circulating liposomes via application of Quality by Design (QbD)

Journal of Liposome Research

ISSN: 0898-2104 (Print) 1532-2394 (Online) Journal homepage: http://www.tandfonline.com/loi/ilpr20

Optimization of prednisolone-loaded longcirculating liposomes via application of Quality by Design (QbD) approach Bianca Sylvester, Alina Porfire, Dana-Maria Muntean, Laurian Vlase, Lavinia Lupuţ, Emilia Licarete, Alina Sesarman, Marius Costel Alupei, Manuela Banciu, Marcela Achim & Ioan Tomuţă To cite this article: Bianca Sylvester, Alina Porfire, Dana-Maria Muntean, Laurian Vlase, Lavinia Lupuţ, Emilia Licarete, Alina Sesarman, Marius Costel Alupei, Manuela Banciu, Marcela Achim & Ioan Tomuţă (2016): Optimization of prednisolone-loaded long-circulating liposomes via application of Quality by Design (QbD) approach, Journal of Liposome Research, DOI: 10.1080/08982104.2016.1254242 To link to this article: http://dx.doi.org/10.1080/08982104.2016.1254242

Accepted author version posted online: 27 Oct 2016. Published online: 16 Nov 2016.

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Date: 12 September 2017, At: 22:54

http://informahealthcare.com/lpr ISSN: 0898-2104 (print), 1532-2394 (electronic) Journal of Liposome Research, Early Online: 1–13 ! 2016 Informa UK Limited, trading as Taylor & Francis Group. DOI: 10.1080/08982104.2016.1254242

RESEARCH ARTICLE

Optimization of prednisolone-loaded long-circulating liposomes via application of Quality by Design (QbD) approach Bianca Sylvester1, Alina Porfire1, Dana-Maria Muntean1, Laurian Vlase1, Lavinia Luput¸2,3, Emilia Licarete2,3, Alina Sesarman2,3, Marius Costel Alupei2,3, Manuela Banciu2,3Marcela Achim1, and Ioan Tomut¸a˘1 1

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Department of Pharmaceutical Technology and Biopharmaceutics, Faculty of Pharmacy, University of Medicine and Pharmacy ‘‘Iuliu Hat¸ieganu’’, Cluj-Napoca, Romania, 2Department of Molecular Biology and Biotechnology, Faculty of Biology and Geology, Babes-Bolyai University, Cluj-Napoca, Romania, and 3Molecular Biology Centre, Institute for Interdisciplinary Research in Bio-Nano-Sciences, Babes-Bolyai University, Cluj-Napoca, Romania Abstract

Keywords

Quality by design principles (QbD) were used to assist the formulation of prednisolone-loaded long-circulating liposomes (LCL-PLP) in order to gain a more comprehensive understanding of the preparation process. This approach enables us to improve the final product quality in terms of liposomal drug concentration, encapsulation efficiency and size, and to minimize preparation variability. A 19-run D-optimal experimental design was used to study the impact of the highest risk factors on PLP liposomal concentration (Y1- mg/ml), encapsulation efficiency (Y2-%) and size (Y3-nm). Out of six investigated factors, four of them were identified as critical parameters affecting the studied responses. PLP molar concentration and the molar ratio of DPPC to MPEG2000-DSPE had a positive impact on both Y1 and Y2, while the rotation speed at the formation of the lipid film had a negative impact. Y3 was highly influenced by prednisolone molar concentration and extrusion temperature. The accuracy and robustness of the model was further on confirmed. The developed model was used to optimize the formulation of LCL-PLP for efficient accumulation of the drug to tumor tissue. The cytotoxicity of the optimized LCL-PLP on C26 murine colon carcinoma cells was assessed. LCL-PLP exerted significant antiangiogenic and anti-inflammatory effects on M2 macrophages, affecting indirectly the C26 colon carcinoma cell proliferation and development.

Design space, drug targeting, PEGylated liposomes, prednisolone, QbD

Introduction Previous studies demonstrated that prednisolone disodium phosphate encapsulated in long-circulating liposomes (LCLPLP) inhibited drastically tumor growth in two experimental murine cancer models, B16.F10 melanoma and C26 colon carcinoma model (Schiffelers et al., 2006). For efficient delivery of PLP into tumors after intravenous (i.v.) administration, the authors used polyethylene glycol (PEG)-coated liposomes (Banciu et al., 2008a; Schiffelers et al., 2006). It is known that PEG protects liposomes from recognition and rapid removal from the circulation by the mononuclear phagocyte system (MPS), enabling the liposomes to stay in the circulation for a prolonged period of time and allowing them to substantially extravasate and accumulate in tumors (Banciu et al., 2008b; Gabizon, 2001). Taking the advantage

Address for correspondence: Alina Porfire, Department of Pharmaceutical Technology and Biopharmaceutics, Faculty of Pharmacy, University of Medicine and Pharmacy ‘‘Iuliu Hat¸ieganu’’, 41 V. Babe¸s street, 400012, Cluj-Napoca, Romania. Tel: 0040-745889030. E-mail: [email protected]

History Received 11 July 2016 Revised 21 September 2016 Accepted 24 October 2016 Published online 15 November 2016

of these previous findings, our study aimed to optimize the encapsulation efficiency of PEG-based long-circulating liposomes for efficient accumulation of PLP to tumor tissue and to minimize the preparation variability. The EE% of previous LCL-PLP formulations for tumor targeting were very low, with a maximum of only 5% and high-preparation variability (Banciu et al., 2008a; Schiffelers et al., 2006). A possible explanation would be that the development of the liposome formulation was done mostly empirical, without taking into account the critical formulation and process parameters that can affect the quality of the final product. Therefore, using the Quality by Design concept, to assist formulation and process design could be a promising way to understand the sources of variability in order to improve the product quality (Xu et al., 2012a). Furthermore, the disadvantages of liposomal formulations such as the stability problems (aggregation during storage, fusion of the membranes, loss of encapsulated material, hydrolysis and oxidation of the lipids) (Davis et al., 2008) and manufacturing problems (time consuming and complex nature on the preparation method, the difficulty to scale-up, high-manufacturing costs due to low reproducibility and low entrapment of therapeutic agents and difficulties associated with the identification and control of

