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BY-NC-ND 3.0 license Open Access Published by De Gruyter November 30, 2017

Analytical techniques and methods for study of drug-lipid membrane interactions

  • Hewen Li , Tao Zhao EMAIL logo and Zhihua Sun EMAIL logo
From the journal Reviews in Analytical Chemistry
https://doi.org/10.1515/revac-2017-0012
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Abstract

A better elucidation of molecular mechanisms underlying drug-membrane interaction is of great importance for drug research and development. To date, different biochemical and biophysical methods have been developed to study biological membranes at molecular level. This review focuses on the recent applications and achievements of modern analytical techniques in the study of drug interactions with lipid membranes, including chromatography, spectrometry, calorimetry, and acoustic sensing. The merits and limitations of these techniques were compared and critically discussed. Moreover, various types of biomimetic model membranes including liposomes, lipid monolayers, and supported lipid monolayers/bilayers were described. General mechanisms underlying drug-membrane interaction process were also briefly introduced.

Keywords: bioanalysis; drug-membrane interactions; drugs; lipid membrane

Introduction

The cell membrane is a very complex and highly diverse system, which is mainly composed of a large variety of different lipids, proteins, and polysaccharides. The matrix of cell membrane is a continuous lipid bilayer that consists mainly of amphipathic phospholipids. As a cell’s boundary, the cell membrane plays an important role in the absorption, distribution, metabolism, and excretion (ADME) of a drug (Seddon et al., 2009).

After being administrated, a drug molecule has to move into the bloodstream (the absorption process) and then be transported to its action sites (the distribution process). Obviously, the drug action can involve several of the movements across biomembranes, including the barrier membranes in the gastrointestinal tract, the walls of the tiny capillaries that line the gut, and the blood-brain barrier (BBB). Increasing evidences have shown that drug molecules, displaying a wide range of pharmaceutical effects and diverse chemical structures, can directly or indirectly interact with the lipid membrane, which, in turn, leads to changes in their physicochemical properties, pharmacological activity, and bioavailability (Escriba et al., 2008). Unfavorable drug interactions with lipid membrane can have adverse effects, such as drug resistance and severe side effects derived from low drug specificity (Gashaw et al., 2012). Examples include nonsteroidal anti-inflammatory drugs (NSAIDs) (Pereira-Leite et al., 2012), anesthetics (Tsuchiya & Mizogami, 2013), and anticancer drugs (Peetla, Vijayaraghavalu & Labhasetwar, 2013). Therefore, understanding the intrinsic interactions between drug and biomembranes is of great importance for both biomedical researchers and the pharmaceutical industry.

On the other hand, most of the targets currently being addressed by drug discovery are membrane proteins which are either permanently embedded (integral proteins) or temporarily attached to lipid bilayers or to the other integral proteins (peripheral proteins) (Imming, Sinning & Meyer, 2006; Raskandersen, Almen & Schioth, 2011). Indeed, the membrane lipids uphold the structure of integral protein and maintain its biological activities. It is well documented that the lipid composition and the structure of biomembrane play important roles in the functioning and conformation of embedded membrane proteins (Escriba et al., 2008). The membrane lipids can also regulate the distribution and localization of peripheral proteins and then participate in numerous important cellular activities such as cell adhesion and cell signaling (Van Meer, Voelker & Feigenson, 2008). Actually, there are many drugs that exert their activity through direct interaction with lipid bilayers. For example, local anesthetic drugs exert a nerve-blocking effect by expanding the lipid bilayer regions of nervous membranes as well as changing the membrane fluidity (Rosenberg & Alila, 1982; Jendrossek & Handrick, 2003). Therefore, it is suggested that membrane lipids might constitute novel drug targets whose therapeutic action are based on both direct and indirect modulations of membrane protein function (Escriba, 2006; Lucio, Lima & Reis, 2010).

In addition, the increasing use of biomimetic model systems in drug delivery also requires greater knowledge of drug-lipid membrane interactions (Mouritsen & Jorgensen, 1998).

In this review, we aim to introduce the recent insights and advances with respect to drug-lipid membrane interaction studied by various analytical methods with biomimetic model systems. The general mechanisms involved in drug-membrane interaction will also be discussed.

General mechanisms of drug-membrane interactions

An important function of biological membranes is to define the enclosed spaces or compartments in which cells may maintain a biochemical environment that differs from the outside. It is crucial for biomembranes to be selectively permeable and be able to regulate the movement of substances in and out of cells or organelles. Due to the cell membrane’s hydrophobic nature, small electrically neutral molecules pass through the membrane more easily than those charged, large ones. Thus, the drug’s ability to cross a membrane is largely dependent on the physicochemical properties of both the membrane and the drug per se. Generally, the movement of drug molecules across a membrane can be either “passive”, occurring without the input of cellular energy, or “active”, requiring the cell to expend energy in a transporting process. There are various mechanisms involved in the transportation of both endogenous substances and drug molecules across biological membranes, such as passive osmosis and diffusion, protein-mediated transport, and endocytosis/exocytosis. An excellent review of this topic has been reported (Engvall & Lundahl, 2006).

Passive diffusion

Passive diffusion is the net movement of substances from an area with high concentration to an area with lower concentration (Fick’s law) (Figure 1A). It is one of the primary pathways of drug absorption and permeation. Drug molecules can either directly cross the membrane or laterally diffuse in the membrane to approach their binding sites embedded in the lipid bilayer (Zervou et al., 2014; Kellici, Tzakos & Mavromoustakos, 2015). The driving force of passive diffusion can be either the concentration gradient of a drug or the difference in degree of saturation (i.e. equilibrium solubility) of a drug at the two sides of the membrane (Borbas et al., 2016). Small, hydrophobic drug molecules such as caffeine (Mose et al., 2008), alcohol (Rajendram, Hunter & Preedy, 2013), small peptides (Davis, 1990), and steroid hormones (Oren et al., 2004) can move rapidly across the plasma membrane by passive diffusion due to their ability to interact with the hydrophobic tail of the lipid bilayer. By contrast, hydrophilic or ionized molecules do not diffuse across the bilayer easily unless they are very small and with an optimal net charge (Pauletti, Okumu & Borchardt, 1997; Baciu et al., 2006). On the other hand, high hydrophobicity prevents the active pharmaceutical ingredients from being bioavailable since they may remain trapped in the lipid membrane by strong hydrophobic bonding. This undesired drug-membrane binding was found to destroy the membrane integrity as a protective barrier (Nunes et al., 2011a). Therefore, an optimal affinity of the drug for the lipophilic membrane environment, i.e., lipophilicity, is required.

Figure 1: Illustration of the movement of drug molecules (pink dots) across cell membranes. (A) Passive diffusion moves down the concentration gradient. (B) Facilitated diffusion mediated either by carrier protein or by channel protein. (C) Active transport against the concentration gradient with input of energy. (D) Endocytosis by formation of a lipid vesicle.
Figure 1:

Illustration of the movement of drug molecules (pink dots) across cell membranes. (A) Passive diffusion moves down the concentration gradient. (B) Facilitated diffusion mediated either by carrier protein or by channel protein. (C) Active transport against the concentration gradient with input of energy. (D) Endocytosis by formation of a lipid vesicle.

The partition coefficient (P) obtained in n-octanol/water and liposome/water partitioning systems is the most widely used measure of the lipophilicity of a chemical compound. Partition coefficient is defined as the ratio of concentrations of a compound (referring specifically to a unionized solute) in organic (Corg) and aqueous (Caq) phases at equilibrium, and the logarithm of the ratio is thus log P:

(1)logP=logCorgCaq

Most drugs, however, contain one or more ionizable groups, and their lipophilicities are pH-dependent (Comer & Tam, 2007). For these ionizable drugs, another quantitative descriptor called the distribution coefficient (D) is often used. Log D refers to the contributions of all neutral and ionized species present at a given pH and can be calculated according to Eqs. (2a) and (2b) (Rutkowska, Pajak & Joźwiak, 2013):

(2)logDacids=log⁡P+log[1(1+10pH−pKa)]
(3)logDbases=log⁡P+log[1(1+10pKa−pH)]

where Ka is dissociation equilibrium constant.

A number of experimental methods for lipophilicity measurement have been reported, such as shake-flask method, potentiometric titration method, and separation-based approaches. These methods differ widely in their ease of use, accuracy, speed, reproducibility, and sensitivity. Previous reviews compared some of these experimental methods (Danielsson & Zhang, 1996; Hartmann & Schmitt, 2004; Liang & Lian, 2015).

Protein-mediated transport

This mode of transport involves specific membrane proteins that provide continuous protein-lined pathways through lipid bilayers and facilitate the flux of some drug molecules to enter or exit cells. The two main types of proteins involved in such transport are generally categorized as either channels or carriers. Channel proteins form membrane-spanning pores that allow water-soluble molecules (those that are either charged or have polar groups) to pass through a process called facilitated diffusion, while carrier proteins literally change their conformation and carry specific molecules across the membranes through a process called active transport.

Facilitated diffusion is the spontaneous passage of large polar molecules or ions across a biological membrane via specific transport proteins (Figure 1B). The transport does not directly require energy input; rather, drug molecules move down their concentration or electrochemical gradient. For example, 5-fluorouracil, a widely applied medication in the treatment of cancer, uses a Na+-dependent facilitated transport system to cross the cell membrane (Bronk, Lister & Lynch, 1987); levodopa, the most effective therapeutic drug used to treat patients with Parkinson’s disease, is transported into the brain by the facilitative amino acid transporter (L1) (Contin & Martinelli, 2010).

Active transport is capable of moving drug molecules against a concentration gradient, polar repulsion, or other resistance in an energy-dependent manner (Figure 1C). Certain drugs that bear significant structural similarity to the endogenous substances (e.g. ions, vitamins, sugars, and amino acids) can cross the membrane by active transport. The rate of active transport is not related to the drug concentration but depends on the amount of transport proteins to which the drug molecules can be bound. The energy source of active transport can be either from the hydrolysis of adenosine triphosphate (so-called primary active transport) or from the electrochemical potential (e.g. sodium or proton gradients) (so-called secondary active transport). Examples of active transport include the uptake of metformin, the mainstay of therapy for diabetes mellitus (Pernicova & Korbonits, 2014), and the cholesterol-lowering drugs, statins (Mangravite, Thorn & Krauss, 2006).

Endocytosis is a special form of active transport that can assist the transmembrane movement of large polar molecules such as proteins and polysaccharides (Gundelfinger, Kessels & Qualmann, 2003; Doherty & Mcmahon, 2009) (Figure 1D). This type of transport involves engulfment of drug molecules by cell membrane in an energy-consuming process. Cellular transport of water-soluble drugs (e.g. vitamin B12) can be facilitated by endocytosis (Nielsen et al., 2012). Macromolecular drugs directly attached to a targeting agent or loaded in nanoparticles can also be endocytosed into cells through both specific and nonspecific interactions with proteins/receptors on cell surfaces (Park et al., 2006; Bareford & Swaan, 2007). Specifically, therapeutic peptide and protein drugs conjugating with specific peptides or antibodies can pass through the BBB by receptor-mediated endocytosis (Guo et al., 2011; Xiao & Gan, 2013). Examples can also be found in targeting delivery of anticancer drugs (Bareford & Swaan, 2007; Sun et al., 2014).

Biomimetic membranes for studying drug-membrane interactions

Due to the high complexity and diversity of natural membranes, various model biomimetic membranes have been developed and applied in drug-membrane interaction studies. Biomimetic membranes allow specific investigations of a given biological phenomenon occurring at membrane level under very defined and controlled conditions (Chan & Boxer, 2007). The most common biomimetic systems used for drug-membrane interactions include lipid vesicles or liposomes, Langmuir monolayers, and solid supported lipid monolayers/bilayers (Knobloch et al., 2015). All these systems exhibit certain advantages and disadvantages. To choose an appropriate membrane model for study of drug-membrane interaction, it is necessary to understand their intrinsic properties and the nature of the study. For example, Langmuir monolayers are usually adapted for investigation of drug interactions with lipid head groups, while vesicles enable the study of drug permeability.

Liposomes

Lipid vesicles or liposomes are the most commonly used lipid bilayer model systems since they have an aqueous core surrounded by two lipid leaflets, which is similar to the natural cell membrane structures. The major types of liposomes are the multilamellar vesicles (MLVs, >1 μm), the small unilamellar vesicles (SUVs, <0.1 μm), the large unilamellar vesicles (LUVs, 0.1 ∼ 1 μm), and the giant unilamellar vesicles (GUVs, >1 μm) (Figure 2). Several methods for forming liposomes have been developed, including spontaneous assembly, extrusion, sonication, electroformation, freeze-drying, hydration or swelling, and ink-jet injection (Jesorka & Orwar, 2008). Microfluidic formation of liposomes for a high-throughput lab-on-a-chip analysis has also been well established (Van Swaay & Demello, 2013). Both liposome dispersion and immobilized liposomes have been widely used to study the interaction of drug molecules and membranes of various compositions. Pinheiro et al. (2013) studied the effect of antibiotic rifabutin at different concentrations on human and bacterial cell membrane models by using MLVs and revealed the mechanism of drug action. Liposome/water partitioning systems are ideal for evaluating the interaction of ionized compounds with biomembranes (Van Balen et al., 2004). Matos, Moutinho, and Lobão (2012) used liposomes to determine the partition coefficient of antitumoral drug daunorubicin and was able to quantify the number of lipid molecules associated with one drug molecule by applying mathematical formalism. Immobilized liposomes are also commonly used as the stationary phase in chromatography for rapid screening of drug partition in lipid bilayers (Lundahl & Beigi, 1997; Liu et al., 2002). Detailed information on immobilized liposome stationary phase can be found in Section “Chromatographic techniques”. Besides, liposomes were applied to evaluate the contribution of drug-membrane interactions to the efficacy profile of drugs. Lucio et al. (2008) used fluorescence-labeled LUVs that consisted of egg phosphatidylcholine (EPC) to investigate the protective effect of a NSAID, etodolac, on lipid peroxidation. They found that the drug can scavenge free radicals to a variable extent, depending on the assayed media and the reactive species. The same group also studied the interactions of vitamin E and Trolox with the membrane. Their antioxidant activities were reported (Lucio et al., 2009). A comparison on NSAIDs-lipid membrane interactions using different model liposome systems was reported by Pereiraleite, Nunes, and Reis (2013).

Figure 2: Schematic illustrations of the most commonly applied liposomes, including the small unilamellar vesicle (SUV, <0.1 μm), the large unilamellar vesicle (LUV, 0.1 ∼ 1 μm), the giant unilamellar vesicle (GUV, >1 μm), and the multilamellar vesicle (MLV, >1 μm).
Figure 2:

Schematic illustrations of the most commonly applied liposomes, including the small unilamellar vesicle (SUV, <0.1 μm), the large unilamellar vesicle (LUV, 0.1 ∼ 1 μm), the giant unilamellar vesicle (GUV, >1 μm), and the multilamellar vesicle (MLV, >1 μm).

Supported lipid bilayers

Solid supported lipid bilayers (SLBs) maybe one of the most popular model systems used for studying surface biochemistry (Abdulhalim & Nese, 2016). SLBs have relatively simple geometry, maintain sufficient mobility for the lipid molecule, and offer a high bilayer stability (Castellana & Cremer, 2006) (Figure 3A). Different substrates can be used for SLBs formation, such as silica, mica, silicon dioxide, metal, metal/polymer-coated glass plates, and carbon nanotubes (Cremer & Boxer, 1999; Tanaka & Sackmann, 2005; Richter, Berat & Brisson, 2006; Zhou et al., 2007; Eeman & Deleu, 2010). There are three classical techniques to prepare SLBs: vesicle fusion, transfer of Langmuir-Blodgett deposition followed by Langmuir-Shaefer deposition, and a combination of the former two techniques (Cremer & Boxer, 1999; Castellana & Cremer, 2006). SLBs can also be fabricated via the adsorption of micellar lipid-surfactant mixtures (Vacklin, Tiberg & Thomas, 2005). Compared with lipid vesicles, SLBs afford the ability to perform heterogeneous assays and are accessible to a large variety of surface sensitive analytical techniques such as atomic force microscopy (AFM) (Mingeotleclercq et al., 2008), quartz crystal microbalance with dissipation (QCM-D) (Cho et al., 2010), and spectroscopic techniques (Generosi et al., 2006; Gedig, Faiss & Janshoff, 2008; Reich et al., 2008; Huang et al., 2013). Therefore, SLBs have been widely used to study drug-membrane interaction in vitro. Huang et al. (2013) studied the affinity between tetracaine (TTC), an anesthetic drug, and supported phospholipid bilayers deposited on glass coverslips by fluorescence spectroscopy. Redondomorata et al. (2016) used AFM imaging and nanomechanical mapping to monitor the effect of statins, a family of hypolipidemic drugs, on the nanomechanical properties of SLBs. Interactions between large molecule drugs such as antimicrobial peptides (AMPs) and SLBs have also been studied by neutron reflectometry (Fernandez et al., 2012) and sum frequency generation (SFG) (Chen & Chen, 2006). Moreover, SLBs can be easily integrated into an on-chip platform and provide higher throughput analysis (Nguyen & Conboy, 2011; Watanabe et al., 2014).

Figure 3: Schematic illustrations of (A) supported lipid bilayers (SLBs) and (B) Langmuir monolayers.
Figure 3:

Schematic illustrations of (A) supported lipid bilayers (SLBs) and (B) Langmuir monolayers.

