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Related Concept Videos

Physiological Pharmacokinetic Models: Assumption with Protein Binding01:13

Physiological Pharmacokinetic Models: Assumption with Protein Binding

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Physiological models with protein binding in pharmacokinetics offer a sophisticated approach to understanding drug disposition. These models consider drug-protein interactions, enabling them to effectively predict drug concentrations in different organs and tissues. This precision aids in accurate drug dosing, providing a significant advantage over conventional models. A key process within these models is equilibration, which ensures that drug concentrations achieve a steady state within the...
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Related Experiment Video

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Adhesion Frequency Assay for In Situ Kinetics Analysis of Cross-Junctional Molecular Interactions at the Cell-Cell Interface
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Characterization of nanoparticle binding dynamics in microcirculation using an adhesion probability function.

Salman Sohrabi1, Doruk Erdem Yunus1, Jiang Xu2

  • 1Department of Mechanical Engineering & Mechanics, Lehigh University, Bethlehem, PA 18015, USA.

Microvascular Research
|July 18, 2016
PubMed
Summary
This summary is machine-generated.

A new numerical model accurately predicts nanoparticle transport and adhesion in microcirculation. The model reveals non-linear binding probabilities, crucial for understanding drug delivery dynamics.

Keywords:
Binding densityBinding probability functionDrug deliveryMicrofluidic channelNano-particle

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Area of Science:

  • Biomedical Engineering
  • Nanotechnology
  • Fluid Dynamics

Background:

  • Understanding nanoparticle transport and adhesion in microcirculation is crucial for drug delivery but challenging due to complex fluid dynamics and imaging.
  • Existing theoretical models often fail to accurately predict experimental results of particle deposition.
  • In-vitro microfluidic experiments highlight the influence of shear rate, carrier size, particle-substrate chemistry, and vessel geometry on deposition.

Purpose of the Study:

  • To develop and validate a numerical model for predicting nanoparticle transport and binding dynamics in microcirculation.
  • To investigate the relationship between binding probability and actual deposition rate under various conditions.
  • To explore the efficacy of a particulate model compared to continuum approaches for simulating drug carrier behavior.

Main Methods:

  • Development of a numerical model incorporating a binding probability function to simulate nanoparticle attachment and detachment.
  • Verification of the model using previously acquired in-vitro experimental data from microfluidic channels.
  • Analysis of particle transport, adhesion mechanisms, and deposition rates under varying shear rates, particle sizes, and channel geometries.

Main Results:

  • The numerical model accurately predicts nanoparticle transport and binding dynamics, aligning well with experimental data.
  • A non-linear correlation between binding probability and actual deposition rate was observed due to complex transport and adhesion mechanisms.
  • Small particle binding probability showed minimal change with shear rate, while large particle binding decreased exponentially with increasing shear.
  • The particulate model successfully simulated phenomena like drug particle accumulation near vessel walls, which continuum models cannot capture.
  • The study extensively discusses the impact of channel geometry and antibody density on particle binding.

Conclusions:

  • The developed numerical model serves as a simple yet efficient predictive tool for studying drug carrier binding in microcirculation.
  • The findings underscore the importance of considering complex particle dynamics and non-linear binding probabilities for accurate predictions.
  • The particulate approach offers advantages over continuum models in capturing specific microcirculation phenomena relevant to drug delivery.