METHODS: The Multi-Layered Perceptron (MLP) neural network was used to predict the dissolution profiles of theophylline pellets containing different ratios of microcrystalline cellulose (MCC) and glyceryl monostearate (GMS). The concepts of leave-one-out as well as a time-point by time-point estimation basis were used to predict the rate of drug release for each matrix ratio. All the data were used for training, except for one set which was selected to compare with the predicted output. The closeness between the predicted and the reference dissolution profiles was investigated using similarity factor (f2).
RESULTS: The f2 values were all above 60, indicating that the predicted dissolution profiles were closely similar to the dissolution profiles obtained from physical experiments.
CONCLUSION: The MLP network could be used as a model for predicting the dissolution profiles of matrix-controlled release theophylline pellet preparation in product development.
SUMMARY: A 29-year-old woman undergoing contrast-enhanced computed tomography developed lesions over her trunk starting 6 hours after imaging. Although initially diagnosed as an allergy to the radiocontrast agent, the condition progressively worsened into toxic epidermal necrolysis-drug reaction with eosinophilia and systemic symptoms overlap syndrome, despite adequate hydration and treatment. Investigation of the patient's medications revealed that she had been switched from brand-name to generic levetiracetam a week before the onset of symptoms. Levetiracetam was immediately discontinued, with the patient recovering after 2 weeks of intensive care. Adverse drug reaction analysis identified excipients in generic levetiracetam as the likely cause of the severe reaction.
CONCLUSION: This is the first reported case of severe cutaneous drug allergy after a brand-to-generic switch for levetiracetam. Brand-to-generic switches of medications can potentially cause severe allergic reactions due to differences in excipients.
Objective: This work aimed to explore the possibility of using Fourier-transform infrared (FTIR) spectroscopy and chemometrics to develop multivariate models to authenticate the "halal-ity" of pharmaceutical excipients with controversial halal status (e.g., magnesium stearate).
Materials and Methods: The FTIR spectral fingerprints of the substance were used to build principal component analysis (PCA) models. The effects of different spectral pretreatment processes such as auto-scaling, baseline correction, standard normal variate (SNV), first, and second derivatives were evaluated. The optimization of the model performance was established to ensure the sensitivity, specificity, and accuracy of the predicted models.
Results: Significant peaks corresponding to the properties of the compound were identified. For both bovine and plant-derived magnesium stearate, the peaks associated can be seen within the regions 2900cm-1 (C-H), 2800cm-1 (CH3), 1700cm-1 (C=O), and 1000-1300cm-1 (C-O). There was not much difference observed in the FTIR raw spectra of the samples from both sources. The quality and accuracy of the classification models by PCA and soft independent modeling classification analogy (SIMCA) have shown to improve using spectra optimized by first derivative followed by SNV smoothing.
Conclusion: This rapid and cost-effective technique has the potential to be expanded as an authentication strategy for halal pharmaceuticals.