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Calculating drug dosage and accumulation in multiple-dose regimens is crucial for achieving therapeutic efficacy while avoiding toxicity. This involves determining the plasma drug concentrations over time to optimize dosing schedules. The principle of superposition is fundamental in this process, allowing for the prediction of drug concentration in plasma following multiple doses based on single-dose data.The principle of superposition asserts that the plasma concentration-time curves from...
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The Quantification of Injectability by Mechanical Testing
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Exploring drug-protein interactions using the relationship between injection volume and capacity factor.

Xinfeng Zhao1, Qian Li1, Jiejun Chen2

  • 1Key Laboratory of Resource Biology and Biotechnology in Western China, Ministry of Education, College of Life Sciences, Northwest University, Xi'an 710069, China.

Journal of Chromatography. A
|March 27, 2014
PubMed
Summary

A new mathematical model for affinity chromatography offers a faster, ligand-conserving method for analyzing drug-protein and protein-protein interactions, validated by drug binding to HSA and β2-AR.

Keywords:
Affinity chromatographyBeta(2)-adrenoceptorDrug–protein interactionsHuman serum albuminMathematical model

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Area of Science:

  • Biochemistry
  • Analytical Chemistry
  • Pharmacology

Background:

  • Affinity chromatography is a key technique for studying drug-protein and protein-protein interactions.
  • Existing methods like frontal analysis and zonal elution have limitations, including long analysis times and high ligand consumption.
  • There is a need for improved mathematical models for data processing in affinity chromatography.

Purpose of the Study:

  • To develop and validate a novel mathematical model for affinity chromatography.
  • To enable faster and more efficient analysis of molecular interactions.
  • To provide an alternative method for characterizing drug-protein and protein-protein binding.

Main Methods:

  • Developed a new mathematical model based on the relationship between molar amount of injected solute and capacity factor.
  • Validated the model by analyzing the binding of drugs to human serum albumin (HSA).
  • Further validated the model by analyzing the binding of drugs to β2-adrenoceptor (β2-AR).

Main Results:

  • Determined association constants for omeprazole, propranolol, and promethazine binding to HSA, consistent with literature values.
  • Quantified association constants for salbutamol, norepinephrine, isoprenaline, bamethane, and methoxyphenamine binding to β2-AR.
  • Demonstrated a positive correlation between the model's results and radio-ligand binding assay data.

Conclusions:

  • The proposed mathematical model is a rapid and ligand-conserving approach for affinity chromatography.
  • The model accurately determines binding constants for drug-protein interactions.
  • This novel method has potential as an alternative for efficiently studying drug-protein and protein-protein interactions.