Development of a blood-brain barrier model in a membrane-based microchip for characterization of drug permeability and cytotoxicity for drug screening

Since most of the central nervous system (CNS) drug candidates show poor permeability across the blood-brain barrier (BBB), development of a reliable platform for permeability assay will greatly accelerate drug discovery. Herein, we constructed a microfluidic BBB model to mimic drug delivery into the brain to induce cytotoxicity at target cells. To reconstitute the in vivo BBB properties, human cerebral microvessel endothelial cells (hCMEC/D3) were dynamically cultured in a membrane-based microchannel. Sunitinib, a model drug, was then delivered into the microchannel and forced to permeate through the BBB model. The permeated amount was directly quantified by an electrospray ionization quadrupole time-of-flight mass spectrometer (ESI-Q-TOF MS) after on-chip SPE (μSPE) pretreatment. Moreover, the permeated drug was incubated with glioma cells (U251) cultured inside agarose gel in the downstream to investigate drug-induced cytotoxicity. The resultant permeability of sunitinib was highly correlated with literature reported value, and it only required 30 min and 5 μL of sample solution for each permeation experiment. Moreover, after 48 h of treatment, the survival rate of U251 cells cultured in 3D scaffolds was nearly 6% higher than that in 2D, which was in accordance with the previously reported results. These results demonstrate that this platform provides a valid tool for drug permeability and cytotoxicity assays which have great value for the research and development of CNS drugs.     

血脑屏障通透性增效方法的研究进展

血脑屏障作为脑部的屏障系统,具有较强的保护作用,维持着中枢神经系统的内环境的稳定,同时也阻止药物进入脑部治疗中枢神经系统疾病。多年来,在提高血脑屏障通透性的研究方面有了很大进展,让药物靶向入脑,为治疗中枢神经系统疾病提供了很大的帮助。本文系统介绍血脑屏障的结构,其中主要介绍产生血脑屏障的解剖和功能结构,并对提高其通透性的增效方法和机制进行了概括,主要从物理、化学、生物学和纳米给药载体等方面阐述了提高血脑屏障透过方式,并简要介绍了一些具体药物的输送的应用。     

Toward eradicating HIV reservoirs in the brain: inhibiting P-glycoprotein at the blood-brain barrier with prodrug abacavir dimers

Eradication of HIV reservoirs in the brain necessitates penetration of antiviral agents across the blood-brain barrier (BBB), a process limited by drug efflux proteins such as P-glycoprotein (P-gp) at the membrane of brain capillary endothelial cells. We present an innovative chemical strategy toward the goal of therapeutic brain penetration of the P-gp substrate and antiviral agent abacavir, in conjunction with a traceless tether. Dimeric prodrugs of abacavir were designed to have two functions: inhibit P-gp efflux at the BBB and revert to monomeric therapeutic within cellular reducing environments. The prodrug dimers are potent P-gp inhibitors in cell culture and in a brain capillary model of the BBB. Significantly, these agents demonstrate anti-HIV activity in two T-cell-based HIV assays, a result that is linked to cellular reversion of the prodrug to abacavir. This strategy represents a platform technology that may be applied to other therapies with limited brain penetration due to P-glycoprotein.     

Shuttle-mediated drug delivery to the brain

Advances in the field of shuttle-mediated drug delivery have been made in the last decade; however, the treatment of brain disorders still remains a great challenge because of the presence of the blood-brain barrier (BBB), a structure that limits the access of drugs to their site of action in the central nervous system. Several strategies have been proposed to enhance the transport of drugs across the BBB. In this Review, we focus on the vector-mediated approach, in which a drug is coupled to a molecule (shuttle) that has the ability to cross the BBB and deliver the drug to the brain.     

Computational models to predict blood–brain barrier permeation and CNS activity

The blood-brain permeation of a structurally diverse set of 281 compounds was modeled using linear regression and a multivariate genetic partial least squares (G/PLS) approach. Key structural features affecting the logarithm of blood-brain partitioning (logBB) were captured through statistically significant quantitative structure-activity relationship (QSAR) models. These relationships reveal the importance of logP, polar surface area, and a variety of electrotopological indices for accurate predictions of logBB. The best models reveal an excellent correlation (r > 0.9) for a training set of 58 compounds. Likewise, the comparison of the average logBB values obtained from an ensemble of QSAR models with experimental values also verifies the statistical quality of the models (r > 0.9). The models provide good agreement (r approximately 0.7) between the predicted logBB values for 34 molecules in the external validation set and the experimental values. To further validate the models for use during the drug discovery process, a prediction set of 181 drugs with reported CNS penetration data was used. A >70% success rate is obtained by using any of the QSAR models in the qualitative prediction for CNS permeable (active) drugs. A lower success rate (approximately 60%) was obtained for the best model for CNS impermeable (inactive) drugs. Combining the predictions obtained from all the models (consensus) did not significantly improve the discrimination of CNS active and CNS inactive molecules. Finally, using the therapeutic classification as a guiding tool, the CNS penetration capability of over 2000 compounds in the Synthline database was estimated. The results were very similar to the smaller set of 181 compounds.     

