Liposome drugs' loading efficiency: A working model based on loading conditions and drug's physicochemical properties

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Journal of Controlled Release j o u r n a l h o m e p a g e : w w w. e l s ev i e r. c o m / l o c a t e / j c o n r e l

Liposome drugs' loading efficiency: A working model based on loading conditions and drug's physicochemical properties Daniel Zucker a, David Marcus b, Yechezkel Barenholz a,⁎, Amiram Goldblum b a b

Department of Biochemistry, The Hebrew University-Hadassah Medical School, P.O. Box 12272, Jerusalem 91120, Israel Department of Medicinal Chemistry and Natural Products, School of Pharmacy, Hebrew University of Jerusalem, P.O. Box 12272, Jerusalem 91120, Israel

a r t i c l e

i n f o

Article history: Received 10 March 2009 Accepted 29 May 2009 Available online 7 June 2009 Keywords: Liposomes Remote loading Loading efficiency Loading conditions Physicochemical properties

a b s t r a c t Remote loading of liposomes by transmembrane gradients is one of the best approaches for achieving the high enough drug level per liposome required for the liposomal drug to be therapeutically efficacious. This breakthrough, which enabled the approval and clinical use of nanoliposomal drugs such as DoxilTM, has not been paralleled by an in-depth understanding that allows predicting loading efficiency of drugs. Here we describe how applying data-mining algorithms on a data bank based on Barenholz’s laboratory's 15 years of liposome research experience on remote loading of 9 different drugs enabled us to build a model that relates drug physicochemical properties and loading conditions to loading efficiency. This model enables choosing candidate molecules for remote loading and optimizing loading conditions according to logical considerations. The model should also help in designing pro-drugs suitable for remote loading. Our approach is expected to improve and accelerate development of liposomal formulations for clinical applications. © 2009 Published by Elsevier B.V.

1. Introduction Liposomes are one of the best drug delivery systems for low molecular weight drugs, imaging agents, peptides, proteins, and nucleic acids [1–4]. Nanoliposomes (d. ≤ 120 nm), when administrated intravenously, may improve the therapeutic index of some drugs due to the enhancement of permeability and retention effect [1,5–7], while larger liposomes (d. b 2–6 µm) have a similar effect when administrated locally [8,9]. Thus more than 11 liposome-based drugs are in clinical use. A major achievement in liposome medical application is the ability to load a sufficient amount of drug needed to achieve therapeutic efficacy. In most cases this requires a remote loading approach (Fig. 1) [10–12], where the drug is loaded into preformed liposomes. For this, the drug should be able to change from uncharged species, which can diffuse across the membrane, to charged ones that are not capable of it. The agent's degree of ionization is dependent on its pKa and on the local pH [11]. The encapsulated agent should also be fairly soluble. Only amphipathic weak acids or bases fit all these requirements. Another requirement for remote loading is the driving force caused by the trans-membrane gradient, which will “pump” the drug from the extraliposome medium into the liposome and maintain the drug there. Nichols and Deamer [13] were the first to demonstrate this approach by loading amphipathic amines into liposomes via a pH gradient. Barenholz’s laboratory upgraded these methods by using

⁎ Corresponding author. Tel.: +972 2 6757615; fax: +972 2 6757499. E-mail address: [email protected] (Y. Barenholz). 0168-3659/$ – see front matter © 2009 Published by Elsevier B.V. doi:10.1016/j.jconrel.2009.05.036

liposomes in which pH and ion gradients were created by salts of either weak bases (e.g., ammonium) [5,15,16] or weak acids (e.g., acetate) [10,14]. These ions can be present (depending on pH) as charged and uncharged species [5]. Such ions can traverse the liposome membrane only in their uncharged form. Therefore, the trans-membrane gradient of such ions is the driving force for remote loading as they can be exchanged with amphipathic drugs. The counterion (e.g. sulfate or chloride for ammonium, and calcium or sodium for acetate) can be selected so that it will effect precipitation of the drug-counterion salt that entrapped inside the liposome, thereby contributing to the control of drug release at various temperatures [16,17]. Many drugs are remotely loaded successfully into preformed liposomes using various gradients [7,18]. These breakthroughs, which made possible the clinical use of liposomes [18–20] such as Doxil™ [6], have not been paralleled by an ability to forecast loading efficiency. There is a growing interest in the remote loading method [19]; therefore, we have developed a theoretical model of loading drugs into liposomes by analyzing the results of both previously published and new experiments. The model is based on the drug's physicochemical properties and on loading conditions; it takes into consideration the multifactorial nature of the process. There are two previous quantitative models that describe the loading of drugs into the liposomes [11,21]. Our proposed model is different in that it is based on a much larger set of experimental data, takes into account the drug properties and the loading conditions, and exploits developments in computational data-mining [22]. Our model enables choosing candidate molecules from a drug repertoire or designing pro-drugs suitable for remote loading and

