Relationship between immobilised artificial membrane chromatographic retention and the brain penetration of structurally diverse drugs

Recent developments in the application of immobilized artificial membrane (IAM) chromatography to drug discovery

INTRODUCTION

Immobilized artificial membrane (IAM) chromatography is widely used in many aspects of drug discovery. It employs stationary phases, which contain phospholipids combining simulation of biological membranes with rapid measurements.

AREAS COVERED

Advances in IAM stationary phases, chromatographic conditions and the underlying retention mechanism are discussed. The potential of IAM chromatography to model permeability and drug-membrane interactions as well as its use to estimate pharmacokinetic properties and toxicity endpoints including ecotoxicity, is outlined. Efforts to construct models for prediction IAM retention factors are presented.

EXPERT OPINION

IAM chromatography, as a border case between partitioning and binding, has broadened its application from permeability studies to encompass processes involving tissue binding. Most IAM-based permeability models are hybrid models incorporating additional molecular descriptors, while for the estimation of pharmacokinetic properties and binding to off targets, IAM retention is combined with other biomimetic properties. However, for its integration into routine drug discovery protocols, reliable IAM prediction models implemented in relevant software should be developed, to enable its use in virtual screening and the design of new molecules. Conversely, preparation of new IAM columns with different phospholipids or mixed monomers offers enhanced flexibility and the potential to tailor the conditions according to the target property.

Development of QSAR models to predict blood-brain barrier permeability

Assessing drug permeability across the blood-brain barrier (BBB) is important when evaluating the abuse potential of new pharmaceuticals as well as developing novel therapeutics that target central nervous system disorders. One of the gold-standard in vivo methods for determining BBB permeability is rodent log BB; however, like most in vivo methods, it is time-consuming and expensive. In the present study, two statistical-based quantitative structure-activity relationship (QSAR) models were developed to predict BBB permeability of drugs based on their chemical structure. The in vivo BBB permeability data were harvested for 921 compounds from publicly available literature, non-proprietary drug approval packages, and University of Washington's Drug Interaction Database. The cross-validation performance statistics for the BBB models ranged from 82 to 85% in sensitivity and 80-83% in negative predictivity. Additionally, the performance of newly developed models was assessed using an external validation set comprised of 83 chemicals. Overall, performance of individual models ranged from 70 to 75% in sensitivity, 70-72% in negative predictivity, and 78-86% in coverage. The predictive performance was further improved to 93% in coverage by combining predictions across the two software programs. These new models can be rapidly deployed to predict blood brain barrier permeability of pharmaceutical candidates and reduce the use of experimental animals.

Prediction Models for Brain Distribution of Drugs Based on Biomimetic Chromatographic Data

The development of high-throughput approaches for the valid estimation of brain disposition is of great importance in the early drug screening of drug candidates. However, the complexity of brain tissue, which is protected by a unique vasculature formation called the blood−brain barrier (BBB), complicates the development of robust in silico models. In addition, most computational approaches focus only on brain permeability data without considering the crucial factors of plasma and tissue binding. In the present study, we combined experimental data obtained by HPLC using three biomimetic columns, i.e., immobilized artificial membranes, human serum albumin, and α1-acid glycoprotein, with molecular descriptors to model brain disposition of drugs. Kp,uu,brain, as the ratio between the unbound drug concentration in the brain interstitial fluid to the corresponding plasma concentration, brain permeability, the unbound fraction in the brain, and the brain unbound volume of distribution, was collected from literature. Given the complexity of the investigated biological processes, the extracted models displayed high statistical quality (R2 > 0.6), while in the case of the brain fraction unbound, the models showed excellent performance (R2 > 0.9). All models were thoroughly validated, and their applicability domain was estimated. Our approach highlighted the importance of phospholipid, as well as tissue and protein, binding in balance with BBB permeability in brain disposition and suggests biomimetic chromatography as a rapid and simple technique to construct models with experimental evidence for the early evaluation of CNS drug candidates.

Bioengineered microfluidic blood-brain barrier models in oncology research

Metastasis is the major reason for most brain tumors with up to a 50% chance of occurrence in patients with other types of malignancies. Brain metastasis occurs if cancer cells succeed to cross the 'blood-brain barrier' (BBB). Moreover, changes in the structure and function of BBB can lead to the onset and progression of diseases including neurological disorders and brain-metastases. Generating BBB models with structural and functional features of intact BBB is highly important to better understand the molecular mechanism of such ailments and finding novel therapeutic agents targeting them. Hence, researchers are developing novel in vitro BBB platforms that can recapitulate the structural and functional characteristics of BBB. Brain endothelial cells-based in vitro BBB models have thus been developed to investigate the mechanism of brain metastasis through BBB and facilitate the testing of brain targeted anticancer drugs. Bioengineered constructs integrated with microfluidic platforms are vital tools for recapitulating the features of BBB in vitro closely as possible. In this review, we outline the fundamentals of BBB biology, recent developments in the microfluidic BBB platforms, and provide a concise discussion of diverse types of bioengineered BBB models with an emphasis on the application of them in brain metastasis and cancer research in general. We also provide insights into the challenges and prospects of the current bioengineered microfluidic platforms in cancer research.

