Quantitative analysis for lipophilic drug transport through a model lipid membrane with membrane retention

Accepted Manuscript Quantitative analysis for lipophilic drug transport through a model lipid membrane with membrane retention

Yohan Lee, Siyoung Q. Choi PII: DOI: Reference:

S0928-0987(19)30164-2 https://doi.org/10.1016/j.ejps.2019.04.020 PHASCI 4915

To appear in:

European Journal of Pharmaceutical Sciences

Received date: Revised date: Accepted date:

30 January 2019 17 April 2019 17 April 2019

Please cite this article as: Y. Lee and S.Q. Choi, Quantitative analysis for lipophilic drug transport through a model lipid membrane with membrane retention, European Journal of Pharmaceutical Sciences, https://doi.org/10.1016/j.ejps.2019.04.020

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Quantitative analysis for lipophilic drug transport through a model lipid membrane with membrane retention Yohan Lee and Siyoung Q. Choi*

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Department of Chemical and Biomolecular engineering, KAIST Institute for the Nanocentury, Korea Advanced Institute of Science and Technology (KAIST), 291 Daehak-ro, Yuseong-gu, Daejeon, 34141 Republic of Korea

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*Corresponding author, E-mail: [email protected]

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1. Introduction For convenience, many drugs are administered orally. In the human body, the absorption of these drug molecules mostly takes place in the intestine (Pang, 2003; Zhang and Benet, 2001).

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Drug absorption in the intestine is a quite complicated process and is affected by various factors,

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such as physicochemical and physiological properties, the formulation of the drugs and so on

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(Martinez and Amidon, 2002). In 1995, Amidon et al. (Amidon et al., 1995) proposed that the key parameters controlling the rate and extent of drug absorption were aqueous solubility and the

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intestinal permeability of the drug. They introduced the Biopharmaceutics Classification System

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(BCS), which categorizes drugs into four classes based on their aqueous solubility and intestinal permeability: Class 1 (high solubility, high permeability), Class 2 (low solubility, high

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permeability), Class 3 (high solubility, low permeability) and Class 4 (low solubility, low

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permeability).

In modern drug discovery, with the emergence of combinatorial chemistry and high

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throughput screening, the proportion of poorly water-soluble or even water-insoluble drugs has

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increased significantly (Buckley et al., 2012; Dahan et al., 2016, 2009; Ghadi and Dand, 2017; Miller et al., 2012; Porter et al., 2007). According to several reports, more than 40% of new drug

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candidates are lipophilic and poorly water-soluble, making the experimental determination of intestinal permeability for these compounds more demanding than that for highly soluble ones. (Buckley et al., 2012; Ghadi and Dand, 2017). Therefore, development of an in vitro permeability assay that can effectively estimate the intestinal permeability of lipophilic compounds, possibly those classified as either BCS class 2 or class 4, is becoming increasingly necessary in the pharmaceutical industry. One of the important transport routes in the intestine, passive transcellular transport, is 2

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driven by a concentration gradient of molecules across the cell membrane (Sugano et al., 2010), and various in vitro artificial membrane-based permeability assays have been developed that only consider passive transcellular transport (Di Cagno et al., 2015; Eyer et al., 2014; Flaten et al., 2006; Gantzsch et al., 2014; Kansy et al., 1998; Li et al., 2011; Runas and Malmstadt, 2015).

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For instance, in the parallel artificial membrane permeability assay (PAMPA) introduced by

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Kansy et al. (Kansy et al., 1998), an artificial membrane is used, made by dipping a porous filter into a lipid solution. To test compounds with poor aqueous solubility in PAMPA, organic

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solvents such as dimethyl sulfoxide (DMSO) or surfactants are frequently added to the aqueous phase as solubilizing agents (Avdeef et al., 2001; Balimane et al., 2006; Bendels et al., 2006;

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Chen et al., 2008; Kansy et al., 1998; Liu et al., 2003; Sugano et al., 2002, 2001; Zhu et al.,

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2002). Although PAMPA is an effective method for estimating permeability, the membrane used

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in PAMPA is known to contain oil residues within it, and the microscopic structure of this membrane is unknown (Avdeef, 2012; Sugano, 2007). This property can potentially lead to drug

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molecules accumulating in the membrane, causing an underestimation of drug permeability in certain cases. This can be caused not only by interaction between the drug and phospholipid

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molecules, but also the complicated filter membrane structure and residual oil solvent. In

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addition to PAMPA, two other permeability assays targeting passive transcellular transport, known as phospholipid vesicle-based permeation assays (PVPA) (Flaten et al., 2006) and Permeapad (Di Cagno et al., 2015), have been also introduced, but these methods might have issues similar to PAMPA. To solve these problems, inspired by the droplet interface bilayer techniques (Bayley et al., 2008; Jeong et al., 2016; Taylor and Sarles, 2015), we previously reported a new in vitro permeability assay for hydrophilic small molecules (BCS class 1 and class 3) which uses a 3

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freestanding planar lipid bilayer with a definite structure that has no residual solvent in it (Lee et al., 2018). The lipid bilayer was created inside a commonly used UV cuvette by adhesion between the lipid monolayer at the planar oil-water interface and another monolayer at the oilwater droplet interface. The water droplet contained the drug molecules inside and these drug

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molecules were transported from the droplet (donor) to the bottom water phase (acceptor)

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because of the concentration gradient across the lipid bilayer. The number of transported molecules was tracked by measuring the UV absorbance of the drug molecules in real time. All

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of the measurements were fully automated, so that laborious and time-consuming sampling processes were avoided.

