Szlęk Jakub, Khalid Mohammad Hassan, Pacławski Adam, Czub Natalia, Mendyk Aleksander
Department of Pharmaceutical Technology and Biopharmaceutics, Jagiellonian University Medical College, Medyczna 9, 30-688 Kraków, Poland.
Pharmaceutics. 2022 Apr 13;14(4):859. doi: 10.3390/pharmaceutics14040859.
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 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.
片剂是药品最常见的剂型。虽然片剂占上市药品的大多数，但仍有相当数量的患者发现吞咽传统片剂困难。这些困难导致患者依从性降低。口腔崩解片（ODT），有时也称为口腔分散片，是吞咽困难患者的首选剂型。ODT被定义为一种固体剂型，在吞咽前能快速崩解。因此，崩解时间是ODT最重要且可优化的关键质量属性（CQA）之一。当前优化ODT崩解时间的策略基于传统的试错方法，即使用少量样品作为整批产品合规性的代表。我们提出了一种替代的机器学习方法，通过H2O自动机器学习平台，基于多种机器学习（ML）模型来优化崩解时间。ML模型的输入来自Han等人最初提供的数据库，该数据库经过增强和整理，纳入了代表活性药物成分（API）特性的化学描述符。获得了一个深度学习模型，其10倍交叉验证的NRMSE为8.1%，R为0.84。使用SHAP方法来解释ML模型预测，确定了影响直接压片ODT崩解的关键参数。现在可以在Heroku平台上获得一个可重复使用的开源工具——ODT计算器。