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the critical formulation and process design factors) (Guan et al., 2011; Xu et al., 2011), could be overcome by a using Quality by Design approach. Pharmaceutical QbD is a systematic, risk-based and proactive approach to pharmaceutical development which uses quality-improving scientific methods upstream in the research, development and design phases, in order to assure that quality is designed into product at as early stage as possible (Vogt, 1992). QbD identifies characteristics that are critical to quality from the perspective of patients and manufacturers, translates them into attributes that the drug should poses and establishes how the critical formulation and process parameters can be varied to constantly produce a drug product with the desired characteristics (Yu, 2008). Moreover QbD study comprises all elements of pharmaceutical development mentioned in ICH guideline Q8: (1) defining the target product quality profile, (2) determination of critical quality attributes (CQAs) and critical process parameters (CPPs), (3) risk assessment, (4) development of an experimental design in order to study the impact of CPPs on CQAs and establish a design space, (5) designing and implementing a control strategy and ensuring a continuous improvement (Eon-Duval et al., 2012; ICH Q8 (R2), 2009; Sangshetti et al., 2014). In our study the desired characteristics of the LCL-PLP were drug encapsulation efficiency higher than that obtained for previous similar LCL-PLP formulations (about 5%) (Banciu et al., 2008a,b; Schiffelers et al., 2006), low and predictable variation in the drug encapsulation efficiency, particle size range of 100–150 nm and narrow size distribution. After defining the desired characteristics of the liposomes and establishing the potential CQAs, we identified CPPs through a risk assessment based on preliminary formulation data and general scientific knowledge. Further on, the impact of CPPs on CQAs was studied using multivariate design of experiment (DoE) tools. The model that we generated through DoE was further on tested for accuracy and robustness and allowed us to construct a design space, in order to obtain liposomes with desired characteristics. Finally, the cytotoxicity of the optimized LCL-PLP on C26 murine colon carcinoma cells cultivated in monoculture as well as in co-culture with murine peritoneal macrophages and the mechanisms of this cytotoxicity were assessed.

Materials and methods Materials Prednisolone sodium phosphate was purchased from Sigma Aldrich (Darmstadt, Germany). The phospholipids used for liposome preparation: 1,2-dipalmitoyl-sn-glycero-3-phosphocholine (DPPC) and N-(carbonyl methoxypolyethylenglycol2000)–1,2-distearoylsn-glycero-3-phosphoethanolamine (Na-salt; MPEG-2000-DSPE) were purchased from Lipoid GmbH (Ludwigshafen am Rhein, Germany). Cholesterol (CHO) from sheep wool was provided by Sigma-Aldrich (Darmstadt, Germany). All the other reagents used were of analytic grade purity, commercially available. Liposome preparation LCL-PLP were prepared using the film hydration method, as described previously by Schiffelers et al. (2005).

Journal of Liposome Research, Early Online: 1–13

Phospholipids (DPPC and MPEG-2000-DSPE) and cholesterol were dissolved in an appropriate amount of ethanol in a round-bottomed flask. The solvent was evaporated under reduced pressure at 60  C in a rotary evaporator, leading to the formation of a thin film at the bottom of the flask. The remaining residual solvent was removed by maintaining the flask under a stream of nitrogen for 1 h. Liposomes were formed by hydrating the film with 5 ml solution of phosphate buffered saline (PBS, pH ¼ 7.4) in which the water soluble PLP was dissolved, for 20 min at 60  C. Liposomal dispersion was subsequently extruded under high pressure five times through a 0.8 mm polycarbonate membrane and five times through a 0.2 mm polycarbonate membrane using LiposoFast LF-50 equipment (Avestin Europe GmbH, Mannheim, Germany). Unencapsulated drug was removed by dialysis in a Slide-A-Lyzer cassette with a molecular weight cutoff of 10 kDa at 4  C, with repeated changes of buffer over a period of 24 h. Liposomes were stored at a temperature of 4  C, until analysis. Measurement of liposomal size Liposomal size and polydispersity index (PDI) value were determined by dynamic light scattering method, using Zetasizer Nano ZS analyzer (Malvern Instruments Co., Malvern, UK). The measurement was performed at 25  C with a scattering angle of 90  . The dynamic light scattering data was collected using a helium laser source and mean results were provided by photon correlation spectroscopy (PCS). Determination of PLP content and encapsulation-efficiency The PLP content of the liposomes was determined through an HPLC/UV method, after complete dissolution of liposomes in methanol. Analyses were performed on a Agilent 1100 HPLC system (Agilent Technologies, Santa Clara, CA), equipped with an ultraviolet (UV) detector. Chromatographic separation was carried out using a Zorbax SB C18 column (100  3 mm, internal diameter 3.5 mm) from Agilent (Agilent Technologies, Santa Clara, CA). The mobile phase was 60% acetonitrile and 40% 0.1% phosphoric acid solution. Chromatographic conditions set for the method were: flow rate 1 ml/min, column temperature 40  C, UV detection at 240 nm and injection volume 10 ml. The retention time for PLP was 1.6 min. Liposomal PLP was expressed both as concentration (mg/ml) and encapsulation efficiency (%). The EE was calculated using the following equation, and represents the percentage of entrapped drug: EE ð%Þ ¼ ðEntrapped PLP=Total PLPÞ  100

ð1Þ

QbD steps Defining the quality target product profile (QTPP) and determination of critical quality attributes (CQAs) The first step in this QbD case study is to identify potential CQAs of the product, based on the quality target product profile (QTPP), by means of a risk assessment.

Optimization of LCL-PLP via application of QbD

DOI: 10.1080/08982104.2016.1254242

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Table 1. D-optimal design table and results. Formulation code

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N1 N2 N3 N4 N5 N6 N7 N8 N9 N10 N11 N12 N13 N14 N15 N16 N17 N18 N19

X1 (mM)

X2

X3 (mM)

X4 ( C)

X5 ( C)

X6 (rot/min)

Y1 (mg/ml)

Y2 (%)

Y3 (nm)

40 100 40 100 40 100 40 100 40 100 40 100 40 100 40 100 70 70 70

5 5 10 10 5 5 10 10 5 5 10 10 5 5 10 10 7.5 7.5 7.5

50 50 50 50 100 100 100 100 50 50 50 50 100 100 100 100 75 75 75

9 9 9 9 9 9 9 9 19 19 19 19 19 19 19 19 14 14 14

40 60 60 40 60 40 40 60 40 60 60 40 60 40 40 60 50 50 50

60 60 180 180 180 180 60 60 180 180 60 60 60 60 180 180 120 120 120

3165.19 3005.96 2422.11 2221.12 8603.41 8536.81 9632.55 9283.69 3760.73 4079.79 4997.39 4789.58 10615.91 10535.62 8613.81 8951.78 6487.43 6190.37 6922.74

13.06 12.41 10.01 9.17 17.76 17.62 19.88 19.16 15.52 16.84 20.63 19.77 21.91 21.75 17.78 18.48 17.85 17.03 19.05

208.7 150.9 154.9 203.2 128.4 153.6 138.7 105.5 169.3 139.0 137.6 226.7 143.1 182.2 191.3 97.0 133.5 127.4 100.5

X1: phospholipids molar concentration (mM); X2: the molar ratio of phospholipids to cholesterol, X3: PLP molar concentration (mM); X4: molar ratio of DPPC to MPEG-2000-DSPE; X5: extrusion temperature (  C); X6: rotation speed at the hydration of the lipid film (rot/min) and Y1: Liposomal concentration of PLP (mM); Y2: Encapsulation efficiency (%); Y3: Liposomal size (nm).