Langmuir monolayers

Langmuir lipid monolayer, also referred to as lipid monolayer, is another useful model system to characterize drug-lipid membrane interactions at a molecular level. Langmuir monolayers are formed by spreading amphiphilic lipids at an air-water interface (Figure 3B). The interactions of drug molecules with the lipid monolayers can be deduced by measuring the surface pressure (π) changes of the Langmuir film as a function of the mean molecular area (A) of the lipids, i.e., the surface pressure-area (π − A) isotherms (Eeman & Deleu, 2010). The advantages of monolayer system over multilamellar or unilamellar bilayer dispersions are (1) parameters such as lipid composition, subphase, pH, and temperature can be chosen without any limitation; (2) they have the ability to regulate lipid lateral-packing density; and (3) the curvature of the lipid surface is fixed and the exact geometry of the dispersion is known (Brockman, 1999; Magetdana, 1999). Lipid monolayers have been extensively used for evaluating the penetration of AMPs, such as melittin, cardiotoxins, and defensin A, into various phospholipids (Magetdana, 1999). Other examples can be found in studies of membrane lipids interaction with anti-cancer drugs (Peetla et al., 2010; Ambike et al., 2011), NSAIDs (Nunes et al., 2011b; Pereiraleite, Nunes & Reis, 2013), antibiotics (Michot et al., 2005), etc.

Supported lipid monolayers

Lipid monolayers formed on solid supports are also useful platforms for membrane study (Castellana & Cremer, 2006). Monolayers of phospholipid analogs can be covalently bonded to the surface of porous (Tsopelas, Vallianatou & Tsantilikakoulidou, 2016a) or nonporous (Lukacova et al., 2007) silica spheres, i.e., immobilized artificial membranes (IAMs). IAMs are often utilized as stationary phases for high-performance liquid chromatography (HPLC) and have been used successfully to study drug partitioning and binding interactions with membranes. Compared with immobilized liposome chromatography (ILC), IAMs are considered more stable and have more reproducibility (Yang et al., 1997; Kuroda, Hamaguchi & Tanimoto, 2014). Detailed information on IAMs can be found in Section “Chromatographic techniques”.

Analytical methods to study drug-membrane interactions

Various analytical methods can be used to study drug-membrane interactions, including chromatography, spectrometry, calorimetry, and acoustic sensing technology (Pignatello, 2013). To get a comprehensive understanding of how drug interacts with lipid membranes, hyphenated technologies and combinations of complementary experimental methods are usually required. This review is focused on the most common experimental approaches currently used for the study of drug-membrane interactions. The techniques, such as UV spectroscopy (Abdiche & Myszka, 2004; Seydel & Wiese, 2009; Yamada et al., 2016), circular dichroism (Gallois, Fiallo & Garnier-Suillerot, 1998), thin layer chromatography (TLC) (Hatziantoniou and Demetzos 2012), dynamic light scattering (Gaber et al., 1998; Matos, Moutinho & Lobão, 2012), Brewster angle microscopy (Więcek et al., 2008), neutron reflectometry (Fernandez et al., 2012), and AFM (Rakowska et al., 2013) as well as computational approaches can be found elsewhere and therefore are excluded from this review.

Chromatographic techniques

Chromatography is a group of analytical techniques used to separate, identify, and quantify specific components in a mixture. The basis of chromatographic techniques is the interaction and differential partition of different compounds between a stationary phase and a mobile phase. Technologies like spectroscopy and electrochemical methods are routinely added to enhance detection of separated components. According to the choice of stationary phase and mobile phase, chromatography can be classified into TLC, liquid chromatography (LC), gas chromatography, capillary liquid chromatography, supercritical fluid chromatography, etc. Here, we will focus on HPLC-based techniques, which are commonly used in the study of drug-membrane interactions.

IAM-HPLC

IAM stationary phase in HPLC is developed for more accurate estimation of the partitioning of ionic and zwitterionic compounds in various phases. IAM stationary phases (commercially available from Regis Technologies) basically consist of monolayers of phospholipids (mostly phosphatidylcholine) covalently bonded to porous silica spheres (Figure 4A). Thus, better insight for biological partition and biological activities could be achieved by using IAMs. Moreover, compared with the conventional determination of drug partitioning in liposome/water systems, IAM-HPLC measurement is simple, rapid, and reproducible and, therefore, is more suitable for a medium- or high-throughput screening in early drug discovery (Yang et al., 1997; Tsopelas, Vallianatou & Tsantilikakoulidou, 2016a). A lot of comparisons have been made between lipophilicity obtained from IAM (expressed as the capacity factor, log⁡kWIAM) and traditional n-octanol/water and liposome/water partitioning systems (logP and logD) (Rutkowska, Pajak & Joźwiak, 2013). The results confirmed that IAM-HPLC is more accurate and more effective for drug-membrane partition determination. Actually, although logP is not able to measure some important intermolecular recognition forces such as ionic bonds, the electrostatic interactions between electrically charged drug molecules and phospholipids may partially compensate the ionization effect (Barbato, La Rotonda & Quaglia, 1997; Barbato et al., 2005; Grumetto, Carpentiero & Barbato, 2012). Therefore, it is common to obtain better correlation of log⁡kWIAM with logP than logD. Recently, based on the study of various structurally diverse neutral, basic, and acidic drugs, Grumetto and coworkers proposed a new physicochemical parameter Δ/Δ′log⁡kWIAM (obtained by combining log⁡kWIAM with logP or logD) as a measure of polar and electrostatic forces involved in drug-membrane interactions (Grumetto et al., 2013; Grumetto, Russo & Barbato, 2014). They claimed that this parameter was effective in evaluating the capability of a compound to cross the BBB. Research has also been made to develop nonlinear models between IAM indices and human oral absorption (%HOA) (Shin et al., 2009; Tsopelas, Vallianatou & Tsantilikakoulidou, 2016b). The properties of IAM columns and their applications to predict compound’s interaction with biological membranes were reviewed in detail by Taillardatbertschinger et al. (2003) and Valko (2004).

Figure 4: Schematic representations of stationary phases of immobilized artificial membrane (IAM) and immobilized liposome chromatography (ILC). (A) In IAM, phospholipid monolayers are covalently attached to the amine-modified silica particles. (B) In ILC, liposomes are usually integrated into agarose-based gels.
Figure 4:

Schematic representations of stationary phases of immobilized artificial membrane (IAM) and immobilized liposome chromatography (ILC). (A) In IAM, phospholipid monolayers are covalently attached to the amine-modified silica particles. (B) In ILC, liposomes are usually integrated into agarose-based gels.

In 2007, a new phospholipid monolayer system consisting of uniform nonporous octadecylated silica (ODS) particles was developed (Lukacova et al., 2007). This system overcomes the potentially slow transport of some chemicals due to their diffusion in the particle pores in IAMs column. The partitioning equilibria of 81 tested compounds were successfully dissected into the head group and core contributions, allowing a prediction of the bilayer/water partition coefficients with a standard deviation of 0.26 log units (Ikegami & Tanaka, 2016). Kuroda, Hamaguchi, and Tanimoto (2014) recently claimed that phospholipid-modified ODS column has the potential to predict the affinity of drugs to various biomembranes from their database.

Immobilized liposome chromatography

Another biomemetic system based HPLC is ILC (Lundahl & Beigi, 1997; Moravcova, Planeta & Wiedmer, 2013) (Figure 4B). In ILC, liposomes in sterically immobilized gel beads are applied as the stationary phase for screening and analysis of permeable compounds (Zhang et al., 2012). Compared with IAMs, the biophysical properties of the lipid environment in ILC columns can be altered through the use of different membrane composition. There are discrepancies in correlation between the lipophilicity index determined by ILC (logKs) and other methods. A comparative study implied that significant correlations were only found between logKs and the lipophilicity indices obtained by IAM, n-octanol/water, and liposome/H2O systems for structurally related compounds (Liu et al., 2010). This suggested that the balance of hydrophobic and electrostatic interactions dominated the partitioning of drugs in these systems. Three main drawbacks of ILC include the difficulties in column preparation, limited liposome stability, and large amount of sample requirement. However, a new procedure reported recently indicated that liposomes on silica-based particle surface allowed simpler column modification and a reduced consumption of liposome dispersions (Moravcova, Planeta & Wiedmer, 2013).

Electrokinetic chromatography

Electrokinetic chromatography (EKC), also called electrokinetic capillary chromatography, is a capillary electromigration technique based on a combination of electrophoresis and HPLC. EKC measures the differential partitioning of analytes between a lipid dispersion (pseudo-stationary phase) and a surrounding aqueous buffer solution (mobile phase), as well as the electrophoretic mobility of analytes (Wiedmer & Lokajova, 2013). Carrozzino and Khaledi (2005) used liposome electrokinetic chromatography to determine the pH effects on the partitioning of basic drugs into liposomes of various compositions. It was found that an increase in pH resulted in a smaller degree of ionization of basic drugs and consequently led to a lower degree of drug interactions with negatively charged membranes. Wan, Ahman, and Holmen (2009) coupled microemulsion electrokinetic chromatography (MEEKC) with mass spectrometry (MS) for the prediction of biopartitioning of central nervous system drugs in brain tissue. Recently, Henchoz et al. (2010) developed a high-throughput logP determination by MEEKC coupled with UV and MS detections (Figure 5). This method can determine the lipophilicity of neutral, basic, and acidic compounds with logP ranging from 0 to 5. Although the simplicity and efficiency of EKC have made it an attractive technique for measuring drug/phospholipid affinity, its reproducibility is affected by the stability of micelles or liposomes.

Figure 5: Schematic illustration of a MEEKC-UV determination of six calibration compounds (1–6) with two markers (EOF and ME) at pH 10.0. (A) The measured retention time (min) enabled the calculation of the retention factor k. (B) The calibration line obtained from the retention factors k of the six calibration compounds as a function of their logPoct was characterized by a high determination coefficient (r2 = 0.9996), suggesting that a simple least-squares linear regression model could be used to estimate logPoct of basic or neutral compounds from their retention factors. Adapted from Henchoz et al. (2010).
Figure 5:

Schematic illustration of a MEEKC-UV determination of six calibration compounds (1–6) with two markers (EOF and ME) at pH 10.0. (A) The measured retention time (min) enabled the calculation of the retention factor k. (B) The calibration line obtained from the retention factors k of the six calibration compounds as a function of their logPoct was characterized by a high determination coefficient (r2 = 0.9996), suggesting that a simple least-squares linear regression model could be used to estimate logPoct of basic or neutral compounds from their retention factors. Adapted from Henchoz et al. (2010).

Capillary electrochromatography

The recently developed capillary electrochromatography (CEC) is a special case of capillary liquid chromatography, where the movement of the mobile phase through a capillary is driven by electroosmotic flow. In CEC measurements, a fused-silica capillary is packed with an IAM stationary phase, and therefore more stable lipid coatings are achieved (Deeb et al., 2014). Barbato et al. (2011) demonstrated linear relationships between CEC and HPLC data in a study of 16 structurally unrelated compounds. They also pointed out that although CEC was more complex in manipulation, it required fewer amounts of analyte, eluent, and stationary phase as compared with IAM-HPLC. The application of different capillary electromigration techniques for the study of interactions between analytes and lipid membranes was just recently reviewed (Wiedmer & Lokajova, 2013).

Spectroscopic techniques

Here, we will outline the commonly used spectroscopic techniques in the study of drug-membrane interactions, including fluorescence spectroscopy, nuclear magnetic resonance (NMR) spectroscopy, electron paramagnetic resonance (EPR), mass spectroscopy (MS), vibrational spectroscopy, X-ray diffraction (XRD), and small-angle neutron scattering (SANS). Typical examples of the spectroscopic techniques are summarized in Figure 6.

Figure 6: Schematic illustrations of typical examples of the spectroscopic techniques introduced in this review. (A) Fluorescence spectra represent relative fluorescence intensity change vs. TTC concentration for phosphatidylcholine (PC) bilayer with different cholesterol concentrations, suggesting that cholesterol decreased the affinity between TTC and the bilayers. Adapted from Huang et al. (2013). (B) 31P NMR spectra represent the chemical shift anisotropy (CSA, ppm) at 298 K on pure DMPC (upper line) and DMPC/amphotericin B (AMB) MLV system (lower line). The arrows show significant CSA increase for AMB containing MLV, indicating a higher fluidity of the bilayer induced by drug-membrane interaction. Adapted from Debouzy et al. (2017). (C) EPR experimental (full line) and calculated (dotted line) spectra of spin probes 5-DS in pig ear stratum corneum and model membrane. The maximum hyperfine splitting (2Amax) reflects the rotational freedom of lipid (i.e. fluidity) close to the polar head groups in the bilayer. Adapted from Yonar et al. (2013). (D) Mass spectrum of dihydroartemisinin (DHAn):DPPC system. The peaks of the ions characteristic to DHAn and the peaks corresponding to the ions characteristic to DPPC were registered in the plot. Adapted from Pashynska et al. (2015). (E) FTIR spectra represent the temperature-dependent changes in the CH vibrational stretching region of pyrimidine analog of FPh (FPh-prm) /sphingomyelin (SM) mixture. The maxima of the symmetric (νsCH2) and asymmetric (νasCH2) stretching vibrations of the lipid CH2 groups are around 2854 and 2922 cm−1, respectively; the vibrational bands of FPh-prm dominating the high temperature spectra with maxima around 2940 cm−1. Adapted from Kuc et al. (2015). (F) SAXS data exhibit changes in the diffraction patterns of egg PC (EPC) in addition of resveratrol (RSV). Inset boxes represent the magnified images of first-order Bragg peaks. Adapted from Neves et al. (2016).
Figure 6:

Schematic illustrations of typical examples of the spectroscopic techniques introduced in this review. (A) Fluorescence spectra represent relative fluorescence intensity change vs. TTC concentration for phosphatidylcholine (PC) bilayer with different cholesterol concentrations, suggesting that cholesterol decreased the affinity between TTC and the bilayers. Adapted from Huang et al. (2013). (B) 31P NMR spectra represent the chemical shift anisotropy (CSA, ppm) at 298 K on pure DMPC (upper line) and DMPC/amphotericin B (AMB) MLV system (lower line). The arrows show significant CSA increase for AMB containing MLV, indicating a higher fluidity of the bilayer induced by drug-membrane interaction. Adapted from Debouzy et al. (2017). (C) EPR experimental (full line) and calculated (dotted line) spectra of spin probes 5-DS in pig ear stratum corneum and model membrane. The maximum hyperfine splitting (2Amax) reflects the rotational freedom of lipid (i.e. fluidity) close to the polar head groups in the bilayer. Adapted from Yonar et al. (2013). (D) Mass spectrum of dihydroartemisinin (DHAn):DPPC system. The peaks of the ions characteristic to DHAn and the peaks corresponding to the ions characteristic to DPPC were registered in the plot. Adapted from Pashynska et al. (2015). (E) FTIR spectra represent the temperature-dependent changes in the CH vibrational stretching region of pyrimidine analog of FPh (FPh-prm) /sphingomyelin (SM) mixture. The maxima of the symmetric (νsCH2) and asymmetric (νasCH2) stretching vibrations of the lipid CH2 groups are around 2854 and 2922 cm−1, respectively; the vibrational bands of FPh-prm dominating the high temperature spectra with maxima around 2940 cm−1. Adapted from Kuc et al. (2015). (F) SAXS data exhibit changes in the diffraction patterns of egg PC (EPC) in addition of resveratrol (RSV). Inset boxes represent the magnified images of first-order Bragg peaks. Adapted from Neves et al. (2016).

Fluorescence spectroscopy

Fluorescence spectroscopy monitors intermolecular interaction by measuring the changes in fluorescence intensity. Compared with other techniques, fluorescence spectroscopy offers outstanding sensitivity (single-molecule level), high spatial resolution (hundreds of nanometers level), and flexibility (Weiss, 1999). Since biological systems do not usually possess intrinsic fluorescence, fluorescent probes are regularly used. An introduction to fluorescence probing of biological membranes was published by Demchenko et al. (2015). In recent years, the use of bioenvironment-sensitive fluorophores has increased gradually in the study of complicated biological processes. These fluorophores, such as Laurdan, Rhodamine, Nile Red, 9-(2-Carboxy-2-cyanovinyl)julolidine (CCVJ), and their derivatives can change their fluorescence characteristics in response to the alterations of locally environmental parameters (e.g. polarity, pH, viscosity, and temperature) (Demchenko et al., 2009). Recently, our group demonstrated the use of a molecular rotor for detection of interactions between local anesthetic TTC with SUVs based on local viscosity modulation (Xu, Zhao & Sun, 2016). By conjugating the fluorophore to an amphiphilic group embedded within the bilayer, we were able to monitor the effect of TTC on the lipid chain order profile and the lipid phase transitions. Similar application of a Rhodamine-based pH-sensitive probe was reported by Huang et al. (2013). It appears that environment-sensitive probes can be exploited as facile and efficient new tools to study drug-membrane interactions in a label-free manner. It should be noted that the use of natural lipids or cholesterol analogs as an anchor for the fluorescent moiety is crucial for selective localization and stable integration of the probe at the membrane. Besides, novel classes of fluorophores, including near-infrared and multi-photon dyes, as well as ratiometric probes, may also potentially be applied in this field for higher resolution imaging and biocompatible detection (Fernandezsuarez & Ting, 2008; Kim et al., 2007; Shynkar et al., 2007; Wu et al., 2016; Zhang et al., 2017).

