Classification tree models for the prediction of blood-brain barrier passage of drugs

The use of classification trees for modeling and predicting the passage of molecules through the blood-brain barrier was evaluated. The models were built and evaluated using a data set of 147 molecules extracted from the literature. In the first step, single classification trees were built and evaluated for their predictive abilities. In the second step, attempts were made to improve the predictive abilities using a set of 150 classification trees in a boosting approach. Two boosting algorithms, discrete and real adaptive boosting, were used and compared. High-predictive classification trees were obtained for the data set used, and the models could be improved with boosting. In the context of this research, discrete adaptive boosting gives slightly better results than real adaptive boosting.     

In silico prediction of blood-brain barrier permeation using the calculated molecular cross-sectional area as main parameter

The cross-sectional area, AD, of a compound oriented in an amphiphilic gradient such as the air-water or lipid-water interface has previously been shown to be crucial for membrane partitioning and permeation, respectively. Here, we developed an algorithm that determines the molecular axis of amphiphilicity and the cross-sectional area, ADcalc, perpendicular to this axis. Starting from the conformational ensemble of each molecule, the three-dimensional conformation selected as the membrane-binding conformation was the one with the smallest cross-sectional area, ADcalcM, and the strongest amphiphilicity. The calculated, ADcalcM, and the measured, AD, cross-sectional areas correlated linearly (n=55, slope, m=1.04, determination coefficient, r2=0.95). The calculated cross-sectional areas, ADcalcM, were then used together with the calculated octanol-water distribution coefficients, log D7.4, of the 55 compounds (with a known ability to permeate the blood-brain barrier) to establish a calibration diagram for the prediction of blood-brain barrier permeation. It yielded a limiting cross-sectional area (ADcalcM=70 A2) and an optimal range of octanol-water distribution coefficients (-1.4相似文献   

Peptide-mediated blood-brain barrier transport of polymersomes

A polymeric nanocarrier: Polymersomes tagged with a dodecamer peptide that recognizes gangliosides GM1 and GT1b are shown to cross the blood-brain barrier, both in an in?vitro model and in?vivo. The combination of polymeric vesicles with a small GM1-binding peptide and GM1/GT1b gangliosides as targeting sites for blood-brain barrier transport is unprecedented.     

Fast investigations from biological matrices using CE--test of a blood-brain barrier model

In order to adopt a general workflow for complex biological matrices with respect to a new blood–brain barrier (BBB) model, a micellar electrokinetic chromatography method has been developed. The cells forming the BBB have been cultivated in a special cell growth medium in which six drugs (acetaminophen, caffeine, carbamazepine, cimetidine, indometacin and propranolol) have been dissolved and tested for their penetration properties. The results showed good to very good accordance to the reference values. Samples were directly injected onto the capillary without any pretreatment (fused silica capillary, id: 50 μm, L: 48 cm, l: 40 cm). After method development, separations were carried out using a 60 mM borate buffer containing 200 mM of SDS at 30 kV, leading to an analysis time of less than 10 min. Between two runs the capillary was rinsed with a mixture of equal parts of running buffer and isopropanol (70% v/v), which proved to be very effective to remove matrix compounds. An appropriate choice of the detection wavelength (220 or 254 nm) could avoid major interferences between analytes and matrix. The typical RSD% for migration times was approximately 2%, for peak areas, it ranged from 2 to 6%, which was very well acceptable for the generic method used in this study and the low concentrations investigated. The LODs ranged from 10 to 30 ng/mL.     

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.     

Computational methods for protein secondary structure prediction using multiple sequence alignments

Efforts to use computers in predicting the secondary structure of proteins based only on primary structure information started over a quarter century ago [1-3]. Although the results were encouraging initially, the accuracy of the pioneering methods generally did not attain the level required for using predictions of secondary structures reliably in modelling the three-dimensional topology of proteins. During the last decade, however, the introduction of new computational techniques as well as the use of multiple sequence information has lead to a dramatic increase in the success rate of prediction methods, such that successful 3D modelling based on predicted secondary structure has become feasible [e.g., Ref 4]. This review is aimed at presenting an overview of the scale of the secondary structure prediction problem and associated pitfalls, as well as the history of the development of computational prediction methods. As recent successful strategies for secondary structure prediction all rely on multiple sequence information, some methods for accurate protein multiple sequence alignments will also be described. While the main focus is on prediction methods for globular proteins, also the prediction of trans-membrane segments within membrane proteins will be briefly summarised. Finally, an integrated iterative approach tying secondary structure prediction and multiple alignment will be introduced [5].     

