An accurate quantitative analysis of polymorphic content by chemometric X-ray powder diffraction.

Area of Science:

• Analytical Chemistry
• Materials Science
• Pharmaceutical Analysis

Background:

• Carbamazepine (CBZ) exists in multiple polymorphic forms, impacting its bioavailability.
• Accurate quantification of CBZ polymorphs is crucial for pharmaceutical development and quality control.
• Traditional methods for polymorph quantification can be time-consuming or destructive.

Purpose of the Study:

• To develop and validate a robust calibration model for predicting carbamazepine polymorphic content using X-ray powder diffraction (XRD) and partial least-squares regression analysis (PLS).
• To compare the predictive accuracy of the XRD-PLS method with the conventional peak-area method.
• To establish a non-destructive, non-contact analytical approach for polymorph quantification.

Main Methods:

• Characterization of carbamazepine (CBZ) Forms I and III using differential scanning calorimetry (DSC) and X-ray powder diffraction (XRD).
• Preparation of powder mixtures of CBZ Forms I and III at varying ratios (0-100% w/w form III).
• Application of partial least-squares regression analysis (PLS) to XRD data for calibration model development, compared against a peak-area method using five diffraction peaks.

Main Results:

• The partial least-squares regression (PLS) method demonstrated superior predictive accuracy for carbamazepine (CBZ) polymorphic content compared to the peak-area method.
• The developed XRD-PLS model effectively predicted the polymorphic ratios in the prepared mixtures.
• The XRD-PLS approach was validated as a non-destructive and non-contact method, mitigating particle orientation effects.

Conclusions:

• The integration of X-ray powder diffraction (XRD) with partial least-squares regression analysis (PLS) provides a highly accurate and reliable method for quantifying carbamazepine (CBZ) polymorphic content.
• The XRD-PLS method offers significant advantages over traditional techniques, including non-destructiveness and avoidance of particle orientation issues.
• This statistically based approach enhances the efficiency and reliability of polymorph analysis in pharmaceutical quality control.