Biomimetic Chromatographic Studies Combined with the Computational Approach to Investigate the Ability of Triterpenoid Saponins of Plant Origin to Cross the Blood-Brain Barrier

Biomimetic (non-cell based in vitro) and computational (in silico) studies are commonly used as screening tests in laboratory practice in the first stages of an experiment on biologically active compounds (potential drugs) and constitute an important step in the research on the drug design process. The main aim of this study was to evaluate the ability of triterpenoid saponins of plant origin to cross the blood-brain barrier (BBB) using both computational methods, including QSAR methodology, and biomimetic chromatographic methods, i.e., High Performance Liquid Chromatography (HPLC) with Immobilized Artificial Membrane (IAM) and cholesterol (CHOL) stationary phases, as well as Bio-partitioning Micellar Chromatography (BMC). The tested compounds were as follows: arjunic acid (Terminalia arjuna), akebia saponin D (Akebia quinata), bacoside A (Bacopa monnieri) and platycodin D (Platycodon grandiflorum). The pharmacokinetic BBB parameters calculated in silico show that three of the four substances, i.e., arjunic acid, akebia saponin D, and bacoside A exhibit similar values of brain/plasma equilibration rate expressed as logPS_Fubrain (the average logPS_Fubrain: -5.03), whereas the logPS_Fubrain value for platycodin D is -9.0. Platycodin D also shows the highest value of the unbound fraction in the brain obtained using the examined compounds (0.98). In these studies, it was found out for the first time that the logarithm of the analyte-micelle association constant (logK_MA) calculated based on Foley's equation can describe the passage of substances through the BBB. The most similar logBB values were obtained for hydrophilic platycodin D, applying both biomimetic and computational methods. All of the obtained logBB values and physicochemical parameters of the molecule indicate that platycodin D does not cross the BBB (the average logBB: -1.681), even though the in silico estimated value of the fraction unbound in plasma is relatively high (0.52). As far as it is known, this is the first paper that shows the applicability of biomimetic chromatographic methods in predicting the penetration of triterpenoid saponins through the BBB.

In Silico Studies on Triterpenoid Saponins Permeation through the Blood-Brain Barrier Combined with Postmortem Research on the Brain Tissues of Mice Affected by Astragaloside IV Administration

As the number of central nervous system (CNS) drug candidates is constantly growing, there is a strong need for precise a priori prediction of whether an administered compound is able to cross the blood–brain barrier (BBB). The aim of this study was to evaluate the ability to cross the BBB of triterpenoid saponins occurring in Astragalus mongholicus roots. The research was carried out using in silico methods combined with postmortem studies on the brain tissues of mice treated with isolated astragaloside IV (AIV). Firstly, to estimate the ability to cross the BBB by the tested saponins, new quantitative structure–activity relationship (QSAR) models were established. The reliability and predictability of the model based on the values of the blood–brain barrier penetration descriptor (logBB), the difference between the n-octanol/water and cyclohexane/water logP (ΔlogP), the logarithm of n-octanol/water partition coefficient (logP_ow), and the excess molar refraction (E) were both confirmed using the applicability domain (AD). The critical leverage value h* was found to be 0.128. The relationships between the standardized residuals and the leverages were investigated here. The application of an in vitro acetylcholinesterase-inhibition test showed that AIV can be recognized as the strongest inhibitor among the tested compounds. Therefore, it was isolated for the postmortem studies on brain tissues and blood using semi-preparative HPLC with the mobile phase composed of water, methanol, and ethyl acetate (1.7:2.1:16.2 v/v/v). The results of the postmortem studies on the brain tissues show a regular dependence of the final concentration of AIV in the analyzed brain samples of animals treated with 12.5 and 25 mg/kg b.w. of AIV (0.00012299 and 0.0002306 mg, respectively, per one brain). Moreover, the AIV logBB value was experimentally determined and found to be equal to 0.49 ± 0.03.

Application of separation methods for in vitro prediction of blood-brain barrier permeability-The state of the art

Despite many efforts, drug discovery pipeline is still a highly inefficient process. Nowadays, when combinatorial chemistry enables to synthesize hundreds of new drugs candidates, methods for rapid assessment of biopharmaceutical parameters of new compounds are highly desired. Over one-third of drugs candidates is rejected because of unsatisfactory pharmacokinetic properties. In the drug discovery process, the blood-brain barrier (BBB) permeability plays a critical role for central nervous system active drugs candidates as well as non-central nervous system active drugs. For this reason, knowledge on the BBB permeability of compounds is essential in the development of new medicines. The review was focused on the application of different separation methods for BBB permeability assessment. Both chromatographic and electrophoretic methods were described. In the article, the advantages and limitations of well-established chromatographic methods like immobilized artificial membrane chromatography or micellar liquid chromatography, and less common techniques were discussed. Special attention was devoted to methods were microemulsion is used as mobile or pseudostationary phases.