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with the approach of Haran et al. [12] for remote loading of amphipathic weak bases. The approach of Clerc and Barenholz [10] was used for remote loading of amphipathic weak acids. In short, for lipid hydration by formation of multilamellar liposomes (MLV), a mixture of the desired PC (in most cases HSPC), cholesterol, and PEGDSPE2k (54–60:41–40:5–0 mole ratio) was hydrated in either ammonium sulfate (for bases' loading) [16,19], or calcium acetate (for acids' loading) [14] to form MLV by the ethanol injection method. These MLV were downsized to large unilamellar vesicles (LUV; 100 ± 20 nm), by medium-pressure stepwise extrusion through polycarbonate filters (400 to 100 nm pore size) using the Northern Lipids, Inc. (Burnaby, BC, Canada) extruder device. Small unilamellar vesicles (SUV; b100 nm), were formed by an additional extrusion step using a 50 nm pore size polycarbonate filter [14]. Large multivesicular vesicles (LMVV) were prepared by dissolving HSPC/cholesterol or mixture of HSPC/N-palmitoyl-SPM/cholesterol (of different compositions) in tert-butanol. The solution was lyophilized, and the dry lipid “cake” was hydrated with the desired ammonium salt (0–300 mM) at 60 °C to produce MLV. The MLV were then homogenized at 10,000–15,000 psi (EmulsiFlex-C5; Avestin, Ottawa, ON, Canada) to produce SUV. The SUV were then subjected to 10 freeze–thaw cycles between liquid nitrogen and water at 37 °C to form LMVV (1 min freezing per 1 ml dispersion) [9]. The use of SUV maximizes the trapped volume of the LMVV and therefore it improves passive encapsulation. The mechanism of LMVV formation involves inter-liposome fusion during the freezing and thawing; the larger is the exposed surface area of the vesicles—the larger is the fusion. SUV has large exposed surface area. 2.3. Transmembrane ion gradient formation Fig. 1. Remote loading of amphipathic weak base into liposomes using an ammonium sulfate gradient (A) or amphipathic weak acid using a calcium acetate gradient (B). Concentration of (NH4)2SO4 or Ca(C2H3O2)2 in the liposomes is 1000-fold greater than concentration in the extraliposomal medium. D = drug, AcH = acetic acid, Ac− = Acetate. Un-ionized drug base (D–N) or acid (D–COOH) crosses the liposomal membrane and is trapped inside by its ionization and insoluble salt formation with the intraliposome counterion.

optimizing the loading conditions. Thereby, it proposes a rational approach based on physicochemical principles, which is expected to facilitate and accelerates the development of novel liposomal formulations. 2. Materials and methods 2.1. Drugs and lipids Vincristine (VCR) sulfate was obtained from Avachem Scientific (San Antonio, TX, USA); topotecan (TPT) from Sinova (Bethesda, MD, USA); bupivacaine (Bup) HCl, from Orgamol (Evionnaz, Switzerland); lidocaine and acridine orange (AO) from Sigma (St. Louis, MO, USA); tempamine (TMN), from Aldrich (Milwaukee, Wl, USA); doxorubicin (DXR), from Farmitalia Carlo Erba (Milan, Italy); methylprednisolone succinate (MPS), from Pharmacia (Puurs, Belgium; and β-methasone succinate (BMS) from Steraloids (Newport, RI, USA). Hydrogenated soybean phosphatidylcholine (HSPC) was obtained from Lipoid (Ludwigshafen, Germany). Cholesterol was obtained from Sigma. 1,2-distearoyl-sn-glycero-3-phosphoethanolamine-N-[methoxy(polyethylene glycol)-2000] (PEG-DSPE2k) was obtained from Genzyme Pharmaceuticals (Liestal, Switzerland). Phospholipid concentration was determined using a modified Bartlett procedure [23]. 2.2. Preparation of liposomes The approach and lipid composition employed by Peleg-Shulman et al. [24] was used for preparation of nanoliposomes. It was combined

The salt gradient was created by passive encapsulation of the gradient-forming salt (e.g. ammonium salt) in the liposomes. Creation of the gradient in liposomes involves replacing the salt in the external liposome medium with iso-osmotic solution (with regard to plasma) of saline or sucrose. In all cases we used three repeated dialysis cycles using 100–250 volumes of dialyzing medium for 1 volume of the liposome dispersion at 40 °C for 1 h, followed by a fourth dialysis step against 250–500 volumes of dialysis medium for 12–24 h. This results in trans-membrane ion gradients' magnitude of at least 1000 [12,14]. 2.4. Drug encapsulation Topotecan (TPT) and vincristine (VCR), both anticancer drugs, were mixed with a preformed LUV (HSPC:cholesterol:PEG-DSPE2k— 54:41:5 or 59:41:0 mole ratio) dispersion exhibiting a transmembrane ammonium salt gradient. The remote loading (Fig. 1) was achieved by incubation of the above liposomes for 10–480 min at 37, 50, or 65 °C, then cooling to 4 °C, followed by dialyzing against saline or 5% dextrose to remove ammonia and residual unloaded drug. Alternatively, in some cases, unloaded drug and ammonia (released during the loading process) were removed by the cation exchange resin Dowex 50WX-4 [12,25]. Bupivacaine (Bup) and lidocaine, both local anesthetics, were remotely loaded into preformed LMVV by a gradient as previously described for doxorubicin [12]. They were loaded by incubating the liposomal formulations with 50 mg/ml of drug at a pH range of 5.0– 7.2 for 30 min at 65 °C (Fig. 1). Non-entrapped drug was separated from the LMVV by centrifuging in saline (1000 g, 5 min, 4 °C). The LMVV pellet was then washed by resuspension in saline, and the process was repeated four times. The other drugs were remotely loaded into preformed liposomes as previously described: acridine orange (AO) [11]; tempamine (TMN), an antioxidant, [16,26]; doxorubicin (DXR), an anticancer drug, [12]; methylprednisolone succinate (MPS) and β-methasone succinate (BMS), both anti-inflammatory steroidal drugs, [14,27].