High-throughput prediction of blood-brain partitioning: a thermodynamic approach

A high-throughput in silico screening tool for potentially CNS active compounds was developed on the basis of the correlation of solvation free energies and blood-brain partitioning (log(cbrain/cblood) = log BB) data available from experimental sources. Utilizing a thermodynamic approach, solvation free energies were calculated by the fast and efficient generalized Born/surface area continuum solvation model, which enabled us to evaluate more than 10 compounds/min. Our training set involved a structurally diverse set of 55 compounds and yielded a function of log BB = 0.035Gsolv + 0.2592 (r = 0.85, standard error 0.37). Calculation of solvation free energies for 8700 CNS active compounds (CIPSLINE database) revealed that Gsolv is higher than -50 kJ/mol for the 96% of these compounds which can be used as suitable criteria for the identification of compounds preferable for CNS penetration.     

Prediction of intestinal absorption and blood-brain barrier penetration by computational methods

This review surveys the computational methods that have been developed with the aim of identifying drug candidates likely to fail later on the road to market. The specifications for such computational methods are outlined, including factors such as speed, interpretability, robustness and accuracy. Then, computational filters aimed at predicting "drug-likeness" in a general sense are discussed before methods for the prediction of more specific properties--intestinal absorption and blood-brain barrier penetration--are reviewed. Directions for future research are discussed and, in concluding, the impact of these methods on the drug discovery process, both now and in the future, is briefly considered.     

Investigation of the metabolism of substance P at the blood-brain barrier using capillary electrophoresis with laser-induced fluorescence detection.

Substance P (SP) metabolism was investigated upon exposure to a monolayer of bovine brain microvessel endothelial cells (BBMECs), a cell culture model of the blood-brain barrier. SP was incubated with the BBMECs and its metabolism was followed as a function of time over a 5-h period. The resulting samples were derivatized with naphthalene-2,3-dicarboxaldehyde (NDA)/cyanide, separated, and detected using cyclodextrin-modified electrokinetic chromatography with laser-induced fluorescence detection (CDMEKC-LIF). Upon exposure to the BBMEC monolayer, SP rapidly degraded to produce the N-terminal (1-9), (1-4) and (1-7) and C-terminal (2-11) and (3-11) fragments. These results were compared with those in an earlier report from our laboratory, where SP metabolism was investigated in vivo by microdialysis sampling in rat striatum.     

Design, synthesis and biological evaluation of a novel series of potent, orally active adenosine A1 receptor antagonists with high blood-brain barrier permeability

A novel series of 3-(2-substituted-3-oxo-2,3-dihydropyridazin-6-yl)-2-phenylpyrazolo[1,5-a]pyridines (5-38) were synthesized and evaluated for their in vitro adenosine A1 and A(2A) receptor binding activities, and in vitro metabolism by rat liver in order to search for orally active compounds. Most of the test compounds were potent adenosine A1 receptor antagonists with high A1 selectivity and the A1 affinity and A1 selectivity of carbonyl derivatives (5-11) was particularly high. In particular, compound 7 was an extremely potent and selective adenosine A1 antagonist with high A1 selectivity (Ki=0.026 nM, A(2A)/A1=5400). In terms of metabolic stability, 2-oxopropyl (5), 2-hydroxypropyl (12), N-methylacetamide (16), 2-(piperidin-1-yl)ethyl (28) and 1-methylpiperidin-4-yl (32, FR194921) were the most stable compounds in this series of analogues. Further in vivo evaluation indicated that compounds 5, 13, 17, 28 and 32 were detected in both plasma and brain after oral administration in rats. In particular, 32 displayed good plasma and brain concentrations (dose: 32 mg/kg (n=3); after 30 min, plasma conc.=3390+/-651nM, brain conc.=3670+/-496nM; after 60min, plasma conc.=1580+/-348nM, brain conc.=2143+/-434nM), and a good brain/plasma ratio (1.11+/-0.060 (30min), 1.39+/-0.172 (60min)). As a result, we could show that 32 is a good candidate for an orally active adenosine A1 receptor antagonist with high blood-brain barrier permeability and good bioavailability (Ki=6.6nM, A(2A)/A1=820, BA=60.6+/-4.9% (32 mg/kg)).     

