A reusable, open-source tool, the ODT calculator, is now available at Heroku platform. By purchasing the personalized artwork, You will get 1 Master file with a picture of yourself, family member or a friend with a disintegration kind of effect. The critical parameters influencing the disintegration of the directly compressed ODTs were ascertained using the SHAP method to explain ML model predictions. A deep learning model with a 10-fold cross-validation NRMSE of 8.1% and an R 2 of 0.84 was obtained. ML models are presented with inputs from a database originally presented by Han et al., which was enhanced and curated to include chemical descriptors representing active pharmaceutical ingredient (API) characteristics. We present an alternative machine learning approach to optimize the disintegration time based on a wide variety of machine learning (ML) models through the H2O AutoML platform. Current strategies to optimize ODT disintegration times are based on a conventional trial-and-error method whereby a small number of samples are used as proxies for the compliance of whole batches. The disintegration time, therefore, is one of the most important and optimizable critical quality attributes (CQAs) for ODTs. ODTs are defined as a solid dosage form for rapid disintegration prior to swallowing. Orally disintegrating tablets (ODT), sometimes called oral dispersible tablets, are the dosage form of choice for patients with swallowing difficulties. Such difficulties lead to reduced patient compliance. While tablets represent the majority of marketed pharmaceutical products, there remain a significant number of patients who find it difficult to swallow conventional tablets. Tablets are the most common dosage form of pharmaceutical products. Disintegrants – Pharmaceutical Excipients. ![]()
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