In all cases, chemical stability of liposome lipids and drugs was not affected by the remote loading process. 2.5. Drug quantification 2.5.1. HPLC The system included Kontron 420 HPLC pump, Kontron HPLC 460 autosampler and Kontron 450 data system (Switzerland). TPT was quantified using a Waters Symmetry C18 column (150 mm × 4.6 mm, 5 µm) with a fluorescence detector (Jasco Model FP-210) at excitation/emission wavelengths of 416/522 nm. The mobile phase consists of water (with 0.6% acetic acid and 1.5% triethylamine) and acetonitrile (82.5:17.5, v/v) [28,29]. Vincristine was quantified using an ACE C18 column (150 mm × 4.6 mm, 5 µm) with UV detector (Kontron, Model 430) at 221 nm; Samples were eluted with mixture of phosphate buffer 0.04 M, pH 3 and methanol. The separation consisted of a gradient method, beginning at 30% methanol and increasing to 70% methanol [30,31]. For both drugs, flow speed was 1.0 ml/min and injection volume was 20 µl. The other drugs were quantified by previously described methods: bupivacaine and lidocaine by HPLC [9,32]. BMS and MPS by HPLC [33], AO and DXR by fluorescence measurements[11,12], TMN by EPR [26,34]. 2.6. Calculations of physicochemical properties The following programs were used for calculating the properties of the drugs (Supplementary Table 1): Advanced Chemistry Development (ACD/Labs) Software V8.14 for Solaris (through CAS registry database); Calculator Plugins, Marvin 5.0.4, 2008 ChemAxon; and Molecular Operating Environment (MOE), version 2007.09, Chemical Computing Group Inc. Montreal, Quebec, Canada. Total surface area is the van der Waals surface area. Non-polar surface area was calculated by subtracting the polar surface area from the total surface area. 2.7. Data analysis Loading efficiency was calculated by dividing the drug/lipid mole ratio at the end of the loading by the initial drug/lipid ratio (used for loading). All loading efficiency measurements from various sources were collected in a database (Supplementary Table 2), and corresponding molecular properties (Supplementary Table 1) were added according to the drug and the experimental environment (e.g., pH). The database was analyzed using various classification tools in Weka 3.4 software [22,35] and validated using 10% leave-group-out (LGO) cross validation. 3. Results and discussion 3.1. Drugs loading efficiency database—the training set The physicochemical properties of all 9 drugs evaluated are described in Supplementary Table 1. Chemical structural formulas of all the drugs investigated are depicted in Supplementary Fig. 1. All drugs are of low molecular weight. BMS and MPS are amphipathic weak acids and their charge is −1, while the rest are amphipathic weak bases and their charge is ~ + 1. The solubility of the bases increases with the reduction in their solution pH. When comparing the polar and non-polar surface areas of the drugs, the largest diversity is in the parameter of non-polar/polar ratio. There is also large diversity in the solubility of the drugs: lidocaine is the most soluble while DXR is the least soluble. Based on logP, TMN is the most hydrophilic while AO is the most hydrophobic. Loading can be described in two terms: One, the drug/lipid ratio, which characterizes the liposome product and determines the amount

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of liposomes to be administrated per treatment. The second, the loading efficiency percentage, is important for pharmaceutical reasons and cost. Low efficiency may lead to a non-viable product. Loading efficiency enables one to calculate the final drug/lipid mole ratio because the initial drug/lipid mole ratio is known. Optimal remote loading by a pH or ammonium or acetate gradients requires that the loaded molecules will have: logD at pH 7 in the range of − 2.5 to 2 and pKa ≤ 11 for an amphipathic weak base or pKa N 3 for an amphipathic weak acid. All drugs in our database are good candidates for remote loading. For instance, β-methasone phosphate [27] and prednisolone phosphate [36] have encapsulation efficiencies b3% when loaded under the same conditions as MPS, which has an encapsulation efficiency of 92–98% (Supplementary Table 2, experiments 200–203) because they are too hydrophilic (logD at pH 7.2 ≈ − 4). We gathered results of 204 loading experiments with 9 different drugs that were performed in Barenholz’s laboratory in the recent 15 years (Supplementary Table 2 and Fig. 2). This constitutes the “training” set of the model design. Fig. 2 describes the loading condition variables that we checked. Seventy percent of the experiments were performed in electrolytic external medium, like saline (Fig. 2A). The pH range of the external liposome medium was 4.6–8.0 (Fig. 2B). Amphipathic weak bases (TPT, VCR, TMN, Bup, AO, lidocaine, and DXR) were loaded mainly by ammonium sulfate gradient, and amphipathic weak acids (MPS, BMS) were loaded mainly by calcium acetate gradient (Fig. 2C and D). Wide ranges of loading temperature (8–65 °C) and loading duration (5– 1440 min) were tested (Fig. 2E and F). We used large multivesicular vesicles (LMVV), multilamellar vesicles (MLV), large unilamellar vesicles (LUV, d. ≥ 100 nm) and small unilamellar vesicles (SUV, d. b100 nm) for drug loading (Fig. 2G). Usually (82% of experiments), we used high-magnitude (N1000) trans-membrane gradients (Fig. 2H and K). We have tried a wide range (0.005–24) of drug/lipid mole ratios at the beginning of the loading (Fig. 2I). Most of our liposomes were composed of ~ 60% phosphatidylcholines and ~40% cholesterol, which according to our scale (Fig. 2J, Supplementary Table 3) corresponds to membrane rigidity of 3.6–3.5. We thus have a wide distribution of external pH, loading temperature, loading duration, liposome type, initial drug/lipid ratio, and encapsulation efficiency (Fig. 2L), but a narrow distribution in gradient magnitude, salt concentration inside the liposomes, and lipid composition/rigidity. 3.2. Model for loading efficiency prediction We used the data-mining software Weka 3.4 to create a model that will connect the drug properties, loading conditions, and loading efficiency. The best results were achieved by a J48 decision tree [37] (Fig. 3), which uses the drug properties described in Table 1. Regression model gave less accurate results (Supplementary Fig. 2) with a low correlation coefficient (0.63). The most important loading condition that affects loading efficiency in the decision tree (Fig. 3) is the initial drug/lipid mole ratio. When it is too high (N0.95) the loading efficiency is low, probably due to excess of drug that exceeds the liposomal loading capacity. In some cases, such overloading damages the liposome membrane leading to drug release and therefore lower final drug/lipid mole ratio. This branch was created from the experiments with Bup, lidocaine, MPS, and TPT. In some cases (e.g., Bup), we would start with high drug/lipid ratio in order to achieve the high final drug/lipid ratio needed for prolonged analgesia [9,32] in spite the “cost” of low loading efficiencies. When the ratio is lower (≤0.95), it is possible to achieve better loading efficiency. High positive charge (N1) of the drug leads to high encapsulation efficiency. This branch resulted from the experiments with vincristine, which is the only drug in the database that has two amine groups with 4 b pKa b 9; therefore at low pH (b7.6), its charge is higher than +1.

