Dhruv Gupta, Anuj A Biswas, Rohan Chand Sahu, Sanchit Arora, Dinesh Kumar, Ashish K Agrawal
{"title":"通过计算推进制药智能化 利用机器学习模型预测快速崩解片剂的体外参数","authors":"Dhruv Gupta, Anuj A Biswas, Rohan Chand Sahu, Sanchit Arora, Dinesh Kumar, Ashish K Agrawal","doi":"10.1016/j.ejpb.2024.114508","DOIUrl":null,"url":null,"abstract":"<div><p>The field of Machine Learning (ML) has garnered significant attention, particularly in healthcare for predicting disease severity. Recently, the pharmaceutical sector has also adopted ML techniques in various stages of drug development. Tablets are the most common pharmaceutical formulations, with their efficacy influenced by the physicochemical properties of active ingredients, in-process parameters, and formulation components. In this study, we developed ML-based prediction models for disintegration time, friability, and water absorption ratio of fast disintegration tablets. The model development process included data visualization, pre-processing, splitting, ML model creation, and evaluation. We evaluated the models using root mean square error (RMSE) and R-squared score (R<sup>2</sup>). After hyperparameter tuning and cross-validation, the voting regressor model demonstrated the best performance for predicting disintegration time (RMSE: 21.99, R<sup>2</sup>: 0.76), surpassing previously reported models. The random forest regressor achieved the best results for friability prediction (RMSE: 0.142, R<sup>2</sup>: 0.7), and the K-nearest neighbor (KNN) regressor excelled in predicting the water absorption ratio (RMSE: 10.07, R<sup>2</sup>: 0.94). Notably, predicting friability and water absorption ratio using ML models is unprecedented in the literature. The developed models were deployed in a web app for easy access by anyone. These ML models can significantly enhance the tablet development phase by minimizing experimental iterations and material usage, thereby reducing costs and saving time.</p></div>","PeriodicalId":12024,"journal":{"name":"European Journal of Pharmaceutics and Biopharmaceutics","volume":"204 ","pages":"Article 114508"},"PeriodicalIF":4.4000,"publicationDate":"2024-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Advancing pharmaceutical Intelligence via computationally Prognosticating the in-vitro parameters of fast disintegration tablets using Machine Learning models\",\"authors\":\"Dhruv Gupta, Anuj A Biswas, Rohan Chand Sahu, Sanchit Arora, Dinesh Kumar, Ashish K Agrawal\",\"doi\":\"10.1016/j.ejpb.2024.114508\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>The field of Machine Learning (ML) has garnered significant attention, particularly in healthcare for predicting disease severity. Recently, the pharmaceutical sector has also adopted ML techniques in various stages of drug development. Tablets are the most common pharmaceutical formulations, with their efficacy influenced by the physicochemical properties of active ingredients, in-process parameters, and formulation components. In this study, we developed ML-based prediction models for disintegration time, friability, and water absorption ratio of fast disintegration tablets. The model development process included data visualization, pre-processing, splitting, ML model creation, and evaluation. We evaluated the models using root mean square error (RMSE) and R-squared score (R<sup>2</sup>). After hyperparameter tuning and cross-validation, the voting regressor model demonstrated the best performance for predicting disintegration time (RMSE: 21.99, R<sup>2</sup>: 0.76), surpassing previously reported models. The random forest regressor achieved the best results for friability prediction (RMSE: 0.142, R<sup>2</sup>: 0.7), and the K-nearest neighbor (KNN) regressor excelled in predicting the water absorption ratio (RMSE: 10.07, R<sup>2</sup>: 0.94). Notably, predicting friability and water absorption ratio using ML models is unprecedented in the literature. The developed models were deployed in a web app for easy access by anyone. These ML models can significantly enhance the tablet development phase by minimizing experimental iterations and material usage, thereby reducing costs and saving time.