Quantitative structure-property relationship modeling of remote liposome loading of drugs.
Quantitative structure-property relationship modeling of remote liposome loading of drugs.
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DOI：
10.1016/j.jconrel.2011.11.029
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发表时间：
2012-06-10
期刊：
影响因子：
--
通讯作者：
Goldblum A
中科院分区：
文献类型：
--
作者：
Cern A;Golbraikh A;Sedykh A;Tropsha A;Barenholz Y;Goldblum A
通过跨膜梯度的脂质体远程负载被用于实现治疗有效的脂质体内药物浓度。我们已经开发了远程脂质体装载的定量结构性质关系（QSPR）模型，该模型包括在内部或其他地方进行的366次装载实验中研究的60种药物。将实验条件和计算的化学描述符作为自变量来预测达到高负载效率所需的初始药脂比（D/L）。二元（区分高与低初始D/L）和连续（预测真实D/L值）模型都是使用先进的机器学习方法和五次外部验证生成的。二元模型的外部预测精度高达91 ~ 96%；对于连续模型，预测值与实测值之间回归的平均系数R2为0.76-0.79。我们认为，QSPR模型可以用于识别具有高远程负载能力的候选药物，同时优化配方实验设计。
Remote loading of liposomes by trans-membrane gradients is used to achieve therapeutically efficacious intra-liposome concentrations of drugs. We have developed Quantitative Structure Property Relationship (QSPR) models of remote liposome loading for a dataset including 60 drugs studied in 366 loading experiments internally or elsewhere. Both experimental conditions and computed chemical descriptors were employed as independent variables to predict the initial drug/lipid ratio (D/L) required to achieve high loading efficiency. Both binary (to distinguish high vs. low initial D/L) and continuous (to predict real D/L values) models were generated using advanced machine learning approaches and five-fold external validation. The external prediction accuracy for binary models was as high as 91–96%; for continuous models the mean coefficient R2 for regression between predicted versus observed values was 0.76–0.79. We conclude that QSPR models can be used to identify candidate drugs expected to have high remote loading capacity while simultaneously optimizing the design of formulation experiments.
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