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Puzzle out Machine Learning Model-Explaining Disintegration Process in ODTs

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. Ora...

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Autores principales: Szlęk, Jakub, Khalid, Mohammad Hassan, Pacławski, Adam, Czub, Natalia, Mendyk, Aleksander
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9044744/
https://www.ncbi.nlm.nih.gov/pubmed/35456693
http://dx.doi.org/10.3390/pharmaceutics14040859
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author Szlęk, Jakub
Khalid, Mohammad Hassan
Pacławski, Adam
Czub, Natalia
Mendyk, Aleksander
author_facet Szlęk, Jakub
Khalid, Mohammad Hassan
Pacławski, Adam
Czub, Natalia
Mendyk, Aleksander
author_sort Szlęk, Jakub
collection PubMed
description 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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spelling pubmed-90447442022-04-28 Puzzle out Machine Learning Model-Explaining Disintegration Process in ODTs Szlęk, Jakub Khalid, Mohammad Hassan Pacławski, Adam Czub, Natalia Mendyk, Aleksander Pharmaceutics Article 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. MDPI 2022-04-13 /pmc/articles/PMC9044744/ /pubmed/35456693 http://dx.doi.org/10.3390/pharmaceutics14040859 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Szlęk, Jakub
Khalid, Mohammad Hassan
Pacławski, Adam
Czub, Natalia
Mendyk, Aleksander
Puzzle out Machine Learning Model-Explaining Disintegration Process in ODTs
title Puzzle out Machine Learning Model-Explaining Disintegration Process in ODTs
title_full Puzzle out Machine Learning Model-Explaining Disintegration Process in ODTs
title_fullStr Puzzle out Machine Learning Model-Explaining Disintegration Process in ODTs
title_full_unstemmed Puzzle out Machine Learning Model-Explaining Disintegration Process in ODTs
title_short Puzzle out Machine Learning Model-Explaining Disintegration Process in ODTs
title_sort puzzle out machine learning model-explaining disintegration process in odts
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9044744/
https://www.ncbi.nlm.nih.gov/pubmed/35456693
http://dx.doi.org/10.3390/pharmaceutics14040859
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