Dataset Information

Predict drug permeability to blood-brain-barrier from clinical phenotypes: drug side effects and drug indications.

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 ?Availability and implementationhttps://github.com/bioinformatics-gao/CASE-BBB-prediction-Data.

Contact

rxx@case.edu.

Supplementary information

Supplementary data are available at Bioinformatics online.

SUBMITTER: Gao Z

PROVIDER: S-EPMC5860495 | biostudies-literature | 2017 Mar

REPOSITORIES: biostudies-literature

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Publications

Predict drug permeability to blood-brain-barrier from clinical phenotypes: drug side effects and drug indications.

Gao Zhen Z Chen Yang Y Cai Xiaoshu X Xu Rong R

Bioinformatics (Oxford, England) 20170301 6

<h4>Motivation</h4>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 ...[more]

PMID: 27993785

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Project description:The blood-brain barrier (BBB) is formed by specialized tight junctions between endothelial cells that line brain capillaries to create a highly selective barrier between the brain and the rest of the body. A major problem to overcome in drug design is the ability of the compound in question to cross the BBB. Neuroactive drugs are required to cross the BBB to function. Conversely, drugs that target other parts of the body ideally should not cross the BBB to avoid possible psychotropic side effects. Thus, the task of predicting the BBB permeability of new compounds is of great importance. Two gold-standard experimental measures of BBB permeability are logBB (the concentration of drug in the brain divided by concentration in the blood) and logPS (permeability surface-area product). Both methods are time-consuming and expensive, and although logPS is considered the more informative measure, it is lower throughput and more resource intensive. With continual increases in computer power and improvements in molecular simulations, in silico methods may provide viable alternatives. Computational predictions of these two parameters for a sample of 12 small molecule compounds were performed. The potential of mean force for each compound through a 1,2-dioleoyl-sn-glycero-3-phosphocholine bilayer is determined by molecular dynamics simulations. This system setup is often used as a simple BBB mimetic. Additionally, one-dimensional position-dependent diffusion coefficients are calculated from the molecular dynamics trajectories. The diffusion coefficient is combined with the free energy landscape to calculate the effective permeability (Peff) for each sample compound. The relative values of these permeabilities are compared to experimentally determined logBB and logPS values. Our computational predictions correlate remarkably well with both logBB (R(2) = 0.94) and logPS (R(2) = 0.90). Thus, we have demonstrated that this approach may have the potential to provide reliable, quantitatively predictive BBB permeability, using a relatively quick, inexpensive method.

Project description:Efficient delivery of therapeutics across the neuroprotective blood-brain barrier (BBB) remains a formidable challenge for central nervous system drug development. High-fidelity in vitro models of the BBB could facilitate effective early screening of drug candidates targeting the brain. In this study, we developed a microfluidic BBB model that is capable of mimicking in vivo BBB characteristics for a prolonged period and allows for reliable in vitro drug permeability studies under recirculating perfusion. We derived brain microvascular endothelial cells (BMECs) from human induced pluripotent stem cells (hiPSCs) and cocultured them with rat primary astrocytes on the two sides of a porous membrane on a pumpless microfluidic platform for up to 10 days. The microfluidic system was designed based on the blood residence time in human brain tissues, allowing for medium recirculation at physiologically relevant perfusion rates with no pumps or external tubing, meanwhile minimizing wall shear stress to test whether shear stress is required for in vivo-like barrier properties in a microfluidic BBB model. This BBB-on-a-chip model achieved significant barrier integrity as evident by continuous tight junction formation and in vivo-like values of trans-endothelial electrical resistance (TEER). The TEER levels peaked above 4000 Ω · cm2 on day 3 on chip and were sustained above 2000 Ω · cm2 up to 10 days, which are the highest sustained TEER values reported in a microfluidic model. We evaluated the capacity of our microfluidic BBB model to be used for drug permeability studies using large molecules (FITC-dextrans) and model drugs (caffeine, cimetidine, and doxorubicin). Our analyses demonstrated that the permeability coefficients measured using our model were comparable to in vivo values. Our BBB-on-a-chip model closely mimics physiological BBB barrier functions and will be a valuable tool for screening of drug candidates. The residence time-based design of a microfluidic platform will enable integration with other organ modules to simulate multi-organ interactions on drug response. Biotechnol. Bioeng. 2017;114: 184-194. © 2016 Wiley Periodicals, Inc.

Dataset Information

Predict drug permeability to blood-brain-barrier from clinical phenotypes: drug side effects and drug indications.

Motivation

Results

Contact

Supplementary information

Publications

Predict drug permeability to blood-brain-barrier from clinical phenotypes: drug side effects and drug indications.

Similar Datasets

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