On the other hand, various instrumental setups and fluorescence-based methodologies have also been developed to study drug-membrane interactions in recent years (Alexander, 2015). For example, a precise determination of the molecular organization, localization, and orientation of antifungal antibiotic amphotericin B (AMB) in GUVs models was carried out by fluorescence lifetime imaging microscopy (FLIM) (Grudzinski et al., 2016). This technique is based on confocal fluorescence microscopy and measures the mean lifetime of the fluorophore, rather than its intensity, and thus has the advantage of minimized photon scattering effect and high spatial resolution for multi-layer samples, e.g. natural cells (Bastiaens & Squire, 1999). Using FLIM, Zhou et al. (2010) found that indomethacin, an anti-inflammatory drug, altered membrane nanoclustering in both model and natural baby hamster kidney cell membranes.

NMR spectroscopy

NMR is the magnetic property of the nucleus of an atom. The principle behind NMR spectroscopy is that the nuclei of certain atoms possess a magnetic moment, which gives rise to different energy levels and resonance frequencies in an external magnetic field (Aubin, Freedberg & Keire, 2015). Lipid molecules have a rich number of such nuclei in atoms including 1H, 13C, 31P, 17O, and 14N. Lipids can also be chemically labeled with deuterium (2H), fluorine (19F), or with other nuclei of interest (Osanai et al., 2013). Any intermolecular interaction (e.g. drug binding to the lipid) may lead to variations in the local electronic environment surrounding the nucleus and thus cause a specific chemical shift in NMR spectrum with respect to a reference resonant frequency (Szakács & Sánta, 2015). Nowadays, NMR is considered as a useful tool for studying drug-membrane interactions since it provides detailed molecular level information (at a sub-Ångstrom resolution) on the structure and dynamics of lipid membranes (Warschawski et al., 2011). Moreover, NMR measurements can be performed at different temperatures, which allows for an observation of the phase transition of membrane lipids (Pentak 2014a; 2014b). Besides, NMR is a non-invasive, non-destructive, and quantitative spectroscopy that can achieve such measurements (Osanai et al., 2013).

Both solution- and solid-state NMR have been applied to identify the specific mechanisms underlying drug incorporation into lipid bilayer process (Jelesarov & Bosshard, 1999; Kajiya et al., 2008; Scheidt & Huster, 2008; Chiu & Prenner, 2011; Husch et al., 2011; Ntountaniotis et al., 2011; Fotakis et al., 2012; Selvaraj et al., 2013; Ntountaniotis et al., 2014; Debouzy et al., 2017). Although solution NMR is currently cheaper and more sensitive, solid-state NMR (SSNMR) is gaining a lot of interest in biological sample analysis. This is mainly due to the facts that (1) SSNMR does not have the size restriction caused by dipolar broadening of large biomolecules (over 30 kDa) that occurs in solution NMR and (2) SSNMR measures the dihedral angle directly which is crucial for getting the absolute orientation of the specific sites in the lipid membranes (Dufourc, 2007; Weaver et al., 2013). Actually, high-resolution SSNMR spectra can provide the same type of information which is available from the corresponding solution NMR spectra, by combining with special techniques such as magic angle spinning (MAS) and/or cross polarization (CP) pulse sequence, special 2D experiments, and enhanced probe electronics (Weaver et al., 2013). Using 31P and 2H SSNMR, Kajiya et al. (2008) was able to acquire direct information on the orientation and dynamics of epigallocatechin gallate (ECg), a green tea polyphenol, incorporated into dimyristoyl phosphatidylcholine (DMPC) MLVs and bicelles. The results showed that catechin molecules have an axially symmetric motion with slightly different wobbling amplitudes in lipid bilayers. Uekusa et al. (2011) incorporated the isotope labeled [13C]-ECg into DMPC MLVs and determined the intermolecular-interatomic distance between the carbonyl carbon of [13C]-ECg and the phosphorus of the phospholipid to be 5.3 ± 0.1 Å by CP/MAS31P and 13C SSNMR. Besides, 13C SSNMR CP/MAS spectra provided direct evidence for the incorporation of dipalmitoyl-phosphatidylcholine (DPPC) bilayers in the absence and presence of cholesterol (Ntountaniotis et al., 2011). The significant chemical shift changes were mainly found in the A ring of the steroidal part of cholesterol, which illustrated that olmesartan (an angiotensin II receptor antagonist) located itself at the head group region and upper segment of the lipid bilayers. The application of SUVs (Pentak 2014a; 2014b) and LUVs (Hervé et al., 1985) was also routinely used in NMR measurements to investigate drug-membrane interactions.

EPR

EPR, also known as electron spin resonance spectroscopy, allows the direct detection of paramagnetic species containing unpaired electrons (Junk, 2012). The basic concepts of EPR are analogous to those of NMR, but its magnetic moments arise from electrons instead of nuclei. Since the magnetic moment of the electron is larger than that of the nucleus, one can use less powerful magnets to acquire higher resonance frequencies compared with NMR frequencies. This explains the greater sensitivity of EPR than that of NMR. Since most materials are diamagnetic, paramagnetic spin labels have to be introduced as tracer molecules into the system of interest. Spin labeled fatty acids, which are oriented like the lipids in bilayer, have been extensively used to study the drug-membrane interactions. Zhao et al. (2007) applied 5-doxyl stearic acid (5-DSA) and 16-DSA as spin labels to determine the molecular interactions between paclitaxel, a chemotherapy drug used to treat a wide spectrum of cancers, and phospholipid bilayers. The local motional profiles of the different alkyl chain regions in the lipid bilayer revealed the binding site of paclitaxel to the phospholipid. In another research, spin labeling EPR was used to search the interaction of a surface active antidepressant drug, clomipramine (CLO), with natural and model membranes (Yonar et al., 2013). The results indicated that CLO provoked a fluidizing effect in a membrane regardless of its composition and consequently facilitate its transport into the membrane. Other examples were also reported (Bartucci et al., 1998; Carrozzino & Khaledi, 2004). It should be pointed out that spin labeling introduces modification into the analyte structure which may alter its properties and function.

Mass Spectroscopy

MS separates the components of a sample based on their mass-to-charge (m/z) ratio. The electrospray ionization mass spectroscopy (ESI-MS) provides a sensitive, efficient, and reliable tool to study interactions between biomolecules at femtomole quantities in microliter sample volumes (Ho et al., 2003). Two excellent reviews addressing the biochemical applications of ESI-MS can be found elsewhere (Wyttenbach & Bowers, 2007; Hilton & Benesch, 2012). The coupling of HPLC with MS is considered as one of the most widely used analysis strategies. Currently, the ultra performance liquid chromatography-tandem mass spectrometric (UPLC/MS/MS) for the drug permeability assessment has been developed, which has a four-fold increase of throughput as well as a significant increase in sensitivity compared with the general LC/MS methods (Mensch et al., 2007). ESI-MS can also help establish the modeling of drug-membrane interactions at the molecular level. For example, a recent ESI-MS research studied the competing intermolecular interactions of artemisinin-type drugs and aspirin with membrane phospholipids and revealed the possible molecular mechanisms involved in the modification of drug bioactivity resulting from multiple drug use (Pashynska et al., 2015).

In recent years, detection technique coupling of microchips to mass spectrometers is becoming one of the most interesting MS-based experiments. It offers the advantages of being capable of performing high-throughput, cost-effective, and online qualitative and quantitative bioanalysis (Wang et al., 2015). In 2012, an isotope labeling assisted microfluidic chip-ESI-MS platform was developed for cell metabolism studies and drug absorption analysis (Chen et al., 2012). Later, Gao et al. (2013) reported a label-free microfluidics-ESI-MS for the detection of drug permeability across intestinal epithelial Caco-2 cell monolayers cultured on a semipermeable polycarbonate membrane. The experimental setup of this technique includes micro solid-phase extraction columns for sample clean-up and concentration prior to MS detection, thus promoting the automation of sample pretreatment and analysis. The latest achievements in the field of microchip-MS for bioanalytical applications have been summarized (He et al., 2014; Oedit et al., 2015; Wang et al., 2015).

Secondary ion mass spectrometry (SIMS) is a highly sensitive surface analytical technique method that combines the power of MS to identify complex molecules based on mass with 3D sample imaging capabilities and high lateral resolution (Mcdonnell & Heeren, 2007). In SIMS analysis, a sample surface is bombarded by primary ions, which leads to the sputtering of secondary ions from the surface. The resulting secondary ions are collected and detected by a mass spectrometer to determine the elemental, isotopic, or molecular composition of the surface. SIMS is well suited to biological membrane studies, as it provides high spatial resolution on the nanometer length scale (Gozen & Jesorka, 2012; Kraft & Klitzing, 2014). Various membrane components in biomimetic membrane systems, biological tissues, and natural cell membranes on the nano- to-micrometer length scale have been studied by SIMS (Boxer, Kraft & Weber, 2009). Rakowska et al. (2013) studied the effect of an AMP on SLBs by combining AFM with nanoscale SIMS. The nanoscale imaging revealed the lateral expansion of AMP-induced pores in lipid bilayers. Vorng et al. (2016) found that the SIMS positive ionization efficiency for small-molecule drugs is a simple power-law relationship to the logP value, which promoted the understanding of the SIMS ionization process in small molecules. Besides, Yang et al. (2011) demonstrated that liquid surfaces can be studied in situ by SIMS under vacuum using a microfluidic device. These developments imply that SIMS has the potential to be exploited as a sensitive screen for drug development and analysis.

Vibrational spectroscopy

Vibrational spectroscopy, commonly concerned with infrared absorption and Raman scattering, analyzes the nuclear vibration features of an atom with minimal perturbation. It provides the most definitive means of identifying membrane structure and composition, bilayer assemblies, and membrane behaviors (Schultz & Levin, 2011). An investigation on drug-membrane interactions can be achieved by measuring the drug-induced vibrational changes assigned to the specific chemical functional groups within membrane systems.

Fourier transform infrared spectroscopy (FTIR) is the most commonly IR method used in biophysical studies. Monitoring the frequency variation in CH2 stretching, C=O stretching, and PO2− stretching modes by FTIR may provide detailed information on analyte interactions with lipid membrane at molecular level (Movasaghi, Rehman & Rehman, 2008). The interaction of alliin, an antioxidant agent, with DMPC MLVs was studied by FTIR (Ezer, Sahin & Kazanci, 2017). The increase of the CH2 asymmetric stretching frequency (2920 cm−1) of DMPC suggested that alliin had a disordering effect on lipid bilayer in both the gel and liquid crystalline phases. Besides, the antisymmetric bands of C=O (1730 cm−1) and PO2− (1230 cm−1) groups provided important clues to the interfacial and head group regions of phospholipids, implying an increase of H-bond interactions around the carbonyl and phosphate groups of DMPC. In order to estimate the potential of liposomes as anticancer drug carriers, Pentak (2014b) applied FTIR to investigate the location and competition between etoposide and cytarabine in DPPC SUVs. The results showed that the interaction between the drug molecule and the lipid bilayer did not induce significant changes in the structure of the membrane. Therefore, it is possible to obtain stable preparations of liposomes containing cytarabine and etoposide for the treatment of cancer. The use of lipid monolayers (deposited on water or solid surface) in FTIR measurements was also reported (Caetano et al., 2001; Zhao & Feng, 2006). In addition, comparative studies using FTIR and differential scanning calorimetry (DSC) showed that FTIR is supportive for the determination of phase transition temperatures of phospholipids (Pentak 2014a; 2014b). Thus, it is suggested that FTIR is a good complement to the DSC analysis of membrane. Attenuated total reflectance (ATR)-FTIR yields an additional level of sensitivity for surfaces and thin layer measurements due to the small light penetration depth of around 1 or 2 μm. Compared with traditional IR, ATR-FTIR is non-destructive and requires easier sample preparation, in addition, it avoids the problem of strong attenuation of IR signal in highly absorbing media, such as aqueous solutions. An ATR-FTIR study of membrane perturbing potency of the pyrimidine analog fluphenazine (FPh-prm) was carried out by using DPPC SUVs (Kuc et al., 2015). The data revealed that FPh-prm induced changes in the phase transition of sphingomyelin membrane and led to strong inhibition of the membrane P-glycoprotein activity during an anti-multidrug resistance process.

Raman spectroscopy relies on the scattering of light by the vibrating molecules. In general, relatively neutral bonds (e.g. C-C, C-H, and C=C) that suffer large changes in polarizability during a vibration and exhibit relatively weak IR signals are strong Raman scatterers. Thus, Raman spectroscopy yields similar but complementary information to IR in studying of phase transition and conformational order of membranes. Extensive studies of drug-membrane interactions have been reported by using confocal Raman spectroscopy (CRM), which provides depth-resolved measurements (Fox, Horton & Harris, 2006; Tfayli et al., 2007; Gotter, Faubel & Neubert, 2010; Tfaili et al., 2014). A study of the lateral penetration of the drug dithranol (DI) within an artificial membrane was carried out by combining CRM and FTIR (Gotter, Faubel & Neubert, 2010). While FTIR allowed for pursuing the lateral distribution of DI, CRM additionally provided a 3D (1.5 μm to 49 μm in depth) profile of the drug diffusion in the membrane. One of the main drawbacks of Raman spectroscopy is the moderate sensitivity due to the fact that spontaneous Raman scattering is typically very weak. However, orders of magnitude enhancement in Raman intensity has been achieved by surface-enhanced Raman spectroscopy (SERS) technology. Ascribed to the additional surface area provided by the roughening of the surface, SERS may be sensitive enough to detect single molecules (Sharma et al., 2012). Besides, SERS is relatively easy to utilize and requires smaller sample volumes than the traditional Raman detections and thus is well suited for biosensing (Hudson & Chumanov, 2009; Bantz et al., 2011). The interactions between drugs and lipid membrane models or natural cells investigated by SERS were reported (Breuzard et al., 2004; Fabriciova et al., 2004; Levin et al., 2008).

Nonlinear vibrational measurements such as SFG are specifically developed to study the vibrations of molecules at interfaces and surfaces. In a typical SFG setup, a tunable IR laser beam is mixed with a VIS beam to generate an output beam at the sum frequency of the two input beams. As the output beam is based on nonlinear optical selection rules, SFG is only sensitive to the regions of a material where the inversion symmetry is necessarily broken. Hence, SFG is background-free and highly surface specific compared with other vibrational spectroscopic methods such as FTIR and Raman spectroscopy. Extensive SFG measurements have been made to study small-molecule and peptide drugs association to supported lipid membranes (Chen & Chen, 2006; Chen et al., 2006; Nguyen, Rembert & Conboy, 2009; Yang, Wu & Chen, 2013; Wu et al. 2014a; 2014b; Soblosky, Ramamoorthy & Chen, 2015; Tan, Ye & Luo, 2015). Important information, such as the drug-induced lipid accumulation (Tan, Ye & Luo, 2015), the dynamic bilayer structure change (Chen et al., 2006), the flip-flop rate change of lipid bilayers (Wu et al., 2014b), and the detailed membrane orientation of drugs (Yang, Wu & Chen, 2013) have been revealed. It is suggested that SFG provides a real-time, in situ, and label-free measurement of drug-membrane interaction with unprecedented sensitivity (Chen & Chen, 2006). Although SFG currently possesses the restriction of model dimensions, a recent theoretical research offered a promising modeling approach to reconstruct images of a spherical liposome with dimensions comparable with the employed wavelength (Volkov & Perry, 2016). Besides, the SFG based technique, second harmonic generation, is also very effective for probing the drug-membrane interaction, especially when the SFG frequency is resonant with an electronic transition of the molecule of interest (Nguyen & Conboy, 2011; Nasir et al., 2012).

X-ray diffraction

XRD measurements rely on the fact that a portion of the incoming X-ray beam is scattered if its wavelength is of similar dimension as the inter-atomic distances present in the sample (Woolfson, 1997). By analyzing the angular distribution of the scattered intensity, XRD offers a direct and non-invasive way to identify the structural properties, chemical composition, and physical properties of the sample. Moreover, XRD has advantages of determining bilayer thickness (down to Ångstrom length scales) of unsupported lipid membranes in near-native conditions (Tyler, Law & Seddon, 2015). The applications of XRD to the study of biomimetic model membranes have been reviewed in detail (Pabst et al., 2010; Pabst, Heberle & Katsaras, 2013; Tyler, Law & Seddon, 2015).

According to the scattering angle, there are small-angle X-ray scattering (SAXS) (typically 0.1–5°) and wide-angle X-ray scattering (WAXS) (typically >5°). SAXS probes microscale or nanoscale structures, whereas WAXS detects sub-nanometer structures. By using a two-dimensional detector, switching between SAXS and WAXS can be easily done in a single run that enables evaluation of a wide range of distribution of sample sizes. Both SAXS and WAXS are very useful for studying drug-membrane interactions (Adachi et al., 1995; Pili et al., 2009; Pabst et al., 2012; Pinheiro et al., 2013; How et al., 2014; Pinheiro et al. 2014a; 2014b; Sun et al., 2015; Neves et al., 2016; Sakuragi et al., 2016). In general, SAXS determines the phase symmetry and the lattice parameters, and WAXS reveals the nature of hydrocarbon chain packing and differentiates the various lamellar phases. An example is the recent study of resveratrol interaction with lipid bilayers (Neves et al., 2016). The SAXS diffraction patterns confirmed the formation of lipid domains composed of resveratrol, while the WAXS diffraction patterns revealed the resveratrol-induced changes in lipid packing. It is therefore concluded that resveratrol controls the lipid packing of bilayers by inducing the organization of lipid rafts. Another XRD study was focused on the effects of antitumoral catechin and its tyrosinase-processed product on the structural properties of DPPC membranes (How et al., 2014). Based on the SAXS and WAXS data, it was found that the presence of catechin did not alter the macroscopic bilayer organization of DPPC but induced a decrease of the bilayer thickness accompanied with the formation of an interdigitated gel phase. Pinheiro et al. (2014a) studied the biophysical interactions of anti-tuberculosis drug rifampicin (RIF) with the three-dimensional macrophage membrane models under environments with physiological and pathological relevance in tuberculosis. The SAXS diffraction patterns showed that RIF increased both the bilayer and the adjacent water layer thickness in a pH-dependent manner in both gel and liquid phases.

