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Related Concept Videos

Model Approaches for Pharmacokinetic Data: Compartment Models01:14

Model Approaches for Pharmacokinetic Data: Compartment Models

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Compartmental analysis is a widely adopted approach to characterizing drug pharmacokinetics. It uses compartment models that conceptualize the body as a collection of reversibly communicating compartments, each representing a group of tissues exhibiting similar drug distribution characteristics. The movement rate of the drug between these compartments is typically described by first-order kinetics.
Two primary types of compartment models are recognized: mammillary and catenary. The more...
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Model Approaches for Pharmacokinetic Data: Physiological Models01:15

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Physiological models in pharmacokinetics are instrumental in understanding the distribution and elimination of drugs within the body. These models describe the drug concentration within target organs, influenced by factors such as drug uptake, tissue volume, and blood flow. Drug uptake is governed by the partition coefficient, which signifies the drug concentration ratio in tissue to that in the blood. The blood flow rate to a specific tissue is expressed as Qt, and the rate of change in tissue...
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Model-Independent Approaches for Pharmacokinetic Data: Noncompartmental Analysis00:59

Model-Independent Approaches for Pharmacokinetic Data: Noncompartmental Analysis

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Noncompartmental analyses offer an alternative method for describing drug pharmacokinetics without relying on a specific compartmental model. In this approach, the drug's pharmacokinetics are assumed to be linear, with the terminal phase log-linear. This assumption allows for simplified analysis and interpretation of the drug's behavior in the body.
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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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Analysis Methods of Pharmacokinetic Data: Model and Model-Independent Approaches01:14

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Drug disposition in the body is a complex process and can be studied using two major approaches: the model and the model-independent approaches.
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Pharmacokinetics: Overview01:10

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Pharmacokinetics is a scientific discipline that focuses on the journey of a drug within the body, encompassing four key stages: absorption, distribution, metabolism, and elimination. The first stage, absorption, involves the drug's transfer into the bloodstream. Several factors dictate the extent and speed of this process. For example, the liver often metabolizes oral drugs before they reach systemic circulation, leading to only partial absorption. In contrast, intravenous (IV)...
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Pharmacokinetic profiling of anticancer phytocompounds using computational approach.

Ashish Sharma1, Shilpa Sharma1, Mansi Gupta1

  • 1Bioinformatics Division, ICMR-National Institute of Cancer Prevention and Research, Noida, India.

Phytochemical Analysis : PCA
|April 19, 2018
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Summary

Natural products show promise for cancer treatment, but poor pharmacokinetics hinder development. This study identified anticancer phytomolecules with favorable absorption, distribution, metabolism, excretion, and toxicity (ADMET) profiles, creating a valuable database for researchers.

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Area of Science:

  • Pharmacology
  • Natural Products Chemistry
  • Computational Chemistry

Background:

  • Natural products offer diverse chemical scaffolds for drug lead development.
  • Poor pharmacokinetic properties frequently impede the progression of natural products in drug discovery pipelines.
  • Understanding absorption, distribution, metabolism, excretion, and toxicity (ADMET) is crucial for identifying viable drug candidates.

Purpose of the Study:

  • To explore the ADMET profiles of plant-derived anticancer compounds.
  • To identify phytomolecules with favorable drug-like properties using open-access databases.
  • To assess the potential of natural products as anticancer drug leads.

Main Methods:

  • Analysis of open-access databases (NPACT, CancerHSP, TaxKB) for plant-based anticancer molecules.
  • Evaluation of molecules based on physicochemical properties and physiological barriers to predict ADMET.
  • Comparison of predicted ADMET properties against known anticancer drugs.

Main Results:

  • Out of 5086 phytomolecules analyzed, 63% showed oral absorbability and 52% distributability.
  • A significant proportion (45%) were predicted to be metabolizable and excretable.
  • 28% of compounds exhibited favorable non-toxicity profiles for cardiotoxicity and CNS activity, with 28% overall possessing suitable pharmacokinetic properties.
  • Predictions aligned with trends observed for known anticancer drugs, increasing confidence in the results.

Conclusions:

  • An interactive database, ADMETCan, was developed to provide access to predicted ADMET properties of anticancer phytomolecules.
  • This resource is expected to reduce compound failure rates in anticancer drug discovery.
  • ADMETCan aims to benefit the scientific community in identifying and developing novel anticancer agents.