Immobilised artificial membrane liquid chromatography vs liposome electrokinetic capillary chromatography: Suitability in drug/bio membrane partitioning studies and effectiveness in the assessment of the passage of drugs through the respiratory mucosa

This study pioneers a comparison of the application of biomimetic techniques, immobilised artificial membrane liquid chromatography (IAM LC) and liposome electrokinetic capillary chromatography (LEKC), for the prediction of pulmonary drug permeability. The pulmonary absorption profiles of 26 structurally unrelated drug-like molecules were evaluated using their IAM hydrophobicity index (CHI IAM) measured in IAM LC, and the logarithm of distribution constants (log KLEKC) derived from the LEKC experiments. Lipophilicity (phospholipids) parameters obtained from IAM LC and most LEKC analyses were linearly related to the n-octanol/water partitioning coefficients of the neutral forms (i.e., log Po/w values) to a moderate extent. However, the relationship with distribution coefficients at the experimental pH (7.4) (i.e., log D7.4) were weaker overall for IAM LC data and sigmoidal for some liposome compositions (phosphatidyl choline (PC): phosphatidyl inositol (PI) 85:15 mol% and 90:10 mol%) and concentrations (4 mM) in LEKC. This suggests that phospholipid partitioning supports both hydrophobic and electrostatic interactions occurring between ionised drugs and charged phospholipid moieties. The latter interactions are original when compared to those taking place in the more established n-octanol/water partitioning systems. A stronger correlation (R2 > 0.65) was identified between the LEKC retention parameters, and the experimental apparent lung permeability (i.e., log Papp values) as opposed to the values obtained by IAM LC. Therefore, LEKC offers unprecedented advantages over IAM LC in simulating cell membrane partitioning processes in the pulmonary delivery of drugs. Although LEKC has the advantage of more effectively simulating the electrostatic and hydrophobic forces in drug/pulmonary membrane interactions in vitro, the technique is unsuitable for analysing highly hydrophilic neutral or anionic compounds at the experimental pH. Conversely, IAM LC is useful for analysing compounds spanning a wider range of lipophilicity. Its simpler and more robust implementation, and propensity for high-throughput automation make it a favourable choice for researchers in drug development and pharmacological studies.

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.

Into the toxicity potential of an array of parabens by biomimetic liquid chromatography, cell viability assessments and in silico predictions

Five parabens (PBs) i.e., Methylparaben (MP), Ethylparaben (EP), Isopropylparaben (iPrP), Isobutylparaben (iBuP), Benzylparaben (BzP), and their parent compound i.e., para-hydroxy Benzoic Acid (pHBA), were studied both in vitro and in silico. Specifically, we determined their retention on several both protein- (Human Serum Albumin and α1-acidic glycoprotein) and (phospho) lipid- (immobilized artificial membrane (IAM)) based biomimetic stationary phases to evaluate their penetration potential through the biomembranes and their possible distribution in the body. The IAM phases were based either on phosphatidylcholine (PC) analogues i.e., PC.MG and PC.DD2 or on sphingomyelin (SPH). We also assessed their viability effect on breast cancer cells (MCF-7) via MTT assay subjecting the cells to five different PB concentrations i.e., 100 μM, 10 μM, 1 μM, 0.1 μM and 0.01 μM. Finally, their pharmacokinetics and toxicity were assessed by the ADMET Predictor™ software. Isopropylparaben was found to be more active than 17β estradiol (E2) employed as positive control, on the screened cell line inducing cell proliferation up to 150 % more of untreated cells. Other analogues showed only a slight/moderate cell proliferation activity, with parabens having longer/branched side chain showing, on average, a higher proliferation rate. Significant linear direct relationships (for PC.DD2 r2 = 0.89, q2 = 0.86, for SPH r2 = 0.89, q2 = 0.85, for both P value < 0.05) were observed between the difference in proliferative effect between the readout and the control at 0.01 μM concentration and the retention on the IAM phases measured at pH 5.0 for all compounds but pHBA, which is the only analyte of the dataset supporting a carboxylic acid moiety. IAM affinity data measured at pH 7.0 were found to be related to the effective human jejunal permeability as predicted by the software ADMET® Predictor, which is relevant when PBs are added to pharmaceutical and food commodities.