CQAs and QTPP were established based on preliminary formulation studies and review of the literature. According to these, liposomal drug concentration, encapsulation efficiency and average particle size are the parameters which influence the drug delivery system the most. Risk analysis Risk assessment was conducted in order to recognize critical attributes that can affect final quality of the product. In order to identify the potential risk factors, Ishikawa diagrams, also known as cause-effect diagrams, were constructed. Three critical quality attributes, liposomal PLP concentration, encapsulation efficiency and particle size were defined and delineated to identify all potential risks. After the risk analysis, six variables were chosen to be further studied and were included in a D-optimal experimental design. D-optimal design Based on the risk analysis results, six key variables were identified: X1- phospholipids molar concentration (mM), X2molar ratio of phospholipids to cholesterol, X3- PLP molar concentration (mM), X4- molar ratio of DPPC to MPEG2000-DSPE, X5- extrusion temperature (  C) and X6- rotation speed at the hydration of the lipid film (rot/min). A D-optimal experimental design with six factors and two levels was employed to study the influence of these risk factors on the preparation of PLP liposomes. The design of the study was developed using Modde 10 software (Umetrics, Umea˚, Sweden). Three responses (dependent variables) were evaluated: liposomal concentration of PLP (Y1), EE (Y2) and liposomal size after extrusion (Y3). The number of passages through the 0.8 mm and 0.2 mm polycarbonate membranes, the temperature used at the formation/hydration of the lipid film and the rotation speed at the formation of the film were kept constant for all the formulations. The levels of the variables studied in the experimental design were established within the range suggested in literature (Banciu et al., 2008a; Porfire

et al., 2015; Schiffelers et al., 2006; Tefas et al., 2014). The matrix of the experimental design comprising 19 formulations and the results obtained are presented in Table 1. In order to fit the experimental data with the chosen experimental design and to calculate the statistical parameters, the statistical module from Modde 10 software (Umetrics, Umea˚, Sweden) was used. Partial least squares (PLS) method was employed for data fitting and for calculation of statistical parameters. Design space Finally, based on our experimental results, using the same software, we generated the Design Space, where all the specifications mentioned in the QTPP are fulfilled at a specified risk level. The establishment of a Design Space is based on the regression models and an estimation of the probability for failure. Monte Carlo simulations are used to compile the necessary probability statistics and risk analysis. The probability of getting predictions outside the response specifications was quantified as probability of failure expressed in percentage. Cell line and culture conditions C26 murine colon carcinoma cells (Cell Line Services GmbH, Eppelheim, Germany) were cultured as a monolayer in complete RPMI 1640 medium (Lonza) supplemented with 10% heat-inactivated fetal bovine serum, at 37  C in 5% CO2 humidified atmosphere. Isolation of peritoneal macrophages and culture conditions Peritoneal macrophages were isolated from 11 male BALB/c mice (Cantacuzino Institute, Bucharest, Romania). Experiments were performed according to the national regulations and were approved by the local animal experiments ethical committee (registration no.32652/01.07.2014

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and 30149/19.01.2015). Mice were previously injected intraperitoneal with 1 ml of 3% thioglycollate (Fluka) (Calorini et al., 2005). After 3 days, macrophages were collected and cultured as a monolayer in complete RPMI 1640 medium (Lonza) supplemented with 10% heat-inactivated fetal bovine serum, at 37  C in 5% CO2 humidified atmosphere. To obtain M2 phenotype of macrophages, these cells were incubated with 20 ng/ml of IL-4 for 24 h (Martinez et al., 2006). Co-culture of C26 tumor cells with murine macrophages

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After obtaining macrophage cultivation, C26 cells were added to reach a final cell density ratio of 1:4 between macrophages and tumor cells. This ratio has been proved previously that ensures the optimal cytokine interplay between tumor cells and macrophages that provides an approximation of the physiological conditions of colon carcinoma development in vivo (Martinez et al., 2006). Cell proliferation assay Cytotoxicity of different concentrations of PLP as free and liposomal form, ranging from 3.5–150 mg/ml on C26 murine colon carcinoma cells (Cell Line Services GmbH, Eppelheim, Germany) cultivated either alone or with peritoneal macrophages was assessed. Thus, the anti-proliferative effects of LCL-PLP and free PLP were determined by using the ELISA BrdU-colorimetric immunoassay (Roche Applied Science, Mannheim, Germany), according to the manufacturer instructions and as described previously (Alupei et al., 2014). This method is based on the incorporation of the pyridine analog – bromodeoxyuridine (BrdU) – instead of thymidine into the DNA of proliferating cells. After different treatments, cells were incubated with BrdU solution for 24 h and the culture medium was completely removed from each well. Following this step, the cells were fixed and the DNA was denatured. To detect the incorporated BrdU in the newly synthesized cellular DNA, a monoclonal antibody conjugated with peroxidase – anti-BrdU-POD – was added in each well. The antibody was removed after 1 h incubation and the cells were washed three times with phosphate buffered saline. A peroxidase substrate (tetramethyl-benzidine) was added in each well, and the immune complexes were detected by measuring the absorbance of the reaction product at 450 nm with a reference wavelength of 655 nm. The effects of different treatments on C26 cells in both culture conditions were determined in triplicate. Determination of inflammatory and angiogenic protein production To assess the effects of LCL-PLP on the expression levels of inflammatory/angiogenic proteins in M2 macrophages, a screening for 24 proteins involved in angiogenesis and inflammation was performed by using RayBioÕ Mouse Angiogenic Cytokine Antibody Array kit (RayBiotech Inc., Norcross, GA) as described previously (Banciu et al., 2008c). Thus, 150 mg/ml LCL-PLP-treated macrophages as well as untreated M2 macrophages were detached and lysed with cell lysis buffer (containing 10 mM HEPES (pH 7), 200 mM NaCl,