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In this work, we develop an in vitro permeability assay for lipophilic compounds by

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improving several limitations in our previous platform. We use six lipophilic compounds

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(griseofulvin, carbamazepine, warfarin, piroxicam, dapsone and acetazolamide), whose octanolwater partition coefficient is larger than 1, and whose water solubility is low as well, which are

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commonly classified as BCS class 2 and class 4 (Kasim et al., 2004; Lindenberg et al., 2004; Wu and Benet, 2005). Organic solvent, such as DMSO and methanol, was added to enhance the

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solubility of these poorly water-soluble drug compounds while maintaining membrane stability,

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so that enough drug molecules could be provided. At the same time, the drug concentration was increased above the detection limit by reducing the acceptor volume with the control of the size of the incident UV light using a custom light mask. The use of lipophilic drug compounds, along with the geometry of our system, permits several events to occur simultaneously during drug transport through the lipid bilayer, as depicted in Fig. 1b. To determine the contribution of each event involved in drug transport in our system, a comprehensive transport model was established. Following a thorough investigation of 4

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each phenomenon, we were able to confirm that the partitioning process to the oil phase has a negligible contribution on the overall transport. We then measured the permeability of each compound from its concentration change over time. Also, the membrane retention fraction, which indicates the number of molecules trapped in the membrane, was estimated for each

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compound. Lastly, the relationship between membrane retention and lipophilicity for each drug

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compound was discussed.

Fig. 1 Overall scheme of this work. (a) Lipid bilayer formation at the oil-aqueous phase interface. A lipid monolayer is made at the oil-aqueous phase interface by self-assembly of lipid molecules. 5

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An aqueous droplet containing drug molecules inside is then placed into the oil phase using a micropipette, also leading to the lipid monolayer formation at the oil-aqueous droplet interface. When two monolayers are brought into contact, the adhesion between them occurs along with oil drainage, finally resulting in the lipid bilayer formation. (b) Schematic showing the transport

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analysis of lipophilic drug molecules through the lipid bilayer. The drug molecules move from

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the upper droplet (donor) to the acceptor through the lipid bilayer, driven by the concentration gradient between the donor and the acceptor. The number of molecules in the acceptor is tracked

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by measuring the UV absorbance of drug molecules at a fixed wavelength in real time. The permeation flux is indicated as J1. At the same time, some molecules undergo a partitioning

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process from the aqueous phase to the oil phase due to their lipophilic nature, and this

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partitioning flux is indicated as J2. Lastly, some molecules are trapped in the lipid membrane

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during transport, as indicated by the curved arrow in the bilayer region. (c) The Biopharmaceutical Classification System (BCS), which divides drug compounds into 4 classes

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based on their aqueous solubility and intestinal permeability. Drug compounds with poor aqueous solubility are used in this work, and these are classified as either class 2 or class 4

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(shaded parts of the diagram).

2. Experimental section 2.1. Materials 1,2-Dioleoyl-sn-glycero-3-phosphocholine (DOPC) was obtained from Avanti Polar Lipids. Squalane (99%), silicone oil (Silicone Oil AR 20), dimethyl sulfoxide (DMSO, ≥ 99.9%), methanol (≥ 99.9%) and all drug compounds including griseofulvin, carbamazepine, warfarin, 6

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piroxicam (≥ 98%), dapsone, and acetazolamide were obtained from Sigma-Aldrich. All water used in the experiments was deionized (Milli-Q, Merck-Millipore, 18.2 MΩ·cm).

2.2. Preparation of lipid solution

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Lipid solution was prepared as described in our previous work (Lee et al., 2018). DOPC

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solution dissolved in chloroform was placed in a glass vial and dried under a stream of nitrogen gas, leading to a lipid film. A 1:1 (v/v) mixture of squalane and silicone oil was added to this

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lipid film to redissolve it at a concentration of 4 mg mL-1, followed by bath-sonication for 30 min

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at room temperature.

