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Related Concept Videos

Factors Affecting Protein-Drug Binding: Drug Interactions01:23

Factors Affecting Protein-Drug Binding: Drug Interactions

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

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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...
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Factors Affecting Drug Response: Overview01:21

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When it comes to infants and young children, they are typically administered smaller doses of medication in comparison to adults. This is primarily because their organ functions still need to fully develop, meaning their bodies are not as efficient at metabolizing or eliminating drugs. Additionally, their blood-brain barrier is more permeable than in adults. As a result, high concentrations of drugs can easily penetrate the central nervous system (CNS), potentially leading to neurological...
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Factors Affecting Protein-Drug Binding: Protein-Related Factors01:20

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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.
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Factors Affecting Protein-Drug Binding: Patient-Related Factors01:29

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Protein-drug binding, a pivotal aspect of pharmacokinetics, is subject to considerable variability influenced by an array of patient-related factors. The intricate interplay of age, individual differences, and pathological conditions significantly impact the binding dynamics and subsequent pharmacological effects.
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The receptor occupancy theory connects a drug's response to the number of occupied receptors. With higher drug concentrations, more receptors are occupied, leading to increased responses. The formation of drug-receptor complexes involves association and dissociation rates, which reach equilibrium when the forward and backward reactions are equal. The equilibrium association constant (Ka) and its inverse, the equilibrium dissociation constant (Kd), indicate drug affinity. Higher Ka and lower...
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Graph Regularized Probabilistic Matrix Factorization for Drug-Drug Interactions Prediction.

Stuti Jain, Emilie Chouzenoux, Kriti Kumar

    IEEE Journal of Biomedical and Health Informatics
    |April 7, 2023
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    Summary
    This summary is machine-generated.

    Predicting drug-drug interactions (DDIs) is crucial for safe medication use. A new Graph Regularized Probabilistic Matrix Factorization (GRPMF) method shows superior performance in identifying potential DDIs.

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    Area of Science:

    • Pharmacology and Bioinformatics
    • Computational Drug Discovery

    Background:

    • Simultaneous drug administration can lead to adverse drug reactions.
    • Accurate identification of drug-drug interactions (DDIs) is vital for drug development and repurposing.
    • Matrix factorization (MF) is a promising approach for DDI prediction.

    Purpose of the Study:

    • To introduce a novel Graph Regularized Probabilistic Matrix Factorization (GRPMF) method for DDI prediction.
    • To integrate expert knowledge into MF using a graph-based regularization strategy.
    • To develop an efficient optimization algorithm for the GRPMF model.

    Main Methods:

    • Developed a Graph Regularized Probabilistic Matrix Factorization (GRPMF) model.
    • Incorporated expert knowledge via graph-based regularization within the MF framework.
    • Utilized an alternating optimization algorithm to solve the non-convex problem.

    Main Results:

    • Evaluated GRPMF performance on the DrugBank dataset.
    • Compared GRPMF against existing state-of-the-art DDI prediction techniques.
    • Demonstrated superior performance of GRPMF over its counterparts.

    Conclusions:

    • GRPMF effectively predicts drug-drug interactions by leveraging expert knowledge.
    • The proposed method offers an improvement over current DDI prediction techniques.
    • GRPMF holds potential for enhancing drug safety and facilitating drug repurposing.