机器学习模型的开发和比较,用于从微针贴片的体外药物透率预测
Anuj A Biswas1, Madhukiran R Dhondale1, Maan Singh1
1Department of Pharmaceutical Engineering and Technology, Indian Institute of Technology (BHU), Varanasi, Uttar Pradesh, India.
概括
机器学习模型可以预测微针贴片中的药物释放,节省时间和资源. 投票回归模型在预测药物透方面表现出卓越的准确性,优于以前的方法.
科学领域:
- 药理学和制药学 药理学和制药学
- 生物医学工程 生物医学工程
- 数据科学数据科学数据科学
背景情况:
- 机器学习 (ML) 为优化药物输送系统开发提供了潜力.
- 在制造之前预测药物释放模式可以降低成本和实验时间.
- 微针贴片是一种先进的药物输送系统,需要准确的释放预测.
研究的目的:
- 开发和评估ML模型,用于预测微针贴片的体外药物释放和透.
- 为了比较堆叠回归器,人工神经网络 (ANN) 和投票回归器模型的性能.
- 评估预测药物透量与药物透率的准确性.
主要方法:
- 利用文献衍生数据集来训练和评估堆叠回归器,ANN和投票回归器模型.
- 使用R平方得分,RMSE和MAE进行模型性能比较.
- 为表现最佳的模型优化了超参数,并执行了交叉验证.
主要成果:
- 投票回归模型在药物透预测方面取得了最佳表现 (RMSE = 3.24),明显优于堆叠回归模型 (RMSE = 16.54) 和ANN (RMSE = 14).
- 通过预测百分比 (RMSE = 654.94) 预测透量比直接量预测 (RMSE = 669.69) 更准确.
- 所有开发的模型都大大超过了之前的模型 (RMSE = 4447.23).
结论:
- 机器学习,特别是投票回归模型,可以准确地预测微针贴片的药物透率.
- 开发的模型比现有方法有了显著的改进,促进了高效的微针贴片开发.
- 开发了一个网络应用程序,以使药物透的用户友好预测成为可能,展示了ML在药物输送系统中的实际应用.
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