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2.3. Lipid bilayer formation at the oil-aqueous phase interface A freestanding lipid bilayer was created at the oil-aqueous phase interface, prior to the

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drug transport measurement (Fig. 1a). First, 300 μL of the aqueous phase consisting of a 1:4 (v/v)

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mixture of the organic solvent (DMSO or methanol) and deionized water was loaded inside a UV

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cuvette (Stirring cell, Semi-Micro Rectangular, 1.8 mL nominal volume, Starna Cells) that has a rectangular form with the interior dimension of 4 mm (width)ⅹ10 mm (length)ⅹ45 mm (height),

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and then 150-200 μL of the previously prepared DOPC solution was added to the aqueous phase. Several minutes later, a lipid monolayer (termed as a planar monolayer) spontaneously selfassembled at the oil-aqueous phase interface. Then, 1 μL of an aqueous droplet containing drug molecules dissolved at various concentrations (400 μM for griseofulvin, 1 mM for carbamazepine, 800 μM for warfarin, 800 μM for piroxicam, 600 μM for dapsone, and 2 mM for acetazolamide.) was gently delivered into the oil phase using a micropipette, resulting in a lipid monolayer (termed as a droplet monolayer) at the oil- aqueous droplet interface. The volume 7

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fraction of the added organic solvent in the aqueous phase (20%), was the same for both the bottom aqueous phase and the aqueous droplet to prevent the flux of organic solvent across these two compartments. By bringing both the planar and droplet monolayers into contact, the oil between the monolayers drained out, and adhesion between these monolayers occurred, leading

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to a lipid bilayer.

2.4. Fluorescence microscopy visualization for the bilayer formation

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The formation of lipid bilayer was visualized by a fluorescence microscope. Texas Red DHPE (Texas Red 1,2-dihexadecanoyl-sn-glycero-3-phosphoethanolamine, Thermo Fisher

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Scientific) was applied as a fluorescent probe (< 1 mol%). Visualization was achieved using an

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inverted optical microscope (IX73, Olympus) equipped with an EMCCD camera (iXon3, Andor).

2.5. Measurement of drug transport using UV spectroscopy

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Once the lipid bilayer was created, drug molecules started to diffuse from the aqueous droplet, termed a donor, to the bottom aqueous phase, termed an acceptor, because of the

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concentration gradient across the lipid bilayer. During drug transport, UV light that has a

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constant wavelength was applied to the acceptor, and the real-time UV absorbance of the acceptor was measured by a UV-vis spectrophotometer (UV-2600, Shimadzu). The overall scheme is described in Fig. 1b. We obtained a UV spectrum (from 260 nm to 400 nm) for each drug solution dissolved in the 1:4(v/v) mixture of the organic solvent and deionized water with different concentrations varying from 0 to 10 μM, to convert the UV absorbance at a fixed wavelength into the drug concentration in the acceptor. Then, UV absorbance at each wavelength was plotted against the 8

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corresponding drug concentration, and linearly fitted. A wavelength where the R2 value for the linear fitting was larger than 0.99 was selected, and the UV absorbance was converted to drug concentration using the Beer-Lambert law. During the experiments, the drug molecules were assumed to be uniformly dispersed over the acceptor region due to continuous mixing of the

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acceptor with a magnetic cell stirrer (MS 500, Misung Scientific, with stirring bar of 6 mm x 1.5

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mm at 150 rpm) located inside the UV spectrophotometer. We also confirmed that the UV

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absorbance was not affected by the stirring process.

2.6. Application of a light mask for the acceptor volume reduction

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Because of the low water solubility of the drug compounds used in this study (Fig. 1c), it

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was difficult to provide enough drug molecules in the donor. To solve this problem, the acceptor

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volume needs to be significantly smaller than in our previous work (1 mL). The concentration of drug molecules in the acceptor becomes highly diluted, by a factor of several hundreds or even a

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thousand, because of the large volume difference between the donor and the acceptor, and this may result in insufficient UV absorbance in the acceptor.

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To achieve a smaller acceptor volume, a custom light mask made of aluminum was

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applied (Fig. 2). In this modified setup, a light mask containing a rectangular hole of 8 mm x 0.8 mm was attached to the cell holder, enabling only some part of the incident UV light to travel further into the cuvette through the hole in the mask. We confirmed that when the light mask was applied for both reference and sample UV cells, the UV absorbance was the same as the value obtained from the conventional setup. This is because the measured absorbance is determined as the ratio of absorbance of the reference cell to that of the sample cell. Consequently, the acceptor volume could be reduced to 300 μL, more than 3 times smaller than before. The light mask was 9

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used for all the experiments.

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Fig. 2 Schematic of the conventional and modified setups for the UV absorbance measurement. In the conventional setup, all the incident UV light goes into the cuvette. However, in the

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modified setup, the aluminum light mask with a small hole is attached to the cell holder, which

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allows only some part of the incident UV light to reach the sample through the hole. By applying the light mask, the volume of the acceptor can be significantly reduced, while maintaining the

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same level of UV absorbance.

