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Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

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Pharmacokinetic models are mathematical constructs that represent and predict the time course of drug concentrations in the body, providing meaningful pharmacokinetic parameters. These models are categorized into compartment, physiological, and distributed parameter models.
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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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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.
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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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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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Drug Repurposing Hypothesis Generation Using the "RE:fine Drugs" System
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Deep Probabilistic Matrix Factorization on Graphs: Application to Drug Repositioning in Antimicrobial Resistance.

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    Antimicrobial resistance (AMR) necessitates new treatments. This study introduces a novel algorithm for drug repositioning, outperforming existing methods in identifying effective antibiotics against resistant bacteria.

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    Area of Science:

    • Computational biology
    • Pharmacology
    • Infectious diseases

    Background:

    • Antimicrobial resistance (AMR) is a growing global health crisis.
    • Misuse and overuse of antibiotics lead to treatment failures.
    • Effective treatments for resistant infections are urgently needed.

    Purpose of the Study:

    • To develop a novel algorithm for general-purpose drug repositioning.
    • To address the challenge of ineffective first-line antibiotics due to AMR.
    • To identify potential antibiotic treatments for resistant bacterial infections.

    Main Methods:

    • Developed a new algorithm for drug repositioning using a matrix completion framework on graphs.
    • Employed a probabilistic approach combining deep matrix factorization and graph learning.
    • Curated a new dataset of antibiotic-bacteria associations.

    Main Results:

    • The proposed method demonstrated superior performance compared to benchmarks.
    • The algorithm achieved precise drug repurposing for general applications.
    • Successfully applied to three specific antimicrobial resistance (AMR) case studies.

    Conclusions:

    • The developed algorithm offers a promising approach for drug repositioning in the context of AMR.
    • This method can aid in identifying effective treatments for infections caused by resistant bacteria.
    • The findings contribute to combating the global health challenge of antimicrobial resistance.