Journal of Liposome Research, Early Online: 1–13

1% Triton X, 10 mM MgCl2, 1 mM dithiothreitol (DTT), and protease inhibitor cocktail tablets (Complete, Roche Diagnostics GmbH, Mannheim, Germany)) after 30 min of incubation on ice. Cells were further lysed using a Pottertissue homogenizer (Thomas Scientific, Swedesboro, NJ), centrifuged at 15 000  g for 10 min at 4  C, and the supernatant was collected. After obtaining the pooled cell lysates for each group, the protein content of the cell lysates was determined using the Bradford assay (Bio-Rad, Hercules, CA) (Bradford, 1976). Thereafter, one array membrane containing 24 types of primary antibodies against specific proteins was used for each cell lysate. The array membrane was incubated with 60 mg of proteins of cell lysates, for 2 h at room temperature. Subsequently, a mixture of secondary biotin-conjugated antibodies against the same angiogenic factors as those for primary antibodies, was added on the membranes and incubated overnight at 4  C, followed by incubation with HRP-conjugated streptavidin for 2 h. Each incubation step was followed by five washing steps. Thereafter, the membranes were incubated with a mixture of two detection buffers for 1 min, exposed to an X-ray film (Kodak) for 2 min and then the films were developed. The protein expression level was quantified by measuring the intensity of the color of each spot on the membranes, in comparison to the positive control spots already bound to the membranes, using TotalLab Quant Software version 12 for Windows. The expression of each angiogenic protein in cell lysates from macrophages treated with LCL-PLP and from untreated M2 macrophages was determined in duplicate. Statistical analysis Data from different experiments were expressed as mean ± standard deviation (SD). The differences between the production of each inflammatory/angiogenic proteins in macrophages treated with LCL-PLP and in control M2 macrophages were analyzed by two-way ANOVA with Bonferroni correction for multiple comparisons. All statistical analyses were performed by using GraphPad Prism version 6 for Windows, GraphPad Software (San Diego, CA). A p value 50.05 was considered significant.

Results and discussions Defining the quality target product profile (QTPP) The most important element in using QbD concept to assist formulation and process design is to pre-define the desired final product quality profile (ICH Q8 (R2), 2009; Xu et al., 2012b). According to the definition of ICH Q8 (R2) (2009), the Quality Target Product Profile (QTPP) is ‘‘a prospective summary of the quality characteristics of a drug product that ideally will be achieved to ensure the desired quality, taking into account safety and efficacy of the drug product’’ (ICH Q8 (R2), 2009). In order to establish the QTPP, the following considerations must be taken into account: route of administration, dosage form, delivery system, attributes affecting pharmacokinetic characteristics, stability and drug release appropriate for the intended final product (ICH Q8 (R2), 2009). In this study we aimed to develop liposomes containing prednisolone

DOI: 10.1080/08982104.2016.1254242

for systemic administration that would remain in the circulation for a prolonged period of time and substantially extravasate and accumulate in tumors. Therefore, the QTPP of a liposomal formulation designed for the mentioned purpose will be represented by: an appropriathte vesicle size for tumor accumulation (100–150 nm); PEGylation of the liposomes for long-circulation properties; an optimal cholesterol concentration for stability; a high concentration of incorporated drug for therapeutic efficacy.

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Determination of critical quality attributes A Critical Quality Attribute (CQA) is, according to the ICH Q8 (R2) definition, ‘‘a physical, chemical, biological, or microbiological property or characteristic that should be within an appropriate limit, range, or distribution to ensure the desired product quality’’ (ICH Q8 (R2), 2009). Based on the QTPP of the long-circulating liposomes defined above, drug concentration, encapsulation efficiency and liposomal size were selected as the CQAs, affecting the therapeutic potential and the targeting ability, as it has been reported previously in the literature (Liu et al., 2012; Pandey et al., 2014; Rahman et al., 2010). Compared to hydrophobic drugs, which can be successfully incorporated into the liposome bilayer with high encapsulation efficiencies, the encapsulation of hydrophilic drugs into liposomes presents unique challenges (Morel et al., 1996; Xu et al., 2011, 2012b,c). High-water solubility of PLP makes it difficult to obtain a high and constant degree of entrapment. The EE% of previous LCL-PLP formulation for tumor targeting only achieved 5% (Banciu et al., 2008a; Schiffelers et al., 2006). In a preliminary formulation step of our experiments we obtained very low EE%, with a maximum of 10% and highpreparation variability. Thus, achieving a higher percentage of encapsulation, especially for water-soluble active principle ingredients, represents an advantage for both manufacturers and patients. A higher percentage of encapsulated drug can increase drug concentration in the final product and so, reduce production costs, allow greater flexibility in dosing, increase dosing intervals and hence improve patient compliance (Xu et al., 2011). It was observed that certain circumstances such as inflammation/hypoxia, tumors or infarcts can determine an enhanced permeability of the endothelial lining of the blood vessel wall, compared with the normal state of the tissue (Shaji & Lal, 2013). Particles, such as nanocarriers (in the size range of 20–200 nm), can extravasate and accumulate inside the interstitial space. This spontaneous accumulation or ‘‘passive’’ targeting, which works especially good with tumors and inflamed areas, is currently known as enhanced permeability and retention (EPR) effect (Torchilin, 2000). In order to maximize this effect and also to allow sterile filtration of the final product, the size of the liposomes should range between 100 and 200 nm (Mayer et al., 1989). Based on this knowledge, the current study’s aim is to apply QbD concept in order to optimize the formulation of LCL-PLP in terms of drug content, EE% and size and to minimize the preparation variability. Further on, we performed a risk analysis to assess the risks associated with these three key product qualities and we

