对基于树的模型进行基因算法优化,以预测影响脂质体中药物释放的货物和载体相关因素
Fatemeh Taghizadeh1, Ali Hashemi Baghi2, Fatemeh Khodadadi1
1Department of Pharmaceutics and Pharmaceutical Nanotechnology, School of Pharmacy, Shahid Beheshti University of Medical Sciences, Tehran, Iran. a_haeri@sbmu.ac.ir.
使用随机森林和XGBoost算法的预测模型可以预测脂质体药物释放概况. 这些模型分析了关键的配方因素,以指导优化脂肪体药物递送系统的开发.
科学领域:
- 制药科学 制药科学
- 药物运输 药物运输 药物运输
- 计算化学计算化学
背景情况:
- 脂质体药物递送系统为改善治疗结果提供了显著的优势.
- 精确的体外脂质体表征对于预测体内性能至关重要.
- 在体外释放测试对于指导有效脂肪体配方的开发至关重要.
研究的目的:
- 开发脂质体药物释放特征的预测模型.
- 确定影响药物释放的关键配方参数.
- 促进脂肪体药物递送系统的合理设计.
主要方法:
- 实施随机森林 (RF) 和XGBoost遗传算法用于预测建模.
- 在从文献中获取的203种脂质体配方数据集上的训练模型.
- 使用夏普利增量解释 (SHAP) 方法来实现模型的可解释性.
主要成果:
- 开发的模型准确地预测了具有高统计指标的脂质体药物释放概况.
- 确定了影响药物释放的关键因素,包括相位过渡温度,药物特性 (log P,可溶性,分子量) 和配方参数 (囊泡大小,胆固醇比率).
- 证明了基因算法在优化基于树的模型超参数方面的有效性.
结论:
- 开发的预测模型可以指导具有所需释放特性的脂质体配方的设计.
- 了解各种参数的影响可以提高体内药物释放的可预测性.
- 这种方法支持量身定制的脂质体药物递送策略的发展.
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