2.7. Analysis of partitioning kinetics Unlike our previous work, where relatively hydrophilic drug compounds were used, lipophilic compounds were investigated in this study. But as a result, it may be possible that some of the drug molecules undergo a partitioning process from the donor to the surrounding oil phase during drug transport through the lipid bilayer, indicated as a flux J2 in Fig. 1b. 10

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Therefore, partitioning kinetics were investigated to quantify how fast this process occurs and how much effect this process has on the overall drug transport in our system (Fig. 3). This analysis was conducted for four compounds (griseofulvin, carbamazepine, warfarin and piroxicam) that are quite lipophilic. Two other compounds, dapsone and acetazolamide, were

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confirmed to undergo practically no partitioning in our system (data not shown). Drug molecules

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at a concentration of 50 µM were initially dissolved in an aqueous phase consisting of a 1:4 (v/v) mixture of DMSO and deionized water, and started to become distributed in the DOPC solution

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dissolved in a 1:1 (v/v) mixture of squalane and silicone oil at a concentration of 4 mg/mL, located on top of the aqueous phase. The UV absorbance of the aqueous phase was measured

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during the entire partitioning process. This UV absorbance can be converted into drug

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concentration in the aqueous phase in the same way described earlier. Also, continuous mixing

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was applied to the aqueous phase using a magnetic cell stirrer during measurement to keep the aqueous phase spatially homogeneous. It should be noted that stirring in the oil phase could also

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maintain the oil phase spatially homogeneous, possibly giving more accurate partitioning information. However, given that the oil phase in our experiments has a few millimeters of

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height, and the time it takes for the drug molecules to diffuse that distance is almost comparable

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to time interval for the measurements, we assumed that our analysis would be reliable even without applying stirring in the oil phase. A partitioning flux, J, can be expressed by 𝐽(𝑡) = 𝑘 [𝐶𝑊 (𝑡) −

𝐶𝑆 (𝑡) 𝑉𝑆 𝑑𝐶𝑆 (𝑡) ]= , 𝑄 𝑆 𝑑𝑡

(1)

where CW and CS are the concentrations of the aqueous and the oil phases respectively, k is the partitioning rate constant, Q is the partition coefficient for our system, defined as Q=CS,eq/ CW,eq 11

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where CW,eq and CS,eq are the equilibrium concentrations of the aqueous and the oil phases respectively, S is the area of the oil-aqueous interphase and VS is the volume of the oil phase. Also, mass conservation becomes 𝑉𝑊 𝐶𝑊 (0) = 𝑉𝑊 𝐶𝑊 (𝑡) + 𝑉𝑆 𝐶𝑆 (𝑡),

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(2)

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where VW is the volume of the aqueous phase. Combining Eq. (1) and Eq. (2), an ordinary

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differential equation with respect to CS(t) could be established, leading to

(3)

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𝐶𝑆 (𝑡) 1 1 = 1 − 𝑒𝑥𝑝 [−𝑘𝑆 ( + ) 𝑡] , 𝐶𝑆,𝑒𝑞 𝑄𝑉𝑆 𝑉𝑊

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where CS,eq = QVWCW(0)/(VW+QVS). By fitting Eq. (3) to the experimental CS(t) curve,

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partitioning rate constant k is obtained.

Fig. 3 Partitioning kinetics analysis. (a) Schematic of the experimental method investigating partitioning kinetics. Drug molecules were initially dissolved in the aqueous phase and 12

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underwent a partitioning from the aqueous phase to the oil phase containing lipids. During the partitioning, UV light with a constant wavelength was applied to the aqueous phase, to measure the UV absorbance of the drug molecules over time. (b) Representative CS(t)/CS,eq curve using carbamazepine. Due to the partitioning of the drug molecules, the concentration of drug

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molecules in the oil phase gradually increases and reaches equilibrium. Solid gray dots are the

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experimental data, and the black dotted line indicates the fitted result from Eq. (3).

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2.8. Permeability calculations

The transport of molecules through the membrane, driven by a concentration gradient

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(more correctly, a chemical potential gradient), has often been modeled by Fick’s first law at

𝑉𝐴 𝑑𝐶𝐴 (𝑡) , 𝐴[𝐶𝐷 (𝑡) − 𝐶𝐴 (𝑡)] 𝑑𝑡

(4)

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𝑃=

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steady state, resulting in the permeability of the molecules

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where P is the permeability, A is the membrane area, VA is the acceptor volume (300 μL), CA and CD are the concentration of the acceptor and the donor respectively. Note that Eq. (4) has been

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developed for non-sink conditions, where the acceptor concentration is not set to zero during

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drug transport, and thus the back flux from the acceptor is also taken into account. (Avdeef, 2012; Avdeef et al., 2001; Hubatsch et al., 2007; Palm et al., 1999). The bilayer area was calculated as A = πR2, where R is the radius of the bilayer estimated from the fluorescence microscopic observations (Video 1). Meanwhile, some molecules can become trapped in the lipid bilayer, whose interior has a hydrophobic nature. It is known that drug molecules become trapped in the artificial membrane within a short time, so the total number of molecules trapped in the membrane remains constant 13

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over time (Avdeef, 2012). The effect of partitioning to the oil phase was ignored when considering mass conservation, and this is explained in detail in the Results and Discussion. Taking the number of trapped molecules within the membrane into account, mass conservation

(5)

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(1 − 𝑅)𝑉𝐷 𝐶𝐷 (0) = 𝑉𝐷 𝐶𝐷 (𝑡) + 𝑉𝐴 𝐶𝐴 (𝑡),

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for our system was expressed as

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where VD is the volume of the donor, and R is the retention fraction, defined as the ratio of the

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number of trapped molecules in the membrane to the total number of initially given molecules. To determine the retention fraction, the equilibrium value of the measured CA(t) curve was firstly