A new descriptor via bio-mimetic chromatography and modeling for the blood brain barrier (Part II)

Within the context of drug design methodology for the central nervous system (CNS), a predictive model which can shorten the process of finding new candidate drugs was developed. Therefore, the retention time of 51 molecules which are clinically established to enter the blood brain barrier (BBB), were recorded on two HPLC columns. For this purpose, a lipophilic butyl (C₄) stationary phase was used to simulate the behavior of a drug regarding BBB permeability and a zwitterionic-HILIC to simulate blood. The results were plotted as Y variables on two Partial Least Squares (PLS) models, while 25 specific physicochemical properties (significant for lipid bilayers BBB permeation or blood) were used as X descriptors. Both models can be utilized to predict the drugability of a new molecule avoiding needless animal experiments, as well as time and material consuming syntheses. The developed models were validated (R² ≥ 0.90, Q² ≥ 0.83), and based on the results specific variables were proved to be significant for the studied phenomenon. Additionally, a new factor symbolized as MT was introduced. MT incorporated the experimental results and it was calculated by the fraction of the sum of the retention time of the drug on the two columns (t_r(butyl) + t_r(HILIC)) divided by the molecular volume (V_m) of each analyte. This new descriptor was used as an equivalent to the logarithm of BBB permeability (logBB) and may indicate the ability of a new molecule to act as a candidate drug able to enter the BBB. Comprehending the extend of contribution of several molecular attributes to the in vivo distribution of a drug may enlighten the knowledge on pharmacokinetics and clinical variation, and enable scientists to design more efficient drug molecules.

Screening therapeutics according to their uptake across the blood-brain barrier: A high throughput method based on immobilized artificial membrane liquid chromatography-diode-array-detection coupled to electrospray-time-of-flight mass spectrometry

The Blood-Brain Barrier (BBB) plays an essential role in protecting the brain tissues against possible injurious substances. In the present work, 79 neutral, basic, acidic and amphoteric structurally unrelated analytes were considered and their chromatographic retention coefficients on immobilized artificial membrane (IAM) stationary phase were determined employing a mass spectrometry (MS)-compatible buffer based on ammonium acetate. Their BBB passage predictive strength was evaluated and the statistical models based on IAM indexes and in silico physico-chemical descriptors showed solid statistics (r² (n - 1) = 0.78). The predictive strength of the indexes achieved by the MS-compatible method was comparable to that achieved by employing the more "biomimetic" Dulbecco's phosphate buffered saline, even if some differences in the elution order were observed. The method was transferred to the MS, employing a diode-array-detection coupled to an electrospray ionization source and a time-of-flight analyzer. This setup allowed the simultaneous analysis of up to eight analytes, yielding a remarkable acceleration of the analysis time.

Lipophilicity and biomimetic properties to support drug discovery

INTRODUCTION

Lipophilicity, expressed as the octanol-water partition coefficient, constitutes the most important property in drug action, influencing both pharmacokinetic and pharmacodynamics processes as well as drug toxicity. On the other hand, biomimetic properties defined as the retention outcome on HPLC columns containing a biological relevant agent, provide a considerable advance for rapid experimental - based estimation of ADME properties in early drug discovery stages. Areas covered: This review highlights the paramount importance of lipophilicity in almost all aspects of drug action and safety. It outlines problems brought about by high lipophilicity and provides an overview of the drug-like metrics which incorporate lower limits or ranges of logP. The fundamental factors governing lipophilicity are compared to those involved in phospholipophilicity, assessed by Immobilized Artificial Membrane Chromatography (IAM). Finally, the contribution of biomimetic properties to assess plasma protein binding is evaluated. Expert opinion: Lipophilicity and biomimetic properties have important distinct and overlapping roles in supporting the drug discovery process. Lipophilicity is unique in early drug design for library screening and for the identification of the most promising compounds to start with, while biomimetic properties are useful for the experimentally-based evaluation of ADME properties for the synthesized novel compounds, supporting the prioritization of drug candidates and guiding further synthesis.

Combined computational-experimental approach to predict blood-brain barrier (BBB) permeation based on "green" salting-out thin layer chromatography supported by simple molecular descriptors

The objective of this paper is to build QSRR/QSAR model for predicting the blood-brain barrier (BBB) permeability. The obtained models are based on salting-out thin layer chromatography (SOTLC) constants and calculated molecular descriptors. Among chromatographic methods SOTLC was chosen, since the mobile phases are free of organic solvent. As consequences, there are less toxic, and have lower environmental impact compared to classical reserved phases liquid chromatography (RPLC). During the study three stationary phase silica gel, cellulose plates and neutral aluminum oxide were examined. The model set of solutes presents a wide range of log BB values, containing compounds which cross the BBB readily and molecules poorly distributed to the brain including drugs acting on the nervous system as well as peripheral acting drugs. Additionally, the comparison of three regression models: multiple linear regression (MLR), partial least-squares (PLS) and orthogonal partial least squares (OPLS) were performed. The designed QSRR/QSAR models could be useful to predict BBB of systematically synthesized newly compounds in the drug development pipeline and are attractive alternatives of time-consuming and demanding directed methods for log BB measurement. The study also shown that among several regression techniques, significant differences can be obtained in models performance, measured by R² and Q², hence it is strongly suggested to evaluate all available options as MLR, PLS and OPLS.