Transition-metal-free C-arylation at room temperature by arynes

A facile, fluoride-induced transition-metal-free chemoselective α-arylation of β-dicarbonyl compounds (malonamide esters) at room temperature using aryne intermediates has been demonstrated. Selective mono- or diarylation and generation of a quaternary benzylic stereocenter have also been achieved. The methodology will be highly useful for the synthesis of a library of CNS depressant barbiturate drugs like Phenobarbital.     

基于随机森林与Chemistry Development Kit描述符的P-gp底物识别

应用随机森林方法、开放源代码软件-CDK(Chemistry Development Kit)描述符与170个化合物的训练数据集[其中96个为磷糖蛋白(P-gp)底物], 建立了P-gp底物的识别模型. 研究了CDK描述符与P-gp底物识别的关系, 结果表明, 原子极化性和电荷偏面积等分子属性对P-gp底物识别起到重要作用. 该模型对训练集的预测正确率为99.42%; 对外部测试集(42个化合物, 其中24个为P-gp底物)的预测结果为P-gp底物、非底物及总测试集的识别正确率分别为87.50%, 83.33%和85.71%. 212个化合物数据集上的Leave-One-Out交叉验证识别正确率为77.4%.     

Creation and application of psychoactive designer drugs data library using liquid chromatography with photodiode array spectrophotometry detector and gas chromatography-mass spectrometry

In order to quickly confirm a potentially hazardous psychoactive designer drug (a compound in which part of the molecular structure of a stimulant or narcotic has been modified), we created a psychoactive drugs data library by performing analysis using liquid chromatography with photodiode array spectrophotometry (LC/PDA) and gas chromatography-mass spectrometry (GC/MS). The data in this library consist of the LC capacity factor (k′) ratios in relation to the internal standard, the ultraviolet (UV) spectra and the MS spectra of 104 compounds. By performing a comparative study of the data in this report with the analytical data for commercial and illegal drug products, it is possible to quickly identify the psychoactive designer drugs in 205 purchased products by using the library. Further, it is possible to analogize the structure of drugs for which there is no matching data in the library using similar data.Furthermore, when structural isomers of controlled substances have detected from the presented library, similarity of their biological effects on human will be predicted, thus leading to regulate their public circulation. Examples of these types of isomers include, for instance, the narcotic 3,4,5-trimethoxyamphetamine (TMA) and its positional isomers 2,4,5-trimethoxyamphetamine (TMA-2) and 2,4,6-trimethoxyamphetamine (TMA-6), or the narcotic 1-(3-chlorophenyl)piperazine (3CPP) and its isomers 1-(o-chlorophenyl)piperazine (2CPP) and 1-(p-chlorophenyl)piperazine (4CPP). Differentiation of these compounds is necessary in regulating them, and we report here the results of a study of a method to confirm these compounds using the present library.     

SDS-Based Biomembrane Mimetic Chromatography for Prediction of Human Drug Transport as an in Vitro Technique

The main oral drug absorption barriers are fluid cell membranes, and generally drugs are absorbed by a passive diffusion mechanism. On the other hand, the blood–brain barrier (BBB) is considered to be the main barrier to drug transport into the central nervous system (CNS). The BBB restricts the passive diffusion of many drugs from blood to brain. Biopartitioning micellar chromatography (BMC), a mode of micellar liquid chromatography that uses micellar mobile phases in adequate experimental conditions, can be useful as an in vitro system in mimicking the drug partitioning process into biological systems. In this study, relationships between the BMC retention data of a heterogeneous set of 12 drugs and their pharmacokinetics parameters (human oral drug absorption and BBB penetration ability) are studied and the predictive ability of the models is evaluated. Modeling of log k _BMC of these compounds was established by multiple linear regression in two different concentrations (0.07 and 0.09 M) of sodium dodecyl sulfate (SDS). The results showed a fair correlation between human oral drug absorption and BMC retention data in 0.09 M SDS (R ² = 0.864) and a good correlation between the blood–brain distribution coefficient and BMC retention data in 0.07 M of SDS (R ² = 0.887). Application of the developed models to a prediction set demonstrated that the model is also reliable with good predictive accuracy. The external and internal validation results showed that the predicted values are in good agreement with the experimental value.     

