Data-smart machine learning methods for predicting composition-dependent Young's modulus of pharmaceutical compacts.
Data-smart machine learning methods for predicting composition-dependent Young's modulus of pharmaceutical compacts.
复制标题
用于预测药物压片的成分依赖性杨氏模量的数据智能机器学习方法。
DOI：
10.1016/j.ijpharm.2020.120049
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发表时间：
2021
影响因子：
5.8
通讯作者：
Akseli,Ilgaz
中科院分区：
文献类型：
--
作者：
Thomas,Stephen;Palahnuk,Hannah;Amini,Hossein;Akseli,Ilgaz
仅从成分性质预测活性药物成分（API）和辅料的压实粉末混合物的机械性能的能力可以减少配方设计中涉及的“试错”的数量。机器学习（ML）可以减少模型开发的时间和工作量，并且需要足够的历史数据。这项工作描述了线性和非线性ML模型的效用，用于预测已知赋形剂和未知原料药的直接压缩混合物的杨氏模量（YM），仅给定原料药的真实密度。模型的训练数据来自三种BCS I类原料药和两种赋形剂在四种药物负荷下混合，三种赋形剂组成，并压实成五种标称固体馏分。采用三种交叉验证（CV）方案测量模型的预测精度。最后，我们演示了该模型在配方设计中的应用。本文还讨论了模型的局限性和未来的工作。
The ability to predict mechanical properties of compacted powder blends of Active Pharmaceutical Ingredients (API) and excipients solely from component properties can reduce the amount of ‘trial-and-error’ involved in formulation design. Machine Learning (ML) can reduce model development time and effort with the imperative of adequate historical data. This work describes the utility of linear and nonlinear ML models for predicting Young’s modulus (YM) of directly compressed blends of known excipients and unknown API mixed at arbitrary compositions given only the true density of the API. The models were trained with data from compacts of three BCS Class I APIs and two excipients blended at four drug loadings, three excipient compositions, and compacted to five nominal solid fractions. The prediction accuracy of the models was measured using three cross-validation (CV) schemes. Finally, we demonstrate an application of the model to enable Quality-by-Design in formulation design. Limitations of the models and future work have also been discussed.