Validation of an immortalized human (hBMEC) in vitro blood-brain barrier model

We recently established and optimized an immortalized human in vitro blood-brain barrier (BBB) model based on the hBMEC cell line. In the present work, we validated this mono-culture 24-well model with a representative series of drug substances which are known to cross or not to cross the BBB. For each individual compound, a quantitative UHPLC-MS/MS method in Ringer HEPES buffer was developed and validated according to current regulatory guidelines, with respect to selectivity, precision, and reliability. Various biological and analytical challenges were met during method validation, highlighting the importance of careful method development. The positive controls antipyrine, caffeine, diazepam, and propranolol showed mean endothelial permeability coefficients (P e) in the range of 17-70 × 10(-6) cm/s, indicating moderate to high BBB permeability when compared to the barrier integrity marker sodium fluorescein (mean P e 3-5 × 10(-6) cm/s). The negative controls atenolol, cimetidine, and vinblastine showed mean P e values < 10 × 10(-6) cm/s, suggesting low permeability. In silico calculations were in agreement with in vitro data. With the exception of quinidine (P-glycoprotein inhibitor and substrate), BBB permeability of all control compounds was correctly predicted by this new, easy, and fast to set up human in vitro BBB model. Addition of retinoic acid and puromycin did not increase transendothelial electrical resistance (TEER) values of the BBB model.

A method to predict blood-brain barrier permeability of drug-like compounds using molecular dynamics simulations

The blood-brain barrier (BBB) is formed by specialized tight junctions between endothelial cells that line brain capillaries to create a highly selective barrier between the brain and the rest of the body. A major problem to overcome in drug design is the ability of the compound in question to cross the BBB. Neuroactive drugs are required to cross the BBB to function. Conversely, drugs that target other parts of the body ideally should not cross the BBB to avoid possible psychotropic side effects. Thus, the task of predicting the BBB permeability of new compounds is of great importance. Two gold-standard experimental measures of BBB permeability are logBB (the concentration of drug in the brain divided by concentration in the blood) and logPS (permeability surface-area product). Both methods are time-consuming and expensive, and although logPS is considered the more informative measure, it is lower throughput and more resource intensive. With continual increases in computer power and improvements in molecular simulations, in silico methods may provide viable alternatives. Computational predictions of these two parameters for a sample of 12 small molecule compounds were performed. The potential of mean force for each compound through a 1,2-dioleoyl-sn-glycero-3-phosphocholine bilayer is determined by molecular dynamics simulations. This system setup is often used as a simple BBB mimetic. Additionally, one-dimensional position-dependent diffusion coefficients are calculated from the molecular dynamics trajectories. The diffusion coefficient is combined with the free energy landscape to calculate the effective permeability (Peff) for each sample compound. The relative values of these permeabilities are compared to experimentally determined logBB and logPS values. Our computational predictions correlate remarkably well with both logBB (R(2) = 0.94) and logPS (R(2) = 0.90). Thus, we have demonstrated that this approach may have the potential to provide reliable, quantitatively predictive BBB permeability, using a relatively quick, inexpensive method.

Methodologies to assess drug permeation through the blood-brain barrier for pharmaceutical research

The drug discovery process for drugs that target the central nervous system suffers from a very high rate of failure due to the presence of the blood-brain barrier, which limits the entry of xenobiotics into the brain. To minimise drug failure at different stages of the drug development process, new methodologies have been developed to understand the absorption, distribution, metabolism, excretion and toxicity (ADMET) profile of drug candidates at early stages of drug development. Additionally, understanding the permeation of drug candidates is also important, particularly for drugs that target the central nervous system. During the first stages of the drug discovery process, in vitro methods that allow for the determination of permeability using high-throughput screening methods are advantageous. For example, performing the parallel artificial membrane permeability assay followed by cell-based models with interesting hits is a useful technique for identifying potential drugs. In silico models also provide interesting information but must be confirmed by in vitro models. Finally, in vivo models, such as in situ brain perfusion, should be studied to reduce a large number of drug candidates to a few lead compounds. This article reviews the different methodologies used in the drug discovery and drug development processes to determine the permeation of drug candidates through the blood-brain barrier.

