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Related Concept Videos

Dose-Response Relationship: Overview01:03

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Agonists can bind with and activate receptors, resulting in the formation of drug-receptor complexes. Once formed, these complexes catalyze many biochemical processes at the cellular level and subsequently induce a pharmacologic response. The degree of response is directly proportional to the fraction of activated receptors, which in turn, depends on the concentration of the drug at the receptor site as well as the sensitivity of the receptor. An increase in the administered dose contributes to...
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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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The plasma drug concentration-time curve is a crucial tool in pharmacokinetics, representing the drug's concentration in plasma at different time intervals post-administration. This curve illustrates the drug's journey from absorption into the systemic circulation, distribution to body tissues, and eventual elimination through excretion or biotransformation.
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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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A Gran plot is used to predict the equivalence volume or endpoint of a potentiometric or acid-base titration without reaching the endpoint. Typically, titration data is collected as a function of the titrant's volume up to a point less than the equivalence volume and then transformed into a linear format. The straight line is extended to the x-axis, indicating the necessary titrant volume to achieve the equivalence point.
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Related Experiment Video

Updated: Nov 16, 2025

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
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Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

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Graph Convolutional Networks for Drug Response Prediction.

Tuan Nguyen, Giang T T Nguyen, Thin Nguyen

    IEEE/ACM Transactions on Computational Biology and Bioinformatics
    |February 19, 2021
    PubMed
    Summary
    This summary is machine-generated.

    GraphDRP, a novel graph convolutional network method, enhances drug response prediction by representing drugs as molecular graphs. This approach improves accuracy and provides insights into genomic contributions to drug response.

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    Last Updated: Nov 16, 2025

    Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
    04:09

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    Area of Science:

    • Computational biology
    • Bioinformatics
    • Machine learning

    Background:

    • Drug response prediction is crucial for personalized medicine.
    • Current machine learning methods often use inadequate drug representations (strings).
    • Interpretability of genomic features influencing drug response is often overlooked.

    Purpose of the Study:

    • To introduce GraphDRP, a novel method for drug response prediction.
    • To represent drugs using molecular graphs and cell lines using genomic aberrations.
    • To improve prediction accuracy and interpretability.

    Main Methods:

    • Utilized graph convolutional networks (GCNs) for feature extraction from molecular graphs.
    • Represented cell lines as binary vectors of genomic aberrations.
    • Integrated drug and cell line features for prediction using a fully-connected neural network.

    Main Results:

    • GraphDRP significantly outperformed tCNNS across all performance metrics.
    • Saliency maps revealed the contribution of specific genomic aberrations to drug responses.
    • Demonstrated the efficacy of graph-based drug representation.

    Conclusions:

    • Representing drugs as molecular graphs improves drug response prediction accuracy.
    • GraphDRP offers enhanced interpretability for understanding drug-genomic interactions.
    • The developed method and code are publicly available for research.