3D QSAR investigation of the blood-brain barrier penetration of chemical compounds

In the present study, we investigated structure-permeability relationships for the blood-brain barrier (BBB) of 16 imipramine and phenothiazine derivatives. The compounds belong to structurally related chemical classes of catamphiphiles, representatives of which have previously been investigated for membrane activity and ability to overcome multidrug resistance (MDR) in tumour cells. These studies show that phenothiazines and structurally related drugs (imipramines, thioxanthenes, acridines) interact with membrane phospholipids, and additionally inhibit the MDR transport P-glycoprotein. This study aimed to identify common 3D structural characteristics of these compounds related to their mechanism of transport across the BBB. For this purpose Genetic Algorithm Similarity Programme (GASP), Comparative Molecular Field Analysis (CoMFA) and Comparative Molecular Similarity Index Analysis (CoMSIA) were applied. The results demonstrate the importance of the spatial distribution of molecular hydrophobicity for the BBB penetration of the investigated compounds. It suggests that the compounds should follow a specific profile of two hydrophobic and one hydrophilic centres in a particular space configuration, for optimal BBB penetration.     

Validity of permeability prediction from NMR measurements

Low field nuclear relaxation measurements applied to porous media can provide a wide variety of information. One important use of NMR measurements in the petroleum industry is the estimation of in-situ permeability as a function of depth. Such information is not available from any other tool and is critical for oil recovery predictions. A large number of empirical relationships have been published without clear explanation of their physical origin. We present some understanding and illustration of the link between NMR relaxation measurements and permeability, which is useful to select the appropriate law as a function of the geological context.     

Generic fast gradient liquid chromatography/tandem mass spectrometry techniques for the assessment of the in vitro permeability across the blood-brain barrier in drug discovery

Rapid, generic gradient liquid chromatography/tandem mass spectrometry (LC/MS/MS) assays, designed to accelerate sample analyses, have been developed to keep pace with the productivity of advanced synthetic procedures. In this study, LC/MS/MS was combined with an in vitro, cell-based, blood-brain barrier (BBB) model to evaluate the potential of new chemical entities (NCEs) to cross the BBB. This in vitro assay provides the permeability of discovery compounds across a monolayer of a primary culture of bovine brain microvessel endothelial cells in a fraction of the time that is required for in vivo studies (brain/plasma concentrations), using only 2 mg of the compound. The results are consistent with in vivo brain/plasma concentration ratio data.     

Computational design and prediction of interesting not-yet-synthesized structures of inorganic materials by using building unit concepts

The computational design of new and interesting inorganic materials is still an ongoing challenge. The motivation of these efforts is to aid the often difficult task of crystal structure determination, to rationalize different but related structure types, or to help limit the domain of structures that are possible in a given system. Over the past decade, simulation methods have continuously evolved towards the prediction of new structures using minimal input information in terms of symmetry, cell parameters, or chemical composition. So far, this task of identifying candidate structures through an analysis of the energy landscape of chemical systems has been particularly successful for predominantly ionic systems with relatively small numbers of atoms or ions in the simulation cell. After an introductory section, the second section of this work presents the historical developments of such simulation methods in this area. The following sections of the work are dedicated to the introduction of the building unit concept in simulation methods: we present simulation approaches to structure prediction employing both primary (aggregate of atoms) and secondary (aggregate of coordination polyhedra) building units. While structure prediction with primary units is a straightforward extension of established approaches, the AASBU method (automated asssembly of secondary building units) focusses on the topology of network-based structures. This method explores the possible ways to assemble predefined inorganic building units in three-dimensional space, opening the way to the manipulation of very large building units (up to 84 atoms in this work). As illustrative examples we present the prediction of candidate structures for Li(4)CO(4), the identification of topological relationships within a family of metalphosphates, ULM-n and MIL-n, and finally the generation of new topologies by using predefined large building units such as a sodalite or a double-four-ring cage, for the prediction of new and interesting zeolite-type structures.