The use of biopartitioning micellar chromatography and immobilized artificial membrane column for in silico and in vitro determination of blood-brain barrier penetration of phenols

Biopartitioning Micellar Chromatography (BMC) is a mode of micellar liquid chromatography that uses C18 stationary phases and micellar mobile phases of Brij35 under adequate experimental conditions and can be useful to mimic human drug absorption, blood-brain barrier distribution or partitioning processes in biological systems. BMC system can be useful in constructing good predictive models because the characteristics of the BMC system are similar to biological barriers and extracellular fluids. Immobilized Artificial Membrane (IAM) chromatography uses stationary phase which consists of a monolayer of phosphatidylcholine covalently immobilized on an inert silica support. IAM columns are thought to mimic very closely a membrane bilayer and are used in a HPLC system with a physiological buffer as eluent. In this paper the usefulness of BMC and IAM system for in silico and in vitro determination of blood-brain barrier (BBB) penetration of phenols has been demonstrated. The most important pharmacokinetic parameters of brain have been obtained for the determination of BBB penetration, i.e. BBB permeability - surface area product (PS), usually given as a logPS, brain/plasma equilibration rate (log(PS×fu,brain)) and fraction unbound in plasma (Fu). Moreover, the relationships between retention of eighteen phenols and different parameters of molecular size, lipophilicity and BBB penetration were studied. Extrapolated to pure water values of the logarithms of retention factors (logkw) have been compared with the corresponding octanol-water partition coefficient (logPo-w) values of the solutes. In addition, different physicochemical parameters from Foley's equation for BMC system have been collated with the chromatographic data. The Linear Solvation Energy Relationship (LSER) using Abraham model for the describing of phenols penetration across BBB has been used. Four equations were developed as a multiple linear regression using retention data from IAM and BMC system (QRAR models) and molecular volume parameter (Vm) and Abraham descriptors to correlate the logBB values. Moreover, in order to establish the relationships between different variables, the principal components analysis (PCA) has been done. The results of PCA were obtained using chromatographic data from IAM and BMC systems as well as from the structures of tested phenols. The four parameters: logkwIAM(exp), logkwBMC(exp), analyte-micelle association constant (Kma) and logPo-w have been checked.

Drug penetration across the blood-brain barrier: an overview

The brain is one of the most protected organs in the body. There are two key barriers that control the access of endogenous substances and xenobiotics (drugs or toxins) to the CNS. These physiological structures are the blood-brain barrier (BBB) and the blood-cerebrospinal fluid barrier. The BBB represents the main determinant of the effective delivery of drugs to the CNS. Good access through the BBB is essential if the target site is located within the CNS or, in contrast, can be a disadvantage if adverse reactions occur at central level. The development of new drugs targeted to the CNS requires a better knowledge of the factors affecting BBB permeation as well as in vitro and in silico predictive tools to optimize screening, and to reduce the attrition rate at later stages of drug development. This review discusses the particular characteristics of the biology and physiology of the BBB with respect to the permeation and distribution of drugs into the brain. The factors affecting rate, extent and distribution into the brain are discussed and a brief description of the in silico, in vitro, in situ and in vivo methods used to measure BBB transport are presented. Finally, the lastest proposals and strategies to enhance transport across the BBB of new CNS drugs are summarized.

Database of published retention factors for immobilized artificial membrane HPLC and an assessment of the effect of experimental variability

A database was collated of published experimental logarithmic values for the relative retention factors (log k(IAM)) measured using an immobilized artificial membrane column and high-performance liquid chromatography (IAM HPLC). Log k(IAM) is an alternative measure of hydrophobicity to the octanol/water partition coefficient (log K(OW)). While there are several accepted methods to measure log K(OW), no standardized method exists to determine log k(IAM). The database of collated log k(IAM) values includes 13 key experimental parameters and contains 1,686 values for 555 compounds, which are predominantly polar organic compounds and include drug molecules and surfactants. These compounds are acidic, basic, and neutral and both ionized and un-ionized under the conditions of analysis. The data compiled demonstrated experimental variability for each experimental parameter considered, including column stationary phase, pH, temperature, and mobile phase. Reducing the experimental variability allowed for greater consistency in the datasets.

Biomembrane models and drug-biomembrane interaction studies: Involvement in drug design and development

Contact with many different biological membranes goes along the destiny of a drug after its systemic administration. From the circulating macrophage cells to the vessel endothelium, to more complex absorption barriers, the interaction of a biomolecule with these membranes largely affects its rate and time of biodistribution in the body and at the target sites. Therefore, investigating the phenomena occurring on the cell membranes, as well as their different interaction with drugs in the physiological or pathological conditions, is important to exploit the molecular basis of many diseases and to identify new potential therapeutic strategies. Of course, the complexity of the structure and functions of biological and cell membranes, has pushed researchers toward the proposition and validation of simpler two- and three-dimensional membrane models, whose utility and drawbacks will be discussed. This review also describes the analytical methods used to look at the interactions among bioactive compounds with biological membrane models, with a particular accent on the calorimetric techniques. These studies can be considered as a powerful tool for medicinal chemistry and pharmaceutical technology, in the steps of designing new drugs and optimizing the activity and safety profile of compounds already used in the therapy.