</p></div>\",\"PeriodicalId\":12024,\"journal\":{\"name\":\"European Journal of Pharmaceutics and Biopharmaceutics\",\"volume\":\"204 \",\"pages\":\"Article 114508\"},\"PeriodicalIF\":4.4000,\"publicationDate\":\"2024-09-19\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"European Journal of Pharmaceutics and Biopharmaceutics\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0939641124003345\",\"RegionNum\":2,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"PHARMACOLOGY & PHARMACY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"European Journal of Pharmaceutics and Biopharmaceutics","FirstCategoryId":"3","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0939641124003345","RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"PHARMACOLOGY & PHARMACY","Score":null,"Total":0}
引用次数: 0
摘要
机器学习(ML)领域备受关注,尤其是在预测疾病严重程度的医疗保健领域。最近,制药行业也在药物开发的各个阶段采用了 ML 技术。片剂是最常见的药物制剂,其药效受活性成分的理化性质、加工过程参数和制剂成分的影响。在本研究中,我们针对快速崩解片剂的崩解时间、易碎性和吸水率开发了基于 ML 的预测模型。模型开发过程包括数据可视化、预处理、拆分、ML 模型创建和评估。我们使用均方根误差(RMSE)和 R 平方得分(R2)对模型进行了评估。经过超参数调整和交叉验证后,投票回归模型在预测解体时间方面表现最佳(RMSE:21.99,R2:0.76),超过了之前报道的模型。随机森林回归模型在易碎性预测方面取得了最佳结果(RMSE:0.142,R2:0.7),K-近邻(KNN)回归模型在吸水率预测方面表现出色(RMSE:10.07,R2:0.94)。值得注意的是,使用 ML 模型预测易碎性和吸水率在文献中是前所未有的。开发的模型被部署在一个网络应用程序中,任何人都可以轻松访问。这些 ML 模型可以最大限度地减少实验迭代和材料使用,从而降低成本并节省时间,从而大大提高片剂开发阶段的效率。
Advancing pharmaceutical Intelligence via computationally Prognosticating the in-vitro parameters of fast disintegration tablets using Machine Learning models
The field of Machine Learning (ML) has garnered significant attention, particularly in healthcare for predicting disease severity. Recently, the pharmaceutical sector has also adopted ML techniques in various stages of drug development. Tablets are the most common pharmaceutical formulations, with their efficacy influenced by the physicochemical properties of active ingredients, in-process parameters, and formulation components. In this study, we developed ML-based prediction models for disintegration time, friability, and water absorption ratio of fast disintegration tablets. The model development process included data visualization, pre-processing, splitting, ML model creation, and evaluation. We evaluated the models using root mean square error (RMSE) and R-squared score (R2). After hyperparameter tuning and cross-validation, the voting regressor model demonstrated the best performance for predicting disintegration time (RMSE: 21.99, R2: 0.76), surpassing previously reported models. The random forest regressor achieved the best results for friability prediction (RMSE: 0.142, R2: 0.7), and the K-nearest neighbor (KNN) regressor excelled in predicting the water absorption ratio (RMSE: 10.07, R2: 0.94). Notably, predicting friability and water absorption ratio using ML models is unprecedented in the literature. The developed models were deployed in a web app for easy access by anyone. These ML models can significantly enhance the tablet development phase by minimizing experimental iterations and material usage, thereby reducing costs and saving time.
期刊介绍:
The European Journal of Pharmaceutics and Biopharmaceutics provides a medium for the publication of novel, innovative and hypothesis-driven research from the areas of Pharmaceutics and Biopharmaceutics.
Topics covered include for example:
Design and development of drug delivery systems for pharmaceuticals and biopharmaceuticals (small molecules, proteins, nucleic acids)
Aspects of manufacturing process design
Biomedical aspects of drug product design
Strategies and formulations for controlled drug transport across biological barriers
Physicochemical aspects of drug product development
Novel excipients for drug product design
Drug delivery and controlled release systems for systemic and local applications
Nanomaterials for therapeutic and diagnostic purposes
Advanced therapy medicinal products
Medical devices supporting a distinct pharmacological effect.