Preparation of Albumin Nanoparticles

The desolvation technique was used for the preparation of albumin nanoparticles as this method is well known to create a less aggregated system, with a homogenous and stable distribution [47]. Ethanol was used as the desolvating agent, owing to its suitable dielectric constant and dipole moment, in addition to its excellent solubilizing property. In addition, ethanol passes through the hydrophobic region of the bovine serum albumin and disrupts the hydrophilic layer of the protein in water, leading to the denaturation of the albumin, and hence the formation of the nanoparticles. Furthermore, as compared to other desolvating agents, ethanol is considered less toxic [39].

Glutaraldehyde was used as the cross-linking agent owing to its high reactivity and low cost, in addition to being a water-soluble bifunctional reagent. Glutaraldehyde is known to react with several functional groups of proteins, such as amine, thiol, phenol, and imidazole, because the most reactive amino acid side chains are nucleophiles [48]. The nucleophiles attack the ε-amino groups of lysine and arginine residues of the protein, where the cross-linking process takes place. Thus, the two carbonyl groups of glutaraldehyde make this and form Schiff bases which are unstable in acidic conditions and are very stable under basic conditions. Furthermore, glutaraldehyde is capable of forming both inter- and intracovalent bonds between the protein units or within them [9, 49].

It has been previously reported that in the preparation of albumin nanoparticles, the drug could be loaded during the preparation or after the formation of nanoparticles, by incubating the drug solution with the formed nanoparticles. The addition of the drug during the preparation will allow the drug to be embedded into the nanoparticles’ matrix as well as to be adsorbed on the surface of the particles [2]. Encapsulating the drug into the nanoparticles could sustain the release of the drug, which satisfies the target of the current study to control the drug release in the blood circulation before reaching the site of action [50].

Quality Target Product Profile

Defining the QTPP is the first step in the QbD, which describes all the characteristics related to the quality that should be present in the product in order to achieve the main target of the study [51]. The entire steps of QbD followed in this study are illustrated in Fig. 2a, which enlists the whole phases taking place to achieve the target. QbD starts by defining the target of the study, which is the optimization of silymarin-albumin nanoparticles, to be used as an anticancer agent. This was followed by risk factor identification, and determination of the CQA, which were found to be PS and EE% in the current study. An experimental design was first developed by screening the main CPP/CMA that may have a direct significant impact on the CQA, using a fractional factorial design. Next was applying a D-optimal design to reach the optimal silymarin-loaded albumin nanoparticles. Then was the creation of a design space and control strategy, ending with continual improvement, with assurance of consistent quality, which was achieved by the further steps applied on the optimized silymarin-loaded albumin nanoparticles.

Fig. 2.

a QTPP of silymarin-albumin nanoparticles, b Ishikawa diagram for the particle size, c Ishikawa diagram for the entrapment efficiency

Risk Assessment

Defining the QTPP could be assessed by determining the most critical parameters that when achieved may result in the desired drug product. Accordingly, in this study, these formulation parameters were the particle size and the drug entrapment efficiency. Consequently, a goal of the current study is to formulate albumin nanoparticles with the smallest vesicular size together with the highest EE%. A reduction in the vesicular size is expected to enhance the drug’s solubility [52]; furthermore, the vesicular size has a dynamic role for targeting tumor tissues, where a size less than 400 nm can potentially target cancerous cells by the enhanced permeability and retention effect [2]. Moreover, a higher entrapment of the drug within the nanoparticles could reduce the manufacturing cost with a greater flexibility in dosing [53].

In order to facilitate risk assessment, Ishikawa diagrams were constructed to help in the identification of the potential risks and the corresponding causes [54]. Accordingly, two Ishikawa diagrams (cause and effect diagrams) were built (Fig. 2b, c) which show the whole factors that may contribute to the quality attribute, including methods, material, and machines used in the preparation and measurements, together with the personnel and environmental factors. One Ishikawa diagram was constructed for the particle size, and the other was constructed for the entrapment efficiency. Analyzing these diagrams resulted in the identification of six key variables, namely, time of cross-linking, albumin amount, pH of albumin solution, silymarin amount, desolvating agent volume, and the type of the solvent, which were recognized as high risk factors and were used for the screening study. The rest of the variables were kept at a constant level, and could be routinely controlled.