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High charge increases vincristine's partitioning to the aqueous phase and its interaction with the counteranions inside the liposomes. Most of the database consists of experiments with lower charges (≤1).

Additional drugs with charge N + 1 are needed in order to understand the effects of high charge on loading. If the charge is ≤ + 1, then low gradient magnitudes (≤100) lead to low loading efficiency (AO, TMN). Increasing the gradient enhances loading since the driving force for the entry of the drug into the liposomes is stronger. When the gradient is N1000, a plateau in the loading is reached. If the gradient magnitude is above 100 then we check the non-polar/ polar surface area ratio of the molecule. The non-polar/polar surface area ratio is N2.31 for the drugs VCR, TPT, TMN, Bup, AO, lidocaine, and BMS. Short loading duration (≤15 min) does not lead to high loading efficiency. This branch is based mainly on loading of TPT. From our experiment with VCR, TPT, MPS, and DXR (Supplementary Table 2), we know that, at optimal conditions, above the Tm [38] of the liposomeforming lipid, the loading kinetics is fast and most of the loading, when done at high temperature, is completed after ~10 min [27,39]. The medium loading in this branch is probably due to suboptimal drug/lipid mole ratios and external pH in these experiments. Low salt concentration inside the liposomes (≤100 mM) or low solubility (≤1.9 mM) leads to medium loading. Basically, the variable salt concentration inside the liposomes resembles the variable of in/ out gradient magnitude. Increase in the intraliposome salt concentration leads to higher loading efficiency until the curve reaches a plateau (when the concentration is ≥250 mM). When salt concentration is N100 and pKa–pHE N 2.2 (see legend of Fig. 3), the loading was medium. For bases, high pKa–pHE value means more charged species of the drug, whereas for acids it is vice versa. It is also possible to reach high loading at relatively low temperatures (≤30 °C). This branch is the result of AO loading with long loading duration. The branch of intraliposome ammonium salt concentration ≥120 mM is classified as high loading, but 5 of 22 cases that are located there are classified as medium loading. Still, it is a close result since in all these cases the loading is 60–70%, and high loading was determined as N70%. If non-polar/polar surface area ratio is ≤2.31 (MPS and DXR), then the external medium type should be checked: Electrolytic medium will lead to high loading (Supplementary Table 2, lines 80–83), however external medium by itself cannot account for good loading. Probably the high loading in these four experiments in this branch is a result of a combination of many favorable conditions, including: high gradient magnitude and low initial drug/lipid ratio. If the external medium is non-electrolytic, then the drug/lipid ratio is a major determinant of the loading efficiency: when it is too low (≤0.14) or too high (N0.41) the loading efficiency is low. Appropriate ratios (0.33–0.41) lead to high loading. Less appropriate ratios (0.14–0.33) lead to medium–low loading: the gradient ion acetate will lead to medium loading and ammonium will generally lead to low loading. It seems that the acetate used for MPS loading is better than ammonium, which was used for DXR loading, although the two drugs are similar in their polar and non-polar surface areas and absolute charge. The reason for this difference may be the use of higher intraliposomal salt concentrations in MPS loading, 200–300 mM (equivalent to 400–600 mM of acetate), in comparison to DXR loading, Fig. 2. Frequency bar charts description of loading conditions database in Supplementary Table 2. The Y axis describes the number of instances in the database. A. External medium type of the liposomes during the loading: electrolytic—based on salt, nonelectrolytic—based on sugar. B. pH of the external medium of the liposomes during the loading. C. The gradient-forming ion, which is responsible for the drug loading. D. The counterion of the gradient-forming ion. E. The temperature during the drug loading process. F. The duration of the drug loading process into the liposomes. G. The type of the liposomes used. H. The concentration ratio of the gradient-forming ion: inside the liposome to outside the liposome. I. The mole drug to lipid ratio at the beginning of the loading process. J. Lipids rigidity—a parameter based on the composition of the membrane (Supplementary Table 3). K. The salt concentration inside the liposomes at the beginning of the loading process. L. The loading efficiency % of the drug: low—0– 40%, medium—40–70%, high—70–100%.

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Fig. 3. J48 decisions tree for predicting loading efficiency based on the loading conditions and drug properties. It was built by analyzing the data in Supplementary Tables 1 and 2 by Weka 3.4 software. Drug/lipid ratio in the tree refers to the input ratio. pHE—the pH of the external medium. MediumE—the type of the external medium. Temp.—the temperature during the loading process in °C. 91.7% of instances (187/204) are classified correctly by this tree and 77.5% of instances were classified correctly using 10% leave-group-out (LGO) cross validation. The single or the first number describes the number of experiments from Supplementary Table 2 that reached this leaf. The second number describes the number of experiments that reached this leaf but were classified incorrectly; when this does not appear, there were no mistakes in this leaf.