Potential of liposomes and lipid membranes for the separation of β-blockers by capillary electromigration and liquid chromatographic techniques

β-Blockers belong to a frequently used class of drugs primarily used to treat heart and circulatory conditions. Here we describe the use of lipid vesicles and liposomes as cell membrane biomimicking models in capillary electromigration (CE) and liquid chromatography (LC) techniques for the investigation of interactions between lipid membranes and β-blockers. In addition to liposomes, the use of commercial intravenous lipid emulsions, and their interactions with β-blockers are also discussed. Different CE and LC instrumental techniques designed for these purposes are introduced. Other methodologies for studying interactions between β-blockers and lipid membranes are also briefly discussed, and the different methodologies are compared. The aim is to give the reader a good overview on the status of the use of liposomes and lipids in CE and LC for studying β-blocker interactions.

Immobilized Keratin HPLC Stationary Phase-A Forgotten Model of Transdermal Absorption: To What Molecular and Biological Properties Is It Relevant?

Chromatographic retention data collected on immobilized keratin (KER) or immobilized artificial membrane (IAM) stationary phases were used to predict skin permeability coefficient (log Kp) and bioconcentration factor (log BCF) of structurally unrelated compounds. Models of both properties contained, apart from chromatographic descriptors, calculated physico-chemical parameters. The log Kp model, containing keratin-based retention factor, has slightly better statistical parameters and is in a better agreement with experimental log Kp data than the model derived from IAM chromatography; both models are applicable primarily to non-ionized compounds.Based on the multiple linear regression (MLR) analyses conducted in this study, it was concluded that immobilized keratin chromatographic support is a moderately useful tool for skin permeability assessment.However, chromatography on immobilized keratin may also be of use for a different purpose-in studies of compounds' bioconcentration in aquatic organisms.

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.

Drug Penetration into the Central Nervous System: Pharmacokinetic Concepts and In Vitro Model Systems

Delivery of most drugs into the central nervous system (CNS) is restricted by the blood-brain barrier (BBB), which remains a significant bottleneck for development of novel CNS-targeted therapeutics or molecular tracers for neuroimaging. Consistent failure to reliably predict drug efficiency based on single measures for the rate or extent of brain penetration has led to the emergence of a more holistic framework that integrates data from various in vivo, in situ and in vitro assays to obtain a comprehensive description of drug delivery to and distribution within the brain. Coupled with ongoing development of suitable in vitro BBB models, this integrated approach promises to reduce the incidence of costly late-stage failures in CNS drug development, and could help to overcome some of the technical, economic and ethical issues associated with in vivo studies in animal models. Here, we provide an overview of BBB structure and function in vivo, and a summary of the pharmacokinetic parameters that can be used to determine and predict the rate and extent of drug penetration into the brain. We also review different in vitro models with regard to their inherent shortcomings and potential usefulness for development of fast-acting drugs or neurotracers labeled with short-lived radionuclides. In this regard, a special focus has been set on those systems that are sufficiently well established to be used in laboratories without significant bioengineering expertise.

Characterization of the new CelerisTM Arginine column: Retentive behaviour through a combination of chemometric tools and potential in drug analysis

Celeris^TM Arginine (ARG) is a mixed-mode stationary phase recently released on the market. To characterize its analytical behavior, the retention factors of a pool (n=100, of which 36 neutrals, 26 acids and 38 bases) of pharmaceutically relevant compounds have been measured on this phase over eight percentages (from 10 to 90% v/v) of acetonitrile (MeCN) as organic modifier. The ARG phase exhibited enhanced affinity for the molecules that are in their anionic form at the experimental pH, whilst basic compounds, albeit over a wide range of lipophilicity and pK_a values, were on average poorly retained. To dissect the separation mechanism of the ARG phase, the overall analytical retention has been deconvoluted into the individual contributions of intermolecular forces by a QSPR/ Partial Least Square (PLS)/Block Relevance (BR) analysis tool recently developed by us. For the neutrals, the most relevant blocks were found to be Size, describing the interaction due to the dimension of the molecule, and O, representing the solute's hydrogen bond donor properties. The change in sign from positive to negative of the Size block, which occurs between 10% and 20% MeCN, allowed to visually appreciate the switch in the separation mode from reversed phase to normal phase. Some good statistic models for rationalizing the analytical behaviour of neutrals were developed from VS+ descriptors. However, their performance in modelling the analytical retention of acids was substandard, probably due to the intrinsic inefficacy of VS+ descriptors in handling electric charges. This instance was addressed by a complimentary MLR strategy, which led to successfully model the retention of acids on the ARG column and to shed light into their retention mechanism, which seemed to be substantially driven by electrostatics.