Screening Using Fractional Factorial Design

The levels of the six CPP/CMA were chosen based on preliminary experiments and based on previous literature. Several research studied the effect of the time of cross-linking and obtained different results [8, 39, 55], so thus would be studied deeply in the current study. Furthermore, the albumin and the drug amount might have a direct impact on the studied CQA [50, 56,57,58]. The pH was chosen to be between 8 and 9, which is above the isoelectric point of albumin (4.7); as it is well reported that the particle size decreases with increasing the pH [39], so it would be screened if this difference would significantly affect the formulation of albumin nanoparticles. It was reported that the albumin nanoparticle formation process depends greatly on the volume of the desolvating agent added, which also may have a direct impact on the vesicular size [2, 8, 9, 37, 50, 55, 59, 60]. Moreover, the sodium chloride solution would be compared with water as the solvent to obtain the solvent that would not interfere with the desolvation and the cross-linking processes [39]. The CPP/CMA that most significantly affected the CQA were further analyzed to be more deeply deliberated in the optimization step.

To screen the most significant CPP/CMA affecting the albumin nanoparticles, a 26–2 fractional factorial design was conducted with six CPP/CMA each at two levels, resulting in 16 formulations (S1–S16). These formulations were prepared and characterized in terms of PS and the EE%, as tabulated in Table 1. Further analysis using ANOVA revealed that the particle size and the EE% were affected by all the CPP/CMA as represented in Table 3. The correlation coefficient R2 was found to be 0.999 for the PS and 0.904 for the EE%, giving a significant fitting to the model.

Table 3 ANOVA study of the fractional factorial design

As can be observed from Table 3, the time of cross-linking (XS1) had a negative impact on the EE%, where a short time of cross-linking resulted in a higher EE%. As for the effect of albumin amount (XS2), it was observed that increasing the albumin amount resulted in a significant increase in both the particle size and the EE%. The relationship between the albumin amount was not linear with the PS or the EE%, and thus would be deeply studied in the further optimization step. Similar results were obtained by [61].

The pH of the albumin solution (XS3) significantly affected both the PS and the EE%. A smaller vesicular size was observed as the pH increased from 8 to 9: this could be attributed to the extension of the BSA backbone with loose unordered parts, by increasing the pH, allowing charged side chains to be accessible [62], which in turn increases the surface charge and reduces the particle attraction and agglomeration, leading to a reduction in the vesicular size [63]. This finding was in accordance with [37]. However, the higher pH resulted in a lower EE%, which could be due to more ionization of the protein at the higher pH, resulting in the hindrance of the incorporation of the drug into the nanoparticles due to the surface charge at that pH, and hence a lower EE% [55]. Furthermore, at high pH, less particle yield could be obtained, which could reduce the incorporation of the drug due to insufficient particle formation [37].

A larger particle size with higher EE% was observed as the drug amount (XS4) increases; however, the relationship was not linear, and thus would be deeply studied in the optimization step. These results were in accordance with that obtained by [61].

The increase in the volume of the desolvating agent (XS5) resulted in a significant increase in the vesicular size, which could be attributed to the direct relationship between the volume of non-solvent and the hydration of the protein. Large amounts of the desolvating agent might reduce the hydration of the albumin, with the consequence of the reduction in the dielectric constant (DEC) of the whole solution [8], which finally increases the vesicular size. It was previously reported that when the volume of the non-solvent is small, it will be insufficient to make the solute reach its supersaturation point; thus, precipitation will not occur efficiently, leading to deformation of the particles [55], and hence an increase in the vesicular size. Furthermore, as stated by Yoshikawa et al. (2012), the increase in the percentage of desolvating agent above 80% may lead to a dramatic change in the structure of protein, which in turn may increase the vesicular size [64]. Moreover, a less entrapment of the drug was observed as the volume of the desolvating agent increased. This could be attributed to the insufficient hydration of the protein, which might not give it the chance to form sufficient nanoparticles, resulting in small yield of the nanoparticles [55].

Sodium chloride solution had a less significant impact than water on increasing the particle size (XS6), which could be due to the higher ionic strength of sodium chloride solution over that of water. This, in turn, reduces the surface charge on the albumin nanoparticles, with the consequence of reducing the electrostatic repulsion between the particles due to the charge screening by the addition of the ions, and the subsequent reduction in the electrophoretic mobility [55]. Accordingly, sodium chloride solution would be used to prepare the albumin nanoparticles in the subsequent optimization step.

As mentioned previously, the constraints of the current study were to attain a small particle size and a high EE%. Thus, the CPP/CMA were all set at constant levels which achieve the aforementioned constraints, except the albumin amount and the drug amount that will be studied deeply in the optimization step. Accordingly, the pH was chosen to be set at 8, as its effect on the PS was greater than on the EE%, with the least time of cross-linking, and the least volume of the desolvating agent.