120 mM (equivalent to 240 mM of ammonia), which results in higher gradient magnitude. In the ammonium gradient branch we can see that longer loading duration (N60 min) at higher temperature (N37 °C) leads to better loading than a shorter loading duration and lower temperature. Based on the aforementioned considerations, the output of the computational tools was revised and an upgraded decisions tree was proposed (Fig. 4). This tree takes into account logical considerations and our “liposomal experience” in addition to data-mining algorithms. The intermediate machine step was employed since human mind

cannot “digest” by itself a large database. For instance, let's analyze by this tree (Fig. 4) two experiments with low and high encapsulation efficiency: Experiment 2 (Supplementary Table 2): the initial drug/lipid mole ratio is 0.005, which is b0.95; the charge of AO in pH 6.7 is 0.99, which is ≤1; the gradient magnitude is 10, which is ≤100; the loading duration is 30 min, which is ≤60. Therefore, the predicted loading is low, 0–40%. This fits the experimental value of 6%. Experiment 200 (Supplementary Table 2): the drug/lipid ratio is 0.39, which is b0.95; the charge of MPS in pH 6.7 is −1, which is ≤1;

Table 1 Drug properties selected by the Weka program to build the decisions tree. Properties

Vincristine

Topotecan

Tempamine

Bupivacaine

Acridine orange

Lidocaine

Doxorubicin

Beta methasone succinate

Methyl prednisolone succinate

pKa Polar surface area A2 Total area A2 Non-polar area A2 Non-polar/polar Solubility (mM) [pH]

7.64a, 6.81b 171a 790.3c 619.3 3.62 0.3 [6], 0.1 [6.5], 0 [7.9–8]a 1.22 [6], 1.12 [6.5], 0.88 [7.9], 0.86 [8]b

7.65a 103a 392.6c 289.6 2.81 141 [4.6], 35 [5], 36 [6], 0.5 [7], 0.2 [8]a 1 [5–4.6], 0.99 [6], 0.95 [7], 0.61 [8]b

8.9b 49.5b 213.4c 163.9 3.31 14.5 [6.7], 7.3 [7]d 1 [6.7–7]b

8.17a 32.3a 327.6c 295.3 9.14 127.4 [4.6], 99 [5], 59 [5.5]a 1 [4.6–5], 0.99 [5.5], 0.73 [7.2]b

8.67b 19.37a 296.7c 277.3 14.32 76 [6.7–7]d

8.53a 32.34b 287.3c 255 7.88 500 [6]a

8.68a 206.07a 453.8c 247.7 1.20 0.2 [5.5]a

4.29a 138.2a 464.4c 326.3 2.36 19.7 [7.6]a

4.29a 138.2a 456.9c 318.7 2.31 30.3 [7.6]a

0.99 [6.7–6.8], 0.98 [7]b

0.97 [6]b

1 [5.5]b

− 1 [7.6]b

− 1 [7.6]b

Charge [pH] a b c d

ACD/Labs. Marvin. MOE. Experimental data (Barenholz’s laboratory).

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Fig. 4. Upgraded decisions tree for predicting loading efficiency based on the loading conditions and drug properties. It was constructed by upgrading the tree in Fig. 3 through logical considerations. 91.2% of instances (186/204) are classified correctly by this tree.

the gradient magnitude is N1000, which is N100; the surface area ratio non-polar/polar is 2.31, which is ≤2.31; the drug/lipid ratio is 0.39, which is N0.33 and ≤0.41. Hence the predicted loading is high, 70–100%. This fits the experimental value of 95%. When logD and pKa fit the aforementioned range, the segregation of the drugs according to their non-polar/polar surface area ratios enables better prediction of the loading efficiencies than using more “popular” properties such as logD or logP. This might be due to the greater sensitivity of this variable and the large diversity in this ratio in the studied drugs. When this ratio is higher, the molecule is more hydrophobic. Hydrophobic molecules may aggregate, and these aggregates have low permeability across the liposomal membrane. Thus, when the non-polar/polar surface area ratio is N2.31 (Fig. 4), it is necessary that the drug would have a reasonable solubility, N1.9 mM, in order to achieve high loading because only soluble uncharged molecules can enter the liposome. We assume that there is an optimal ratio for loading efficiency: below this ratio there is excess of liposomes, hence the loading efficiency diminishes. Above this ratio there is an excess of drug and insufficient loading capacity, hence the loading diminishes. At high temperatures, the loading reaches its maximum faster than at low temperature. However, the loading maximum is higher at low temperature (after a long time) than at high temperature, probably due to the larger intraliposome precipitation at low temperature [11] (Supplementary Table 2, AO). Above 50 °C the loading is fast and reaches maximum after 10 min. Thus, at low temperature the difference between short loading duration and long loading duration can be large. Combination of high temperature and extreme loading pH may cause some hydrolysis of phospholipid acyl ester bonds, which will not be significant if the duration of exposure to high temperature is short (b30 min).

Liposome size and type are highly important for passive loading, as the passive encapsulation is limited by the trapped aqueous volume and the drug's solubility in water. LMVV has the highest trapped volume, which is higher than MLV, LUV, while SUV has the smallest [1,4]. However, remote loading, being affected by other considerations in addition to trapped volume, is more efficient in small liposomes; hence, remote loading is less sensitive to liposome size and type. With respect to external pH, we have to differentiate between the two types of drugs: weak acids (MPS, BMS) and weak bases (all the others used). For bases, the loading is better at lower pH than at higher as long as the medium pH is not too low from the drug pKa. For acids, this issue is now under investigation. Extreme pHs below or above drug pKa reduce dramatically the loading efficiency due to lack of enough neutral drug species (as was demonstrated for remote loading of DXR, which practically did not occur below pH 3.5 [40]) or to drug precipitation. It may also cause lipid hydrolysis. For bases, the ranking of the gradient ions with respect to loading efficiency is ammonia NN all others. For acids, the ranking of the gradient ions is acetate NN chloride.