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obtained by nonlinear fitting. At equilibrium, the concentration of both the donor and the acceptor would be the same, thus by setting CD,eq = CA,eq = Ceq in Eq. (5), where CD,eq and CA,eq

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are the equilibrium concentrations of the donor and the acceptor respectively, the retention

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fraction can be simply expressed as

(6)

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𝑉𝐷 +𝑉𝐴 𝑅 =1−( )𝐶 , 𝑉𝐷 𝐶𝐷 (0) 𝑒𝑞

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To solve Eq. (4), the mass conservation from Eq. (5) and the initial condition of CA(0)=0

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are applied together, leading to 𝐶𝐴 (𝑡) 1 1 = 1 − 𝑒𝑥𝑝 [−𝑃𝐴 ( + ) 𝑡], 𝐶𝑒𝑞 𝑉𝐷 𝑉𝐴

(7)

where Ceq is the equilibrium concentration, defined as Ceq = (1-R)CD(0)VD/(VD+VA) by Eq. (6). Finally, the CA(t) curve was fitted to the model equation of Eq. (7), and the permeability was obtained. For each drug, 3 independent measurements for CA(t) were conducted and the averaged value at each time was used to assess the permeability.

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3. Results and discussion

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3.1. Lipid bilayer formation

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We made a stable (lasting for a day) and highly reproducible freestanding planar lipid

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bilayer with a millimeter scale area (>1 mm2) within a conventional UV cuvette. When the two lipid monolayers, the planar and droplet monolayers, were brought into contact, the adhesion

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process between them led to the formation of a solvent-free lipid bilayer. The solvent-free lipid

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bilayer was created because squalane and silicone oil, the components of our oil phase, could not remain inside the lipid membrane because of their bulky molecular structures (Leptihn et al.,

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2013). Also, silicone oil, a bad solvent for phospholipids, likely enhanced the stability of the

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lipid bilayer by facilitating the adhesion between the two lipid monolayers (Thiam et al., 2012). The lipid bilayer formation was visualized using fluorescence microscopy. Oil drainage

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between the two lipid monolayers was observed approximately 5 min after loading the droplet

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onto the oil-aqueous phase (1:4(v/v) mixture of DMSO and deionized water) interface. A video showing the adhesion process obtained via fluorescence microscopy is in the Supporting

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Information (Video 1).

3.2. Partitioning kinetics The partition coefficients (Q) and partitioning rate constants (k) for the four lipophilic compounds (griseofulvin, carbamazepine, warfarin and piroxicam) are shown in Table 1. The partition coefficients for our system were quite small, even smaller than 1 for carbamazepine, warfarin and piroxicam, and this was due to the presence of DMSO in the aqueous phase. Compared to the pure water system, the addition of DMSO to the water significantly increased 15

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the affinity between the drug molecules and the aqueous phase, resulting in less partitioning of the oil phase at equilibrium.

Table 1. Partition coefficients (Q) and partitioning rate constants (k) for tested drug

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compounds. Partition coefficient (Q)

Partitioning rate constant (k)

Griseofulvin

2.34

21.0ⅹ10-6 cm/s

Carbamazepine

0.29

Warfarin

0.56

Piroxicam

0.05

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Compound

6.6ⅹ10-6 cm/s 2.4ⅹ10-6 cm/s

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9.7ⅹ10-6 cm/s

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As shown in Fig. 4, not only permeation through the lipid bilayer, but also partitioning from the donor to the surrounding oil phase can occur during drug transport. To estimate the

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contribution of the partitioning effect to the overall drug transport in our system, the number of

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transported molecules due to the partitioning process was compared with those from lipid bilayer permeation. For both cases, the maximum flux occurs at the very beginning, where the largest

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concentration gradient exists. Thus, the maximum number of transported molecules for a single

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time interval due to permeation (Nperm,max) and partitioning (Npart,max) could be estimated as Nperm,max = PCD(0)AB∆t and Npart,max = kCD(0)AM∆t, where P is the permeability, k is the partitioning rate constant, ∆t is a time interval for our measurement and AB and AM are the bilayer area and the droplet monolayer area respectively. Because the ratio of bilayer area to monolayer area, AB/AM, is ~ 0.8, the ratio of maximum number of molecules due to the bilayer permeation to that resulting from partitioning can be expressed as Nperm,max/ Npart,max ~ 0.8P/k. This ratio was 7.4, 12.1, 12.2 and 31.0 for griseofulvin, carbamazepine, warfarin and 16

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piroxicam respectively. The permeability from this work listed in Table 2 was used for the calculation. This estimation shows that drug transport through the lipid bilayer is much more significant than transport due to partitioning. Consequently, we conclude that the contribution of

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partitioning to the overall drug transport is reasonably negligible for our system.

Fig. 4 Comparison of lipid bilayer permeation and partitioning to the oil during drug transport in

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our system. The permeation flux is indicated as J1 and the partitioning flux is indicated as J2,

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along with the mathematical expressions for both fluxes. P is the permeability, k is the

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manuscript.

