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Related Concept Videos

Volume of Distribution01:20

Volume of Distribution

The apparent volume of distribution (Vd) is a crucial pharmacokinetic parameter representing the hypothetical body fluid volume into which a drug disperses. It is calculated based on the total amount of drug in the body (estimated from the administered dose and bioavailability) divided by the plasma drug concentration. The total amount of drug in the body does not directly refer to the dose given but is derived by accounting for absorption, distribution, metabolism, and excretion processes.
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Quantitative Aspects of Drug-Receptor Interaction

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 Kd...
Structure-Activity Relationships and Drug Design01:28

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Role of Skin in Vitamin D Synthesis01:23

Role of Skin in Vitamin D Synthesis

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Transducer Mechanism: Nuclear Receptors

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Methodology for Studying Interactions of Vitamin A Membrane Receptors and Opsin Protein with their Ligands in Generating the Retinylidene Protein
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Radial Distribution Function descriptors for predicting affinity for vitamin D receptor.

Maykel Pérez González1, Zoila Gándara, Yagamare Fall

  • 1Molecular Simulation and Drug Design Group, Chemical Bioactive Center, Central University of Las Villas, Santa Clara, Villa Clara, Cuba. mpgonzalez76@yahoo.es

European Journal of Medicinal Chemistry
|December 11, 2007
PubMed
Summary

Quantitative Structure-Activity Relationship (QSAR) models using Radial Distribution Function (RDF) descriptors effectively predict Vitamin D analogues

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Methodology for Studying Interactions of Vitamin A Membrane Receptors and Opsin Protein with their Ligands in Generating the Retinylidene Protein
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Quantitative Analysis of Dietary Vitamin A Metabolites in Murine Ocular and Non-Ocular Tissues Using High-Performance Liquid Chromatography
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Quantitative Analysis of Dietary Vitamin A Metabolites in Murine Ocular and Non-Ocular Tissues Using High-Performance Liquid Chromatography

Published on: December 27, 2024

Area of Science:

  • Medicinal Chemistry
  • Computational Chemistry
  • Drug Discovery

Background:

  • The development of novel Vitamin D analogues is crucial for therapeutic applications.
  • The Vitamin D receptor (VDR) plays a vital role in various physiological processes.
  • Existing methods for identifying VDR-binding analogues can be time-consuming and resource-intensive.

Purpose of the Study:

  • To explore the utility of Quantitative Structure-Activity Relationship (QSAR) modeling for predicting Vitamin D analogue affinity to the VDR.
  • To develop and validate a predictive model using Radial Distribution Function (RDF) descriptors.
  • To assess the performance of the developed QSAR model against other predictive approaches.

Main Methods:

  • Application of Radial Distribution Function (RDF) descriptors to a set of 38 Vitamin D analogues.
  • Development of a QSAR model to correlate RDF descriptors with experimental VDR binding affinity.
  • Validation of the model using leave-one-out, bootstrapping, and external set methods.

Main Results:

  • The developed QSAR model, utilizing RDF descriptors, explained 80% of the experimental variance.
  • The model achieved a standard deviation of 0.35.
  • Validation metrics demonstrated good predictive power, with squared correlation coefficients of 0.72 (leave-one-out), 0.70 (bootstrapping), and 0.79 (external set).

Conclusions:

  • The RDF-based QSAR approach is a viable and effective method for the discovery of new Vitamin D analogues with VDR affinity.
  • The developed model exhibits robust predictive capabilities, outperforming other tested predictive models.
  • This study highlights the potential of computational methods in accelerating drug discovery for Vitamin D-related research.