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Related Experiment Videos

Ranitidine hydrochloride X-ray assay using a neural network.

S Agatonovic-Kustrin1, V Wu, T Rades

  • 1School of Pharmacy, University of Otago, Dunedin, New Zealand. nena.kustrin@stonebow.otago.ac.nz

Journal of Pharmaceutical and Biomedical Analysis
|June 17, 2000
PubMed
Summary

This study introduces an X-ray powder diffraction (XRD) method using artificial neural networks (ANNs) to identify and quantify ranitidine HCl crystal forms. The method accurately analyzes solid-state drug information from mixtures and tablets.

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Area of Science:

  • Analytical Chemistry
  • Materials Science
  • Computational Chemistry

Background:

  • Characterizing solid-state forms of pharmaceuticals like ranitidine HCl is crucial for drug quality and efficacy.
  • Traditional methods for analyzing crystalline mixtures can be complex and time-consuming.
  • Developing rapid and accurate analytical techniques for pharmaceutical solid-state analysis is an ongoing need.

Purpose of the Study:

  • To develop a simple X-ray powder diffractometric (XRD) method combined with artificial neural networks (ANNs) for recognizing and quantifying ranitidine HCl crystal modifications.
  • To apply this method for determining the solid-state information of bulk drug mixtures and quantifying ranitidine HCl from pharmaceutical tablets.
  • To evaluate the performance and potential of ANNs in analyzing complex analytical data, specifically XRD patterns.

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Main Methods:

  • Utilized X-ray powder diffractometry (XRD) for data acquisition.
  • Developed and trained a three-layer artificial neural network (ANN) using a back-propagation learning rule and a sigmoid output function.
  • Employed pattern recognition on entire XRD patterns, exploring data transformations (smoothing) for improved performance.
  • Applied the trained ANN to analyze mixtures of ranitidine HCl polymorphs and quantify ranitidine HCl from tablet formulations.

Main Results:

  • The ANN method accurately recognized and quantified two crystal modifications of ranitidine HCl in mixtures with high precision (mean sum of squared error < 0.02%).
  • Quantification of ranitidine HCl from tablets, even with significant interference from excipients, yielded excellent results (recovery = 98.65%) without data transformations.
  • The study demonstrated that ANN pattern analysis on XRD data is highly effective for solid-state characterization and quantification.
  • ANN performance was influenced by XRD pattern resolution, with smoothed diffractograms sufficient for distinguishing crystalline forms in mixtures.

Conclusions:

  • The developed XRD-ANN method offers a powerful and accurate approach for the solid-state analysis of ranitidine HCl, distinguishing and quantifying its polymorphic forms.
  • ANNs show significant potential for pattern analysis in chemical applications, particularly for interpreting complex analytical data like XRD patterns.
  • This technique provides a valuable tool for pharmaceutical quality control, enabling precise quantification of active pharmaceutical ingredients in various formulations.