Small-angle neutron scattering

SANS shares similar principles with SAXS except that, in the case of SANS, the scattering originates from the neutron rather than from the electron. While SAXS is only sensitive to the hydrophilic part of a lipid bilayer, SANS provides useful information on the hydrophobic tail part. Therefore, SAXS and SANS can be used as complementary techniques for a fine structural description of the biological membranes (Cola, Grillo & Ristori, 2016). The interaction between antibiotic amphotericin B (AmB) and biomimetic membranes were analyzed by SANS. The results showed an ∼4 Å increase of the hydrocarbon chain thickness induced by AmB in both model fungal and mammalian cell membranes (Foglia et al., 2011). The localization of AmB in EPC membrane has also been investigated by SANS (Herec et al., 2007). A recent SANS research studied the kinetics of interaction between AmB-cholesteryl sulfate (SCS) formulation and the sterol-free and sterol-containing membranes (Foglia et al., 2015). It was found that the structural changes in lipid membranes occurred far more rapidly following the exposure to AmB-SCS, and the kinetics of these changes varied with membrane composition. Other drug-membrane interactions studied by SANS were also reported (Foglia et al., 2012; Khadka et al., 2015).

Calorimetric techniques

Calorimetric techniques are based on the measurement of heat effects associated with drug-membrane interaction (Raudino, Sarpietro & Pannuzzo, 2013). In principle, the generation or consumption of heat in a chemical reaction is related to the amount of material involved in the reaction and the heat production rate. Calorimetric techniques can therefore be employed as quantitative and thermodynamic analytical tools. Several advanced calorimetric techniques have been used in pharmacology science (Lewis & McElhaney, 2013). Among them, DSC, isothermal titration calorimetry (ITC), and pressure perturbation calorimetry (PPC) are generally used to characterize the drug interaction with membrane process. Typical examples of the three methods are summarized in Figure 7.

Figure 7: Schematic illustrations of the calorimetric techniques introduced in this review. (A) DSC thermograms show that the addition of CZX resulted in the decreases in the onset temperature of and broadening of the DPPC phase transitions represented by the sharp endothermic peaks. Adapted from Yamada et al. (2016). (B) ITC data represent the heat flow (μcal/s) changes when 1-anilino-8-naphtarenesulfonate (ANS) was titrated with the concentrated egg yolk PC liposomes with aliquots of 8 μL injected at each titration. Adapted from Osanai et al. (2013). (C) PPC experiments allow the evaluation of the thermal expansion coefficient (αV) 2 mg/mL of DMPC and DMPC/primaquine (PQ) liposomes. Adapted from Basso et al. (2011).
Figure 7:

Schematic illustrations of the calorimetric techniques introduced in this review. (A) DSC thermograms show that the addition of CZX resulted in the decreases in the onset temperature of and broadening of the DPPC phase transitions represented by the sharp endothermic peaks. Adapted from Yamada et al. (2016). (B) ITC data represent the heat flow (μcal/s) changes when 1-anilino-8-naphtarenesulfonate (ANS) was titrated with the concentrated egg yolk PC liposomes with aliquots of 8 μL injected at each titration. Adapted from Osanai et al. (2013). (C) PPC experiments allow the evaluation of the thermal expansion coefficient (αV) 2 mg/mL of DMPC and DMPC/primaquine (PQ) liposomes. Adapted from Basso et al. (2011).

Differential scanning calorimetry

DSC is a non-perturbing technique that measures how a material’s heat capacity (Cp) is changed as a function of time and temperature. It was developed by E. S. Watson and M. J. O’Neill in 1962 and was first used to investigate the thermotropic behavior of biomembranes by Chapman in the 1960s (Ladbrooke, Williams & Chapman, 1968). Nowadays, DSC has become a very routine thermal analytical technique applied in drug-membrane interaction study (Chiu & Prenner, 2011). It offers a fast and reliable way to describe the membrane affinity of a drug and the effects of a drug on the membrane properties such as membrane fluidity, phase transition behavior, the membrane location of the drug, and the lipid order and packing (Ezer, Sahin & Kazanci, 2017; Sinha et al., 2014; Weaver et al., 2013; Yamada et al., 2016). Important information regarding the calorimetric enthalpy (ΔH), the transition temperature, the entropy of the phase transition (ΔS), the Gibbs free energy (ΔG), the binding constant (Ka), and stoichiometry (n) of binding can be obtained from DSC analysis. Different model membranes of various lipid compositions can be applied in DSC measurements to elucidate drug-membrane interactions. Weaver et al. (2013) investigated the interaction of choline salts with DPPC unilamellar vesicles via DSC and established that organic salts with high toxicity profiles drastically altered the lipid phase behavior of the tested model membranes. Yamada et al. (2016) examined the cholesterol concentration dependence of the interaction of a cytochrome P450 (CYP) substrate drug, chlorzoxazone (CZX), with MLVs composed of 1-palmitoyl-2-oleoyl-sn-glycero-3-phosphocholine (POPC), DPPC, and cholesterol by using DSC. The results confirmed that the binding and penetration of CYP substrate drugs with lipid bilayers are controlled by cholesterol content.

One of the main advantages of DSC is its ability to study ultratight binding (binding constant up to 1040 mol/L or higher) that cannot be measured by other methods (Brandts & Lin, 1990; Renaud et al., 2016). The major drawback of the conventional DSC is the requirement of large amounts (1.5∼30 nmol) of sample compared with spectroscopic methods, such as NMR (3∼6 nmol) and MS (10∼100 pmol) (Renaud et al., 2016). In recent years, technical improvements have been made enabling increased sensitivity (i.e., less sample consumption) and higher throughput in DSC analysis. For example, with very fast temperature scanning rates (100–500°C/min) in heating and cooling, the sensitivity and throughput of high-speed DSC techniques (PerkinElmer® HyperDSC) have dramatically increased by a factor of 10 over most conventional DSC analysis. As a typical run can be completed in less than 2 min for a simple heating run and in less than 6 min for a heat-cool-heat run, it is possible to run 264–720 samples per day. Other novel DSC techniques, including the new Nano DSC Autosampler System– from TA Instruments and the VP-Capillary DSC– from GE Healthcare (MicroCal, LLC), perform superior signal-to-noise ratio and permit the high sample throughput with automated, unattended sample handling.

Isothermal titration calorimetry

The basic principle of ITC is similar to that of DSC except for performing at a constant temperature and adding a titration module (Velazquezcampoy et al., 2004). Briefly, in the ITC experiment, small amounts of drug are titrated in aliquots into a solution of liposomes and vice versa. The heat flow is recorded at every injection until the saturation of the binding is observed. The binding parameters and thermodynamic quantities for the drug-lipid binding can be obtained from the resulting isotherm (Moreno et al., 2009; Huang et al., 2013; Osanai et al., 2013). It should be noted that when a solution of drug is titrated by amphiphilic phospholipids, self-assembled lipid monolayers (micelles) rather than liposomes form in the solution with increasing lipid concentration above its critical micelle concentration (Marsh, 2012). Compared with DSC, ITC requires larger sample amount (6∼60 nmol) (Renaud et al., 2016) and tends to be used as a real-time measurement for a binding reaction of modest affinity (Jelesarov & Bosshard, 1999). Besides, there is no lipid composition limitation in ITC measurements since the titration can be both forward and reverse (Bouchemal, 2008).

Pressure perturbation calorimetry

PPC is a relatively new thermodynamic technique which measures heat change (ΔQ) resulting from a pressure change (ΔP) above a solution containing proteins or other biomolecules (Heerklotz et al., 2003). PPC can be used to detect the temperature-dependent thermal volume expansion of biomembranes. By using PPC, Basso et al. (2011) found only slight volume change of DMPC membranes in the presence of an antimalarial drug primaquine (PQ), suggesting a very superficial interaction between DMPC and PQ. So far, only a few studies have utilized PPC to investigate drug-membrane interaction (Seeger, Gudmundsson & Heimburg, 2007; Basso et al., 2011). This technique, however, offers an independent way to identify the weak, broad thermotropic transitions in lipid membrane obtained from the DSC measurement (Heiko Heerklotz, 2007). PPC is therefore very useful in looking at the local changes and macroscopic behavior of more complex lipid mixtures such as protein-containing model membranes and natural biological membranes.

Quartz crystal microbalance with dissipation

QCM-D is an acoustic sensing technique which is highly sensitive (1 ng/cm2 sensitivity) to small mass variations due to changes in quartz crystal resonance frequency, as well as viscoelastic property variations due to changes in the dissipation of energy during the crystal oscillation (Zhang & Wang, 2012). It enables real-time, label-free measurements of molecular interactions with surfaces and interactions between molecules. QCM-D has been successfully employed to quantitatively analyze the binding kinetics and dynamics of drug interaction with lipid membranes of different compositions. Mechanistic investigation of AMPs (Piantavigna et al., 2011; Rydberg et al., 2014), bioactive polyphenols (Kamihira et al., 2008), and anesthetics (Paiva et al., 2012) (Figure 8) with supported lipid membranes by QCM-D have been reported in the recent past. It should be pointed out that QCM-D alone cannot directly count the lipid mass bound to the surface because of the bound water in the system (Edvardsson et al., 2009). However, this can be overcome by combining QCM-D with an optical technique, such as ellipsometry (Bittrich et al., 2010; Kananizadeh et al., 2017), electrochemical impedance spectroscopy (EIS) (Briand et al., 2010), SPR (Larsson et al., 2009; Ferhan, Jackman & Cho, 2016), and reflectometry (Wang et al., 2008; Edvardsson et al., 2009), into one setup.

Figure 8: Schematic illustration of a typical QCM-D investigation on the interaction of anesthetic with model lipid membrane adapted from Paiva et al. (2012). The resonance frequency change (Δf/n) is proportional to mass uptake or release at the sensor surface. The dissipation factor change (ΔD) is proportional to the crystal’s oscillation decay time constant, which is related to structural changes of the lipid membrane adhering at the sensor surface. As shown in this figure, the normalized Δf/n and ΔD at various harmonics are presented for (1) adsorption of the DMPC + cholesterol liposome onto the gold-coated quartz crystals, (2) rinsing with buffer, and (3) addition of the TTC solution. The strong increase in Δf/n could be ascribed to liposome disruption and/or water release from the liposome core caused by TTC. On the other hand, ΔD decreased in the higher harmonics and increased in the lower harmonics. This indicated that TTC induced the formation of heterogeneous liposome layer with different fluidity, as the higher harmonics are more sensitive to the variation at the solid interface while the lower harmonics are responsive to the liquid interface of the adsorbed liposomes.
Figure 8:

Schematic illustration of a typical QCM-D investigation on the interaction of anesthetic with model lipid membrane adapted from Paiva et al. (2012). The resonance frequency change (Δf/n) is proportional to mass uptake or release at the sensor surface. The dissipation factor change (ΔD) is proportional to the crystal’s oscillation decay time constant, which is related to structural changes of the lipid membrane adhering at the sensor surface. As shown in this figure, the normalized Δf/n and ΔD at various harmonics are presented for (1) adsorption of the DMPC + cholesterol liposome onto the gold-coated quartz crystals, (2) rinsing with buffer, and (3) addition of the TTC solution. The strong increase in Δf/n could be ascribed to liposome disruption and/or water release from the liposome core caused by TTC. On the other hand, ΔD decreased in the higher harmonics and increased in the lower harmonics. This indicated that TTC induced the formation of heterogeneous liposome layer with different fluidity, as the higher harmonics are more sensitive to the variation at the solid interface while the lower harmonics are responsive to the liquid interface of the adsorbed liposomes.

Future direction

As demonstrated in this review, there has been a tremendous increase in the availability and sensitivity of analytical techniques to characterize biointerfaces and biointeractions. These techniques have already provided much valuable information on drug-membrane interactions, including the drug localization, orientation, and conformation in the membrane; the structural integrity and phase behavior of the drug-inserted membrane; the dynamics of drug interacting with lipid membrane; and the consequences of drug-membrane interaction on the ADME properties of the drug. Indeed, drug-membrane interactions may involve various factors such as van der Waals force, hydrogen bonding, and hydrophobic and electrostatic interactions among specific moieties of lipids, drug molecules, and membrane proteins. Therefore, to get a comprehensive understanding of drug-membrane interaction phenomena, complementary analytical approaches are highly recommended. Besides, the development of powerful novel combinations of techniques, such as lab-on-a-chip hyphenation with MS strategies, will greatly increase the efficiency of on-site screening in the early stage of drug development. Another major future challenge in this research area is to establish ultrasensitive and selective techniques for study of the functional relationships between drugs and lipid membranes and obtain more biologically relevant information.

Acknowledgments

This work was supported by a grant from Shanghai University of Engineering Science (17KY0402). The authors thank Dr. Tinglu Yang at the Pennsylvania State University and Dr. Lihong Li at the Shanghai University of Engineering Science for critical comments on the manuscript.

References

Abdiche, Y. N.; Myszka, D. G. Probing the Mechanism of Drug/Lipid Membrane Interactions Using Biacore. Anal. Biochem.2004, 328, 233–243.10.1016/j.ab.2004.01.018Search in Google Scholar

Abdulhalim, K.; Nese, K. F. Biomimetic Lipid Bilayers on Solid Surfaces: Models for Biological Interactions. Surf. Innovations2016, 4, 141–157.10.1680/jsuin.16.00008Search in Google Scholar

Adachi, T.; Takahashi, H.; Ohki, K.; Hatta, I. Interdigitated Structure of Phospholipid-Alcohol Systems Studied by X-ray Diffraction. Biophys. J.1995, 68, 1850–1855.10.1016/S0006-3495(95)80361-9Search in Google Scholar

Alexander, K. Using Fluorescence for Studies of Biological Membranes: A Review. Methods Appl. Fluoresc.2015, 3, 042003.10.1088/2050-6120/3/4/042003Search in Google Scholar PubMed

Ambike, A. A.; Rosilio; Stella, B.; Lepetremouelhi, S.; Couvreur, P. Interaction of Self-Assembled Squalenoyl Gemcitabine Nanoparticles with Phospholipid-Cholesterol Monolayers Mimicking a Biomembrane. Langmuir2011, 27, 4891–4899.10.1021/la200002dSearch in Google Scholar PubMed

Aubin, Y.; Freedberg, D. I.; Keire, D. A. Biophysical Characterization of Proteins in Developing Biopharmaceuticals; Elsevier: Amsterdam, 2015.Search in Google Scholar

Baciu, M.; Sebai, S. C.; Ces, O.; Mulet, X.; Clarke, J. A.; Shearman, G. C.; Templer, R. H.; Plisson, C.; Parker, C. A.; Gee, A. D. Degradative Transport of Cationic Amphiphilic Drugs Across Phospholipid Bilayers. Philos. Trans. R. Soc., A2006, 364, 2597–2614.10.1098/rsta.2006.1842Search in Google Scholar PubMed

Bantz, K. C.; Meyer, A. F.; Wittenberg, N. J.; Im, H.; Kurtulus, O.; Lee, S. H.; Lindquist, N. C.; Oh, S.; Haynes, C. L. Recent Progress in SERS Biosensing. Phys. Chem. Chem. Phys.2011, 13, 11551–11567.10.1039/c0cp01841dSearch in Google Scholar PubMed PubMed Central

Barbato, F.; Grumetto, L.; Carpentiero, C.; Rocco, A.; Fanali, S. Capillary Electrochromatography as a New Tool to Assess Drug Affinity for Membrane Phospholipids. J. Pharm. Biomed. Anal.2011, 54, 893–899.10.1016/j.jpba.2010.11.037Search in Google Scholar PubMed

Barbato, F.; La Rotonda, M. I.; Quaglia, F. Interactions of Nonsteroidal Antiinflammatory Drugs with Phospholipids: Comparison between Octanol/Buffer Partition Coefficients and Chromatographic Indexes on Immobilized Artificial Membranes. J. Pharm. Sci.1997, 86, 225–229.10.1021/js960233hSearch in Google Scholar PubMed

Barbato, F.; Martino, G.; Grumetto, L.; La Rotonda, M. I. Can Protonated Beta-Blockers Interact with Biomembranes Stronger than Neutral Isolipophilic Compounds? A Chromatographic Study on Three Different Phospholipid Stationary Phases (IAM-HPLC). Eur. J. Pharm. Sci.2005, 25, 379–386.10.1016/j.ejps.2005.03.011Search in Google Scholar PubMed

Bareford, L. M.; Swaan, P. W. Endocytic Mechanisms for Targeted Drug Delivery. Adv. Drug Del. Rev.2007, 59, 748–758.10.1016/j.addr.2007.06.008Search in Google Scholar

Bartucci, R.; Mollica, P.; Sapia, P.; Sportelli, L. Procain Interaction with DPPC Multilayers: An ESR Spin Label Investigation. Appl. Magn. Reson.1998, 15, 181–195.10.1007/BF03161870Search in Google Scholar

