Identification of Sildenafil Compound in Selected Drugs Using X-ray Study and Thermal Analysis.
Izabela JendrzejewskaTomasz GoryczkaEwa PietrasikJoanna KlimontkoJosef JampilekPublished in: Molecules (Basel, Switzerland) (2023)
Twelve drugs containing sildenafil compounds (sildenafil citrate and sildenafil base) were examined using X-ray studies and thermal analysis. According to the manufacturer's information, the presence of sildenafil was confirmed in all investigated drugs. The positions of diffraction lines (value of 2 θ angle) agree with the patterns presented in the ICDD database, Release 2018 (ICDD-International Centre of Diffraction Data). The difference expresses the agreement in the position of the diffraction line between the tested substance and the standard. A good agreement is when this difference is less than 0.2°. The values of interplanar distances d hkl are also compatible with the ICDD database. It indicated that the drug examined was genuine. Because all drugs are mixtures of different substances (API and excipients), the various diffraction line intensities were detected in all observed X-ray images for the tested drugs. The intensity of the diffraction line depends on many factors, like the amount of substance, coexisting phases, and mass absorption coefficient of the mixture. The thermal analysis confirmed the results obtained by the X-ray study. On DSC curves, the endothermic peaks for sildenafil compounds were observed. The determined melting points of sildenafil compounds corresponded to the values available in the literature. The results gathered by connecting two methods, X-ray study and thermal analysis, can help identify irregularities that may exist in pharmaceutical specimens, e.g., distinguishing genuine from counterfeit products, the presence of a correct polymorph, a lack of active substance, an inaccurate amount of the active substance, or excipients in the tested drug.
Keyphrases
- high resolution
- pulmonary hypertension
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- electron microscopy
- healthcare
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- dual energy
- mass spectrometry
- machine learning
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- deep learning
- single molecule
- convolutional neural network
- high intensity
- electronic health record
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