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New predictive models for blood-brain barrier permeability of drug-like molecules.

ABSTRACT: The goals of the present study were to apply a generalized regression model and support vector machine (SVM) models with Shape Signatures descriptors, to the domain of blood-brain barrier (BBB) modeling.The Shape Signatures method is a novel computational tool that was used to generate molecular descriptors utilized with the SVM classification technique with various BBB datasets. For comparison purposes we have created a generalized linear regression model with eight MOE descriptors and these same descriptors were also used to create SVM models.The generalized regression model was tested on 100 molecules not in the model and resulted in a correlation r2 = 0.65. SVM models with MOE descriptors were superior to regression models, while Shape Signatures SVM models were comparable or better than those with MOE descriptors. The best 2D shape signature models had 10-fold cross validation prediction accuracy between 80-83% and leave-20%-out testing prediction accuracy between 80-82% as well as correctly predicting 84% of BBB+ compounds (n = 95) in an external database of drugs.Our data indicate that Shape Signatures descriptors can be used with SVM and these models may have utility for predicting blood-brain barrier permeation in drug discovery.

SUBMITTER: Kortagere S

PROVIDER: S-EPMC2803117 | biostudies-other | 2008 Aug

REPOSITORIES: biostudies-other

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New predictive models for blood-brain barrier permeability of drug-like molecules.

Kortagere Sandhya S Chekmarev Dmitriy D Welsh William J WJ Ekins Sean S

Pharmaceutical research 20080416 8

<h4>Purpose</h4>The goals of the present study were to apply a generalized regression model and support vector machine (SVM) models with Shape Signatures descriptors, to the domain of blood-brain barrier (BBB) modeling.<h4>Materials and methods</h4>The Shape Signatures method is a novel computational tool that was used to generate molecular descriptors utilized with the SVM classification technique with various BBB datasets. For comparison purposes we have created a generalized linear regression ...[more]

PMID: 18415049

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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.

Project description:The strong barrier function of the blood-brain barrier (BBB) protects the central nervous system (CNS) from xenobiotic substances, while the expression of selective transporters controls the transportation of nutrients between the blood and brain. As a result, the delivery of drugs to the CNS and prediction of the ability of specific drugs to penetrate the BBB can be difficult. Although in vivo pharmacokinetic analysis using rodents is a commonly used method for predicting human BBB permeability, novel in vitro BBB models, such as Transwell models, have been developed recently. Induced pluripotent stem cells (iPSCs) have the potential to differentiate into various types of cells, and protocols for the differentiation of iPSCs to generate brain microvascular endothelial cells (BMECs) have been reported. The use of iPSCs makes it easy to scale-up iPSC-derived BMECs (iBMECs) and enables production of BBB disease models by using iPSCs from multiple donors with disease, which are advantageous properties compared with models that utilize primary BMECs (pBMECs). There has been little research on the value of iBMECs for predicting BBB permeability. This study focused on the similarity of iBMECs to pBMECs and investigated the ability of iPSC-BBB models (monoculture and coculture) to predict in vivo human BBB permeability using iBMECs. iBMECs express BMEC markers (e.g., VE-cadherin and claudin-5) and influx/efflux transporters (e.g., Glut-1, SLC7A5, CD220, P-gp, ABCG2, and MRP-1) and exhibit high barrier function (transendothelial electrical resistance, >1000 ??×?cm2) as well as similar transporter expression profiles to pBMECs. We determined that the efflux activity using P-glycoprotein (P-gp) transporter is not sufficient in iBMECs, while in drug permeability tests, iPSC-derived BBB models showed a higher correlation with in vivo human BBB permeability compared with a rat BBB model and the Caco-2 model. In a comparison between monoculture and coculture models, the coculture BBB model showed higher efflux activity for compounds with low CNS permeability (e.g., verapamil and thioridazine). In conclusion, iPSC-BBB models make it possible to predict BBB permeability, and employing coculturing can improve iPSC-BBB function.

Dataset Information

New predictive models for blood-brain barrier permeability of drug-like molecules.

Publications

New predictive models for blood-brain barrier permeability of drug-like molecules.

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