Chemical Modifications on 4-Arylpiperazine-Ethyl Carboxamide Derivatives Differentially Modulate Affinity for 5-HT1A, D4.2, and α2A Receptors: Synthesis and In Vitro Radioligand Binding Studies

A series of substituted 4-aryl-piperazine-ethyl heteroarylcarboxamides were prepared and tested in in vitro radioligand binding studies. The presence of a quinoxaline has a favourable impact in terms of serotonin 5-HT1A versus dopamine D4.2 receptor selectivity. Compounds with a 3-CF3 group at the distal phenyl ring are the most effective in terms of affinity and selectivity for 5-HT1A versus D4.2 receptors. A 4-phenyl-1,2,3,6-tetrahydropyridine in place of the corresponding 4-phenyl-piperazine side chain is also favourable not only for the affinity for 5-HT1A and D4.2 receptors but also in some cases for α 2A-adrenoceptors.

A molecular chromatographic approach to analyze the cell diffusion of arginase inhibitors

Our group demonstrated that arginase inhibition reduces endothelial dysfunction in spontaneously hypertensive rats [C. Demougeot, A. Prigent-Tessier, C. Marie, A. Berthelot, J. Hypertens. 23 (2005) 971; C. Demougeot, A. Prigent-Tessier, T. Bagnost, C. Andre, Y. Guillaume, M. Bouhaddi, C. Marie, A. Berthelot, Life Sci. 80 (2007) 1128] which opens perspectives in the development of drugs against hypertension. In previous papers [T. Bagnost, Y.C. Guillaume, M. Thomassin, J.F. Robert, A. Berthelot, A. Xicluna, C. Andre, J. Chromatogr. B: Analyt. Technol. Biomed. Life Sci. 856 (2007) 113; T. Bagnost, Y.C. Guillaume, M. Thomassin, A. Berthelot, C. Demougeot, C. Andre, J. Chromatogr. B: Analyt. Technol. Biomed. Life Sci. 873 (2008) 37], we developed a biochromatographic column for studying the binding of an arginase inhibitor with this enzyme and the effect of magnesium on this binding. In this paper, the interaction of arginase inhibitors with an immobilized artificial membrane (IAM) has been studied using a biochromatographic approach. This IAM provided a biophysical model system to study the inhibitor passive transport across cells. It was demonstrated that more the inhibitor cross the cell membrane by passive diffusion more it is potent. As well, an analysis of the thermodynamics of the interaction of the arginase inhibitors with the IAM showed that van der Waals, hydrogen and ionic bonds were the main forces between the arginase inhibitors and the polar head groups of the IAM surface.

Characterization of microemulsion liquid chromatography systems by solvation parameter model and comparison with other physicochemical and biological processes

The solvation parameter model has been applied to characterize four microemulsion liquid chromatography (MELC) systems and two micellar liquid chromatography (MLC) systems, and utilized to compare the above systems with other physicochemical and biological processes in this study. The microemulsion mobile phases were composed of sodium dodecyl sulfate (SDS), polyoxyethylene (23) lauryl ether (Brij 35), butanol, heptane and phosphate buffer (pH 7.0) at the designated ratios. The results showed the main difference between the concerned MELC and MLC systems was the decrease of hydrogen-bond basicity of stationary phase with the addition of heptane in microemulsion. Principal component analysis with normalized coefficients can provide consistent results involving the similarities among various systems with that obtained by distance parameter d. Except for some proven similarities of chromatographic systems to octanol-water partition coefficients (logP) and human skin permeation (logK(p)), a microemulsion HPLC system, the mobile phase being 3.3% SDS-6.6% butanol-1.6% heptane-88.5% buffer, was found very similar to drug penetration across blood-brain barrier and its predictive capability for this biological process was originally evaluated in this study.

Characterization of the retention behavior of organic and pharmaceutical drug molecules on an immobilized artificial membrane column with the Abraham model

Data have been compiled from the published literature on the retention factors of 174 organic compounds and drug molecules eluted from a Regis Technologies IAM.PC.DD2 HPLC column using an aqueous mobile phase buffered in the pH range of pH 6.5-7.5. The logarithms of the retention factors are correlated with the Abraham solvation parameter model. The derived correlation contains the five Abraham solute descriptors plus two additional indicator descriptors (I(COOH) and I(amine)) that would be needed whenever carboxylic acid and alkylamine solutes are eluted in ionic form. The derived correlation describes the experimental capacity data of 174 neutral, acidic and basic compounds to within 0.21 log units.