D-optimal Design Analysis

The results of the PS and the EE%, from the D-optimal design, were fitted to polynomial cubic models. The linear regression equations showing the effect of each of the studied CMA, and their interactions on the PS and EE%, are represented in Table 4. A perfect fit of the model was obtained as indicated by the correlation coefficient (R2) values. Perfect results were obtained between the adjusted R2 and the predicted R2, together with an adequate precision greater than 4, indicating an adequate signal, and assuring the ability of the model to navigate the design space.

Table 4 Regression equations, R2, adjusted R2, predicted R2, and adequate precision as obtained from D-optimal design

Further analysis using ANOVA showed significant models of each of the PS, and the EE% at p-level < 0.05, with a non-significant lack of fit as represented in Table 5.

Table 5 ANOVA study of the D-optimal designParticle Size Analysis

It has been reported that nanoparticles smaller than 200 nm decrease phagocytic uptake due to opsonization. This, in turn, enhances drug targeting to cancerous cells. Accordingly, one of the main goals of the current study is to obtain a particle size less than 200 nm [65]. Furthermore, a study showed that the pore size of the capillaries supplying the tumor cells is about 400 nm; thus, nanoparticles with vesicular size less than 400 nm could increase the residence time of the nanoparticles in the systemic circulation, and would passively target the tumor cells through the enhanced permeability and retention effect [2].

As observed from Table 2, particle size ranged from 121.7 ± 16.9 to 223.1 ± 6.8 nm. Further ANOVA analysis showed that the individual effect of each of the studied CMA and their interactions significantly affected the particle size as represented in Table 5. A larger particle size was obtained as the albumin amount (X1) or the drug amount increased (X2), as observed from the positive coefficients in Eq. 4 in Table 4.

The larger size of the albumin nanoparticles due to the increase in the albumin amount (X1) might be attributed to the formation of a stronger intermolecular disulfide bond at the higher albumin concentration. This, in turn, resulted in a greater aggregation of the protein, and hence a larger-sized albumin nanoparticle [50]. Moreover, better hydrophobic interaction takes place at an increased albumin amount, which leads to an increase in the protein coagulation, and finally an increase in the size of the albumin nanoparticles [57]. Furthermore, at a higher BSA concentration, the viscosity increases, which slows down the frequency of transport of the protein from water to ethanol, resulting in a slower nucleation rate and a bigger vesicular size [55].

The positive impact of the silymarin amount (X2) on the particle size could be attributed to the poor solubility of silymarin in water (< 50 μg/mL) [66]. This poor solubility will allow the drug to be incorporated into the protein’s matrix during the desolvation process. As a result, a kind of hydrophobic interaction between the drug and the protein will occur, which increases the vesicular size of the nanoparticles [57]. Moreover, at a high drug amount, no protein binding sites would be available, and thus silymarin would be forced to interact with the nanoparticles at the protein’s surface, causing an increase in the vesicular size [58]. As can be deduced from Eq. 4 in Table 4, there was an antagonistic effect between the albumin amount and the drug amount.

As can be observed from Fig. 3a, a cubic model existed between each of the CQA and the studied CMA. The 3-D plot showed an initial reduction in the particle size as each of the albumin amount or the drug amount increased. This was followed by an increase in the particle size with a further increase in both CMA.

Entrapment Efficiency Analysis

The percentage yield for all formulations were found to be from 93.67 ± 3.7 to 98.77 ± 1.87. whereas, the drug loading % was found to be from 40.31% ± 2.76 to 77.65 ± 5.65%.

A major goal of the current study is to maximize the entrapment of the drug into the nanoparticles in order to increase the drug concentration at the site of action [50].

As observed in Table 2, the EE% ranged from 33.36% ± 5.8 to 94.57% ± 3.4, with a cubic best fitting model as represented in Table 5. The entrapment efficiency was found to increase by increasing each of the albumin amount (X1) and the drug amount (X2) individually, as observed in Eq. 5 in Table 4. The increase in the EE% at a higher albumin amount is due to the higher availability of the albumin surrounding the drug [67]. As the albumin concentration increases, more nanoparticles would be formed, which in turn increases the chance for better drug encapsulation.

A higher EE% was observed when the drug amount increased. This may be due to the higher interaction of the drug with the active sites in the protein at the higher drug amount, causing a higher EE% [58]. Moreover, a larger particle size was observed with the increase in the drug amount, which gives a larger volume of the nanoparticles to encapsulate more drug [56].

Equation 5 shows a negative coefficient of XAB, indicating a synergistic effect between the studied MA on the EE%. Figure 3b shows the non-linear effect of each of the albumin amount and the drug amount.