Basso, L. G. M.; Rodrigues, R. Z.; Naal, R. M. Z. G.; Costa-Filho, A. J. Effects of the Antimalarial Drug Primaquine on the Dynamic Structure of Lipid Model Membranes. Biochim. Biophys. Acta2011, 1808, 55–64.10.1016/j.bbamem.2010.08.009Search in Google Scholar

Bastiaens, P. I. H.; Squire, A. Fluorescence Lifetime Imaging Microscopy: Spatial Resolution of Biochemical Processes in the Cell. Trends Cell Biol.1999, 9, 48–52.10.1016/S0962-8924(98)01410-XSearch in Google Scholar

Bittrich, E.; Rodenhausen, K. B.; Eichhorn, K.; Hofmann, T.; Schubert, M.; Stamm, M.; Uhlmann, P. Protein Adsorption on and Swelling of Polyelectrolyte Brushes: A Simultaneous Ellipsometry-Quartz Crystal Microbalance Study. Biointerphases2010, 5, 159–167.10.1116/1.3530841Search in Google Scholar PubMed

Borbas, E.; Sinko, B.; Tsinman, O.; Tsinman, K.; Kiserdei, E.; Demuth, B.; Balogh, A.; Bodak, B.; Domokos, A.; Dargo, G. Investigation and Mathematical Description of the Real Driving Force of Passive Transport of Drug Molecules from Supersaturated Solutions. Mol. Pharm.2016, 13, 3816–3826.10.1021/acs.molpharmaceut.6b00613Search in Google Scholar PubMed

Bouchemal, K. New Challenges for Pharmaceutical Formulations and Drug Delivery Systems Characterization Using Isothermal Titration Calorimetry. Drug Discov. Today2008, 13, 960–972.10.1016/j.drudis.2008.06.004Search in Google Scholar PubMed

Boxer, S. G.; Kraft, M. L.; Weber, P. K. Advances in Imaging Secondary Ion Mass Spectrometry for Biological Samples. Annu. Rev. Biophys.2009, 38, 53–74.10.1146/annurev.biophys.050708.133634Search in Google Scholar PubMed

Brandts, J. F.; Lin, L. N. Study of Strong to Ultratight Protein Interactions Using Differential Scanning Calorimetry. Biochemistry1990, 29, 6927–6940.10.1021/bi00481a024Search in Google Scholar PubMed

Breuzard, G.; Angiboust, J. F.; Jeannesson, P.; Manfait, M.; Millot, J. Surface-Enhanced Raman Scattering Reveals Adsorption of Mitoxantrone on Plasma Membrane of Living Cells. Biochem. Biophys. Res. Commun.2004, 320, 615–621.10.1016/j.bbrc.2004.05.203Search in Google Scholar PubMed

Briand, E.; Zach, M.; Svedhem, S.; Kasemo, B.; Petronis, S. Combined QCM-D and EIS Study of Supported Lipid Bilayer Formation and Interaction with Pore-Forming Peptides. Analyst2010, 135, 343–350.10.1039/B918288HSearch in Google Scholar

Brockman, H. L. Lipid Monolayers: Why Use Half a Membrane to Characterize Protein-Membrane Interactions? Curr. Opin. Struct. Biol.1999, 9, 438–443.10.1016/S0959-440X(99)80061-XSearch in Google Scholar

Bronk, J. R.; Lister, N.; Lynch, S. E. Absorption of 5-Fluorouracil and Related Pyrimidines in Rat Small Intestine. Clin. Sci.1987, 72, 705–716.10.1042/cs0720705Search in Google Scholar

Caetano, W.; Ferreira, M.; Tabak, M.; Mosquera Sanchez, M. I.; Oliveira, O. N.; Krüger, P.; Schalke, M.; Lösche, M. Cooperativity of Phospholipid Reorganization Upon Interaction of Dipyridamole with Surface Monolayers on Water. Biophys. Chem.2001, 91, 21–35.10.1016/S0301-4622(01)00145-4Search in Google Scholar

Carrozzino, J. M.; Khaledi, M. G. Interaction of Basic Drugs with Lipid Bilayers Using Liposome Electrokinetic Chromatography. Pharm. Res.2004, 21, 2327–2335.10.1007/s11095-004-7685-3Search in Google Scholar PubMed

Carrozzino, J. M.; Khaledi, M. G. pH Effects on Drug Interactions with Lipid Bilayers by Liposome Electrokinetic Chromatography. J. Chromatogr. A2005, 1079, 307–316.10.1016/j.chroma.2005.04.008Search in Google Scholar PubMed

Castellana, E. T.; Cremer, P. S. Solid Supported Lipid Bilayers: From Biophysical Studies to Sensor Design. Surf. Sci. Rep.2006, 61, 429–444.10.1016/j.surfrep.2006.06.001Search in Google Scholar PubMed PubMed Central

Chan, Y. M.; Boxer, S. G. Model Membrane Systems and Their Applications. Curr. Opin. Chem. Biol.2007, 11, 581–587.10.1016/j.cbpa.2007.09.020Search in Google Scholar PubMed PubMed Central

Chen, Q.; Wu, J. M.; Zhang, Y.; Lin, J. Qualitative and Quantitative Analysis of Tumor Cell Metabolism via Stable Isotope Labeling Assisted Microfluidic Chip Electrospray Ionization Mass Spectrometry. Anal. Chem.2012, 84, 1695–1701.10.1021/ac300003kSearch in Google Scholar PubMed

Chen, X.; Chen, Z. SFG Studies on Interactions between Antimicrobial Peptides and Supported Lipid Bilayers. Biochim. Biophys. Acta2006, 1758, 1257–1273.10.1016/j.bbamem.2006.01.017Search in Google Scholar PubMed

Chen, X.; Tang, H.; Even, M. A.; Wang, J.; Tew, G. N.; Chen, Z. Observing a Molecular Knife at Work. J. Am. Chem. Soc.2006, 128, 2711–2714.10.1021/ja057029tSearch in Google Scholar

Chiu, M. H.; Prenner, E. J. Differential Scanning Calorimetry: An Invaluable Tool for a Detailed Thermodynamic Characterization of Macromolecules and Their Interactions. J. Pharm. BioAllied Sci.2011, 3, 39.10.4103/0975-7406.76463Search in Google Scholar

Cho, N.; Frank, C. W.; Kasemo, B.; Hook, F. Quartz Crystal Microbalance with Dissipation Monitoring of Supported Lipid Bilayers on Various Substrates. Nat. Protoc.2010, 5, 1096–1106.10.1038/nprot.2010.65Search in Google Scholar

Cola, E. D.; Grillo, I.; Ristori, S. Small Angle X-ray and Neutron Scattering: Powerful Tools for Studying the Structure of Drug-Loaded Liposomes. Pharmaceutics2016, 8, 10.10.3390/pharmaceutics8020010Search in Google Scholar

Comer, J.; Tam, K. Pharmacokinetic Optimization in Drug Research; Verlag Helvetica Chimica Acta: Schweizerische Eidgenossenschaft, 2007.Search in Google Scholar

Contin, M.; Martinelli, P. Pharmacokinetics of Levodopa. J. Neurol.2010, 257, 253–261.10.1007/s00415-010-5728-8Search in Google Scholar

Cremer, P. S.; Boxer, S. G. Formation and Spreading of Lipid Bilayers on Planar Glass Supports. J. Phys. Chem. B1999, 103, 2554–2559.10.1021/jp983996xSearch in Google Scholar

Danielsson, L. R.; Zhang, Y. Methods for Determining n-Octanol-Water Partition Constants. Trends Anal. Chem.1996, 15, 188–196.10.1016/0165-9936(96)00003-9Search in Google Scholar

Davis, S. S. Overcoming Barriers to the Oral Administration of Peptide Drugs. Trends Pharmacol. Sci.1990, 11, 353–355.10.1016/0165-6147(90)90174-7Search in Google Scholar

Debouzy, J.; Mehenni, L.; Crouzier, D.; Lahiani-Skiba, M.; Nugue, G.; and Skiba, M. NMR and ESR Study of Amphotericin B Interactions with Various Binary Phosphatidylcholine/Phosphatidylglycerol Membranes. Int. J. Pharm2017, 521, 384–394.10.1016/j.ijpharm.2017.02.034Search in Google Scholar PubMed

Deeb, S. E.; Wätzig, H.; Elhady, D. A.; Albishri, H. M.; de Griend, C. S.; Scriba, G. K. Recent Advances in Capillary Electrophoretic Migration Techniques for Pharmaceutical Analysis. Electrophoresis2014, 35, 170.10.1002/elps.201300411Search in Google Scholar PubMed

Demchenko, A. P.; Duportail, G.; Oncul, S.; Klymchenko, A. S.; Mely, Y. Introduction to Fluorescence Probing of Biological Membranes. Methods Mol. Biol.2015, 1232, 19–43.10.1007/978-1-4939-1752-5_3Search in Google Scholar PubMed

Demchenko, A. P.; Mély, Y.; Duportail, G.; Klymchenko, A. S. Monitoring Biophysical Properties of Lipid Membranes by Environment-Sensitive Fluorescent Probes. Biophys. J.2009, 96, 3461–3470.10.1016/j.bpj.2009.02.012Search in Google Scholar PubMed PubMed Central

Doherty, G. J.; Mcmahon, H. T. Mechanisms of Endocytosis. Annu. Rev. Biochem.2009, 78, 857–902.10.1146/annurev.biochem.78.081307.110540Search in Google Scholar PubMed

Dufourc, E. J. Chemical Biology; John Wiley & Sons, Ltd: Hoboken, New Jersey, 2007.Search in Google Scholar

Edvardsson, M.; Svedhem, S.; Wang, G.; Richter, R. P.; Rodahl, M.; Kasemo, B. QCM-D and Reflectometry Instrument: Applications to Supported Lipid Structures and Their Biomolecular Interactions. Anal. Chem.2009, 81, 349–361.10.1021/ac801523wSearch in Google Scholar PubMed

Eeman, M.; and Deleu, M. From Biological Membranes to Biomimetic Model Membranes. Biotechnol., Agron., Soc. Environ2010, 14, 719–736.Search in Google Scholar

Engvall, C.; Lundahl, P. Reviews in Cell Biology and Molecular Medicine; Wiley-VCH Verlag GmbH & Co. KGaA: Weinheim, Deutschland, 2006.Search in Google Scholar

Escriba, P. V. Membrane-Lipid Therapy: a New Approach in Molecular Medicine. Trends Mol. Med.2006, 12, 34–43.10.1016/j.molmed.2005.11.004Search in Google Scholar PubMed

Escriba, P. V.; Gonzalezros, J. M.; Goni, F. M.; Kinnunen, P. K. J.; Vigh, L.; Sanchezmagraner, L.; Fernandez, A. M.; Busquets, X.; Horvath, I.; Barcelocoblijn, G. Membranes: A Meeting Point for LIPIDS, Proteins and Therapies. J. Cell. Mol. Med.2008, 12, 829–875.10.1111/j.1582-4934.2008.00281.xSearch in Google Scholar PubMed PubMed Central

Ezer, N.; Sahin, I.; Kazanci, N. Alliin Interacts with DMPC Model Membranes to Modify the Membrane Dynamics: FTIR and DSC Studies. Vib. Spectrosc2017, 89, 1–8.10.1016/j.vibspec.2016.12.006Search in Google Scholar

Fabriciova, G.; Sanchezcortes, S.; Garciaramos, J. V.; Miskovsky, P. Surface-Enhanced Raman Spectroscopy Study of the Interaction of the Antitumoral Drug Emodin with Human Serum Albumin. Biopolymers2004, 74, 125–130.10.1002/bip.20058Search in Google Scholar PubMed

Ferhan, A. R.; Jackman, J. A.; Cho, N.-J. Integration of Quartz Crystal Microbalance-Dissipation and Reflection-Mode Localized Surface Plasmon Resonance Sensors for Biomacromolecular Interaction Analysis. Anal. Chem.2016, 88, 12524–12531.10.1021/acs.analchem.6b04303Search in Google Scholar PubMed

Fernandez, D. I.; Brun, A. P. L.; Lee, T.; Bansal, P. S.; Aguilar, M.; James, M.; Separovic, F. Structural Effects of the Antimicrobial Peptide Maculatin 1.1 on Supported Lipid Bilayers. Eur. Biophys. J.2012, 42, 47–59.10.1007/s00249-012-0796-6Search in Google Scholar PubMed

Fernandezsuarez, M.; Ting, A. Y. Fluorescent Probes for Super-Resolution Imaging in Living Cells. Nat. Rev. Mol. Cell Biol.2008, 9, 929–943.10.1038/nrm2531Search in Google Scholar

Foglia, F.; Drake, A. F.; Terry, A. E.; Rogers, S. E.; Lawrence, M. J.; Barlow, D. J. Small-Angle Neutron Scattering Studies of the Effects of Amphotericin B on Phospholipid and Phospholipid-Sterol Membrane Structure. Biochim. Biophys. Acta2011, 1808, 1574–1580.10.1016/j.bbamem.2011.02.012Search in Google Scholar

Foglia, F.; Lawrence, M. J.; Demė, B.; Fragneto, G.; Barlow, D. J. Neutron Diffraction Studies of the Interaction between Amphotericin B and Lipid-Sterol Model Membranes. Sci. Rep.2012, 2, 778–778.10.1038/srep00778Search in Google Scholar

Foglia, F.; Rogers, S. E.; Webster, J. R. P.; Akeroyd, F. A.; Gascoyne, K.; Lawrence, M. J.; Barlow, D. J. Neutron Scattering Studies of the Effects of Formulating Amphotericin B with Cholesteryl Sulfate on the Drug’s Interactions with Phospholipid and Phospholipid-Sterol Membranes. Langmuir2015, 31, 8042–8051.10.1021/acs.langmuir.5b01365Search in Google Scholar

Fotakis, C.; Megariotis, G.; Christodouleas, D.; Kritsi, E.; Zoumpoulakis, P.; Ntountaniotis, D.; Zervou, M.; Potamitis, C.; Hodzic, A.; Pabst, G. Comparative Study of the AT 1 Receptor Prodrug Antagonist Candesartan Cilexetil with Other Sartans on the Interactions with Membrane Bilayers. Biochim. Biophys. Acta2012, 1818, 3107–3120.10.1016/j.bbamem.2012.08.009Search in Google Scholar

Fox, C. B.; Horton, R. A.; Harris, J. M. Detection of Drug-Membrane Interactions in Individual Phospholipid Vesicles by Confocal Raman Microscopy. Anal. Chem.2006, 78, 4918–4924.10.1021/ac0605290Search in Google Scholar

Gaber, M. H.; Ghannam, M. M.; Ali, S. A.; Khalil, W. A. Interaction of Doxorubicin with Phospholipid Monolayer and Liposomes. Biophys. Chem.1998, 70, 223–229.10.1016/S0301-4622(97)00125-7Search in Google Scholar

Gallois, L.; Fiallo, M.; Garnier-Suillerot, A. Comparison of the Interaction of Doxorubicin, Daunorubicin, Idarubicin and Idarubicinol with Large Unilamellar Vesicles: Circular Dichroism Study. Biochim. Biophys. Acta1998, 1370, 31–40.10.1016/S0005-2736(97)00241-1Search in Google Scholar

Gao, D.; Liu, H.; Lin, J.; Wang, Y.; Jiang, Y. Characterization of Drug Permeability in Caco-2 Monolayers by Mass Spectrometry on a Membrane-Based Microfluidic Device. Lab Chip2013, 13, 978–985.10.1039/c2lc41215bSearch in Google Scholar PubMed

Gashaw, I.; Ellinghaus, P.; Sommer, A.; Asadullah, K. What Makes a Good Drug Target. Drug Discov. Today2012, 16, 1037–1043.10.1016/j.drudis.2011.09.007Search in Google Scholar PubMed

Gedig, M.; Faiss, S.; and Janshoff, A. Melting and Interdigitation of Microstructured Solid Supported Membranes Quantified by Imaging Ellipsometry. Biointerphases2008, 3, FA51–FA58.10.1116/1.2901179Search in Google Scholar PubMed

Generosi, J.; Margaritondo, G.; Sanghera, J. S.; Aggarwal, I. D.; Tolk, N. H.; Piston, D. W.; Castellano, A. C.; Cricenti, A. Spectroscopic Infrared Near-Field Microscopy and X-ray Reflectivity Studies of Order and Clustering in Lipid Membranes. Appl. Phys. Lett.2006, 89, 233906–233908.10.1063/1.2403184Search in Google Scholar

Gotter, B.; Faubel, W.; Neubert, R. H. H. FTIR Microscopy and Confocal Raman Microscopy for Studying Lateral Drug Diffusion from a Semisolid Formulation. Eur. J. Pharm. Biopharm.2010, 74, 14–20.10.1016/j.ejpb.2009.07.006Search in Google Scholar PubMed

Gozen, I.; Jesorka, A. Instrumental Methods to Characterize molecular Phospholipid Films on Solid Supports. Anal. Chem.2012, 84, 822–838.10.1021/ac203126fSearch in Google Scholar PubMed

Grudzinski, W.; Sagan, J.; Welc, R.; Luchowski, R.; Gruszecki, W. I. Molecular Organization, Localization and Orientation of Antifungal Antibiotic Amphotericin B in a Single Lipid Bilayer. Sci. Rep.2016, 6, 32780.10.1038/srep32780Search in Google Scholar PubMed PubMed Central

