...
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Protein-Drug Binding: Determination Methods01:22

Protein-Drug Binding: Determination Methods

189
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...
189
Factors Affecting Protein-Drug Binding: Drug-Related Factors01:18

Factors Affecting Protein-Drug Binding: Drug-Related Factors

108
Drug binding to proteins is a complex phenomenon influenced by various drug-related factors, each playing a significant role in the interaction between drugs and proteins within the body.
One crucial factor in drug-protein binding is the drug's lipophilicity or its affinity for fat. More lipophilic drugs tend to have higher binding extents. For example, highly lipophilic drugs like cloxacillin exhibit substantial protein binding, with as much as 95% of the drug binding to proteins. In...
108
Factors Affecting Protein-Drug Binding: Drug Interactions01:23

Factors Affecting Protein-Drug Binding: Drug Interactions

141
Drug interactions are a critical aspect of pharmacology and can occur when two or more drugs compete for the same binding site. This competition can result in one drug displacing another, altering the effect of the displaced drug. Drug interactions are complex processes that rely heavily on how much of the displacer drug is present and how strongly it can bind to the same sites as the displaced drug.
Displacement interactions can have varying outcomes, ranging from toxicity to virtually...
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Drug Distribution: Plasma Protein Binding01:29

Drug Distribution: Plasma Protein Binding

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Drugs predominantly attach to plasma proteins, with only a small percentage remaining unbound. The unbound portion can be calculated as one minus the bound fraction. Acidic drugs form large, inactive complexes by reversibly binding to plasma albumin, which prevents them from diffusing across biological barriers. These drug-protein complexes act as reservoirs for the drugs. As the concentration of unbound drugs decreases, these complexes quickly dissociate to release the free drug, maintaining...
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Author Spotlight: Streamlining Protein Target Prediction and Validation via Molecular Docking and CETSA
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Dual Representation Learning for Predicting Drug-Side Effect Frequency Using Protein Target Information.

Sungjoon Park, Sangseon Lee, Minwoo Pak

    IEEE Journal of Biomedical and Health Informatics
    |January 19, 2024
    PubMed
    Summary

    This study introduces a novel deep learning model for predicting drug side effect frequencies, outperforming existing methods, especially for new drugs. It effectively integrates diverse drug features for improved accuracy in pharmacovigilance and drug repurposing.

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    Area of Science:

    • Pharmacology
    • Computational Biology
    • Artificial Intelligence in Medicine

    Background:

    • Understanding drug side effects is crucial for treatment risk assessment and drug repurposing.
    • Existing methods for predicting drug side effects often fail to predict frequency, handle unseen drugs, or utilize diverse drug features.
    • Current prediction models lack the integration of drug target information, limiting their comprehensive applicability.

    Purpose of the Study:

    • To develop a novel deep learning model for predicting drug-side effect frequencies.
    • To improve the prediction accuracy for both known and unseen drugs by integrating heterogeneous drug features.
    • To enhance drug repurposing and pharmacovigilance by providing more accurate side effect frequency predictions.

    Main Methods:

    • A deep learning model was developed to predict drug-side effect frequencies using heterogeneous drug features, including target protein information, molecular graphs, fingerprints, and chemical similarity.
    • The model creates simultaneous drug embeddings and learns dual representation vectors for drugs and side effects in a common vector space.
    • The Adaboost method was employed to extend the model's predictive capabilities to drugs lacking clear target proteins.

    Main Results:

    • The proposed model achieved state-of-the-art performance in predicting drug-side effect frequencies, significantly outperforming existing methods.
    • The model demonstrated superior predictive power, particularly for unseen drugs, highlighting its robustness and generalizability.
    • Ablation studies confirmed the effective integration and utilization of heterogeneous drug features, and drugs with explicit targets showed better prediction accuracy.

    Conclusions:

    • The novel deep learning model offers a significant advancement in predicting drug-side effect frequencies, addressing limitations of previous approaches.
    • The integration of diverse drug features and the dual representation learning contribute to the model's high accuracy and ability to generalize to new drugs.
    • This approach holds promise for improving drug safety monitoring, risk assessment, and facilitating efficient drug repurposing efforts.