Investigation of the lipophilic behaviour of some thiazolidinediones. Relationships with PPAR-gamma activity

Various lipophilicity aspects of five well-known PPAR-gamma ligands, belonging to the thiazolidinedione (TZD) class, ciglitazone (CSZ), troglitazone (TGZ), netoglitazone (NGZ) and the ampholytic pioglitazone (PGZ) and rosiglitazone (RGZ), have been explored. The compounds were found to be highly lipophilic as assessed by direct octanol-water partitioning experiments and further confirmed by reversed phase HPLC measurements under different conditions. Immobilised artificial membrane (IAM) chromatographic indices were also determined as an alternative expression of lipophilicity. They were found to show less diversity forming two clusters. Experimental logD/logP values were compared to those predicted by three widely used calculation systems. For the two ampholytic TZDs, the lipophilicity and retention/pH profiles were established over a broad pH range and compared to the corresponding calculated profiles. Lipophilicity indices derived under the different conditions were further compared to biological activity, concerning in vitro transactivation (pEC(50)) and binding affinity (pK(i)) data, taken from literature. The most active TZD (RGZ) in both transactivation and binding assay proved to be the less lipophilic analogue. An equation relating pEC(50) data to experimental logD(7.4) or reversed-phase logk(w) values could be established, while pK(i) data did not lead to satisfactory correlation.

Rapidly profiling blood–brain barrier penetration with liposome EKC

This report intended to study the potential of liposome EKC (LEKC) as a convenient and high-throughput screening tool to assess drug penetration across the blood-brain barrier (BBB). The retention factors (k) of 24 structurally diverse compounds were determined with LEKC and vesicle EKC (VEKC), respectively. Principal component analysis of the steady-state concentrations ratio of compounds in the brain and in the blood expressed as log BB, log k(LEKC), log k(VEKC), and other lipophilic descriptors including octanol/water partition coefficient (Clog P), octanol/water distribution coefficients (log D(7.4)), and polar surface area (PSA), showed the maximum similarity of partitioning processes in LEKC to drug penetration across the BBB. Furthermore, the log BB were correlated with the above five lipophilic descriptors, and the results showed that log k(LEKC) gave the better correlation coefficient (r(2) = 0.811, p <0.0001) than those of log D(7.4), Clog P, PSA, and log k(VEKC) (r(2) = 0.730, 0.672, 0.627, and 0.620, p <0.0001). This is the first report of the use of LEKC as a promising rapid tool to profile drug penetration across the BBB.

Quantitative structure-(chromatographic) retention relationships

Since the pioneering works of Kaliszan (R. Kaliszan, Quantitative Structure-Chromatographic Retention Relationships, Wiley, New York, 1987; and R. Kaliszan, Structure and Retention in Chromatography. A Chemometric Approach, Harwood Academic, Amsterdam, 1997) no comprehensive summary is available in the field. Present review covers the period of 1996-August 2006. The sources are grouped according to the special properties of kinds of chromatography: Quantitative structure-retention relationship in gas chromatography, in planar chromatography, in column liquid chromatography, in micellar liquid chromatography, affinity chromatography and quantitative structure enantioselective retention relationships. General tendencies, misleading practice and conclusions, validation of the models, suggestions for future works are summarized for each sub-field. Some straightforward applications are emphasized but standard ones. The sources and the model compounds, descriptors, predicted retention data, modeling methods and indicators of their performance, validation of models, and stationary phases are collected in the tables. Some important conclusions are: Not all physicochemical descriptors correlate with the retention data strongly; the heat of formation is not related to the chromatographic retention. It is not appropriate to give the errors of Kovats indices in percentages. The apparently low values (1-3%) can disorient the reviewers and readers. Contemporary mean interlaboratory reproducibility of Kovats indices are about 5-10 i.u. for standard non polar phases and 10-25 i.u. for standard polar phases. The predictive performance of QSRR models deteriorates as the polarity of GC stationary phase increases. The correlation coefficient alone is not a particularly good indicator for the model performance. Residuals are more useful than plots of measured and calculated values. There is no need to give the retention data in a form of an equation if the numbers of compounds are small. The domain of model applicability of models should be given in all cases.

Air to fat and blood to fat distribution of volatile organic compounds and drugs: Linear free energy analyses

Partition coefficients, K(fat), from air to human fat and to rat fat have been collected for 129 volatile organic compounds, VOCs. A linear free energy relationship, LFER, correlates the 129 values of log K(fat) with R(2)=0.958 and a standard deviation, S.D., of 0.194 log units. Use of training and test sets gives a predictive assessment of around 0.20 log units. Combination of log K(fat) with our previously listed values of log K(blood) enables blood/plasma to fat partition coefficients, as log P(fat), to be obtained for 126 VOCs. These values can be correlated with R(2)=0.847, S.D.=0.304 log units; the latter is also our assessment of the predictive capability of the LFER. Values of log P(fat) have been collected for 46 drugs, and can be fitted to an LFER with R(2)=0.811 and S.D.=0.355 log units. Unlike partition into brain or muscle, the data for VOCs and drugs cannot be combined. There are marked discrepancies for PCBs for which partition from blood/plasma into fat is very much less than that calculated from the data on VOCs or from the data on drugs.