Optimization of LCL-PLP via application of QbD

5

studied how critical formulation and process parameters can be varied to consistently produce a drug with the desired characteristics (Yu, 2008). Risk assessment According to ICH guideline Q9, risk is defined as ‘‘the combination of the probability of occurrence of harm and the severity of that harm. Risk assessment consists of the identification of hazards and the analysis and evaluation of risks associated with exposure to those hazards’’ (ICH Q9, 2005). Risk assessment helps to increase the quality of the process and to recognize critical attributes that are going to affect the final quality of the product (Sangshetti et al., 2014). In this study, liposomal PLP concentration, encapsulation efficiency, particle size and size distribution were identified as critical quality attributes of the final product. Therefore, understanding the potential risks affecting these major product qualities is very important. In order to accomplish this, two cause and effect diagrams (Ishikawa diagrams) were constructed to identify the potential causes of product variability (ICH Q8 (R2), 2009), as shown in Figures 1 and 2. Risk analysis: PLP liposomal concentration and EE% Factors affecting PLP liposomal concentration and EE% were analyzed together, as they are very similar, and they were divided into three categories: formulation, process and analytical method, as illustrated in Figure 1. No factors were selected from the analytical method, because, as we observed from preliminary formulation studies, they could be very well controlled. The most suitable purification method was selected to be dialysis and the EE% was calculated based on the encapsulated drug, since it was the most accurate. Formulation factors that were taken into account in the study were phospholipid molar concentration, the molar ratio of phospholipids to cholesterol and PLP molar concentration. In order to prepare the liposomes, drug containing solutions are used to hydrate the lipid film, thus the molar concentration of that solution has an impact on the final liposomal drug concentration and the EE%. The lipid bilayer composition influences lipid bilayer fluidity and hence permeability (Kirby et al., 1980; Lee et al., 2005) and plays an important role in the circulating lifetime of liposomal carriers (Drummond et al., 1999; Porfire et al., 2015). Different phospholipids may induce, depending on their shape, charge, different properties of the bilayer. Cholesterol is known to increase stability of liposomes, contributing to their longcirculating properties, by reducing membrane fluidity and permeability. A high content of cholesterol may on the other hand have a negative influence on drug entrapment, hence the optimum concentration of cholesterol should be determined (Gabizon, 2001). Critical process parameters (CPPs) are process parameters whose variability have an impact on a CQA and therefore should be monitored or controlled to ensure the process produces the desired quality and the specifications in the QTPP are met (ICH Q8 (R2), 2009). In our study, the critical process parameters for the production method that were chosen to be further investigated were the rotation speed at the hydration of the lipid film and the extrusion temperature.

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Journal of Liposome Research, Early Online: 1–13

Figure 1. Ishikawa diagram illustrating factors that might have impact on liposomal PLP concentration and the encapsulation efficiency (EE%).

Figure 2. Ishikawa diagram illustrating factors that might have impact on liposomal size.

The other critical process parameters, represented in Figure 1, were determined during preliminary formulation studies and were kept constant. The formation of the lipid film and further on the hydration process are vital steps in the preparation of liposomes. The temperature used in these critical steps was 60  C, which is above the Tm (liquid crystal transition temperature) of the lipids and the hydration medium pH was kept at 7.4 with a solution of phosphate buffered saline.

Risk analysis: liposomal size As shown in Figure 2, the two major categories of the Ishikawa diagram are formulation and processing conditions, from which several factors were chosen to be investigated by means of the D-optimal experimental design: phospholipids molar concentration, the molar ratio of phospholipids to cholesterol, the molar ratio of DPPC to MPEG-2000-DSPE and extrusion temperature.

Optimization of LCL-PLP via application of QbD

DOI: 10.1080/08982104.2016.1254242

Table 2. Analysis of variance for liposomal PLP concentration. Degrees of freedom

Source

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Total corrected Regression Residual Lack of fit Pure error

19 6 12 10 2

Sum of squares

Mean square

F value

Table 3. Analysis of variance for EE%. p values

940785000 49515000 146075000 24345800 352.956 0.001 827723 68976.9 556354 55635.4 0.410 0.863 271369 135684

Although formulation factors have a significant impact on liposomal size and size distribution of the final product, these characteristics of the liposomes largely depend on the final step of the preparation, the extrusion. Since the membrane pore size and the number of passages through the membrane have a big influence on the desired size of liposomes and the homogeneity of the product, both factors were determined during preliminary formulation studies. The extrusion pressure was kept constant at 8 atmospheres for the entire experiment, while extruder temperature was varied between 40 and 60  C to see how the increase of temperature influences lipid bilayer flexibility and hence passage through the membrane pores. The influence of studied factors on PLP liposomal concentration As shown in Table 1, PLP liposomal concentration varied from 2221.12 mg/ml to 10615.91 mg/ml for various factor combinations. A good correlation between the observed and predicted values was obtained during statistical analysis. The correlation coefficient (r2) had a value of 0.992. The results from the ANOVA test, presented in Table 2, showed a significant influence of variables on the response (liposomal PLP concentration) (p50.01) and that the model did not present a significant lack of fit (p ¼ 0.863). As can be seen from the equation describing the influence of formulation and process related factors on liposomal PLP concentration, out of the six investigated main factors, only three of them showed statistical significance, X3, X4 and X6. A significant interaction between X2 and X6 can be observed as well. Y1 ¼ 6464 þ 2730:12X3 þ 558:24X4  520:68X6  230:69X2 X6

7

ð2Þ

A positive value of the regression coefficient indicates an effect that favors the response, while a negative value suggests an inverse relation between the factor and the response. The influence of studied factors on encapsulation efficiency (EE%) According to the data presented in Table 1, EE % varied from 9.17 to 21.91% for the studied factor combinations. The prediction confidence level of the model was 95% and a good correlation between observed and predicted values was obtained, reflected by the r2 value of 0.955. The ANOVA test results, illustrated in Table 3, showed a significant influence of variables on PLP encapsulation efficiency (p50.001).

Source Total corrected Regression Residual Lack of fit Pure error

Degrees of freedom

Sum of squares

Mean square

18 5 13 11 2

0.204704 0.195583 0.009121 0.007931 0.001189

0.011372 0.039116 0.000701 0.000721 0.000594

F value p values 55.747

0.001

1.212

0.563

The value of p ¼ 0.536 shows that the proposed model did not present a significant lack of fit. The equation describing the influence of formulation and process parameters on EE % is the following: Y2 ¼ 1:222 þ 0:063X3 þ 0:057X4  0:041X6  0:045X3  X4 ð3Þ The factors that influenced significantly the EE % were, according to the equation, PLP molar concentration, the molar ratio of DPPC to MPEG-2000-DSPE and the rotation speed at the hydration of the lipid film (rot/min), the same factors that influenced the first response Y1. This result was expected considering EE% is dependent on the intra-liposomal concentration. An interaction between factors X3 and X4 is present. The effect of PLP molar concentration on liposomal PLP concentration and on EE% Out of all investigated factors, PLP molar concentration had the highest impact on both liposomal concentration and EE%. An increase in PLP liposomal concentration is observed when the molar concentration of the hydration solution is increased, as illustrated in Figure 3(A). The EE% increased when increasing PLP molar concentration, with the maximum encapsulation occurring at the concentration of 100 mM. A negative curvature of the response surface diagram for predicting EE% with respect to PLP molar concentration and the molar ratio of DPPC to MPEG-2000-DSPE was observed, due to an interaction between the two factors (Figure 4A). Therefore EE% increases with the PLP molar concentration in a greater extent when the ratio of DPPC to MPEG-2000-DSPE increases, but reaches a plateau at the ratio of 18:1. These findings, that both liposomal PLP concentration and EE% are positively influenced by PLP molar concentration, can be explained by the fact that more PLP is available in the hydration medium to be incorporated into the liposomes. Due to drug-lipid interactions, that have been observed also in previous studies (Xu et al., 2011, 2012b) and explained by the ‘‘pocket theory’’, EE% manifests an increase dependent on the ratio of DPPC to MPEG-2000-DSPE. Inside the lipid bilayer, numerous pockets can be generated, their size depending on the lipid composition and on the amount of cholesterol present. These pockets allow a favorable interaction of the drug with the lipid membrane. A higher extent of cholesterol and MPEG-2000-DSPE leads to the formation of smaller pockets and thus decreases the lipid-drug interactions and hence the EE%, while a higher extent of DPPC has the opposite effect. After a certain concentration of DPPC, the