Grumetto, L.; Carpentiero, C.; Barbato, F. Lipophilic and Electrostatic Forces Encoded in IAM-HPLC Indexes of Basic Drugs: Their Role in Membrane Partition and Their Relationships with BBB Passage Data. Eur. J. Pharm. Sci.2012, 45, 685–692.10.1016/j.ejps.2012.01.008Search in Google Scholar PubMed

Grumetto, L.; Carpentiero, C.; Di Vaio, P.; Frecentese, F.; Barbato, F. Lipophilic and Polar Interaction Forces between Acidic Drugs and Membrane Phospholipids Encoded in IAM-HPLC LC Indexes: Their Role in Membrane Partition and Relationships with BBB Permeation Data. J. Pharm. Biomed. Anal.2013, 75, 165–172.10.1016/j.jpba.2012.11.034Search in Google Scholar PubMed

Grumetto, L.; Russo, G.; Barbato, F. Indexes of Polar Interactions between Ionizable Drugs and Membrane Phospholipids Measured by IAM–HPLC: Their Relationships with Data of Blood–Brain Barrier Passage. Eur. J. Pharm. Sci.2014, 65, 139–146.10.1016/j.ejps.2014.09.015Search in Google Scholar PubMed

Gundelfinger, E. D.; Kessels, M. M.; Qualmann, B. Temporal and Spatial Coordination of Exocytosis and Endocytosis. Nat. Rev. Mol. Cell Biol.2003, 4, 127–139.10.1038/nrm1016Search in Google Scholar PubMed

Guo, J.; Gao, X.; Su, L.; Xia, H.; Gu, G.; Pang, Z.; Jiang, X.; Yao, L.; Chen, J.; Chen, H. Aptamer-Functionalized PEG–PLGA Nanoparticles for Enhanced Anti-Glioma Drug Delivery. Biomaterials2011, 32, 8010–8020.10.1016/j.biomaterials.2011.07.004Search in Google Scholar PubMed

Hartmann, T.; Schmitt, J. Lipophilicity – Beyond Octanol/Water: A Short Comparison of Modern Technologies. Drug Discovery Today: Technol.2004, 1, 431–439.10.1016/j.ddtec.2004.10.006Search in Google Scholar PubMed

Hatziantoniou, S.; Demetzos, C. Liposomes: Methods and Protocols; Humana Press: Clifton, New Jersey, 2012.Search in Google Scholar

He, X.; Chen, Q.; Zhang, Y.; Lin, J. Recent Advances in Microchip-Mass Spectrometry for Biological Analysis. Trends Anal. Chem.2014, 53, 84–97.10.1016/j.trac.2013.09.013Search in Google Scholar

Heerklotz, H.; Winter, R.; Royer, C.; Seelig, J. Advances in High Pressure Bioscience and Biotechnology II, Proceedings of the 2nd International Conference on High Pressure Bioscience and Biotechnology, Springer Berlin Heidelberg: Berlin, Heidelberg, 2003.Search in Google Scholar

Heiko Heerklotz, P. D. Methods in Membrane Lipids; Humana Press: Totowa, NJ, 2007.Search in Google Scholar

Henchoz, Y.; Romand, S.; Schappler, J.; Rudaz, S.; Veuthey, J.; Carrupt, P. High-Throughput log p Determination by MEEKC Coupled with UV and MS Detections. Electrophoresis2010, 31, 952–964.10.1002/elps.200900540Search in Google Scholar PubMed

Herec, M.; Islamov, A.; Kuklin, A. I.; Gagoś, M.; Gruszecki, W. I. Effect of Antibiotic Amphotericin B on Structural and Dynamic Properties of Lipid Membranes Formed with Egg Yolk Phosphatidylcholine. Chem. Phys. Lipids2007, 147, 78–86.10.1016/j.chemphyslip.2007.03.007Search in Google Scholar PubMed

Hervé, M.; Cybulska, B.; Gary-Bobo, C. Cation Permeability Induced by Valinomycin, Gramicidin D and Amphotericin B in Large Lipidic Unilamellar Vesicles Studied by 31P-NMR. Eur. Biophys. J.1985, 12, 121–128.10.1007/BF00254070Search in Google Scholar

Hilton, G. R.; Benesch, J. L. P. Two Decades of Studying Non-Covalent Biomolecular Assemblies by Means of Electrospray Ionization Mass Spectrometry. J. R. Soc., Interface2012, 9, 801–816.10.1098/rsif.2011.0823Search in Google Scholar PubMed PubMed Central

Ho, C. S.; Lam, C. W.; Chan, M. H.; Cheung, R. C.; Law, L. K.; Lit, L. C.; Ng, K. F.; Suen, M. W.; Tai, H. L. Electrospray Ionisation Mass Spectrometry: Principles and Clinical Applications. Clin Biochem Rev2003, 24, 3–12.Search in Google Scholar

How, C. W.; Teruel, J. A.; Ortiz, A.; Montenegro, M. F.; Rodriguezlopez, J. N.; Aranda, F. J. Effects of a Synthetic Antitumoral Catechin and its Tyrosinase-Processed Product on the Structural Properties of Phosphatidylcholine Membranes. Biochim. Biophys. Acta2014, 1838, 1215–1224.10.1016/j.bbamem.2014.01.025Search in Google Scholar PubMed

Huang, D.; Zhao, T.; Xu, W.; Yang, T.; Cremer, P. S. Sensing Small Molecule Interactions with Lipid Membranes by Local pH Modulation. Anal. Chem.2013, 85, 10240–10248.10.1021/ac401955tSearch in Google Scholar PubMed

Hudson, S. D.; Chumanov, G. Bioanalytical Applications of SERS (Surface-Enhanced Raman Spectroscopy). Anal. Bioanal. Chem.2009, 394, 679–686.10.1007/s00216-009-2756-2Search in Google Scholar PubMed

Husch, J.; Dutagaci, B.; Glaubitz, C.; Geppert, T.; Schneider, G.; Harms, M.; Mullergoymann, C. C.; Fink, L.; Schmidt, M. U.; Setzer, C. Structural Properties of So-Called NSAID-Phospholipid-Complexes. Eur. J. Pharm. Sci.2011, 44, 103–116.10.1016/j.ejps.2011.06.010Search in Google Scholar PubMed

Ikegami, T.; Tanaka, N. Recent progress in monolithic silica columns for high-speed and high-selectivity separations. Rev. Anal. Chem2016, 9, 317–342.10.1146/annurev-anchem-071114-040102Search in Google Scholar PubMed

Imming, P.; Sinning, C.; Meyer, A. Drugs, Their Targets and the Nature and Number of Drug Targets. Nat. Rev. Drug Discov.2006, 5, 821–834.10.1038/nrd2132Search in Google Scholar

Jelesarov, I.; Bosshard, H. R. Isothermal Titration Calorimetry and Differential Scanning Calorimetry as Complementary Tools to Investigate the Energetics of Biomolecular Recognition. J. Mol. Recognit.1999, 3–18.10.1002/(SICI)1099-1352(199901/02)12:1<3::AID-JMR441>3.0.CO;2-6Search in Google Scholar

Jendrossek, V.; Handrick, R. Membrane Targeted Anticancer Drugs: Potent Inducers of Apoptosis and Putative Radiosensitisers. Curr. Med. Chem. Anticancer Agents2003, 3, 343–353.10.2174/1568011033482341Search in Google Scholar

Jesorka, A.; Orwar, O. Liposomes: Technologies and Analytical Applications. Rev. Anal. Chem2008, 1, 801–832.10.1146/annurev.anchem.1.031207.112747Search in Google Scholar

Junk, M. J. N. Assessing the Functional Structure of Molecular Transporters by EPR Spectroscopy; Springer Berlin Heidelberg: Berlin, Heidelberg, 2012.10.1007/978-3-642-25135-1Search in Google Scholar

Kajiya, K.; Kumazawa, S.; Naito, A.; Nakayama, T. Solid-state NMR Analysis of the Orientation and Dynamics of Epigallocatechin Gallate, a Green Tea Polyphenol, Incorporated into Lipid bilayers. Magn. Reson. Chem.2008, 46, 174–177.10.1002/mrc.2157Search in Google Scholar

Kamihira, M.; Nakazawa, H.; Kira, A.; Mizutani, Y.; Nakamura, M.; Nakayama, T. Interaction of Tea Catechins with Lipid Bilayers Investigated by a Quartz-Crystal Microbalance Analysis. Biosci. Biotechnol. Biochem.2008, 72, 1372–1375.10.1271/bbb.70786Search in Google Scholar

Kananizadeh, N.; Rice, C.; Lee, J.; Rodenhausen, K. B.; Sekora, D.; Schubert, M.; Schubert, E.; Bartelthunt, S. L.; Li, Y. Combined Quartz Crystal Microbalance with Dissipation (QCM-D) and Generalized Ellipsometry (GE) to Characterize the Deposition of Titanium Dioxide Nanoparticles on Model Rough Surfaces. J. Hazard. Mater.2017, 322, 118–128.10.1016/j.jhazmat.2016.03.048Search in Google Scholar

Kellici, T. F.; Tzakos, A. G.; Mavromoustakos, T. Rational Drugsign and Synthesis of Molecules Targeting the Angiotensin II type 1 and type 2 receptors. Molecules2015, 20, 3868–3897.10.3390/molecules20033868Search in Google Scholar

Khadka, N. K.; Cheng, X.; Ho, C. S.; Katsaras, J.; Pan, J. Interactions of the Anticancer Drug Tamoxifen with Lipid Membranes. Biophys. J.2015, 108, 2492–2501.10.1016/j.bpj.2015.04.010Search in Google Scholar

Kim, H. M.; Choo, H.; Jung, S.; Ko, Y.; Park, W. J.; Jeon, S.; Kim, C. H.; Joo, T.; Cho, B. R. A Two-Photon Fluorescent Probe for Lipid Raft Imaging: C-laurdan. Chem. Bio. Chem.2007, 8, 553–559.10.1002/cbic.200700003Search in Google Scholar

Knobloch, J.; Suhendro, D. K.; Zieleniecki, J. L.; Shapter, J. G.; Koper, I. Membrane–Drug Interactions Studied Using Model Membrane Systems. Saudi J. Biol. Sci.2015, 22, 714–718.10.1016/j.sjbs.2015.03.007Search in Google Scholar

Kraft, M. L.; Klitzing, H. A. Imaging Lipids with Secondary Ion Mass Spectrometry. Biochim. Biophys. Acta2014, 1841, 1108–1119.10.1016/j.bbalip.2014.03.003Search in Google Scholar

Kuc, M.; Cieślikboczula, K.; Świątek, P.; Jaszczyszyn, A.; Gąsiorowski, K.; Malinka, W. FTIR-ATR Study of the Influence of the Pyrimidine Analog of Fluphenazineine on the Chain-Melting Phase Transition of Sphingomyelin Membranes. Chem. Phys.2015, 458, 9–17.10.1016/j.chemphys.2015.06.010Search in Google Scholar

Kuroda, Y.; Hamaguchi, R.; Tanimoto, T. Phospholipid-Modified Ods Monolithic Column for Affinity Prediction of Hydrophobic Basic Drugs to Phospholipids. Chromatographia2014, 77, 405–411.10.1007/s10337-013-2621-5Search in Google Scholar

Ladbrooke, B. D.; Williams, R. M.; Chapman, D. Studies on Lecithin-Cholesterol-Water Interactions by Differential Scanning Calorimetry and X-ray Diffraction. Biochim. Biophys. Acta1968, 150, 333–340.10.1016/0005-2736(68)90132-6Search in Google Scholar

Larsson, E. M.; Edvardsson, M.; Langhammer, C.; Zoric, I.; Kasemo, B. A Combined Nanoplasmonic and Electrodeless Quartz Crystal Microbalance Setup. Rev. Sci. Instrum.2009, 80, 125105.10.1063/1.3265321Search in Google Scholar PubMed

Levin, C. S.; Kundu, J.; Janesko, B. G.; Scuseria, G. E.; Raphael, R. M.; Halas, N. J. Interactions of Ibuprofen with Hybrid Lipid Bilayers Probed by Complementary Surface-Enhanced Vibrational Spectroscopies. J. Phys. Chem. B2008, 112, 14168–14175.10.1021/jp804374eSearch in Google Scholar PubMed PubMed Central

Lewis, R. N. A. H.; McElhaney, R. N. Encyclopedia of Biophysics; Springer Berlin Heidelberg: Berlin, Heidelberg, 2013.Search in Google Scholar

Liang, C.; Lian, H. Recent Advances in Lipophilicity Measurement by Reversed-Phase High-Performance Liquid Chromatography. Trends Anal. Chem.2015, 68, 28–36.10.1016/j.trac.2015.02.009Search in Google Scholar

Liu, X.; Fan, P.; Chen, M.; Hefesha, H.; Scriba, G. K. E.; Gabel, D.; Fahr, A. Drug-Membrane Interaction on Immobilized Liposome Chromatography Compared to Immobilized Artificial Membrane (IAM), Liposome/Water, and Octan-1-ol/Water Systems. Helv. Chim. Acta2010, 93, 203–211.10.1002/hlca.200900233Search in Google Scholar

Liu, X.; Nakamura, C.; Yang, Q.; Kamo, N.; Miyake, J. Immobilized Liposome Chromatography to Study Drug–Membrane Interactions. J. Chromatogr. A2002, 961, 113–118.10.1016/S0021-9673(02)00505-8Search in Google Scholar

Lucio, M.; Ferreira, H.; Lima, J. L. F. C.; Reis, S. Use of Liposomes as Membrane Models to Evaluate the Contribution of Drug-Membrane Interactions to Antioxidant Properties of Etodolac. Redox Rep.2008, 13, 225–236.10.1179/135100008X308939Search in Google Scholar

Lucio, M.; Lima, J. L.; Reis, S. Drug-Membrane Interactions: Significance for Medicinal Chemistry. Curr. Med. Chem.2010, 17, 1795–1809.10.2174/092986710791111233Search in Google Scholar

Lucio, M.; Nunes, C.; Gaspar, D.; Ferreira, H.; Lima, J. L. F. C.; Reis, S. Antioxidant Activity of Vitamin e and Trolox: Understanding of the Factors that Govern Lipid Peroxidation Studies In Vitro. Food Biophys.2009, 4, 312–320.10.1007/s11483-009-9129-4Search in Google Scholar

Lukacova, V.; Peng, M.; Fanucci, G. E.; Tandlich, R.; Hinderliter, A.; Maity, B. C.; Manivannan, E.; Cook, G. R.; Balaz, S. Drug-Membrane Interactions Studied in Phospholipid Monolayers Adsorbed on Nonporous Alkylated Microspheres. J. Biomol. Screen.2007, 12, 186–202.10.1177/1087057106297063Search in Google Scholar

Lundahl, P.; Beigi, F. Immobilized Liposome Chromatography of Drugs for Model Analysis of Drug-Membrane Interactions. Adv. Drug Del. Rev.1997, 23, 221–227.10.1016/S0169-409X(96)00437-1Search in Google Scholar

Magetdana, R. The Monolayer Technique: A Potent Tool for Studying the Interfacial Properties of Antimicrobial and Membrane-Lytic Peptides and Their Interactions with Lipid Membranes. Biochim. Biophys. Acta1999, 1462, 109–140.10.1016/S0005-2736(99)00203-5Search in Google Scholar

Mangravite, L. M.; Thorn, C. F.; Krauss, R. M. Clinical Implications of Pharmacogenomics of Statin Treatment. Pharmacogenom. J.2006, 6, 360–374.10.1038/sj.tpj.6500384Search in Google Scholar PubMed

Marsh, D. Thermodynamics of Phospholipid Self-Assembly. Biophys. J.2012, 102, 1079–1087.10.1016/j.bpj.2012.01.049Search in Google Scholar PubMed PubMed Central

Matos, C.; Moutinho, C.; Lobão, P. Liposomes as a Model for the Biological Membrane: Studies on Daunorubicin Bilayer Interaction. J. Membr. Biol.2012, 245, 69–75.10.1007/s00232-011-9414-2Search in Google Scholar PubMed

Mcdonnell, L. A.; Heeren, R. M. A. Imaging Mass Spectrometry. Mass Spectrom. Rev.2007, 26, 606–643.10.1002/mas.20124Search in Google Scholar PubMed

Mensch, J.; Noppe, M.; Adriaensen, J.; Melis, A.; Mackie, C.; Augustijns, P.; Brewster, M. E. Novel Generic UPLC/MS/MS Method for High Throughput Analysis Applied to Permeability Assessment in Early Drug Discovery. J. Chromatogr. B2007, 847, 182–187.10.1016/j.jchromb.2006.10.031Search in Google Scholar PubMed

Michot, J.; Seral, C.; Van Bambeke, F.; Mingeotleclercq, M.; Tulkens, P. M. Influence of Efflux Transporters on the Accumulation and Efflux of Four Quinolones (Ciprofloxacin, Levofloxacin, Garenoxacin, and Moxifloxacin) in J774 Macrophages. Antimicrob. Agents Chemother.2005, 49, 2429–2437.10.1128/AAC.49.6.2429-2437.2005Search in Google Scholar PubMed PubMed Central

Mingeotleclercq, M.; Deleu, M.; Brasseur, R.; Dufrene, Y. F. Atomic Force Microscopy of Supported Lipid Bilayers. Nat. Protoc.2008, 3, 1654–1659.10.1038/nprot.2008.149Search in Google Scholar PubMed

Moravcova, D.; Planeta, J.; Wiedmer, S. K. Silica-Based Monolithic Capillary Columns Modified by Liposomes for Characterization of Analyte-Liposome Interactions by Capillary Liquid Chromatography. J. Chromatogr. A2013, 1317, 159–166.10.1016/j.chroma.2013.08.031Search in Google Scholar PubMed

