Using singular value decomposition to analyze drug/β-cyclodextrin mixtures: insights from X-ray powder diffraction...

Area of Science:

• Pharmaceutical Sciences
• Materials Science
• Computational Chemistry

Background:

• Crystalline structures and interconversion pathways of drug complexes are crucial for drug formulation and efficacy.
• Beta-cyclodextrin (β-CD) is widely used to improve drug solubility and stability.
• Understanding drug-β-CD interactions requires advanced analytical techniques.

Purpose of the Study:

• To apply mathematical models and linear algebra, specifically Singular Value Decomposition (SVD), to analyze drug-β-CD complexes.
• To identify crystalline structures and interconversion pathways of specific drug-β-CD mixtures.
• To determine the capability of SVD in resolving individual drug polymorphs within complex mixtures.

Main Methods:

• Preparation and analysis of drug-β-CD mixtures (indomethacin, diclofenac, famotidine, cimetidine with β-CD).
• Characterization using Differential Scanning Calorimetry (DSC), X-ray Powder Diffraction (XRPD), and Proton Nuclear Magnetic Resonance (¹H-NMR).
• Application of Singular Value Decomposition (SVD) for analyzing complex diffractogram datasets.

Main Results:

• SVD analysis was employed to identify polymorphs and interconversion pathways in drug-β-CD systems.
• The SVD procedure successfully extracted six independent basis functions from the diffractogram data.
• However, SVD failed to fully resolve specific forms, namely the indomethacin (INM) α-form and the cimetidine (CIM) B-form.

Conclusions:

• Linear algebra and SVD offer powerful tools for analyzing complex crystalline mixtures.
• Data processing limitations may restrict the resolution of all individual components in intricate systems.
• The study highlights the challenges and limitations of computational methods in fully representing complex real-world scenarios.