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interactions become insignificant, so increasing the molar ratio of DPPC to MPEG-2000-DSPE no longer increases the EE%, a plateau being reached. The effect of the molar ratio of DPPC to MPEG-2000-DSPE on liposomal PLP concentration and on EE%

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Three different molar ratios of DPPC to MPEG-2000-DSPE were employed in this study (9:1, 14:1 and 19:1). It was observed that a higher amount of DPPC used at the preparation of the liposomes led to higher PLP liposomal concentration and EE%, with the maximum concentration and EE% occurring when the ration of DPPC to MPEG-2000-DSPE was 19:1 (Figure 3A), and 18:1 ratio respectively (Figure 4A). This effect was attributed to the interactions between PLP and DPPC/cholesterol, which may be weaker or not even exist between PLP and MPEG-2000-DSPE/cholesterol, resulting in

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a smaller PLP liposomal concentration and EE% when the ratio between the two lipids decreases. The interaction lipidPLP is a hydrophobic coupling between the hydrophobic domain of PLP and the bilayer hydrophobic core (Andersen & Koeppe, 2007). The effect of the rotation speed at the hydration of the lipid film on liposomal PLP concentration and on EE% As illustrated in Figure 3(B), liposomal PLP concentration increases at lower rotation speeds. Also, an interaction between the rotation speed at the hydration of the lipid film and the molar ratio of phospholipids to cholesterol has been evidenced (p50.005), therefore, the increase in liposomal PLP concentration with the rotation speed decrease is more important when the molar ratio of phospholipids to cholesterol is higher.

Figure 3. Response surface for predicting liposomal PLP concentration (Y1) with respect to (A) X3: PLP molar concentration and X4: molar ratio of DPPC to MPEG-2000-DSPE and (B) X2: the molar ratio of phospholipids to cholesterol and X6: rotation speed at the formation of the lipid film.

Figure 4. Response surface for predicting EE% (Y2) with respect to (A) X3: PLP molar concentration and X4: molar ratio of DPPC to MPEG-2000DSPE and (B) X3: PLP molar concentration and X6: rotation speed at the formation of the lipid film.

Optimization of LCL-PLP via application of QbD

DOI: 10.1080/08982104.2016.1254242

Table 4. Analysis of variance for liposomal size. Source Total corrected Regression Residual Lack of fit Pure error

Degrees of freedom

Sum of squares

Mean square

19 8 10 8 2

0.19656 0.14461 0.05195 0.04321 0.00874

0.01092 0.01807 0.00519 0.00541 0.00437

F value

p values

3.47

0.024

1.235

0.511

9

EE% increases with the decrease of the rotation speed at the hydration of the lipid film, as illustrated in Figure 4(B). The method of preparation can markedly influence the characteristics of the liposomes, the rotation speed at the hydration of the lipid film being identified as one of the process related risk factors that might affect PLP liposomal concentration and EE%. Drug entrapment can be enhanced by hydrating a thinner film of dry lipids, at temperatures above the Tm (liquid crystal transition temperature) of the lipids and by slower rate of hydration and gentle mixing (Tefas et al., 2014; Vemuri & Rhodes, 1995).

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The influence of studied factors on the liposomal size

Figure 5. Response surface for predicting liposomal size (Y3) with respect to X3: PLP molar concentration and X5: extrusion temperature.

The size of the liposomes ranged from 97 to 226.7, as seen in Table 1. All prepared liposomes had a narrow size distribution (PDI50.1). Statistical analysis showed a good correlation between the observed and predicted values, with a value of the correlation coefficient r2 ¼ 0.796. The ANOVA analysis of the model is shown in Table 4. As can be seen from the table, there is a significant influence of variables on liposomal size (p50.05). The proposed model did not present a significant lack of fit, since the p values for lack of fit was 0.511. We investigated the impact of six high risk factors on liposomal size. Out of these factors only two of them had a statistically significant impact on the studied response, the PLP molar concentration and the extrusion temperature, as it can be clearly observed from the equation describing the

Figure 6. Probability of failure (%). Design space for Liposomal PLP concentration (Y1), EE% (Y2) and liposomal size (Y3), with respect to: (A) PLP molar concentration and molar ratio of DPPC to MPEG-2000-DSPE, (B) PLP molar concentration and extrusion temperature and (C) PLP molar concentration and rotation speed at the formation of the lipid film. The numbers inside the white squares represent the probability of failure.

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influence of formulation and process-related factors on liposomal size: Y3 ¼ 2:170  0:044X3  0:069X5

ð4Þ

The effect of PLP molar concentration on liposomal size PLP molar concentration has a negative impact on liposomal size, an increase in the molar concentration of PLP leading to a decrease in size of the vesicles, as can be seen from Figure 5. This could be explained by the interactions between PLP and the phospholipids from the lipid bilayer which can

Table 5. Range of variables that can guarantee a successful prediction using the developed model.