A data base for partition of volatile organic compounds and drugs from blood/plasma/serum to brain, and an LFER analysis of the data

Literature values of the in vivo distribution (BB) of drugs from blood, plasma, or serum to rat brain have been assembled for 207 compounds (233 data points). We find that data on in vivo distribution from blood, plasma, and serum to rat brain can all be combined. Application of our general linear free energy relationship (LFER) to the 207 compounds yields an equation in log BB, with R2=0.75 and a standard deviation, SD, of 0.33 log units. An equation for a training set predicts the test set of data with a standard deviation of 0.31 log units. We further find that the in vivo data cannot simply be combined with in vitro data on volatile organic and inorganic compounds, because there is a systematic difference between the two sets of data. Use of an indicator variable allows the two sets to be combined, leading to a LFER equation for 302 compounds (328 data points) with R2=0.75 and SD=0.30 log units. A training equation was then used to predict a test set with SD=0.25 log units.

Chromatographic Estimation of Drug Disposition Properties by Means of Immobilized Artificial Membranes (IAM) and C18 Columns

Chromatographic retention measurement in immobilized artificial membranes (IAMs) is considered a fast and reliable method to predict biological properties (drug distribution) because of the IAM structure, which consists of phospholipid analogues bonded covalently to silica particles. A new parameter (d) is proposed to estimate the similarity between IAM columns, conventional HPLC columns, and drug distribution systems, and thus the performance of chromatographic systems to predict drug distribution. An IAM.PC.DD2 column has been used for this study, together with two XTerra columns (MSC18 and RP18), at several acetonitrile-water mobile phases. According to the d parameter, good correlations should be obtained between chromatographic systems (both IAM and C18) and octanol-water partition coefficient (log P), and thus both types of columns could be used to obtain log P values. The IAM.PC.DD2 system shows a close similarity to human skin partition, tadpole narcosis, and blood-brain permeability processes, showing that it can be useful as a model for these biological processes. Controversially, it is shown that human skin permeation is more similar to C18 partition than to IAM partition. Other biological processes such as blood-brain distribution and tissue-blood partition show a poor similarity to IAM and C18 systems, demonstrating that estimation of these drug distribution processes by chromatographic measurements may not be adequate.

Methods to assess drug permeability across the blood-brain barrier

Much research has focussed on the development of novel therapeutic agents to target various central nervous system disorders, however less attention has been given to determining the potential of such agents to permeate the blood-brain barrier (BBB), a factor that will ultimately govern the effectiveness of these agents in man. In order to assess the potential for novel compounds to permeate the BBB, various in-vitro, in-vivo and in-silico methods may be employed. Although in-vitro models (such as primary cell culture and immortalized cell lines) are useful as a screening method and can appropriately rank compounds in order of BBB permeability, they often correlate poorly to in-vivo brain uptake due to down-regulation of some BBB-specific transporters. In-vivo models (such as the internal carotid artery single injection or perfusion, intravenous bolus injection, brain efflux index and intracerebral microdialysis) provide more accurate information regarding brain uptake, and these can be complemented with novel imaging techniques (such as magnetic resonance imaging and positron emission tomography), although such methods are not suited to high-throughput permeability assessment. This paper reviews current methods used for assessing BBB permeability and highlights the particular advantages and disadvantages associated with each method, with a particular focus on methods suitable for moderate- to high-throughput screening.

In silico prediction of blood brain barrier permeability: an Artificial Neural Network model

This paper has two objectives: first to develop an in silico model for the prediction of blood brain barrier permeability of new chemical entities and second to find the role of active transport specific to the P-glycoprotein (P-gp) substrate probability in blood brain barrier permeability. An Artificial Neural Network (ANN) model has been developed to predict the ratios of the steady-state concentrations of drugs in the brain to those in the blood (logBB) from their molecular structural parameters. Seven descriptors including P-gp substrate probability have been used for model development. The developed model is able to capture a relationship between P-gp and logBB. The predictive ability of the ANN model has also been compared with earlier computational models.

Different retention behavior of structurally diverse basic and neutral drugs in immobilized artificial membrane and reversed-phase high performance liquid chromatography: comparison with octanol-water partitioning

The retention behavior of 43 structurally diverse neutral and basic drugs in immobilized artificial membrane chromatography was investigated and compared to the reversed-phase retention and octanol-water partitioning. IAM chromatography was performed using morpholinepropanesulfonic acid (MOPS) or phosphate buffer saline (PBS) at pH 7.4 as the aqueous component of the mobile phase. The differences in the retention factors were attributed to increased electrostatic interactions in the MOPS environment, dependent on the fraction of charged species. Electrostatic interactions were found to play a key role in the relationships with reversed-phase retention factors determined under two different mobile phase conditions as well as in the relationships with lipophilicity data. IAM retention factors correlated better with octanol-water partition coefficients log P than with log D(7.4), as a result of the contribution of electrostatic forces in IAM retention. With log D(7.4) the relationships were improved when the fraction of charged species was taken into consideration. In any case the regression coefficient of log P or log D(7.4) was considerably lower than 1 reflecting the reduced hydrophobic environment of the IAM stationary phase. The different data sets were submitted to principal component analysis for further exploration of their similarities/dissimilarities.