Journal
PHARMACEUTICS
Volume 14, Issue 4, Pages -Publisher
MDPI
DOI: 10.3390/pharmaceutics14040859
Keywords
ODTs; machine learning; AutoML; shapley values; partial dependence plots; explainable models; orally disintegrating tablets
Categories
Funding
- Uniwersytet Jagielloski Collegium Medicum [N42/DBS/000205]
- qLIFE Priority Research Area under the program Excellence Initiative-Research University at Jagiellonian University
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This article presents an alternative machine learning approach to optimize the disintegration time of orally disintegrating tablets (ODTs). Various machine learning models are used to predict the disintegration time, and chemical descriptors representing the active pharmaceutical ingredient (API) characteristics are included. A deep learning model with good performance is obtained, and the critical parameters influencing disintegration are determined using the SHAP method.
Tablets are the most common dosage form of pharmaceutical products. While tablets represent the majority of marketed pharmaceutical products, there remain a significant number of patients who find it difficult to swallow conventional tablets. Such difficulties lead to reduced patient compliance. Orally disintegrating tablets (ODT), sometimes called oral dispersible tablets, are the dosage form of choice for patients with swallowing difficulties. ODTs are defined as a solid dosage form for rapid disintegration prior to swallowing. The disintegration time, therefore, is one of the most important and optimizable critical quality attributes (CQAs) for ODTs. 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. 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. 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. A deep learning model with a 10-fold cross-validation NRMSE of 8.1% and an R-2 of 0.84 was obtained. The critical parameters influencing the disintegration of the directly compressed ODTs were ascertained using the SHAP method to explain ML model predictions. A reusable, open-source tool, the ODT calculator, is now available at Heroku platform.
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