3.3. Stability and drug release Membrane rigidity, counterion and liposome type were not included in the tree by the program. Our experience shows that membrane lipid composition and counterion are more important for the release rate at the temperature range relevant for long-term stability (2–8 °C) and for therapeutic efficacy (37 °C), than for loading efficiency per se. Most of the experiments in our database were performed with “good” lipid compositions (HSPC/Cholesterol of mole ratio 54–60:33–41) and counterions optimal to achieve low release

rate at 4 °C, and low but sufficient drug release at 37 °C. Thus, our database is not diverse enough in that respect (Fig. 2D and J). Wasserman [16,41] has shown that TMN release from the liposomes depends on the counteranion of the ammonium ion due to the different capacity of TMN to aggregate with the different anions. We have similar data for most of our drugs including MPS, DXR and Bup. For amphipathic weak bases, the ranking of the gradient ions 2+ with respect to drug release rate is NH+ b H+, and the ranking 4 b Mg of the counterions is sulfate b phosphate b citrate b glucoronate. For amphiphatic weak acids, the ranking of the gradient ions is acetate b chloride, and the acid counterion is calcium b sodium. Regarding effect of lipid composition, liposomes composed of lipids with high Tm (e.g., HSPC) and ~40 mole % cholesterol release less drug than liposomes composed of lipids with low Tm (e.g., EPC) and the same or lower mole % cholesterol [12,42–45]. The use of long, saturated acyl chains and the presence of an optimal level of cholesterol in the lipid bilayer reduce membrane “defects” and “free volume”, thereby, lowering membrane permeability. The cost of stable loading may be a too slow drug release at the target site at body temperature. Using ‘leakier’ liposomes will result in drug release, while the liposomes are still in circulation, thereby reaching the extravascular disease site with drug-poor liposomes. This limitation can be surmounted by designing a liposomal system that is stable upon storage and while circulating in vivo in the plasma, but loses stability once the liposomes reach the target site, as was done for DOXIL, where the conditions in the tumor interstitial fluid differ from the conditions in the plasma. Comparison between the fate and therapeutic efficacy of DOXIL and SPI-077 nanoliposomes loaded with cis-platin by passive loading [46] suggests that the collapse of the ammonium sulfate gradient plays a more major role in drug release than the phospholipases. This explains why doxorubicin is released from DOXIL in vivo in tumor-bearing mice and in humans [47], while SPI-077, in which the ion gradient does not play any role in the release, lacks efficacy [48]. 3.4. Testing the model This decisions tree (Fig. 4) was examined by an external set of experiments done by others and referred to as “external test set”. For this validation we used loading efficiencies of molecules, which differ from those used in the training set: the anticancer agent CKD-602 [49,50], the anti-anxiety muscle relaxant drug diclofenac [51], and the antibiotic ciprofloxacin [52]. Their relevant physicochemical characterizations are described in Supplementary Table 4 and Supplementary Fig. 3. Their loading efficiencies were obtained by others [50–52] and are described in Supplementary Table 5. The loading efficiencies match the predictions of the tree (Fig. 4) in 91% of cases. 4. Conclusions We have herein summarized 15 years of liposomes research in Barenholz’s laboratory into one database, from previously published and from new results, which used to construct a “training data set” for the design of a quantitative model to predict efficiency of drug loading into liposomes. This is the first publication of a large database describing remote loading of drugs into liposomes. Data-mining algorithms enabled us to analyze this database and to build a model that suggests new ideas and confirms some previous assumptions. This is the first model of its kind to take into consideration so many drugs (9), drug properties (4), and loading conditions (10). The utility of our model (Fig. 4) was demonstrated by its validation using the “external test set” with 3 additional drugs. Our model is the first of its kind enabling one to select the appropriate loading conditions for efficient loading of various drugs according to the drug's properties. Additionally, it provides guidance for design of pro-drugs that are suitable for liposome remote loading (e.g., attachment of succinate to

