Dataset Information

Development of Human in vitro Brain-blood Barrier Model from Induced Pluripotent Stem Cell-derived Endothelial Cells to Predict the in vivo Permeability of Drugs.

ABSTRACT: An in vitro blood-brain barrier (BBB) model is critical for enabling rapid screening of the BBB permeability of the drugs targeting on the central nervous system. Though many models have been developed, their reproducibility and renewability remain a challenge. Furthermore, drug transport data from many of the models do not correlate well with the data for in vivo BBB drug transport. Induced-pluripotent stem cell (iPSC) technology provides reproducible cell resources for in vitro BBB modeling. Here, we generated a human in vitro BBB model by differentiating the human iPSC (hiPSC) line GM25256 into brain endothelial-type cells. The model displayed BBB characteristics including tight junction proteins (ZO-1, claudin-5, and occludin) and endothelial markers (von Willebrand factor and Ulex), as well as high trans-endothelial electrical resistance (TEER) (1560 ?.cm² ± 230 ?.cm²) and ?-GTPase activity. Co-culture with primary rat astrocytes significantly increased the TEER of the model (2970 ?.cm² to 4185 ?.cm²). RNAseq analysis confirmed the expression of key BBB-related genes in the hiPSC-derived endothelial cells in comparison with primary human brain microvascular endothelial cells, including P-glycoprotein (Pgp) and breast cancer resistant protein (BCRP). Drug transport assays for nine CNS compounds showed that the permeability of non-Pgp/BCRP and Pgp/BCRP substrates across the model was strongly correlated with rodent in situ brain perfusion data for these compounds (R² = 0.982 and R² = 0.9973, respectively), demonstrating the functionality of the drug transporters in the model. Thus, this model may be used to rapidly screen CNS compounds, to predict the in vivo BBB permeability of these compounds and to study the biology of the BBB.

SUBMITTER: Li Y

PROVIDER: S-EPMC6864025 | biostudies-literature | 2019 Dec

REPOSITORIES: biostudies-literature

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Development of Human in vitro Brain-blood Barrier Model from Induced Pluripotent Stem Cell-derived Endothelial Cells to Predict the in vivo Permeability of Drugs.

Li Yuan Y Sun Xueying X Liu Houfu H Huang Liang L Meng Guofeng G Ding Yu Y Su Wenji W Lu Jiaqi J Gong Sophie S Terstappen Georg C GC Zhang Ru R Zhang Wandong W

Neuroscience bulletin 20190511 6

An in vitro blood-brain barrier (BBB) model is critical for enabling rapid screening of the BBB permeability of the drugs targeting on the central nervous system. Though many models have been developed, their reproducibility and renewability remain a challenge. Furthermore, drug transport data from many of the models do not correlate well with the data for in vivo BBB drug transport. Induced-pluripotent stem cell (iPSC) technology provides reproducible cell resources for in vitro BBB modeling. H ...[more]

PMID: 31079318

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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.

Project description:The blood-brain barrier plays a crucial role in regulating the passage of 98% of the compounds that enter the central nervous system (CNS). Compounds with high permeability must be identified to enable the synthesis of brain medications for the treatment of various brain diseases, such as Parkinson's, Alzheimer's, and brain tumors. Throughout the years, several models have been developed to solve this problem and have achieved acceptable accuracy scores in predicting compounds that penetrate the blood-brain barrier. However, predicting compounds with "low" permeability has been a challenging task. In this study, we present a deep learning (DL) classification model to predict blood-brain barrier permeability. The proposed model addresses the fundamental issues presented in former models: high dimensionality, class imbalances, and low specificity scores. We address these issues to enhance the high-dimensional, imbalanced dataset before developing the classification model: the imbalanced dataset is addressed using oversampling techniques and the high dimensionality using a non-linear dimensionality reduction technique known as kernel principal component analysis (KPCA). This technique transforms the high-dimensional dataset into a low-dimensional Euclidean space while retaining invaluable information. For the classification task, we developed an enhanced feed-forward deep learning model and a convolutional neural network model. In terms of specificity scores (i.e., predicting compounds with low permeability), the results obtained by the enhanced feed-forward deep learning model outperformed those obtained by other models in the literature that were developed using the same technique. In addition, the proposed convolutional neural network model surpassed models used in other studies in multiple accuracy measures, including overall accuracy and specificity. The proposed approach solves the problem inevitably faced with obtaining low specificity resulting in high false positive rate.

Project description:Acoustic trauma, autoimmune inner ear disease, and presbycusis feature loss of the integrity of the blood-labyrinth barrier (BLB). Normal BLB function depends on endothelial structural integrity, which is supported and maintained by tight junctions and adherens junctions within the microvascular endothelial layer. When these junctions are disrupted, vascular leakage occurs. Tight junctions and adherens junctions are functionally and structurally linked, but the exact signaling pathways underlying their interaction remain unknown. In addition, solute carriers (SC) are essential for optimal exchange through BLB. Previously, we found that SC family member, the sodium-hydrogen exchanger NHE6, was expressed in all wildtype cochlear tissues, and that Nhe6-knockout mice displayed moderate hearing loss. Moreover, NHE6 depletion affected Trk protein turnover and endosomal signaling. Here, we investigated whether NHE6 might impact BLB integrity. We found that Nhe6-knockout, BLB-derived endothelial cells showed reduced expression of major junctional genes: Tjp1, F11r, Ocln, Cdh5, and Cldn5. Co-culturing BLB-derived endothelial cells with pericytes and/or perivascular resident macrophage-like melanocytes in a transwell system showed that monolayers of Nhe6-knockout BLB-derived cells had lower electrical resistance and higher permeability, compared to wildtype endothelial monolayers. Additionally, another SC, NKCC1, which was previously linked to congenital deafness, was downregulated in our Nhe6-knockout mouse model. Blocking NKCC1 with a NKCC1-specific inhibitor, bumetanide, in wildtype BLB-derived endothelial cells also caused the downregulation of major junctional proteins, particularly Tjp1 and F11r, which encode the zonula occludens and junctional adhesion molecule-1 proteins, respectively. Moreover, bumetanide treatment increased cell permeability. In conclusion, we showed that the lack or inhibition of NHE6 or NKCC1 affected the permeability of endothelial BLB-derived cells. These findings suggested that NHE6 and NKCC1 could serve as potential targets for modifying BLB permeability to facilitate drug delivery across the BLB to the cochlea or to protect the cochlea from ototoxic insults.

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

Development of Human in vitro Brain-blood Barrier Model from Induced Pluripotent Stem Cell-derived Endothelial Cells to Predict the in vivo Permeability of Drugs.

Publications

Development of Human in vitro Brain-blood Barrier Model from Induced Pluripotent Stem Cell-derived Endothelial Cells to Predict the in vivo Permeability of Drugs.

Similar Datasets

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