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partitioning rate constant and Q is the partition coefficient, which are previously described in the

3.3. Permeability assay 3.3.1. Real-time drug transport measurements We used six different lipophilic drug compounds with low water solubility (BCS class 2 or 4), griseofulvin, carbamazepine, warfarin, piroxicam, dapsone and acetazolamide, whose physicochemical properties and experimental values are listed in Table 2. All of these drugs, except acetazolamide, are known to be absorbed mainly through passive transport in the intestine 17

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(Zhu et al., 2002), but we could not determine whether acetazolamide is completely absorbed in the gastrointestinal membrane without information about its absorption mechanism (Granero et al., 2008). Drug molecules started to diffuse from the donor to the acceptor through the lipid bilayer,

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and the UV absorbance of the drug molecules in the acceptor was continuously measured over

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time. Each curve of the acceptor concentration over time, CA(t), was obtained by converting the UV absorbance into the concentration, and was normalized by the equilibrium concentration, Ceq

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= (1 - R)CD(0)VD/(VD + VA) (Fig. 5). Each normalized curve, CA(t)/Ceq, was then fitted to the model equation of Eq. (7), and the R2 value was larger than 0.98 for each fitting process, as

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shown in the last column of Table 2.

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In the time evolution of the acceptor concentration for each drug compound (Fig. 5),

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each concentration increases gradually over time and finally reaches an equilibrium concentration, which is Ceq = (1 - R)CD(0)VD/(VD + VA). It should be noted that when defining

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the equilibrium concentration, the molecules accumulated in the membrane are excluded, and only free molecules are considered. Hence, the fact that CA(t)/Ceq reaches 1 eventually indicates

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the oil phase.

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that all free molecules move down to the acceptor, rather than undergo a partitioning process to

The main goal of this research was to measure the membrane permeability of drug compounds classified as BCS class 2 (griseofulvin, carbamazepine, warfarin, piroxicam and dapsone) and BCS class 4 (acetazolamide) whose permeability values are expected to differ significantly. The transport kinetics were different for each drug compound, and an especially remarkable difference was observed between BCS class 2 and class 4 drug compounds (Fig. 5). The characteristic time scale, τ = [PA(1/VD + 1/VA)] -1, varied from 400 s (griseofulvin) to 30000 18

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s (acetazolamide) demonstrating that compounds with orders of magnitude different permeability

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could be successfully measured with our assay.

Fig. 5 Real-time acceptor concentration curve. Each concentration curve of the six tested drugs

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(griseofulvin, carbamazepine, warfarin, piroxicam, dapsone and acetazolamide) over time at the

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acceptor was normalized by the equilibrium concentrations, Ceq = (1 - R)CD(0)VD/(VD + VA),

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respectively. Here, because molecules that are retained in the lipid bilayer are excluded in the definition of Ceq, each normalized curve reaches 1 at equilibrium. Each data set is the averaged

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values from three independent measurements, and each dotted line corresponds to the fitted

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curve from Eq. (7). Note that the curve for dapsone is fitted from the inflection point (900 s) for a better fitting. The inset is an entire curve for acetazolamide, which takes over 60000 s to reach equilibrium. The error bars indicate standard deviations.

Table 2 shows the permeability and other physicochemical properties measured by our assay. Our work shows 11-37 times higher permeability than PAMPA, the most popular in vitro permeability assay using an artificial membrane. Our work uses the lipid bilayer, which has a 19

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thickness of a few nanometers, as an artificial membrane, which is much thinner than that of PAMPA which is generally over 100 μm thick. This significant difference in membrane thickness might be responsible for the much higher permeability in our work, since the membrane permeability is inversely proportional to membrane thickness (Avdeef, 2012). Meanwhile, the

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permeability rank in our work is not exactly the same with that in previous PAMPA studies. This

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discrepancy might be caused by some variations in the experimental conditions, such as the

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stirring condition and the effect of the unstirred water layer.

Table 2. Physicochemical properties of the drug compounds and their experimental values

Mw

log P

class b

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Compound

a

Organic

permeability

(nm)

(10 cm/s)

(10 cm/s)

fraction (%)

296

5.3

194.0 ± 28.9

44.2 ± 1.6

0.987

D

11.3

146.2 ± 19.8

28.4 ± 3.0

0.996

solvent (D: DMSO

Applied

wavelength

M

BCS

-6

c

Permeability

Membrane

from this work

retention

-6

R2 (Fig. 5)

352

2.18

2

D

Carbamazepine

236

2.45

2

285

Warfarin

308

2.52

2

D

308

12.3

100.7 ± 25.8

27.7 ± 1.5

0.987

Piroxicam

331

1.97

2

D

358

8.2

92.9 ± 6.6

23.0 ± 5.3

0.986

Dapsone

248

0.97

2

M

292

0.1

52.3 ± 5.8

2.4 ± 4.5

0.999

Acetazolamide

222

0.14

4

D

268

-

2.8 ± 0.3

2.7 ± 0.7

0.991

CE

AC

a

PT

Griseofulvin

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M: Methanol)