Moreno, M.; Garidel, P.; Suwalsky, M.; Howe, J.; Brandenburg, K. The Membrane-Activity of Ibuprofen, Diclofenac, and Naproxen: A Physico-Chemical Study with Lecithin Phospholipids. Biochim. Biophys. Acta2009, 1788, 1296–1303.10.1016/j.bbamem.2009.01.016Search in Google Scholar PubMed

Mose, T.; Kjaerstad, M. B.; Mathiesen, L.; Nielsen, J. B.; Edelfors, S.; Knudsen, L. E. Placental Passage of Benzoic Acid, Caffeine, and Glyphosate in an Ex Vivo Human Perfusion System. J. Toxicol. Environ. Health2008, 71, 984–991.10.1080/01932690801934513Search in Google Scholar PubMed

Mouritsen, O. G.; Jorgensen, K. A New Look at Lipid-Membrane Structure in Relation to Drug Research. Pharm. Res.1998, 15, 1507–1519.10.1023/A:1011986613392Search in Google Scholar

Movasaghi, Z.; Rehman, S.; Rehman, I. U. Fourier Transform Infrared (FTIR) Spectroscopy of Biological Tissues. Appl. Spectrosc. Rev.2008, 43, 134–179.10.1080/05704920701829043Search in Google Scholar

Nasir, M. N.; Benichou, E.; Guez, J. S.; Jacques, P.; Brevet, P.-F.; Besson, F. Second Harmonic Generation to Monitor the Interactions of the Antimicrobial Mycosubtilin with Membrane-Mimicking Interfacial Monolayers. BioNanoScience2012, 2, 108–112.10.1007/s12668-012-0037-6Search in Google Scholar

Neves, A. R.; Nunes, C.; Amenitsch, H.; Reis, S. Resveratrol Interaction with Lipid Bilayers: A Synchrotron X-ray Scattering Study. Langmuir2016, 32, 12914–12922.10.1021/acs.langmuir.6b03591Search in Google Scholar PubMed

Nguyen, T. T.; Conboy, J. C. High-Throughput Screening of Drug-Lipid Membrane Interactions via Counter-Propagating Second Harmonic Generation Imaging. Anal. Chem.2011, 83, 5979–5988.10.1021/ac2009614Search in Google Scholar PubMed PubMed Central

Nguyen, T. T.; Rembert, K. B.; Conboy, J. C. Label-Free Detection of Drug-Membrane Association Using Ultraviolet-Visible Sum-Frequency Generation. J. Am. Chem. Soc.2009, 131, 1401–1403.10.1021/ja8070607Search in Google Scholar PubMed PubMed Central

Nielsen, M.; Rasmussen, M. R.; Andersen, C. B. F.; Nexo, E.; Moestrup, S. K. Vitamin b12 Transport from Food to the Body’s Cells – a Sophisticated, Multistep Pathway. Nat. Rev. Gastroenterol. Hepatol.2012, 9, 345–354.10.1038/nrgastro.2012.76Search in Google Scholar PubMed

Ntountaniotis, D.; Kellici, T.; Tzakos, A.; Kolokotroni, P.; Tselios, T.; Becker-Baldus, J.; Glaubitz, C.; Lin, S.; Makriyannis, A.; Mavromoustakos, T. The Application of Solid-State NMR Spectroscopy to Study Candesartan Cilexetil (TCV-116) Membrane Interactions. Comparative Study with the AT1R Antagonist Drug Olmesartan. Biochim. Biophys. Acta2014, 1838, 2439–2450.10.1016/j.bbamem.2014.06.003Search in Google Scholar PubMed PubMed Central

Ntountaniotis, D.; Mali, G.; Grdadolnik, S. G.; Maria, H.; Skaltsounis, A.-L.; Potamitis, C.; Siapi, E.; Chatzigeorgiou, P.; Rappolt, M.; Mavromoustakos, T. Thermal, Dynamic and Structural Properties of Drug AT 1 Antagonist Olmesartan in Lipid Bilayers. Biochim. Biophys. Acta2011, 1808, 2995–3006.10.1016/j.bbamem.2011.08.001Search in Google Scholar PubMed

Nunes, C.; Brezesinski, G.; Lopes, D.; Lima, J. L.; Reis, S.; Lucio, M. Lipid-Drug Interaction: Biophysical Effects of Tolmetin on Membrane Mimetic Systems of Different Dimensionality. J. Phys. Chem. B2011a, 115, 12615–12623.10.1021/jp206013zSearch in Google Scholar PubMed

Nunes, C.; Brezesinski, G.; Pereiraleite, C.; Lima, J. L.; Reis, S.; Lucio, M. NSAIDs Interactions with Membranes: A Biophysical Approach. Langmuir2011b, 27, 10847–10858.10.1021/la201600ySearch in Google Scholar PubMed

Oedit, A.; Vulto, P.; Ramautar, R.; Lindenburg, P. W.; Hankemeier, T. Lab-on-a-Chip Hyphenation with Mass Spectrometry: strategies for bioanalytical applications. Curr. Opin. Biotechnol.2015, 31, 79–85.10.1016/j.copbio.2014.08.009Search in Google Scholar PubMed

Oren, I.; Fleishman, S. J.; Kessel, A.; Bental, N. Free Diffusion of Steroid Hormones Across Biomembranes: A Simplex Search with Implicit Solvent Model Calculations. Biophys. J.2004, 87, 768–779.10.1529/biophysj.103.035527Search in Google Scholar PubMed PubMed Central

Osanai, H.; Ikehara, T.; Miyauchi, S.; Shimono, K.; Tamogami, J.; Nara, T.; Kamo, N. A Study of the Interaction of Drugs with Liposomes with Isothermal Titration Calorimetry. J. Biophys. Chem.2013, 04, 11–21.10.4236/jbpc.2013.41002Search in Google Scholar

Pabst, G.; Kucerka, N.; Nieh, M.; Rheinstadter, M. C.; Katsaras, J. Applications of Neutron and X-ray Scattering to the Study of Biologically Relevant Model Membranes. Chem. Phys. Lipids2010, 163, 460–479.10.1016/j.chemphyslip.2010.03.010Search in Google Scholar PubMed

Pabst, G.; Zweytick, D.; Prassl, R.; Lohner, K. Use of X-ray Scattering to Aid the Design and Delivery of Membrane-Active Drugs. Eur. Biophys. J.2012, 41, 915–929.10.1007/s00249-012-0821-9Search in Google Scholar PubMed

Pabst, G.; Heberle, F. A.; Katsaras, J. Encyclopedia of Biophysics; Springer Berlin Heidelberg: Berlin, Heidelberg, 2013.Search in Google Scholar

Paiva, J. G.; Paradiso, P.; Serro, A. P.; Fernandes, A.; Saramago, B. Interaction of Local and General Anaesthetics with Liposomal Membrane Models: A QCM-D and DSC Study. Colloids Surf. B. Biointerfaces2012, 95, 65–74.10.1016/j.colsurfb.2012.02.027Search in Google Scholar PubMed

Park, J. S.; Han, T. H.; Lee, K. Y.; Han, S. S.; Hwang, J. J.; Moon, D. H.; Kim, S. Y.; Cho, Y. W. N-acetyl Histidine-Conjugated Glycol Chitosan Self-Assembled Nanoparticles for Intracytoplasmic Delivery of Drugs: Endocytosis, Exocytosis and Drug Release. J. Control. Release2006, 115, 37–45.10.1016/j.jconrel.2006.07.011Search in Google Scholar PubMed

Pashynska, V.; Stepanian, S. G.; Gomory, A.; Vekey, K.; Adamowicz, L. Competing Intermolecular Interactions of Artemisinin-Type Agents and Aspirin with Membrane Phospholipids: Combined Model Mass Spectrometry and Quantum-Chemical Study. Chem. Phys.2015, 455, 81–87.10.1016/j.chemphys.2015.04.014Search in Google Scholar

Pauletti, G. M.; Okumu, F. W.; Borchardt, R. T. Effect of Size and Charge on the Passive Diffusion of Peptides Across Caco-2 Cell Monolayers via the Paracellular Pathway. Pharm. Res.1997, 14, 164–168.10.1023/A:1012040425146Search in Google Scholar

Peetla, C.; Bhave, R.; Vijayaraghavalu, S.; Stine, A.; Kooijman, E. E.; Labhasetwar, V. Drug resistance in breast cancer cells: biophysical characterization of and doxorubicin interactions with membrane lipids. Mol. Pharm.2010, 7, 2334–2348.10.1021/mp100308nSearch in Google Scholar PubMed PubMed Central

Peetla, C.; Vijayaraghavalu, S.; Labhasetwar, V. Biophysics of Cell Membrane Lipids in Cancer Drug Resistance: Implications for Drug Transport and Drug Delivery with Nanoparticles. Adv. Drug Del. Rev.2013, 65, 1686–1698.10.1016/j.addr.2013.09.004Search in Google Scholar PubMed PubMed Central

Pentak, D. Alternative Methods of Determining Phase Transition Temperatures of Phospholipids that Constitute Liposomes on the Example of DPPC and DMPC. Thermochim. Acta2014a, 584, 36–44.10.1016/j.tca.2014.03.020Search in Google Scholar

Pentak, D. Physicochemical Properties of Liposomes as Potential Anticancer Drugs Carriers. Interaction of Etoposide and Cytarabine with the Membrane: Spectroscopic Studies. Spectrochim. Acta, Pt. A: Mol. Biomol. Spectrosc.2014b, 122, 451–460.10.1016/j.saa.2013.11.061Search in Google Scholar PubMed

Pereira-Leite, C.; Nunes, C.; Lima, J. L. F. C.; Reis, S.; Lúcio, M. Interaction of Celecoxib with Membranes: The Role of Membrane Biophysics on its Therapeutic and Toxic Effects. J. Phys. Chem. B2012, 116, 13608–13617.10.1021/jp304037vSearch in Google Scholar PubMed

Pereiraleite, C.; Nunes, C.; Reis, S. Interaction of Nonsteroidal Anti-Inflammatory Drugs with Membranes: In Vitro Assessment and Relevance for Their Biological Actions. Prog. Lipid Res.2013, 52, 571–584.10.1016/j.plipres.2013.08.003Search in Google Scholar PubMed

Pernicova, I.; Korbonits, M. Metformin – Mode of Action and Clinical Implications for Diabetes and Cancer. Nat. Rev. Endocrinol.2014, 10, 143–156.10.1038/nrendo.2013.256Search in Google Scholar PubMed

Piantavigna, S.; Mccubbin, G. A.; Boehnke, S.; Graham, B.; Spiccia, L.; Martin, L. L. A Mechanistic Investigation of Cell-Penetrating tat Peptides with Supported Lipid Membranes. Biochim. Biophys. Acta2011, 1808, 1811–1817.10.1016/j.bbamem.2011.03.002Search in Google Scholar PubMed

Pignatello, R. Drug–Biomembrane Interaction Studies. Sawston, Cambridge: Woodhead Publishing, 2013.10.1533/9781908818348Search in Google Scholar

Pili, B.; Bourgaux, C.; Meneau, F.; Couvreur, P.; Ollivon, M. Interaction of an Anticancer Drug, Gemcitabine, with Phospholipid Bilayers. J. Therm. Anal. Calorim.2009, 98, 19–28.10.1007/s10973-009-0229-7Search in Google Scholar

Pinheiro, M.; Nunes, C.; Caio, J. M.; Moiteiro, C.; Lucio, M.; Brezesinski, G.; Reis, S. The Influence of Rifabutin on Human and Bacterial Membrane Models: Implications for its Mechanism of Action. J. Phys. Chem. B2013, 117, 6187–6193.10.1021/jp403073vSearch in Google Scholar PubMed

Pinheiro, M.; Pisco, S.; Silva, A. S.; Nunes, C.; Reis, S. Evaluation of the Effect of Rifampicin on the Biophysical Properties of the Membranes: Significance for Therapeutic and Side Effects. Int. J. Pharm.2014a, 466, 190–197.10.1016/j.ijpharm.2014.03.005Search in Google Scholar PubMed

Pinheiro, M.; Silva, A. S.; Pisco, S.; Reis, S. Interactions of Isoniazid with Membrane Models: Implications for Drug Mechanism of Action. Chem. Phys. Lipids2014b, 183, 184–190.10.1016/j.chemphyslip.2014.07.002Search in Google Scholar PubMed

Rajendram, R.; Hunter, R.; Preedy, V. Encyclopedia of Human Nutrition, 3rd ed.; Academic Press: Waltham, 2013.Search in Google Scholar

Rakowska, P. D.; Jiang, H.; Ray, S.; Pyne, A.; Lamarre, B.; Carr, M.; Judge, P. J.; Ravi, J.; Gerling, U. I. M.; Koksch, B. Nanoscale Imaging Reveals Laterally Expanding Antimicrobial Pores in Lipid Bilayers. Proc. Natl. Acad. Sci. U. S. A.2013, 110, 8918–8923.10.1073/pnas.1222824110Search in Google Scholar PubMed PubMed Central

Raskandersen, M.; Almen, M. S.; Schioth, H. B. Trends in the Exploitation of Novel Drug Targets. Nat. Rev. Drug Discov.2011, 10, 579–590.10.1038/nrd3478Search in Google Scholar PubMed

Raudino, A., Sarpietro, M. G., and Pannuzzo, M., Drug-Biomembrane Interaction Studies. Sawston, Cambridge: Woodhead Publishing, 2013.Search in Google Scholar

Redondomorata, L.; Sanford, R. L.; Andersen, O. S.; Scheuring, S. Effect of Statins on the Nanomechanical Properties of Supported Lipid Bilayers. Biophys. J.2016, 111, 363–372.10.1016/j.bpj.2016.06.016Search in Google Scholar PubMed PubMed Central

Reich, C. G.; Horton, M. R.; Krause, B.; Gast, A. P.; Radler, J. O.; Nickel, B. Asymmetric Structural Features in Single Supported Lipid Bilayers Containing Cholesterol and gm1 Resolved with Synchrotron X-ray Reflectivity. Biophys. J.2008, 95, 657–668.10.1529/biophysj.107.113068Search in Google Scholar PubMed PubMed Central

Renaud, J.-P.; Chung, C.-w.; Danielson, U. H.; Egner, U.; Hennig, M.; Hubbard, R. E.; Nar, H. Biophysics in Drug Discovery: Impact, Challenges and Opportunities. Nat. Rev. Drug Discov.2016, 15, 679–698.10.1038/nrd.2016.123Search in Google Scholar PubMed

Richter, R. P.; Berat, R.; Brisson, A. Formation of Solid-Supported Lipid Bilayers: An Integrated View. Langmuir2006, 22, 3497–3505.10.1021/la052687cSearch in Google Scholar PubMed

Rosenberg, P. H.; Alila, A. Hydrophobic Membrane Interaction of Etidocaine, Bupivacaine and 2-Chloroprocaine. A Spin and Fluorescent Probe Study. Naunyn Schmiedebergs Arch. Pharmacol.1982, 319, 95–100.10.1007/BF00503919Search in Google Scholar PubMed

Rutkowska, E.; Pajak, K.; Joźwiak, K. Lipophilicity – Methods of Determination and its Role in Medicinal Chemistry. Acta Pol. Pharm.2013, 70, 3.Search in Google Scholar

Rydberg, H. A.; Kunze, A.; Carlsson, N.; Altgarde, N.; Svedhem, S.; Norden, B. Peptide-Membrane Interactions of Arginine-Tryptophan Peptides Probed Using Quartz Crystal Microbalance with Dissipation Monitoring. Eur. Biophys. J.2014, 43, 241–253.10.1007/s00249-014-0958-9Search in Google Scholar PubMed PubMed Central

Sakuragi, M.; Zushi, T.; Seguchi, R.; Arai, T.; Taguchi, K.; Kusakabe, K. Locational Analysis of Glutathione in Liposomes by Using Small-Angle X-ray scattering. Chem. Lett.2016, 46, 185–187.10.1246/cl.160932Search in Google Scholar

Scheidt, H. A.; Huster, D. The Interaction of Small Molecules with Phospholipid Membranes Studied by 1H NOESY NMR Under Magic-Angle Spinning. Acta Pharmacol. Sin.2008, 29, 35–49.10.1111/j.1745-7254.2008.00726.xSearch in Google Scholar PubMed

Schultz, Z. D.; Levin, I. W. Vibrational Spectroscopy of Biomembranes. Rev. Anal. Chem.2011, 4, 343–366.10.1146/annurev-anchem-061010-114048Search in Google Scholar

Seddon, A. M.; Casey, D.; Gee, A. D.; Templer, R. H.; Ces, O. Drug Interactions with Lipid Membranes. Chem. Soc. Rev.2009, 38, 2509–2519.10.1039/b813853mSearch in Google Scholar

Seeger, H. M.; Gudmundsson, M. L.; Heimburg, T. How Anesthetics, Neurotransmitters, and Antibiotics Influence the Relaxation Processes in Lipid Membranes. J. Phys. Chem. B2007, 111, 13858–13866.10.1021/jp075346bSearch in Google Scholar

Selvaraj, S.; Mohan, A.; Narayanan, S.; Sethuraman, S.; Krishnan, U. M. Dose-Dependent Interaction of Trans-Resveratrol with Biomembranes: Effects on Antioxidant Property. J. Med. Chem.2013, 56, 970–981.10.1021/jm3014579Search in Google Scholar