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methylprednisolone and β-methasone to form MPS and BMS, respectively) in order to increase their loading efficiency. Furthermore, our approach may be useful in understanding how to prepare clinical formulations of drugs in other nanoparticles such as nanotubes and polymers. Acknowledgements This study was supported by the Barenholz Fund and Camber Fund at The Center of Nanotechnology of The Hebrew University. We would like to thank Y. Avnir, S. Clerc, R. Cohen, O. Garbuzenko, A. Sigal, K. Turjeman, and V. Wasserman for sharing their knowledge and results. Mr. S. Geller is acknowledged for help in editing the manuscript. Appendix A. Supplementary data Supplementary data associated with this article can be found, in the online version, at doi:10.1016/j.jconrel.2009.05.036. References [1] Y. Barenholz, D. Crommelin, Liposomes as Pharmaceutical Dosage Forms, Marcel Dekker Inc, New York, 1994. [2] G. Gregoriadis, Liposome Technology, Informa Healthcare, New York, 2007. [3] D.D. Lasic, Y. Barenholz, Liposomes: Models for Biological Phenomena, CRC Press Inc, Boca Raton, 1996. [4] D. Lichtenberg, Y. Barenholz, Liposomes: Preparation, Characterization, and Preservation, Wiley, New York, 1988. [5] Y. Barenholz, Liposome application: problems and prospects, Curr. Opin. Colloid Interface Sci. 6 (1) (2001) 66–77. [6] Y. Barenholz, Relevancy of drug loading to liposomal formulation therapeutic efficacy, J. Liposome Res. 13 (1) (2003) 1–8. [7] M. Voinea, M. Simionescu, Designing of ‘intelligent’ liposomes for efficient delivery of drugs, J. Cell. Mol. Med. 6 (4) (2002) 465–474. [8] M. Alam, C.T. Hartrick, Extended-release epidural morphine (DepoDur): an old drug with a new profile, Pain Pract. 5 (4) (2005) 349–353. [9] G.J. Grant, Y. Barenholz, E.M. Bolotin, M. Bansinath, H. Turndorf, B. Piskoun, E.M. Davidson, A novel liposomal bupivacaine formulation to produce ultralong-acting analgesia, Anesthesiol. 101 (1) (2004) 133–137. [10] S. Clerc, Y. Barenholz, Loading of amphipathic weak acids into liposomes in response to transmembrane calcium acetate gradients, Biochim. Biophys. Acta 1240 (2) (1995) 257–265. [11] S. Clerc, Y. Barenholz, A quantitative model for using acridine orange as a transmembrane pH gradient probe, Anal. Biochem. 259 (1) (1998) 104–111. [12] G. Haran, R. Cohen, L.K. Bar, Y. Barenholz, Transmembrane ammonium sulfate gradients in liposomes produce efficient and stable entrapment of amphipathic weak bases, Biochim. Biophys. Acta 1151 (2) (1993) 201–215. [13] J.W. Nichols, D.W. Deamer, Catecholamine uptake and concentration by liposomes maintaining pH gradients, Biochim. Biophys. Acta 455 (1) (1976) 269–271. [14] Y. Avnir, R. Ulmansky, V. Wasserman, S. Even-Chen, M. Broyer, Y. Barenholz, Y. Naparstek, Amphipathic weak acid glucocorticoid prodrugs remote-loaded into sterically stabilized nanoliposomes evaluated in arthritic rats and in a Beagle dog: a novel approach to treating autoimmune arthritis, Arthritis Rheum. 58 (1) (2008) 119–129. [15] D.D. Lasic, B. Ceh, M.C. Stuart, L. Guo, P.M. Frederik, Y. Barenholz, Transmembrane gradient driven phase transitions within vesicles: lessons for drug delivery, Biochim. Biophys. Acta 1239 (2) (1995) 145–156. [16] V. Wasserman, P. Kizelsztein, O. Garbuzenko, R. Kohen, H. Ovadia, R. Tabakman, Y. Barenholz, The antioxidant tempamine: in vitro antitumor and neuroprotective effects and optimization of liposomal encapsulation and release, Langmuir 23 (4) (2007) 1937–1947. [17] S. Clerc, Y. Barenholz, A quantitative model for using acridine orange as a transmembrane pH gradient probe, Anal. Biochem. 259 (1) (1998) 104–111. [18] Y. Zhang, D. Ceh, Liposomes in Drug Delivery, Marcel Dekker, New York, 2002. [19] Y. Barenholz, Amphipathic Weak Base Loading into Preformed Liposomes having a Transmembrane Ammonium Ion Gradient: From the Bench to Approved DOXIL, Informa Healthcare, New York, 2007. [20] A. Gabizon, R. Catane, B. Uziely, B. Kaufman, T. Safra, R. Cohen, F. Martin, A. Huang, Y. Barenholz, Prolonged circulation time and enhanced accumulation in malignant exudates of doxorubicin encapsulated in polyethylene-glycol coated liposomes, Cancer Res. 54 (4) (1994) 987–992. [21] B. Ceh, D.D. Lasic, A rigorous theory of remote loading of drugs into liposomes: transmembrane potential and induced pH-gradient loading and leakage of liposomes, J. Colloid Interface Sci. 185 (1) (1997) 9–18. [22] I.H. Witten, E. Frank, Data Mining: Practical Machine Learning Tools and Techniques, Morgan Kaufmann, San Francisco, 2005. [23] H. Shmeeda, S. Even-Chen, R. Honen, R. Cohen, C. Weintraub, Y. Barenholz, Enzymatic assays for quality control and pharmacokinetics of liposome formulations: comparison with nonenzymatic conventional methodologies, Methods Enzymol. 367 (2003) 272–292.

NANOMEDICINE

D. Zucker et al. / Journal of Controlled Release 139 (2009) 73–80

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80

D. Zucker et al. / Journal of Controlled Release 139 (2009) 73–80

[24] T. Peleg-Shulman, D. Gibson, R. Cohen, R. Abra, Y. Barenholz, Characterization of sterically stabilized cisplatin liposomes by nuclear magnetic resonance, Biochim. Biophys. Acta 1510 (1–2) (2001) 278–291. [25] G. Storm, L. van Bloois, M. Brouwer, D.J. Crommelin, The interaction of cytostatic drugs with adsorbents in aqueous media. The potential implications for liposome preparation, Biochim. Biophys. Acta 818 (3) (1985) 343–351. [26] V. Wasserman, Development, characterization, optimization, and pharmacokinetic evaluation of liposome-based piperidine nitroxide formulation for treatment of pathological conditions: tumor and rheumatoid arthritis. Ph.D Thesis, Biochemistry Dept., Hebrew University, Jerusalem, 2001. [27] Y. Avnir, Glucocorticosteroids pro-drugs, which are weak amphiphatic acids that undergo active loading into nano liposomes: from physicochemical principles to therapeutic efficacy. Ph.D Thesis, Biochemistry Dept., Hebrew University, Jerusalem, 2007. [28] X. Li, W.L. Lu, G.W. Liang, G.R. Ruan, H.Y. Hong, C. Long, Y.T. Zhang, Y. Liu, J.C. Wang, X. Zhang, Q. Zhang, Effect of stealthy liposomal topotecan plus amlodipine on the multidrug-resistant leukaemia cells in vitro and xenograft in mice, Eur. J. Clin. Invest. 36 (6) (2006) 409–418. [29] P. Tardi, E. Choice, D. Masin, T. Redelmeier, M. Bally, T.D. Madden, Liposomal encapsulation of topotecan enhances anticancer efficacy in murine and human xenograft models, Cancer Res. 60 (13) (2000) 3389–3393. [30] S.C. Semple, R. Leone, J. Wang, E.C. Leng, S.K. Klimuk, M.L. Eisenhardt, Z.N. Yuan, K. Edwards, N. Maurer, M.J. Hope, P.R. Cullis, Q.F. Ahkong, Optimization and characterization of a sphingomyelin/cholesterol liposome formulation of vinorelbine with promising antitumor activity, J. Pharm. Sci. 94 (5) (2005) 1024–1038. [31] S.J. Van Belle, M. De Smet, W. De Neve, C. Monsaert, G.A. Storme, D.L. Massart, Determination of vinca alkaloids in mouse tissues by high-performance liquid chromatography, J. Chromatogr. 578 (2) (1992) 223–229. [32] G.J. Grant, K. Vermeulen, L. Langerman, M. Zakowski, H. Turndorf, Prolonged analgesia with liposomal bupivacaine in a mouse model, Reg. Anesth. 19 (4) (1994) 264–269. [33] B.D. Anderson, V. Taphouse, Initial rate studies of hydrolysis and acyl migration in methylprednisolone 21-hemisuccinate and 17-hemisuccinate, J. Pharm. Sci. 70 (2) (1981) 181–186. [34] J. Fuchs, N. Groth, T. Herrling, G. Zimmer, Electron paramagnetic resonance studies on nitroxide radical 2,2,5,5-tetramethyl-4-piperidin-1-oxyl (TEMPO) redox reactions in human skin, Free Radic. Biol. Med. 22 (6) (1997) 967–976. [35] E. Frank, Weka 3: Data Mining Software in Java. Waikato. The University of Waikato. http://www.cs.waikato.ac.nz/ml/weka/ (accessed 15 June 2008). [36] J.M. Metselaar, M.H. Wauben, J.P. Wagenaar-Hilbers, O.C. Boerman, G. Storm, Complete remission of experimental arthritis by joint targeting of glucocorticoids with long-circulating liposomes, Arthritis. Rheum. 48 (7) (2003) 2059–2066. [37] J. Quinlan, C4.5: Programs for Machine Learning, Morgan Kaufmann Publishers, San Mateo, CA, 1993.