PAMPA

The octanol-water partition coefficient. (Oja and Maran, 2015; Wu and Benet, 2005; Zhu et al.,

2002) b(Kasim et al., 2004; Wu and Benet, 2005) c(Oja and Maran, 2015; Zhu et al., 2002)

During drug transport in our system, additional solvent movement could also be present as well. As reported in our previous work, the effect of osmotic water flux from the acceptor to the donor on the concentration change during drug transport is negligible. Other than water flux, 20

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flux of the organic solvent could exist. However, because the volume fraction of the organic solvent (DMSO or methanol) is the same for both the donor and the acceptor, there is no organic solvent flux across these two parts. We also confirmed that the flux of the organic solvent to the oil phase during the measurement was almost negligible. After weighing all these considerations,

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it can be concluded that our assay is able to measure the permeability of drug compounds

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without interference from any undesired solvent flux.

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3.3.2. Overcoming technical issues

In many cases, the extremely low solubility of selected drug compounds was a major

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limitation. Because of the poor aqueous solubility, it was quite difficult to make the donor

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concentration high enough to keep the acceptor concentration above the detection limit of our

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UV spectrophotometer. To tackle this problem, some variables can be adjusted to increase the measured acceptor concentration, and these are related to increasing the equilibrium

PT

concentration, Ceq = (1 - R)CD(0)VD/(VD + VA), eventually. First, the donor volume, VD, could be increased. However, the larger the volume of the

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donor, the larger the gravity exerted on the donor, and this might cause a system instability. The

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increased donor volume would also increase the characteristic time scale of the whole drug transport, resulting in a longer measurement time. The characteristic time scale for drug transport in our system is expressed as τ = [PA(1/VD + 1/VA)] -1, and because the acceptor volume is much larger than that of the donor, it can be simplified as τ ~ VD/(PA). If the donor volume (VD) increases by a factor of n, then the characteristic time scale (τ) increases roughly by a factor of n1/3. For example, when VD increases 4 times, τ will increase about 1.6 times. This would not be a critical issue for highly permeable molecules, but if the permeability of the tested molecule is 21

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very low, quite a lot of hours may be needed for the measurement. Second, increasing the initial donor concentration, CD(0), could be considered. In this study, the organic solvent was added to deionized water to enhance the solubility of the drugs. However, our lipid bilayer system was unstable when the volume fraction of DMSO or methanol

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was too high. We found that the appropriate volume fraction of the organic solvent to ensure a

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stable system was up to 20%, and the corresponding initial donor concentration was 400-2000 μM for each drug compound, which is 3-75 times lower than the initial donor concentration from

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our previous work.

To apply this low level of initial donor concentration for a reliable measurement, the

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acceptor volume, VA, the last variable that we could change, should be set smaller. By blocking

M

much of the incident UV light, the acceptor volume could be sufficiently reduced without

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affecting the measured UV absorbance (refer to the Experimental Section). It is expected that by making the acceptor volume much smaller than now, ideally comparable to the donor volume, a

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reliable detection could be achieved even for compounds with extremely low aqueous solubility. Also in other cases, a significant background UV absorption of the organic solvent

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prevented reliable measurements. For example, because the UV absorption of DMSO at short

AC

wavelength (< 260 nm) is substantial, more UV transparent organic solvents, such as alcohol, should be applied for the measurement at short wavelength. We are planning to apply some other organic solvents and verify that various drug compounds could be tested with properly selected solvents in our assay. Lastly, it should be noted that the added organic solvents, DMSO and methanol, could exert an influence on the membrane permeability depending on the concentration of them. Because DMSO is a small amphiphile, it can reside between each head groups of the lipid 22

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molecules, possibly making the membrane thinner. This could lead to the formation of spontaneous water pores at higher DMSO concentration, affecting the membrane permeability (de Ménorval et al., 2012; Notman et al., 2006). Given that the concentration of DMSO in our system is lower than the reported one at which the pore formation in the lipid membrane can be

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induced, the permeability change due to the organic solvent would not be significant in our

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system (Hughes et al., 2012). However, when a high fraction of the organic solvent is applied, a systematic permeability measurement would be required to suggest unbiased values.

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3.3.3. Stirring and the unstirred water layer

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During the measurement, the acceptor region was continuously mixed using a magnetic cell stirrer to make sure that the acceptor concentration was spatially uniform. Otherwise, it

M

would not be possible to obtain a reliable permeability measurement because it would take quite

ED

a long time after lipid bilayer permeation for drug molecules to reach the part, by the diffusion, where the incident UV light covers. In our experiments, stirring rate of 150 rpm was applied. We

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chose this stirring rate because the lipid bilayer located at the interface between the acceptor and

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the donor droplet was destroyed at higher stirring rate than that. In drug permeability experiments, barriers to the drug permeation usually consist of both

