title: Predict drug permeability to blood-brain-barrier from clinical phenotypes: drug side effects and drug indications.
abstract: 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.
  
  
  
  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.
  
  
  
  https://github.com/bioinformatics-gao/CASE-BBB-prediction-Data.
  
  
  
  rxx@case.edu.
  
  
  
  Supplementary data are available at Bioinformatics online.
authors.affiliation: Department of Epidemiology and Biostatistics, Case Western Reserve University, Cleveland, OH, USA.
authors.forename: Zhen
authors.initials: Z
authors.lastname: Gao
authors.affiliation: Department of Epidemiology and Biostatistics, Case Western Reserve University, Cleveland, OH, USA.
authors.forename: Yang
authors.initials: Y
authors.lastname: Chen
authors.affiliation: Department of Electrical Engineering and Computer Science, Case Western Reserve University, Cleveland, OH, USA.
authors.forename: Xiaoshu
authors.initials: X
authors.lastname: Cai
authors.affiliation: Department of Epidemiology and Biostatistics, Case Western Reserve University, Cleveland, OH, USA.
authors.forename: Rong
authors.initials: R
authors.lastname: Xu
mesh_terms: D001692:Biological Transport; D001812:Blood-Brain Barrier / Q000378:metabolism*; D062313:Databases, Pharmaceutical; D004058:Diffusion; D004358:Drug Therapy; D064420:Drug-Related Side Effects and Adverse Reactions; D006801:Humans; D008954:Models, Biological; D010539:Permeability; D004364:Pharmaceutical Preparations / Q000378:metabolism*; D049490:Systems Biology / Q000379:methods*
chemical_list: D004364:Pharmaceutical Preparations