Dataset Information

Physical Ageing of Amorphous Indapamide Characterised by Differential Scanning Calorimetry.

ABSTRACT: The objective of this study was to characterise amorphous indapamide (IND) subjected to the physical ageing process by differential scanning calorimetry (DSC). The amorphous indapamide was annealed at different temperatures below the glass transition, i.e., 35, 40, 45, 65, 75 and 85 °C for different lengths of time, from 30 min up to a maximum of 32 h. DSC was used to characterise both the crystalline and the freshly prepared glass and to monitor the extent of relaxation at temperatures below the glass transition (Tg). No ageing occurred at 35, 40 and 45 °C at the measured lengths of times. Molecular relaxation time constants (?KWW) for samples aged at 65, 75 and 85 °C were determined by the Kohlrausch-Williams-Watts (KWW) equation. The fragility parameter m (a measure of the stability below the glass transition) was determined from the Tg dependence from the cooling and heating rates, and IND was found to be relatively stable ("moderately fragile") in the amorphous state. Temperature-modulated DSC was used to separate reversing and nonreversing processes for unaged amorphous IND. The enthalpy relaxation peak was clearly observed as a part of the nonreversing signal. Heat capacities data for unaged and physically aged IND were fitted to Cp baselines of solid and liquid states of IND, were integrated and enthalpy was presented as a function of temperature.

SUBMITTER: Drogon A

PROVIDER: S-EPMC7558952 | biostudies-literature | 2020 Aug

REPOSITORIES: biostudies-literature

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Physical Ageing of Amorphous Indapamide Characterised by Differential Scanning Calorimetry.

Drogoń Agata A Skotnicki Marcin M Skotnicka Agnieszka A Pyda Marek M

Pharmaceutics 20200825 9

The objective of this study was to characterise amorphous indapamide (IND) subjected to the physical ageing process by differential scanning calorimetry (DSC). The amorphous indapamide was annealed at different temperatures below the glass transition, i.e., 35, 40, 45, 65, 75 and 85 °C for different lengths of time, from 30 min up to a maximum of 32 h. DSC was used to characterise both the crystalline and the freshly prepared glass and to monitor the extent of relaxation at temperatures below th ...[more]

PMID: 32854214

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Project description:The thermoanalytical technique differential scanning calorimetry (DSC) has been applied to characterize protein denaturation patterns (thermograms) in blood plasma samples and relate these to a subject's health status. The analysis and classification of thermograms is challenging because of the high-dimensionality of the dataset. There are various methods for group classification using high-dimensional data sets; however, the impact of using high-dimensional data sets for cancer classification has been poorly understood. In the present article, we proposed a statistical approach for data reduction and a parametric method (PM) for modeling of high-dimensional data sets for two- and three- group classification using DSC and demographic data. We compared the PM to the non-parametric classification method K-nearest neighbors (KNN) and the semi-parametric classification method KNN with dynamic time warping (DTW). We evaluated the performance of these methods for multiple two-group classifications: (i) normal versus cervical cancer, (ii) normal versus lung cancer, (iii) normal versus cancer (cervical + lung), (iv) lung cancer versus cervical cancer as well as for three-group classification: normal versus cervical cancer versus lung cancer. In general, performance for two-group classification was high whereas three-group classification was more challenging, with all three methods predicting normal samples more accurately than cancer samples. Moreover, specificity of the PM method was mostly higher or the same as KNN and DTW-KNN with lower sensitivity. The performance of KNN and DTW-KNN decreased with the inclusion of demographic data, whereas similar performance was observed for the PM which could be explained by the fact that the PM uses fewer parameters as compared to KNN and DTW-KNN methods and is thus less susceptible to the risk of overfitting. More importantly the accuracy of the PM can be increased by using a greater number of quantile data points and by the inclusion of additional demographic and clinical data, providing a substantial advantage over KNN and DTW-KNN methods.

Dataset Information

Physical Ageing of Amorphous Indapamide Characterised by Differential Scanning Calorimetry.

Publications

Physical Ageing of Amorphous Indapamide Characterised by Differential Scanning Calorimetry.

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