AC

the membrane and so-called the unstirred water layer (UWL). The UWL is a stagnant region in the vicinity of the membrane created by incomplete mixing near the membrane surface (Avdeef et al., 2004; Korjamo et al., 2009; Lennernas, 1998). If the resistance of the UWL is larger than that of the membrane, the diffusion in the UWL becomes a rate limiting step for the drug permeation. It is reported that the thickness of the UWL in vivo is 30 – 100 µm, which is negligible in the intestinal drug absorption (Korjamo et al., 2009; Lennernas, 1998), whereas the UWL thickness in vitro could be considerable, over 1000 µm, depending on the experimental 23

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conditions. The UWL thickness could be remarkably reduced by stirring, but it can never be completely eliminated. Based on some reports on the in vitro UWL thickness with agitation (Avdeef et al., 2004; Korjamo et al., 2009), we assumed that the UWL thickness in our system would be hundreds of

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micrometers, though we did not conduct thorough measurements for that. Given that the

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diffusion coefficient of a drug in water is generally in the order of 10-10 m2/s, it would take ~ 100 s for the diffusion of drug molecules in the UWL. Because the shortest time interval for the UV

US

absorbance measurement was 300 s, we assumed that the diffusion in the UWL would have insignificant effect on the overall permeation measurements.

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We agree that even if the stirring condition is maintained as efficiently as possible the

M

UWL still exists, thus diffusion of drug molecules in the stagnant aqueous environment and its

ED

effect on the overall permeation process should not be overlooked (Di Cagno et al., 2018). Also, one should keep in mind that a thorough consideration of UWL in in vitro permeability assay is

PT

necessary when suggesting in vitro-in vivo correlations and elucidating the structure-permeability relationships, though it may not be required in a relatively simple screening based on

AC

CE

permeability rank in the early drug development stages (Korjamo et al., 2009).

3.3.4. Membrane retention The membrane retention fraction indicates how many molecules get retained within the lipid membrane compared to the entire content of molecules. The extent of membrane retention varies depending on the physicochemical property of the applied compound, the lipid composition of the membrane, and so on. The hydrophobic interaction between drug and phospholipid molecules is known to be responsible for membrane retention (Nagar and 24

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Korzekwa, 2012). According to several reports, membrane retention is therefore common with lipophilic compounds, and highly lipophilic compounds have been reported to undergo high retentions in cell-based assays (Avdeef et al., 2001; Balimane et al., 2006; Buckley et al., 2012). The membrane retention fraction from this work is listed in Table 2, and is plotted

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T

against the corresponding octanol-water partition coefficient (log P) for each compound (Fig. 6).

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The membrane retention fraction of drug compounds used in our previous work are also plotted. The retention fractions for compounds with low log P values that are relatively hydrophilic and

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mostly classified as BCS class 1 or class 3, are almost zero. However, as log P values increase retention fractions become higher for the lipophilic compounds used in this work. Because all of

AN

the compounds presented in Fig. 6 have no charge, membrane retention is mainly related to the

M

intensity of hydrophobic interactions between the drug and lipid molecules, which is determined

ED

by the lipophilicity of the drug molecules. Thus, the higher retention fraction might be attributed to the increased drug-lipid interaction, due to the increased lipophilicity of the drug molecules,

PT

which agrees well with the previous reports. This demonstrates that the freestanding and solventfree lipid bilayer used in our assay could be an effective platform to investigate the drug-lipid

AC

CE

membrane interaction as well.

25

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T

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Fig. 6 Relationship between octanol-water partition coefficient (log P) and retention fraction. The drug compounds used in our previous work (A - E)(Lee et al., 2018) and in this work (F – K)

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are all plotted.

4. Conclusion

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In this paper, we introduced an in vitro permeability assay for lipophilic drug compounds

ED

that have poor water solubility, commonly classified as BCS class 2 or class 4. We created a

PT

stable freestanding lipid bilayer in a UV cuvette by exploiting the adhesion between two different lipid monolayers. Prior to the permeability measurement, the kinetics of drug

CE

partitioning to the oil phase was thoroughly investigated, and its contribution to the overall drug

AC

transport was found to be negligible. We successfully obtained the real-time transport curve for six lipophilic drug compounds, and permeabilities were successfully estimated for each compound that were different by orders of magnitude. Also, the membrane retention fraction for each compound was obtained, and it was found that the more highly lipophilic compounds experienced more membrane retention, as had been reported in previous literatures. Since many commercial drug compounds have poor water solubility and highly lipophilic drug candidates are frequently synthesized in modern drug discovery, our assay could 26

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be employed as a simple and precise tool for drug screening. An ongoing challenge of our system is achieving a much smaller acceptor volume to apply more various compounds that have extremely poor aqueous solubility. Based on this improvement, more research has to be done to measure drug transport across the lipid membrane with properly selected organic solvents,

AN

US

CR

which were not able to be investigated in our current system.

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T

possibly suggesting correlations with other in vitro assays for a wide range of drug compounds,

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Conflicts of interest

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Acknowledgments

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There are no conflicts of interest to declare.

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This research was supported by the Basic Science Research Program through the National Research Foundation of Korea (Grants NRF-2012R1A6A3A04040395) and by KAIST Institute

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for the NanoCentury.

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Graphical abstract

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Figure 1

Figure 2

Figure 3

Figure 4

Figure 5

Figure 6