Orodispersible tablets disintegrate rapidly (within 3 minutes) in the oral cavity and release the medicament before swallowing. The mode of disintegrant addition might affect the properties of orodispersible tablets. The objective of this study was to formulate and evaluate orodispersible tablets by studying different modes of disintegration addition with varying concentrations of disintegrants. The wet granulation method was used to produce the orodispersible tablets. Two methods of disintegration addition were compared (i.e., intragranular, extragranular). Three disintegrants (i.e., cornstarch, sodium starch glycolate, crospovidone) were used at three levels (5%, 10%, and 15%) in the study. The formulations were tested for the powder flowability (angle of repose) and characterized physically (hardness, weight, thickness, friability, disintegration time). The mangosteen pericarp extract was used as a model active pharmaceutical ingredient to be incorporated into the optimum formulation. It was observed that the extragranular method produced granules with better flowability compared to that of the intragranular method. Crospovidone was found as the most efficient disintegrant among the three. The optimum formulation selected was one with the highest concentration of crospovidone (15%), which showed the fastest disintegration time. The mode of disintegrant addition into the orodispersible tablets formulation was found to show a marked difference in the disintegration, as well as other physical characteristics of the orodispersible tablets where the extragranular mode of addition showed better property, which caused the orodispersible tablets to disintegrate the fastest.
Automatic classification of colon and lung cancer images is crucial for early detection and accurate diagnostics. However, there is room for improvement to enhance accuracy, ensuring better diagnostic precision. This study introduces two novel dense architectures (D1 and D2) and emphasizes their effectiveness in classifying colon and lung cancer from diverse images. It also highlights their resilience, efficiency, and superior performance across multiple datasets. These architectures were tested on various types of datasets, including NCT-CRC-HE-100K (set of 100,000 non-overlapping image patches from hematoxylin and eosin (H&E) stained histological images of human colorectal cancer (CRC) and normal tissue), CRC-VAL-HE-7K (set of 7180 image patches from N = 50 patients with colorectal adenocarcinoma, no overlap with patients in NCT-CRC-HE-100K), LC25000 (Lung and Colon Cancer Histopathological Image), and IQ-OTHNCCD (Iraq-Oncology Teaching Hospital/National Center for Cancer Diseases), showcasing their effectiveness in classifying colon and lung cancers from histopathological and Computed Tomography (CT) scan images. This underscores the multi-modal image classification capability of the proposed models. Moreover, the study addresses imbalanced datasets, particularly in CRC-VAL-HE-7K and IQ-OTHNCCD, with a specific focus on model resilience and robustness. To assess overall performance, the study conducted experiments in different scenarios. The D1 model achieved an impressive 99.80 % accuracy on the NCT-CRC-HE-100K dataset, with a Jaccard Index (J) of 0.8371, a Matthew's Correlation Coefficient (MCC) of 0.9073, a Cohen's Kappa (Kp) of 0.9057, and a Critical Success Index (CSI) of 0.8213. When subjected to 10-fold cross-validation on LC25000, the D1 model averaged (avg) 99.96 % accuracy (avg J, MCC, Kp, and CSI of 0.9993, 0.9987, 0.9853, and 0.9990), surpassing recent reported performances. Furthermore, the ensemble of D1 and D2 reached 93 % accuracy (J, MCC, Kp, and CSI of 0.7556, 0.8839, 0.8796, and 0.7140) on the IQ-OTHNCCD dataset, exceeding recent benchmarks and aligning with other reported results. Efficiency evaluations were conducted in various scenarios. For instance, training on only 10 % of LC25000 resulted in high accuracy rates of 99.19 % (J, MCC, Kp, and CSI of 0.9840, 0.9898, 0.9898, and 0.9837) (D1) and 99.30 % (J, MCC, Kp, and CSI of 0.9863, 0.9913, 0.9913, and 0.9861) (D2). In NCT-CRC-HE-100K, D2 achieved an impressive 99.53 % accuracy (J, MCC, Kp, and CSI of 0.9906, 0.9946, 0.9946, and 0.9906) with training on only 30 % of the dataset and testing on the remaining 70 %. When tested on CRC-VAL-HE-7K, D1 and D2 achieved 95 % accuracy (J, MCC, Kp, and CSI of 0.8845, 0.9455, 0.9452, and 0.8745) and 96 % accuracy (J, MCC, Kp, and CSI of 0.8926, 0.9504, 0.9503, and 0.8798), respectively, outperforming previously reported results and aligning closely with others. Lastly, training D2 on just 10 % of NCT-CRC-HE-100K and testing on CRC-VAL-HE-7K resulted in significant outperformance of InceptionV3, Xception, and DenseNet201 benchmarks, achieving an accuracy rate of 82.98 % (J, MCC, Kp, and CSI of 0.7227, 0.8095, 0.8081, and 0.6671). Finally, using explainable AI algorithms such as Grad-CAM, Grad-CAM++, Score-CAM, and Faster Score-CAM, along with their emphasized versions, we visualized the features from the last layer of DenseNet201 for histopathological as well as CT-scan image samples. The proposed dense models, with their multi-modality, robustness, and efficiency in cancer image classification, hold the promise of significant advancements in medical diagnostics. They have the potential to revolutionize early cancer detection and improve healthcare accessibility worldwide.
