RESUMO
Objectives: The aim of this study is to examine resolution, identification, and characterization of forced degradation products of netarsudil by liquid chromatography-tandem mass spectrometry by validating a simple and sensitive high-performance liquid chromatography method for the resolution, identification, and quantification of two process-related impurities in netarsudil. Materials and Methods: Chromatographic separation was accomplished on a ZORBAX Eclipse XDB C18 (250 x 4.6 mm; 5 µ id) column at room temperature as the stationary phase and 257 nm as the detector wavelength with the mobile phase consisting of acetonitrile, methanol, and pH 4.6 phosphate buffer in 45:35:20 (v/v) at 1.0 mL/min flow rate in isocratic elution. Results: The method reported very sensitive detection limits of 0.008 µg/mL for impurity 1 and 0.003 µg/mL for impurity 1. The method produces a calibration curve linear in the concentration level of 25-200 for netarsudil and 0.025-0.2 µg/mL for impurities. The proposed method gives acceptable results for other validation parameters such as accuracy, precision, ruggedness, and robustness. The drug was subjected to various stress conditions such as acid, base, peroxide, and thermal and ultraviolet light to investigate the stability-indicating ability of the method. Considerable degradation was observed in stress studies, and the degradation products were well resolved from process-related impurities. The characterization of degradation products was performed on the basis of collision-induced dissociation mass spectral data, and the possible structures of the six degradation compounds of netarsudil were proposed. Conclusion: The outcomes of other validation studies were likewise satisfactory and proven adequate for the regular analysis of netarsudil and its process-related impurities in bulk drug and pharmaceutical dosage forms and can also be applied for the evaluation of the stress degradation mechanism of netarsudil.
RESUMO
Purpose: Optimizers are widely utilized across various domains to enhance desired outcomes by either maximizing or minimizing objective functions. In the context of deep learning, they help to minimize the loss function and improve model's performance. This study aims to evaluate the accuracy of different optimizers employed for autosegmentation of non-small cell lung cancer (NSCLC) target volumes on thoracic computed tomography images utilized in oncology. Materials and Methods: The study utilized 112 patients, comprising 92 patients from "The Cancer Imaging Archive" (TCIA) and 20 of our local clinical patients, to evaluate the efficacy of various optimizers. The gross tumor volume was selected as the foreground mask for training and testing the models. Of the 92 TCIA patients, 57 were used for training and validation, and the remaining 35 for testing using nnU-Net. The performance of the final model was further evaluated on the 20 local clinical patient datasets. Six different optimizers, namely AdaDelta, AdaGrad, Adam, NAdam, RMSprop, and stochastic gradient descent (SGD), were investigated. To assess the agreement between the predicted volume and the ground truth, several metrics including Dice similarity coefficient (DSC), Jaccard index, sensitivity, precision, Hausdorff distance (HD), 95th percentile Hausdorff distance (HD95), and average symmetric surface distance (ASSD) were utilized. Results: The DSC values for AdaDelta, AdaGrad, Adam, NAdam, RMSprop, and SGD were 0.75, 0.84, 0.85, 0.84, 0.83, and 0.81, respectively, for the TCIA test data. However, when the model trained on TCIA datasets was applied to the clinical datasets, the DSC, HD, HD95, and ASSD metrics showed a statistically significant decrease in performance compared to the TCIA test datasets, indicating the presence of image and/or mask heterogeneity between the data sources. Conclusion: The choice of optimizer in deep learning is a critical factor that can significantly impact the performance of autosegmentation models. However, it is worth noting that the behavior of optimizers may vary when applied to new clinical datasets, which can lead to changes in models' performance. Therefore, selecting the appropriate optimizer for a specific task is essential to ensure optimal performance and generalizability of the model to different datasets.