从临床表型预测药物对血脑屏障的通透性：药物副作用和药物适应症。
Predict drug permeability to blood-brain-barrier from clinical phenotypes: drug side effects and drug indications.
作者信息
Gao Zhen, Chen Yang, Cai Xiaoshu, Xu Rong
机构信息
Department of Epidemiology and Biostatistics, Case Western Reserve University, Cleveland, OH, USA.
Department of Electrical Engineering and Computer Science, Case Western Reserve University, Cleveland, OH, USA.
出版信息
Bioinformatics. 2017 Mar 15;33(6):901-908. doi: 10.1093/bioinformatics/btw713.
MOTIVATION
Blood-Brain-Barrier (BBB) is a rigorous permeability barrier for maintaining homeostasis of Central Nervous System (CNS). Determination of compound's permeability to BBB is prerequisite in CNS drug discovery. Existing computational methods usually predict drug BBB permeability from chemical structure and they generally apply to small compounds passing BBB through passive diffusion. As abundant information on drug side effects and indications has been recorded over time through extensive clinical usage, we aim to explore BBB permeability prediction from a new angle and introduce a novel approach to predict BBB permeability from drug clinical phenotypes (drug side effects and drug indications). This method can apply to both small compounds and macro-molecules penetrating BBB through various mechanisms besides passive diffusion.
RESULTS
We composed a training dataset of 213 drugs with known brain and blood steady-state concentrations ratio and extracted their side effects and indications as features. Next, we trained SVM models with polynomial kernel and obtained accuracy of 76.0%, AUC 0.739, and F 1 score (macro weighted) 0.760 with Monte Carlo cross validation. The independent test accuracy was 68.3%, AUC 0.692, F 1 score 0.676. When both chemical features and clinical phenotypes were available, combining the two types of features achieved significantly better performance than chemical feature based approach (accuracy 85.5% versus 72.9%, AUC 0.854 versus 0.733, F 1 score 0.854 versus 0.725; P < e -90 ). We also conducted de novo prediction and identified 110 drugs in SIDER database having the potential to penetrate BBB, which could serve as start point for CNS drug repositioning research.
AVAILABILITY AND IMPLEMENTATION
https://github.com/bioinformatics-gao/CASE-BBB-prediction-Data.
CONTACT
SUPPLEMENTARY INFORMATION
Supplementary data are available at Bioinformatics online.
动机
血脑屏障（BBB）是维持中枢神经系统（CNS）内环境稳定的严格通透性屏障。确定化合物对血脑屏障的通透性是中枢神经系统药物研发的先决条件。现有的计算方法通常根据化学结构预测药物的血脑屏障通透性，并且一般适用于通过被动扩散穿过血脑屏障的小分子化合物。随着时间的推移，通过广泛的临床应用记录了大量关于药物副作用和适应症的信息，我们旨在从一个新的角度探索血脑屏障通透性预测，并引入一种从药物临床表型（药物副作用和药物适应症）预测血脑屏障通透性的新方法。该方法不仅适用于通过被动扩散，还适用于通过各种机制穿过血脑屏障的小分子化合物和大分子。
结果
我们构建了一个包含213种已知脑血稳态浓度比的药物的训练数据集，并提取它们的副作用和适应症作为特征。接下来，我们使用多项式核训练支持向量机（SVM）模型，通过蒙特卡洛交叉验证获得了76.0%的准确率、0.739的曲线下面积（AUC）和0.760的F1分数（宏加权）。独立测试的准确率为68.3%，AUC为0.692，F1分数为0.676。当化学特征和临床表型都可用时，将这两种类型的特征结合起来的性能明显优于基于化学特征的方法（准确率85.5%对72.9%，AUC 0.854对0.733，F1分数0.854对0.725；P < e -90 ）。我们还进行了从头预测，并在SIDER数据库中鉴定出110种具有穿透血脑屏障潜力的药物，这可以作为中枢神经系统药物重新定位研究的起点。
可用性和实现方式
https://github.com/bioinformatics-gao/CASE-BBB-prediction-Data。
联系方式
补充信息
补充数据可在《生物信息学》在线获取。
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