A Recurrent Neural Network model to predict blood–brain barrier permeability

The rapid development of computational methods and the increasing volume of chemical and biological data have contributed to an immense growth in chemical research. This field of study is known as “chemoinformatics,” which is a discipline that uses machine-learning techniques to extract, process, and extrapolate data from chemical structures. One of the significant lines of research in chemoinformatics is the study of blood–brain barrier (BBB) permeability, which aims to identify drug penetration into the central nervous system (CNS). In this research, we attempt to solve the problem of BBB permeability by predicting compounds penetration to the CNS. To accomplish this goal: (i) First, an overview is provided to the field of chemoinformatics, its definition, applications, and challenges, (ii) Second, a broad view is taken to investigate previous machine-learning and deep-learning computational models to solve BBB permeability. Based on the analysis of previous models, three main challenges that collectively affect the classifier performance are identified, which we define as “the triple constraints”; subsequently, we map each constraint to a proposed solution, (iii) Finally, we conclude this endeavor by proposing a deep learning based Recurrent Neural Network model, to predict BBB permeability (RNN-BBB model). Our model outperformed other studies from the literature by scoring an overall accuracy of 96.53%, and a specificity score of 98.08%. The obtained results confirm that addressing the triple constraints substantially improves the classification model capability specifically when predicting compounds with low penetration.     

Characterization of a microfluidic in vitro model of the blood-brain barrier (μBBB)

The blood-brain barrier (BBB), a unique selective barrier for the central nervous system (CNS), hinders the passage of most compounds to the CNS, complicating drug development. Innovative in vitro models of the BBB can provide useful insights into its role in CNS disease progression and drug delivery. Static transwell models lack fluidic shear stress, while the conventional dynamic in vitro BBB lacks a thin dual cell layer interface. To address both limitations, we developed a microfluidic blood-brain barrier (μBBB) which closely mimics the in vivo BBB with a dynamic environment and a comparatively thin culture membrane (10 μm). To test validity of the fabricated BBB model, μBBBs were cultured with b.End3 endothelial cells, both with and without co-cultured C8-D1A astrocytes, and their key properties were tested with optical imaging, trans-endothelial electrical resistance (TEER), and permeability assays. The resultant imaging of ZO-1 revealed clearly expressed tight junctions in b.End3 cells, Live/Dead assays indicated high cell viability, and astrocytic morphology of C8-D1A cells were confirmed by ESEM and GFAP immunostains. By day 3 of endothelial culture, TEER levels typically exceeded 250 Ω cm(2) in μBBB co-cultures, and 25 Ω cm(2) for transwell co-cultures. Instantaneous transient drop in TEER in response to histamine exposure was observed in real-time, followed by recovery, implying stability of the fabricated μBBB model. Resultant permeability coefficients were comparable to previous BBB models, and were significantly increased at higher pH (>10). These results demonstrate that the developed μBBB system is a valid model for some studies of BBB function and drug delivery.     

SDS-Based Biomembrane Mimetic Chromatography for Prediction of Human Drug Transport as an in Vitro Technique

The main oral drug absorption barriers are fluid cell membranes, and generally drugs are absorbed by a passive diffusion mechanism. On the other hand, the blood–brain barrier (BBB) is considered to be the main barrier to drug transport into the central nervous system (CNS). The BBB restricts the passive diffusion of many drugs from blood to brain. Biopartitioning micellar chromatography (BMC), a mode of micellar liquid chromatography that uses micellar mobile phases in adequate experimental conditions, can be useful as an in vitro system in mimicking the drug partitioning process into biological systems. In this study, relationships between the BMC retention data of a heterogeneous set of 12 drugs and their pharmacokinetics parameters (human oral drug absorption and BBB penetration ability) are studied and the predictive ability of the models is evaluated. Modeling of log k _BMC of these compounds was established by multiple linear regression in two different concentrations (0.07 and 0.09 M) of sodium dodecyl sulfate (SDS). The results showed a fair correlation between human oral drug absorption and BMC retention data in 0.09 M SDS (R ² = 0.864) and a good correlation between the blood–brain distribution coefficient and BMC retention data in 0.07 M of SDS (R ² = 0.887). Application of the developed models to a prediction set demonstrated that the model is also reliable with good predictive accuracy. The external and internal validation results showed that the predicted values are in good agreement with the experimental value.

     

Boosted regression trees, multivariate adaptive regression splines and their two-step combinations with multiple linear regression or partial least squares to predict blood-brain barrier passage: a case study

The use of some unconventional non-linear modeling techniques, i.e. classification and regression trees and multivariate adaptive regression splines-based methods, was explored to model the blood-brain barrier (BBB) passage of drugs and drug-like molecules. The data set contains BBB passage values for 299 structural and pharmacological diverse drugs, originating from a structured knowledge-based database. Models were built using boosted regression trees (BRT) and multivariate adaptive regression splines (MARS), as well as their respective combinations with stepwise multiple linear regression (MLR) and partial least squares (PLS) regression in two-step approaches. The best models were obtained using combinations of MARS with either stepwise MLR or PLS. It could be concluded that the use of combinations of a linear with a non-linear modeling technique results in some improved properties compared to the individual linear and non-linear models and that, when the use of such a combination is appropriate, combinations using MARS as non-linear technique should be preferred over those with BRT, due to some serious drawbacks of the BRT approaches.