Seydel, J. K.; Wiese, M. Drug-membrane Interactions: Analysis, Drug Distribution, Modeling; John Wiley & Sons: Hoboken, New Jersey, 2009.Search in Google Scholar

Sharma, B.; Frontiera, R. R.; Henry, A.; Ringe, E.; Van Duyne, R. P. SERS: Materials, Applications, and the Future. Mater. Today2012, 15, 16–25.10.1016/S1369-7021(12)70017-2Search in Google Scholar

Shin, B. S.; Yoon, C. H.; Balthasar, J. P.; Choi, B. Y.; Hong, S. H.; Kim, H. J.; Lee, J. B.; Hwang, S. W.; Yoo, S. D. Prediction of Drug Bioavailability in Humans Using Immobilized Artificial Membrane Phosphatidylcholine Column Chromatography and In Vitro Hepatic Metabolic Clearance. Biomed. Chromatogr.2009, 23, 764–769.10.1002/bmc.1182Search in Google Scholar PubMed

Shynkar, V.; Klymchenko, A. S.; Kunzelmann, C.; Duportail, G.; Muller, C. D.; Demchenko, A. P.; Freyssinet, J. M.; Mely, Y. Fluorescent Biomembrane Probe for Ratiometric Detection of Apoptosis. J. Am. Chem. Soc.2007, 129, 2187–2193.10.1021/ja068008hSearch in Google Scholar PubMed

Sinha, R.; Joshi, A.; Joshi, U. J.; Srivastava, S.; Govil, G. Localization and Interaction of Hydroxyflavones with Lipid Bilayer Model Membranes: A Study Using DSC and Multinuclear NMR. Eur. J. Med. Chem.2014, 80, 285–294.10.1016/j.ejmech.2014.04.054Search in Google Scholar PubMed

Soblosky, L.; Ramamoorthy, A.; Chen, Z. Membrane Interaction of Antimicrobial Peptides Using E. coli Lipid Extract as Model Bacterial Cell Membranes and SFG Spectroscopy. Chem. Phys. Lipids2015, 187, 20–33.10.1016/j.chemphyslip.2015.02.003Search in Google Scholar PubMed PubMed Central

Sun, T.; Zhang, Y. S.; Pang, B.; Hyun, D. C.; Yang, M.; Xia, Y. Engineered Nanoparticles for Drug Delivery in Cancer Therapy. Angew. Chem.2014, 53, 12320–12364.10.1002/anie.201403036Search in Google Scholar

Sun, Y.; Huang, P.; Lin, C.; Lee, K.; Lee, M. Studying Antibiotic-Membrane Interactions via X-ray Diffraction and Fluorescence Microscopy. Biophys. J.2015, 110, 515–521.10.1016/j.fob.2015.06.006Search in Google Scholar

Szakács, Z.; Sánta, Z. Anthropic Awareness; Elsevier: Boston, 2015.Search in Google Scholar

Taillardatbertschinger, A.; Carrupt, P.; Barbato, F.; Testa, B. Immobilized Artificial Membrane HPLC in Drug Research. J. Med. Chem.2003, 46, 655–665.10.1021/jm020265jSearch in Google Scholar PubMed

Tan, J.; Ye, S.; Luo, Y. Observing Peptide-Induced Lipid Accumulation in a Single-Component Zwitterionic Lipid Bilayer. J. Phys. Chem. C.2015, 119, 28523–28529.10.1021/acs.jpcc.5b10632Search in Google Scholar

Tanaka, M.; Sackmann, E. Polymer-Supported Membranes as Models of the Cell Surface. Nature2005, 437, 656–663.10.1038/nature04164Search in Google Scholar PubMed

Tfaili, S.; Josse, G.; Angiboust, J.; Manfait, M.; Piot, O. Monitoring Caffeine and Resveratrol Cutaneous Permeation by Confocal Raman Microspectroscopy. J. Biophotonics2014, 7, 676–681.10.1002/jbio.201300011Search in Google Scholar PubMed

Tfayli, A.; Piot, O.; Pitre, F.; Manfait, M. Follow-up of Drug Permeation Through Excised Human Skin with Confocal Raman Microspectroscopy. Eur. Biophys. J.2007, 36, 1049–1058.10.1007/s00249-007-0191-xSearch in Google Scholar PubMed

Tsopelas, F.; Vallianatou, T.; Tsantilikakoulidou, A. Advances in Immobilized Artificial Membrane (IAM) Chromatography for Novel Drug Discovery. Expert Opin. Drug Discovery2016a, 11, 473.10.1517/17460441.2016.1160886Search in Google Scholar PubMed

Tsopelas, F.; Vallianatou, T.; Tsantilikakoulidou, A. The Potential of Immobilized Artificial Membrane Chromatography to Predict Human oral Absorption. Eur. J. Pharm. Sci.2016b, 81, 82–93.10.1016/j.ejps.2015.09.020Search in Google Scholar PubMed

Tsuchiya, H.; Mizogami, M. Interaction of Local Anesthetics with Biomembranes Consisting of Phospholipids and Cholesterol: Mechanistic and Clinical Implications for Anesthetic and Cardiotoxic Effects. Anesthesiol. Res. Pract.2013, 2013, 297141–297141.10.1155/2013/297141Search in Google Scholar

Tyler, A. I. I.; Law, R. V.; Seddon, J. M. Methods in Membrane Lipids; Springer New York: New York, NY, 2015.Search in Google Scholar

Uekusa, Y.; Kamihiraishijima, M.; Sugimoto, O.; Ishii, T.; Kumazawa, S.; Nakamura, K.; Tanji, K.; Naito, A.; Nakayama, T. Interaction of Epicatechin Gallate with Phospholipid Membranes as Revealed by Solid-State NMR Spectroscopy. Biochim. Biophys. Acta2011, 1808, 1654–1660.10.1016/j.bbamem.2011.02.014Search in Google Scholar PubMed

Vacklin, H. P.; Tiberg, F.; Thomas, R. K. Formation of Supported Phospholipid Bilayers via Co-Adsorption with Beta-D-dodecyl Maltoside. Biochim. Biophys. Acta2005, 1668, 17–24.10.1016/j.bbamem.2004.11.001Search in Google Scholar PubMed

Valko, K. Application of High-Performance Liquid Chromatography Based Measurements of Lipophilicity to Model Biological Distribution. J. Chromatogr. A2004, 1037, 299–310.10.1016/j.chroma.2003.10.084Search in Google Scholar PubMed

Van Balen, G. P.; Martinet, C. A. M.; Caron, G.; Bouchard, G.; Reist, M.; Carrupt, P.; Fruttero, R.; Gasco, A.; Testa, B. Liposome/Water Lipophilicity: Methods, Information Content, and Pharmaceutical Applications. Med. Res. Rev.2004, 24, 299–324.10.1002/med.10063Search in Google Scholar PubMed

Van Meer, G.; Voelker, D. R.; Feigenson, G. W. Membrane Lipids: Where They are and How They Behave. Nat. Rev. Mol. Cell Biol.2008, 9, 112–124.10.1038/nrm2330Search in Google Scholar PubMed PubMed Central

Van Swaay, D.; Demello, A. J. Microfluidic Methods for Forming Liposomes. Lab Chip2013, 13, 752–767.10.1039/c2lc41121kSearch in Google Scholar PubMed

Velazquezcampoy, A.; Ohtaka, H.; Nezami, A.; Muzammil, S.; and Freire, E. Isothermal Titration Calorimetry. Curr. Protoc. Stem. Cell Biol.2004, 62, 17.8.1–17.8.24.10.1002/0471143030.cb1708s23Search in Google Scholar

Volkov, V.; and Perry, C. C. Modeling of Infrared-Visible Sum Frequency Generation Microscopy Images of a Giant Liposome. Microsc. Microanal2016, 22, 1–18.10.1017/S1431927616011752Search in Google Scholar PubMed

Vorng, J. L.; Kotowska, A. M.; Passarelli, M. K.; West, A.; Marshall, P. S.; Havelund, R.; Seah, M. P.; Dollery, C. T.; Rakowska, P. D.; Gilmore, I. S. Semiempirical Rules to Determine Drug Sensitivity and Ionization Efficiency in Secondary Ion Mass Spectrometry Using a Model Tissue Sample. Anal. Chem.2016, 88, 11028–11036.10.1021/acs.analchem.6b02894Search in Google Scholar PubMed

Wan, H.; Ahman, M.; Holmen, A. G. Relationship between Brain Tissue Partitioning and Microemulsion Retention Factors of CNS drugs. J. Med. Chem.2009, 52, 1693–1700.10.1021/jm801441sSearch in Google Scholar PubMed

Wang, G.; Rodahl, M.; Edvardsson, M.; Svedhem, S.; Ohlsson, G.; Hook, F.; Kasemo, B. A Combined Reflectometry and Quartz Crystal Microbalance with Dissipation Setup for Surface Interaction Studies. Rev. Sci. Instrum.2008, 79, 075107.10.1063/1.2957619Search in Google Scholar PubMed

Wang, X.; Yi, L.; Mukhitov, N.; Schrell, A. M.; Dhumpa, R.; Roper, M. G. Microfluidics-to-Mass Spectrometry: A Review of Coupling Methods and Applications. J. Chromatogr. A2015, 1382, 98–116.10.1016/j.chroma.2014.10.039Search in Google Scholar PubMed PubMed Central

Warschawski, D. E.; Arnold, A. A.; Beaugrand, M.; Gravel, A.; Chartrand, É.; Marcotte, I. Choosing Membrane Mimetics for NMR Structural Studies of Transmembrane Proteins. Biochim. Biophys. Acta2011, 1808, 1957–1974.10.1016/j.bbamem.2011.03.016Search in Google Scholar PubMed

Watanabe, R.; Soga, N.; Yamanaka, T.; Noji, H. High-Throughput Formation of Lipid Bilayer Membrane Arrays with An Asymmetric Lipid Composition. Sci. Rep.2014, 4, 7076–7076.10.1038/srep07076Search in Google Scholar PubMed PubMed Central

Weaver, K. D.; Van Vorst, M.; Vijayaraghavan, R.; Macfarlane, D. R.; Elliott, G. D. Interaction of Choline Salts with Artificial Biological Membranes: DSC Studies Elucidating Cellular Interactions. Biochim. Biophys. Acta2013, 1828, 1856–1862.10.1016/j.bbamem.2013.03.017Search in Google Scholar PubMed PubMed Central

Weiss, S. Fluorescence Spectroscopy of Single Biomolecules. Science1999, 283, 1676–1683.10.1126/science.283.5408.1676Search in Google Scholar PubMed

Więcek, A.; Dynarowicz-Łątka, P.; Vila-Romeu, N.; Nieto-Suarez, M.; Flasiński, M. Interactions between An Anticancer Drug-Edelfosine-and DPPC in Langmuir Monolayers. Colloids Surf. Physicochem. Eng. Aspects2008, 321, 201–205.10.1016/j.colsurfa.2007.11.026Search in Google Scholar

Wiedmer, S. K.; Lokajova, J. Capillary Electromigration Techniques for Studying Interactions between Analytes and Lipid Dispersions. J. Sep. Sci.2013, 36, 37–51.10.1002/jssc.201200829Search in Google Scholar PubMed

Woolfson, M. M. An Introduction to X-ray Crystallography. Phys. Today1997, 50, 70–74.10.1063/1.882009Search in Google Scholar

Wu, F.-G.; Yang, P.; Zhang, C.; Han, X.; Song, M.; Chen, Z. Investigation of Drug-Model Cell Membrane Interactions Using Sum Frequency Generation Vibrational Spectroscopy: A Case Study of Chlorpromazine. J. Phys. Chem. C.2014a, 118, 17538–17548.10.1021/jp503038mSearch in Google Scholar

Wu, F. G.; Yang, P.; Zhang, C.; Li, B.; Han, X.; Song, M.; Chen, Z. H. Molecular Interactions between Amantadine and Model Cell Membranes. Langmuir2014b, 30, 8491–8499.10.1021/la501718nSearch in Google Scholar PubMed

Wu, L.; Li, X.; Huang, C.; Jia, N. Dual-Modal Colorimetric/Fluorescence Molecular Probe for Ratiometric Sensing of pH and its Application. Anal. Chem.2016, 88, 8332–8338.10.1021/acs.analchem.6b02398Search in Google Scholar PubMed

Wyttenbach, T.; Bowers, M. T. Intermolecular Interactions in Biomolecular Systems Examined by Mass Spectrometry. Annu. Rev. Phys. Chem.2007, 58, 511–533.10.1146/annurev.physchem.58.032806.104515Search in Google Scholar PubMed

Xiao, G.; Gan, L. Receptor-Mediated Endocytosis and Brain Delivery of Therapeutic Biologics. Int. J. Cell Biol.2013, 2013, 703545–703545.10.1155/2013/703545Search in Google Scholar

Xu, Q.; Zhao, T.; Sun, Z. Monitoring Drug–Lipid Membrane Interactions via a Molecular Rotor Probe. Analyst2016, 141, 4676–4684.10.1039/C6AN00721JSearch in Google Scholar PubMed

Yamada, A.; Shimizu, N.; Hikima, T.; Takata, M.; Kobayashi, T.; Takahashi, H. Effect of Cholesterol on the Interaction of Cytochrome P450 Substrate Drug Chlorzoxazone with the Phosphatidylcholine Bilayer. Biochemistry2016, 55, 3888–3898.10.1021/acs.biochem.6b00286Search in Google Scholar PubMed

Yang, C. Y.; Cai, S. J.; Liu, H.; Pidgeon, C. Immobilized Artificial Membranes – Screens for Drug Membrane Interactions. Adv. Drug Del. Rev.1997, 23, 229–256.10.1016/S0169-409X(96)00438-3Search in Google Scholar

Yang, L.; Yu, X.; Zhu, Z.; Iedema, M. J.; Cowin, J. P. Probing Liquid Surfaces Under Vacuum using SEM and TOF-SIMS. Lab Chip2011, 11, 2481–2484.10.1039/c0lc00676aSearch in Google Scholar PubMed

Yang, P.; Wu, F.-G.; Chen, Z. Dependence of Alamethicin Membrane Orientation on the Solution Concentration. J. Phys. Chem. C.2013, 117, 3358–3365.10.1021/jp3099522Search in Google Scholar PubMed PubMed Central

Yonar, D.; Horasan, N.; Paktas, D. D.; Abramovic, Z.; Strancar, J.; Sunnetcioglu, M. M.; Sentjurc, M. Interaction of Antidepressant Drug, Clomipramine, with Model and Biological Stratum Corneum Membrane as Studied by Electron Paramagnetic Resonance. J. Pharm. Sci.2013, 102, 3762–3772.10.1002/jps.23687Search in Google Scholar PubMed

Zervou, M.; Cournia, Z.; Potamitis, C.; Patargias, G.; Durdagi, S.; Grdadolnik, S. G.; Mavromoustakos, T. Insights into the Molecular Basis of Action of the AT 1 Antagonist Losartan Using a Combined NMR Spectroscopy and Computational Approach. Biochim. Biophys. Acta2014, 1838, 1031–1046.10.1016/j.bbamem.2013.12.012Search in Google Scholar PubMed

Zhang, B.; Wang, Q. Nanotechnology Research Methods for Foods and Bioproducts; Wiley-Blackwell: Hoboken, New Jersey, 2012.Search in Google Scholar

Zhang, C.; Li, J.; Xu, L.; Shi, Z. Fast Immobilized Liposome Chromatography Based on Penetrable Silica Microspheres for Screening and Analysis of Permeable Compounds. J. Chromatogr. A2012, 1233, 78–84.10.1016/j.chroma.2012.02.013Search in Google Scholar PubMed

Zhang, Y.; Guo, S.; Cheng, S.; Ji, X.; He, Z. Label-Free Silicon Nanodots Featured Ratiometric Fluorescent Aptasensor for Lysosomal Imaging and pH Measurement. Biosens. Bioelectron.2017, 94, 478–484.10.1016/j.bios.2017.03.041Search in Google Scholar PubMed

Zhao, L.; Feng, S.-S. Effects of Cholesterol Component on Molecular Interactions between Paclitaxel and Phospholipid within the Lipid Monolayer at the Air–Water Interface. J. Colloid Interface Sci.2006, 300, 314–326.10.1016/j.jcis.2006.03.035Search in Google Scholar PubMed

Zhao, L.; Feng, S.; Kocherginsky, N. M.; Kostetski, I. DSC and EPR Investigations on Effects of Cholesterol Component on Molecular Interactions between Paclitaxel and Phospholipid within Lipid Bilayer Membrane. Int. J. Pharm.2007, 338, 258–266.10.1016/j.ijpharm.2007.01.045Search in Google Scholar PubMed

Zhou, X.; Moranmirabal, J. M.; Craighead, H. G.; Mceuen, P. L. Supported Lipid Bilayer/Carbon Nanotube Hybrids. Nat. Nanotechnol.2007, 2, 185–190.10.1038/nnano.2007.34Search in Google Scholar PubMed

Zhou, Y.; Plowman, S. J.; Lichtenberger, L. M.; Hancock, J. F. The Anti-Inflammatory Drug Indomethacin Alters Nanoclustering in Synthetic and Cell Plasma Membranes. J. Biol. Chem.2010, 285, 35188–35195.10.1074/jbc.M110.141200Search in Google Scholar PubMed PubMed Central

Received: 2017-5-31
Accepted: 2017-10-15
Published Online: 2017-11-30

©2018 Walter de Gruyter GmbH, Berlin/Boston

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Volume 37 Issue 1
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