Unit

Lower limit

Upper limit

mM – mM –  C rot/min

40 5 91 14 56.5 60

100 10 100 17 60 105

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Factor Phospholipids molar concentration Molar ratio of phospholipids to cholesterol PLP molar concentration Molar ratio of DPPC to MPEG-2000-DSPE Extrusion temperature Rotation speed at the hydration of the lipid film

lead to a smaller internal-to-external volume ratio of the liposomes and a lower surface curvature. The effect of extrusion temperature on liposomal size As shown is Figure 5, the extrusion temperature had the biggest impact on the vesicle size, an increase of the temperature in the extrusion step determines the formation of liposomes with smaller size. For constant pressure, membrane pore size and number of passages through the membrane, the increase in temperature in the extrusion step leads to an increase in lipid bilayer fluidity, allowing the liposomes to convert easier from multi-lamellar vesicles (MLV), which are obtained using the method of preparation described, to large uni-lamellar vesicles (LUV), leading to a reduction of the final size of liposomes. Establishment and evaluation of the design space A design space is, according to ICH Q8 (R2), ‘‘a multidimensional combination and interaction of input variables (e.g. material attributes) and process parameters that have been demonstrated to provide assurance of quality’’ (ICH Q8 (R2), 2009). The design space was constructed taking into account the key parameters that had been demonstrated to affect product quality the most, PLP molar concentration, molar

Table 6. Validation of the model.

X1 (mM)

X2

X3 (mM)

X4

X5 (  C)

X6 (rot/min)

Experimental C (mg/ml)

Predicted C (mg/ml)

40.6 100 65.3 91.5 100 p values

10 9.8 9 5 9.3

69.5 99.2 100 99.8 96.7

17.7 19 18.3 12 17.6

49.8 60 59.2 59.8 60

123 62.1 60.3 50 60.2

9227.10 ± 46.35 10308.37 ± 167.13 10321.27 ± 191.58 9217.23 ± 107.81 10697.57 ± 93.33 0.734

9538.4 10523.5 10487.3 9443.6 10473

Experimental EE (%) 19.05 ± 0.10 21.28 ± 0.35 21.31 ± 0.40 19.03 ± 0.22 22.08 ± 0.19 0.383

Predicted EE (%) 19.48 21.8 21.73 21.55 21.8

Experimental size (nm)

Predicted size (nm)

107.6 ± 3.8 114.04 91.8 ± 1.8 96.88 107.8 ± 2.5 105.1 132.4 ± 3.2 122.5 102 ± 1.4 99.26 0.928

Results of additional tests. X1: phospholipids molar concentration (mM); X2: the molar ratio of phospholipids to cholesterol; X3: PLP molar concentration (mM); X4: molar ratio of DPPC to MPEG-2000-DSPE; X5: extrusion temperature ( ?C); X6: rotation speed at the hydration of the lipid film (rot/min); C: PLP intra-liposomal concentration (mg/ml); EE: Encapsulation efficiency (%). Values are the mean of three independent determinations ± SD.

Figure 7. Effects of LCL-PLP and free PLP on C26 murine colon carcinoma cell proliferation (A) 48 h after C26 cell incubation with different concentrations of liposomal (LCL-PLP) and free PLP ranging from 3.5 to 150 mg/ml and (B) 48 h after incubation of co-culture of C26 cells with macrophages with different concentrations of liposomal and free PLP ranging from 3.5 to 150 mg/ml. Values are the mean of three independent determinations ± SD. PLP: cells treated with free PLP (non-encapsulated PLP); LCL-PLP: cells treated with long-circulating liposomal PLP.

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DOI: 10.1080/08982104.2016.1254242

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ratio of DPPC to MPEG-2000-DSPE, the extrusion temperature and the rotation speed at the hydration of the lipid film. Each point from the design space represents a possible LCLPLP formulation having the characteristics indicated in the QTPP, with a certain risk level. The design space is illustrated in Figure 6. All formulation and process variables that were taken into account in this study are listed in Table 5, maintaining each variable within the range ensures that liposomal drug concentration, EE% and size of the liposomes can be successfully and accurately predicted and controlled. The process is controlled in such a way that the CQAs reach their expected values. By establishing the link between the CPPs, process inputs and CQAs and considering all the combinations of CPPs that give satisfying results, we built the design space for liposome preparation at laboratory scale, within which the product quality is assured and the preparation variability is minimized (Roy, 2012).

as a result of endocytosis of the lipid particles by the C26 cells and macrophages.

Validation of the model with additional data

Angiogenic/ inflammatory proteins

‘‘Robustness is the ability of a process to demonstrate acceptable quality and performance while tolerating variability in inputs and it is a function of both formulation and process design’’ (ICH Q8 (R2), 2009). Process performance and variability are factors impacting on robustness, but they can be managed through the choice of the manufacturing technology. Setting appropriate parameter ranges for each factor, ensures a robust process (Amasya et al., 2016; Glodek et al., 2006). In order to evaluate the accuracy and robustness of the obtained model, additional tests were performed. A very good correlation was observed between the experimental data and the prediction sets for all the studied responses, as can be seen from Table 6, proving that the model was robust and accurate. The p values were greater than 0.05, meaning that there was no significant difference between experimental and predicted values. Effects of LCL-PLP on cell proliferation The effects of different treatments on C26 cell proliferation were expressed as percentage of inhibition compared to the proliferation of the control C26 cells (Figures 7A and B). C26 murine colon carcinoma cells cultivated under standard conditions as well as in co-culture with peritoneal macrophages were incubated for 48 h, with increasing PLP concentrations ranging from 3.5 to 150 mg/ml administered as LCLPLP and free PLP formulations. Untreated C26 cells cultivated alone as well as with macrophages were used as controls. Only minor cytotoxic effects of free PLP (about 5% inhibition compared to controls) and LCL-PLP (about 15% inhibition compared to the controls) were noted on standard culture of C26 cells and only at the highest concentration of 150 mg PLP/ml tested (Figure 7A). Interestingly, when C26 cells were cultivated with macrophages, the anti-proliferative actions of LCL-PLP were stronger (ranging from 15–30%) than those noted on C26 cells cultivated alone (ranging from 5 to 15%) (Figure 7A and B). These cytotoxic actions observed only for LCL-PLP might be due to higher intracellular drug concentrations induced by liposomal encapsulation, possibly

Effects of LCL-PLP on the angiogenic and inflammatory capacity of M2 macrophages Our recent in vitro studies demonstrated that long-circulating liposome-encapsulated simvastatin exerted enhanced prooxidant inhibitory effects when the C26 cells were cultivated with macrophages (Porfire et al., 2015). Moreover, it has been previously proven that LCLs have a natural tropism for protumor macrophages (Alupei et al., 2015; Schiffelers et al., 2005). In addition to these data, the in vivo antitumor activity Table 7. Effects of LCL-PLP on the angiogenic and inflammatory capacity of M2 macrophages. % of protein production compared to positive controls

G-CSF GM-CSF M-CSF IGF-II IL-1a IL-1ß IL-6 IL-9 IL-12p40 IL-13 TNF-a MCP-1 Eotaxin FasL bFGF VEGF Leptin TPO TIMP-1 TIMP-2 PF-4 IL-12p70 IFN-g MIG

