ArticleBioinformatics (Oxford, England)2017
Predict drug permeability to blood-brain-barrier from clinical phenotypes: drug side effects and drug indications.
Article in Bioinformatics (Oxford, England), 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 39 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
39 citing papers in PubMed.
- BBBper: A Machine Learning-based Online Tool for Blood-brain Barrier (BBB) Permeability Prediction.CNS & neurological disorders drug targets · 2026Article
- Recent progress on nanotechnologies for enhancing blood-brain barrier permeability.Smart molecules : open access · 2025Review
- Employing Automated Machine Learning (AutoML) Methods to Facilitate theJournal of chemical information and modeling · 2025Article
- Nanomedicines Targeting Metabolic Pathways in the Tumor Microenvironment: Future Perspectives and the Role of AI.Metabolites · 2025Review
- Machine Learning in Drug Development for Neurological Diseases: A Review of Blood Brain Barrier Permeability Prediction Models.Molecular informatics · 2025Review
- Prodrug Approach as a Strategy to Enhance Drug Permeability.Pharmaceuticals (Basel, Switzerland) · 2025Review
- Permeability Benchmarking: Guidelines for ComparingJournal of chemical information and modeling · 2025Review
- Organelle-Targeting Nanoparticles.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Review
- Article
- A review on the role of nanoparticles for targeted brain drug delivery: synthesis, characterization, and applications.EXCLI journal · 2025Review
- Antiseizure Medications: Advancements, Challenges, and Prospects in Drug Development.Current neuropharmacology · 2025Review
- Neurotherapeutics across blood-brain barrier: screening of BBB-permeable and CNS-active molecules for neurodegenerative disease.Frontiers in pharmacology · 2025Article
- Non-animal models for blood-brain barrier permeability evaluation of drug-like compounds.Scientific reports · 2024Article
- Synergistic Potential of Nanomedicine in Prostate Cancer Immunotherapy: Breakthroughs and Prospects.International journal of nanomedicine · 2024Review
- A Comprehensive Study on Nanoparticle Drug Delivery to the Brain: Application of Machine Learning Techniques.Molecular pharmaceutics · 2024Article
- Article
- A least-squares-fitting procedure for an efficient preclinical ranking of passive transport across the blood-brain barrier endothelium.Journal of computer-aided molecular design · 2023Article
- A machine learning-based quantitative model (LogBB_Pred) to predict the blood-brain barrier permeability (logBB value) of drug compounds.Bioinformatics (Oxford, England) · 2023Article
- A critical analysis of neuro-oncology clinical trials.Neuro-oncology · 2023Article
- Repurposing FDA-approved drugs as FXR agonists: a structure based in silico pharmacological study.Bioscience reports · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
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: rxx@case.edu. Supplementary information: Supplementary data are available at Bioinformatics online.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.