[38] R. Biltonen, D. Lichtenberg, The use of differential scanning calorimetry as a tool to characterize liposome preparations, Chem. Phys. Lipids 64 (1993) 129–142. [39] I.V. Zhigaltsev, N. Maurer, Q.F. Akhong, R. Leone, E. Leng, J. Wang, S.C. Semple, P.R. Cullis, Liposome-encapsulated vincristine, vinblastine and vinorelbine: a comparative study of drug loading and retention, J. Control. Release 104 (1) (2005) 103–111. [40] E. Bolotin, R. Cohen, L. Bar, N. Emanuel, S. Ninio, D. Lasic, Y. Barenholz, Ammonium sulfate gradients for efficient and stable remote loading of amphipathic weak bases into liposomes and ligandoliposomes, J. Liposome Res. 4 (1994) 455–479. [41] Y. Barenholz, V. Wasserman, Method for preparing liposome formulations with a predefined release, USA Patent, 2006/0165766 A1USA, Sep 9, 2002. [42] J. de Gier, Osmotic behaviour and permeability properties of liposomes, Chem. Phys. Lipids 64 (1–3) (1993) 187–196. [43] D.W. Deamer, J. Bramhall, Permeability of lipid bilayers to water and ionic solutes, Chem. Phys. Lipids 40 (2–4) (1986) 167–188. [44] G.J. Grant, Y. Barenholz, B. Piskoun, M. Bansinath, H. Turndorf, E.M. Bolotin, DRV liposomal bupivacaine: preparation, characterization, and in vivo evaluation in mice, Pharm. Res. 18 (3) (2001) 336–343. [45] W. Stein, Transport and Diffusion Across Cell Membranes, Academic Press, New York, 1986. [46] D.D. Lasic, J.J. Vallner, P.K. Working, Sterically stabilized liposomes in cancer therapy and gene delivery, Curr. Opin. Mol. Ther. 1 (2) (1999) 177–185. [47] Z. Symon, A. Peyser, D. Tzemach, O. Lyass, E. Sucher, E. Shezen, A. Gabizon, Selective delivery of doxorubicin to patients with breast carcinoma metastases by stealth liposomes, Cancer 86 (1) (1999) 72–78. [48] S. Bandak, D. Goren, A. Horowitz, D. Tzemach, A. Gabizon, Pharmacological studies of cisplatin encapsulated in long-circulating liposomes in mouse tumor models, Anticancer Drugs 10 (10) (1999) 911–920. [49] S.S. Jew, M.G. Kim, H.J. Kim, E.Y. Rho, H.G. Park, J.K. Kim, H.J. Han, H. Lee, Synthesis and in vitro cytotoxicity of C(20)(RS)-camptothecin analogues modified at both B (or A) and E ring, Bioorg. Med. Chem. Lett. 8 (14) (1998) 1797–1800. [50] W.C. Zamboni, S. Strychor, E. Joseph, D.R. Walsh, B.A. Zamboni, R.A. Parise, M.E. Tonda, N.Y. Yu, C. Engbers, J.L. Eiseman, Plasma, tumor, and tissue disposition of STEALTH liposomal CKD-602 (S-CKD602) and nonliposomal CKD-602 in mice bearing A375 human melanoma xenografts, Clin. Cancer Res. 13 (23) (2007) 7217–7223. [51] S.H. Hwang, Y. Maitani, X.R. Qi, K. Takayama, T. Nagai, Remote loading of diclofenac, insulin and fluorescein isothiocyanate labeled insulin into liposomes by pH and acetate gradient methods, Int. J. Pharm. 179 (1) (1999) 85–95. [52] Y.K. Oh, D.E. Nix, R.M. Straubinger, Formulation and efficacy of liposomeencapsulated antibiotics for therapy of intracellular Mycobacterium avium infection, Antimicrob. Agents Chemother. 39 (9) (1995) 2104–2111.