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.     

Evaluation of sphingomyelin,cholester, and phosphatidylcholine-based immobilized artificial membrane liquid chromatography to predict drug penetration across the blood-brain barrier

Over the past decades, several in vitro methods have been tested for their ability to predict drug penetration across the blood-brain barrier. So far, in high-performance liquid chromatography, most attention has been paid to micellar liquid chromatography and immobilized artificial membrane (IAM) LC. IAMLC has been described as a viable approach, since the stationary phase emulates the lipid environment of a cell membrane. However, research in IAMLC has almost exclusively been limited to phosphatidylcholine (PC)-based stationary phases, even though PC is only one of the lipids present in cell membranes. In this article, sphingomyelin and cholester stationary phases have been tested for the first time towards their ability to predict drug penetration across the blood-brain barrier. Upon comparison with the PC stationary phase, the sphingomyelin- and cholester-based columns depict similar predictive performance. Combining data from the different stationary phases did not lead to improvements of the models. Figure

Schematic representation of how IAM-LC is used to predict drug penetration across the blood-brain barrier.     

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.     

Imaging brain tumor by dendrimer-based optical/paramagnetic nanoprobe across the blood-brain barrier

A multimodal optical/paramagnetic nanoprobe, Den-Angio, was developed and demonstrated a capability to circumvent the blood brain barrier (BBB) and visualize brain tumors with high sensitivity in vivo. Den-Angio holds promise to pre-operatively localize brain tumors and make image-guided tumor resection possible during surgery.     

Diketopiperazines as a tool for the study of transport across the blood-brain barrier (BBB) and their potential use as BBB-shuttles

Here we prepared and evaluated two libraries of mono-N-methylated and di-N-methylated diketopiperazines (DKPs) by parallel artificial membrane permeability assay and immobilized artificial membrane chromatography in order to obtain information on the features that govern the passage of peptidic molecules across the blood-brain barrier (BBB) by passive diffusion. On the basis of the results from these two libraries, we prepared and evaluated several DKP-baicalin and DKP-dopamine constructs. The DKPs or cyclic dipeptide scaffolds can be considered a novel family of brain delivery systems (BBB-shuttles) to transport to the brain drugs and other cargos that cannot cross the BBB unaided.     

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.     

Expression of ornithine decarboxylase during the transport of saquinavir across the blood-brain barrier using composite polymeric nanocarriers under an electromagnetic field

The expression of human ornithine decarboxylase (ODC) and permeability of saquinavir (SQV) across the blood-brain barrier were studied using nanoparticles (NPs) composed of poly(lactide-co-glycolide) (PLGA), poly-(γ-glutamic acid) (γ-PGA), and polyethyleneimine (PEI). SQV was encapsulated in the particle core to traverse a monolayer of human brain-microvascular endothelial cells (HBMECs) with the regulation of human astrocytes under an electromagnetic field (EMF). An increase in the weight percentage of PEI enhanced the particle size, zeta potential, and permeability of SQV. However, the viability of HBMECs reduced when the weight percentage of PEI increased. In addition, an increment in the molecular weight of γ-PGA enhanced the particle size and viability of HBMECs, and reduced the zeta potential. The permeability of SQV and expression of ODC were in the order: an EMF with amplitude modulation (AM)>an EMF with frequency modulation>no EMF. At 0.04% PEI, the AM EMF increased 2.38 times the uptake of NPs and 2.72 times the expression of ODC. The combination of PEI/γ-PGA/PLGA NPs and EMF can be an innovative strategy for delivering SQV into the brain.     

Predicting protein-ligand binding affinities using novel geometrical descriptors and machine-learning methods

Inspired by the concept of knowledge-based scoring functions, a new quantitative structure-activity relationship (QSAR) approach is introduced for scoring protein-ligand interactions. This approach considers that the strength of ligand binding is correlated with the nature of specific ligand/binding site atom pairs in a distance-dependent manner. In this technique, atom pair occurrence and distance-dependent atom pair features are used to generate an interaction score. Scoring and pattern recognition results obtained using Kernel PLS (partial least squares) modeling and a genetic algorithm-based feature selection method are discussed.     

血脑屏障药物筛选模型的建立及丹参活性成分筛选分析

ECV304细胞在C6细胞的诱导下生长,通过细胞培养时间优化、渗透系数测定和细胞形态学观察等,建立ECV304/C6共培养血脑屏障(BBB)药物筛选模型.将该模型应用于从丹参提取液中筛选可能作用于中枢神经系统(CNS)的活性成分,结合液相色谱-质谱联用技术(LC-MS)分析,发现丹参提取液中至少有16种成分能够穿越BBB模型,其中4种成分被确认为隐丹参酮、丹参酮Ⅰ、丹参素和原儿茶酸.通过定量构效关系(QASR)分析,进一步从理论上证明所确认的4种化合物均符合CNS靶向药物的特征.研究结果表明,ECV304/C6共培养BBB模型能够在模拟生理状态下从中药复杂体系中筛选分离跨越BBB的活性成分组,可用于CNS药物开发的早期快速筛选,服务于中药现代化研究.     

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.     

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.     

A versatile liposome/cyclodextrin supramolecular carrier for drug delivery through the blood-brain barrier

For many commercial drugs, reaching the central nervous system in large amount without damaging the blood-brain-barrier (BBB) remains a challenging task. We present here a supramolecular strategy aiming at using a well-defined cyclodextrin-coated liposomes as drug carrier and adamantoyl saccharides as BBB-interacting ligands. In this study, the liposome is constituted of n-alkyldimethylammoniumcyclodextrins incorporated in the lipid bilayer of a 3/7 cholesterol/dipalmitoylphosphatidylcholine mixture and the ligand is constituted of an adamantoylglucose molecule whose adamantoyl moiety can be included in the CD cavity. The whole supramolecular assembly has been characterized by light-scattering and ³¹P NMR measurements. Toxicity and permeability studies on an in vitro model of the BBB clearly demonstrated a 5-fold improved ability of the modified liposome to enter the BBB-endothelial cells compared to the non-coated liposome. Fluorescence labelling of these liposomes is also displayed with DiI as a fluorescent probe.