Orally disintegrating tablets are a solid dosage form that will disintegrate rapidly within 3 minutes upon contact with saliva. Fillers or diluents are excipients that are used to make up the volume of orally disintegrating tablets, and some might act as a disintegrant or binder that will affect the physical properties of orally disintegrating tablets. The objective of this study was to formulate and evaluate physical properties of orally disintegrating tablets containing Annona muricata leaves extract by a freeze-drying method using different fillers at different concentrations. In this study, fifteen formulations of orally disintegrating tablets were prepared by a freeze-drying method with different fillers such as starch, lactose, microcrystalline cellulose, StarLac, and CombiLac at 5%, 10%, and 15%. The orally disintegrating tablets were evaluated for hardness, thickness, weight variation, friability, and disintegration time test. The optimum formulation was chosen and incorporated with Annona muricata leaves extract. The results obtained in this work indicated that Formulation 3, with 15% starch, was the most optimum formulation due to the shortest disintegration time (21.08 seconds ± 4.24 seconds), and all the physical tests were within the acceptable range. The orally disintegrating tablets containing Annona muricata leaves extract possessed antioxidant activity and stable at least for 3 months under 60°C and 75% relative humidity.
Orally disintegrating tablets, which were originally developed in the pharmaceutical field to improve the compliance of patients who had difficulty swallowing tablets, have become a preferable choice in solid dosage forms since it brings advantages to the patients and consumers in the healthcare system. Among the advantages of this novel dosage form are a faster onset of action, improved bioavailability, and the ease of administration as it can be taken without water. However, there are still some limitations of orally disintegrating tablets that need to be overcome, including a lack of mechanical strength, an unpleasant taste of the drug in the mouth, and a stability issue due to its hygroscopicity nature. This objective of this study was to identify the composition of co-processed excipients comprising of mannitol, microcrystalline cellulose, xylitol, and crospovidone or croscarmellose sodium in order to formulate orally disintegrating tablets containing memantine hydrochloride. This study was carried out in two stages. Firstly, orally disintegrating tablets containing memantine hydrochloride with 6 different formulations, which differed in the percentage of crospovidone or croscarmellose sodium, were formulated and manufactured. Secondly, the orally disintegrating tablets obtained were evaluated through pre- and post-compression tests based on the standard for orally disintegrating tablets. Formulation 3, which consisted of 10% xylitol, 10% mannitol, 72% microcrystalline cellulose, and 8% crospovidone, was chosen as the optimum formulation for the co-processed excipient since it was the fastest disintegration process among all the formulations in the study. In addition, Formulation 3 also showed the acceptable and satisfying results in other evaluation tests such as - weight variation test, hardness test, and friability test. The co-processed excipient comprising of 10% xylitol, 10% mannitol, 72% microcrystalline cellulose, and 8% crospovidone, which is characterized by improved functionalities such as a fast disintegration process, plays a crucial role in the application of orally disintegrating tablets.
Fast Melt Tablet (FMT) is a newer type of orally disintegrating tablet using the advantage of cocoa butter that melts at body temperature to achieve fast melting effect when the tablet is placed in oral cavity. However, oral disintegrating dosage form must have good palatability so that patients can accept it. The objective of this study is to taste mask a previously developed FMT containing memantine hydrochloride using artificial sweetener namely aspartame and acesulfame K and conduct a palatability study. Six formulations were developed and each sweetener was used at three level (10mg, 20mg and 30mg) to taste mask memantine hydrochloride in FMT. Formulation T7 was selected as the best taste masked formulation. Aspartame 30mg is sufficient to cover the bitter taste of memantine hydrochloride. A taste masked memantine hydrochloride FMT containing 30mg of aspartame was successfully developed. This formulation has hardness of 17.31 (0.18) Newton, 0.51 (0.02) g weight, 6.18 (0.42) mm thickness and in-vitro melting time of 31.16 (1.23) seconds. This novel dosage form has the potential to be commercialized as a patient friendly dosage form to treat Alzheimer's disease.