M2 macrophages

LCL-PLP-treated M2 macrophages

Statistical differences

28.97 ± 2.31 18.79 ± 1.66 32.79 ± 3.80 29.33 ± 0.82 33.66 ± 3.01 25.33 ± 1.05 26.50 ± 2.29 33.25 ± 2.40 13.00 ± 3.28 33.13 ± 2.22 9.69 ± 0.67 32.76 ± 2.27 6.16 ± 1.28 26.21 ± 2.21 26.62 ± 1.21 3.19 ± 0.66 7.20 ± 1.36 59.92 ± 7.55 35.08 ± 0.88 30.67 ± 0.88 34.55 ± 3.04 26.65 ± 0.04 21.79 ± 5.35 4.31 ± 0.81

20.77 ± 0.12 13.57 ± 0.23 21.16 ± 2.17 19.78 ± 1.56 24.71 ± 0.19 17.19 ± 0.26 17.60 ± 2.05 24.32 ± 0.33 6.77 ± 0.83 25.49 ± 0.22 7.29 ± 0.09 25.08 ± 1.74 3.32 ± 1.54 21.78 ± 0.07 14.98 ± 5.12 3.31 ± 0.72 5.17 ± 0.09 21.05 ± 1.67 25.80 ± 2.40 19.49 ± 0.74 22.81 ± 2.58 23.61 ± 2.85 15.60 ± 0.55 2.41 ± 0.11

* ns *** ** ** * ** ** ns * ns * ** ns *** ns ns **** ** *** *** ns ns ns

The protein levels in macrophages after administration of 150 mg PLP/ml as LCL-PLP form are compared to the same protein levels in M2 macrophages. The results are expressed as % of protein production compared to positive controls bound to the array membrane and presented as mean ± SD of two independent measurements. M2 macrophages: protein expression levels in lysates from IL-4-induced M2 macrophages, LCL-PLP-treated M2 macrophages: protein expression levels in lysates M2 macrophages treated with 150 mg of PLP in LCL-PLP for 48 h. Statistical differences were evaluated by using twoway ANOVA with Bonferroni correction for multiple comparisons and are indicated as follows: ns, not significant, p40.05; *p50.05; **p50.01; ***p50.001. G-CSF: granulocyte-colony stimulating factor; GM-CSF: granulocyte-macrophage-colony stimulating factor; M-CSF: monocyte-colony stimulating factor; IGF-II: insulin-like growth factor II; IL-1a: interleukin 1a; IL-1b: interleukin 1b; IL-6: interleukin 6; IL-9: interleukin 9, IL-12 p40: interleukin 12 p40, IL-12 p70: interleukin 12 p70; IL-13: interleukin 13; TNF-a: tumor necrosis factor-a; MCP-1: monocyte chemo-attractant protein-1; FasL: Fas ligand; bFGF: basic fibroblast growth factor; VEGF: vascular endothelial growth factor; TIMP-1: tissue inhibitor of metalloproteinase 1, TIMP-2: tissue inhibitor of metalloproteinase 2; PF-4: platelet factor 4; IL-12 p70: interleukin 12 p70; IFN-g: interferon g; MIG: monokine induced by IFN-g.

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of another liposomal prednisolone formulation on B16. F10 melanoma was exerted only in the presence of the pro-tumor macrophages (so called M2 macrophages) via inhibition of tumor angiogenesis and inflammation in these tumors (Banciu et al., 2008c). Based on these previous findings, we further tested the effects of LCL-PLP on the angiogenic and inflammatory capacity of M2 macrophages. Since the highest inhibition of C26 cell proliferation was noted after C26 cell incubation with 150 mg/ml LCL-PLP in the presence of M2 macrophages this concentration was used throughout the experiments conducted for testing the mechanisms of this cytotoxicity. Therefore, we assessed the effects of LCL-PLP on the production of 24 proteins involved in angiogenesis and inflammation in IL-4-induced tumor associated macrophages (TAMs) in vitro. These results are shown in Table 7. After 48 h of incubation with LCL-PLP the overall reduction of angiogenic and inflammatory protein production in M2 macrophages was about 30% compared to their levels in untreated M2 macrophages (p50.001). More specifically, the LCL-PLP treatment suppressed moderately to strongly (by 30–65% compared to their levels in untreated M2 macrophages) the levels of proteins specific for M2 macrophage phenotype (IGF-II, IL-13, bFGF, thrombopoietin, eotaxin) (Banciu et al., 2008c; Martinez & Gordon, 2014), but also for both M1 and M2 phenotypes (M-CSF, IL-1a, -1 b, -6,- 9) (Martinez & Gordon, 2014). Notably proteins with high levels in M1 macrophages (MCP-1 and TIMP-1, and-2) were also affected by LCL-PLP treatment (Martinez et al., 2009). These findings might suggest that liposomal PLP could be used in combination with conventional M1 stimuli such as IFN-g or IL-12p70 (Martinez & Gordon, 2014; Martinez et al., 2009) to improve the efficacy of future cancer therapies based on this liposomal formulation.

Conclusions The results of this study demonstrated that PLP can be successfully encapsulated into long-circulating liposomes with a higher EE% than that obtained for previous liposomal PLP formulations. The application of quality by design (QbD) proved to be the best approach in this case, allowing us to understand the sources of variability, improve the final product quality in terms on liposomal drug concentration, encapsulation efficiency and size and to minimize preparation variability. PLP molar concentration, molar ratio of DPPC to MPEG-2000-DSPE, extrusion temperature and the rotation speed at the hydration of the lipid film were identified as critical parameters affecting CQAs of the liposomes: liposomal drug concentration, EE% and size. The model that we generated through the D-optimal experimental design was highly predictive and robust, and was used to generate the design space for liposome preparation at laboratory scale. This QbD approach had a significant benefit on improving the properties of liposomes containing a hydrophilic active principle ingredient, PLP, considering the challenges that encapsulation of hydrophilic drugs presents compared to hydrophobic ones. Although LCL-PLP optimized in this study exerted only minor direct cytotoxic effects on C26 carcinoma cells, their modulatory actions on angiogenic and inflammatory potential of pro-tumor macrophages could

Journal of Liposome Research, Early Online: 1–13

create an unfriendly microenvironment for C26 colon carcinoma cell proliferation and development.

Declaration of interest The authors report no conflicts of interest. The authors alone are responsible for the content and writing of this article. This work was supported by a grant of the Romanian National Authority for Scientific Research, CNDI– UEFISCDI, project number PN-II-PT-PCCA-2011–3.2–1060.

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