Predicting the Blood-Brain Barrier Permeability of New Drug-Like Compounds via HPLC with Various Stationary Phases

The permeation of the blood-brain barrier is a very important consideration for new drug candidate molecules. In this research, the reversed-phase liquid chromatography with different columns (Purosphere RP-18e, IAM.PC.DD2 and Cosmosil Cholester) was used to predict the penetration of the blood-brain barrier by 65 newly-synthesized drug-like compounds. The linear free energy relationships (LFERs) model (log BB = c + eE + sS + aA + bB + vV) was established for a training set of 23 congeneric biologically active azole compounds with known experimental log BB (BB = C_blood/C_brain) values (R² = 0.9039). The reliability and predictive potency of the model were confirmed by leave-one-out cross validation as well as leave-50%-out cross validation. Multiple linear regression (MLR) was used to develop the quantitative structure-activity relationships (QSARs) to predict the log BB values of compounds that were tested, taking into account the chromatographic lipophilicity (log k_w), polarizability and topological polar surface area. The excellent statistics of the developed MLR equations (R² > 0.8 for all columns) showed that it is possible to use the HPLC technique and retention data to produce reliable blood-brain barrier permeability models and to predict the log BB values of our pharmaceutically important 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.

Predicting a Drug's Membrane Permeability: A Computational Model Validated With in Vitro Permeability Assay Data

Membrane permeability is a key property to consider during the drug design process, and particularly vital when dealing with small molecules that have intracellular targets as their efficacy highly depends on their ability to cross the membrane. In this work, we describe the use of umbrella sampling molecular dynamics (MD) computational modeling to comprehensively assess the passive permeability profile of a range of compounds through a lipid bilayer. The model was initially calibrated through in vitro validation studies employing a parallel artificial membrane permeability assay (PAMPA). The model was subsequently evaluated for its quantitative prediction of permeability profiles for a series of custom synthesized and closely related compounds. The results exhibited substantially improved agreement with the PAMPA data, relative to alternative existing methods. Our work introduces a computational model that underwent progressive molding and fine-tuning as a result of its synergistic collaboration with numerous in vitro PAMPA permeability assays. The presented computational model introduces itself as a useful, predictive tool for permeability prediction.

Phosphatidylcholine covalently linked to a methacrylate-based monolith as a biomimetic stationary phase for capillary liquid chromatography

In this study a strategy to immobilize phospholipids onto a polymer-based stationary phase is described. Methacrylate-based monoliths in capillary format (150×0.1mm) were modified by soybean phosphatidylcholine through 1-ethyl-3-(3-dimethylaminopropyl)carbodiimide coupling to obtain stationary phases suitable to mimic cell surface membranes. The covalent coupling reaction involves the phosphate group in phospholipids; therefore, the described methodology is suitable for all types of phospholipids. Immobilization of soy bean phosphatidylcholine on the monolith was confirmed by attenuated total reflectance Fourier transform infrared spectroscopy and gas chromatography-mass spectrometry of the fatty alcohol profile, generated upon reductive cleavage of the fatty acyl side chains of the phospholipid on the monolith surface with lithium aluminium hydride. The prepared stationary phases were evaluated through studies on the retention of low-molar mass model analytes including neutral, acidic, and basic compounds. Liquid chromatographic studies confirmed predominant hydrophobic interactions between the analytes and the synthesized stationary phase; however, electrostatic interactions contributed to the retention as well. The synthesized columns showed high stability even with fully aqueous mobile phases such as Dulbecco's phosphate-buffered saline solution.

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.

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.