Fig. 3.

a 3-D response surface plot of the particle size, b 3-D response surface plot of the entrapment efficiency, c design space of silymarin-albumin nanoparticles

Data Optimization and Model Validation, Design Space, and Control Strategy

A design space was constructed based on the key parameters discussed earlier, as represented in Fig. 3c. It shows two regions: a yellow region where working within this region is expected to get the desirable outcomes, and a gray region which shows the undesirable limits. A desirability approach based on a numerical technique was employed to get an optimized formula [18]. Accordingly, a new formula (O1) was chosen, with a desirability of 0.859.

A control space has been established, showing the highest and lowest limits for the MA and CQA, which also ensures the reproducibility of CMA and CQA that could be routinely controlled. Table 6 shows the optimized formula (O1), together with the expected results as suggested by the software. The optimized formula was prepared and characterized in terms of particle size and EE% to calculate the % bias [68]. The low % bias indicates the validity of the design.

Table 6 The optimized formula with the expected and the observed results

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Characterization Tests on the Optimized FormulaTransmission Electron Microscope Analysis

The morphological analysis of albumin nanoparticles in Fig. 4 shows spherical particles of about 100 nm ± 2.8, with a uniform particle size distribution, which coincides with the diffractive light scattering measurements (DLS). It should be mentioned that the slight difference between the diffractive light scattering (DLS) and the TEM might be attributed to the difference in the sample preparation. The DLS measures the hydrodynamic particle diameter, whereas the TEM measures the diameter of the particles in their dried states [39].

Fig. 4

TEM micrographs of the optimized silymarin-albumin nanoparticles

Zeta Potential and Polydispersity Index

Zeta potential gives an indication of the stability of the nanoparticles in the dispersion through electrostatic repulsion between the particles. As the value increases, it gives an indication of more repulsion between the nanoparticles, reducing the tendency of aggregation that could occur between the nanoparticles [2]. The zeta potential for the silymarin nanoparticles was found to be − 12.5 ± 1.2 mV. The negative charge might be due to the preparation of the albumin at a pH higher than the isoelectric point of the protein [69], leading to the ionization of the carboxyl terminal of the protein, and hence imparting a negative charge [39].

Polydispersity index was found to be 0.09 ± 0.007, which indicates the homogeneity of the size distribution within the formed nanoparticles.

In Vitro Drug Release

The release of silymarin from both the standard solution and the optimized formula (O1) is shown in Fig. 5. The release of silymarin from albumin nanoparticles showed a biphasic release, with an initial burst effect, which is followed by a sustained release over 48 h. A sudden burst release of about 45% was observed within 4 h from O1, followed by a slower release over the 48 h, whereas the standard drug was completely released within 3 h. The initial burst effect might be due to the free unencapsulated drug and the drug on the surface of the nanoparticles. The sustained release pattern may be attributed to the drug incorporated in the core of the nanoparticles’ matrix. It should be mentioned that the sustained release effect is in great favor for the cancer targeting as it is required for the anticancer drugs to have a slow release in the blood to reduce its side effects on the normal cells, whereas when it reaches the cancer cells, it should be high [2].

Fig. 5

Release pattern of silymarin from the optimized formula and standard silymarin

Stability Testing

The values of the PS, EE%, PDI, and z-pot of the optimized formula after a 3-month storage at 4 °C were 133.01 ± 2.7 nm, 85.64 ± 0.4%, 0.09 ± 0.1, and − 11.94 ± 0.9 mV respectively. The results of the stored samples show no significant difference as compared to the freshly prepared ones. This validates the stability of the formulation at 4 °C.

Molecular Docking

In an attempt to explore the molecular basis behind the observed cytotoxic activity, silymarin was docked in the active site of bovine serum albumin obtained from the protein databank (PDB ID: 4JK4) using MOE software version 2014.0910.

The docking scores in (kcal/mol) and major interactions of silymarin are provided in Table 7 and Fig. 1b along with the score and interactions of the native compound of the albumin.

Table 7 Docking scores and major interactions. The amino acid with which the ligand interacts with is provided below the relevant interactions

As depicted in both Table 7 and Fig. 1b, it is clear that silymarin displays similar interactions as the native ligand with the essential amino acids Arg 217, Arg 256, and Tyr 149. Additional interaction with Asp 250 was found with silymarin.

Antiproliferative Effects of Silymarin on HepG2 Cells

Silymarin has been used for centuries as a hepatoprotective agent and its anticancer effects on various malignancies have been reported. Silymarin was shown to suppress the proliferation of a variety of tumor cells, including prostate, ovarian, breast, lung, skin, liver, and bladder [