The pearl necklace model in protein A chromatography: Molecular mechanisms at the resin interface

Abstract Staphylococcal protein A chromatography is an established core technology for monoclonal antibody purification and capture in the downstream processing. MabSelect SuRe involves a tetrameric chain of a recombinant form of the B domain of staphylococcal protein A, called the Z‐domain. Little is known about the stoichiometry, binding orientation, or preferred binding. We analyzed small‐angle X‐ray scattering data of the antibody–protein A complex immobilized in an industrial highly relevant chromatographic resin at different antibody concentrations. From scattering data, we computed the normalized radial density distributions. We designed three‐dimensional (3D) models with protein data bank crystallographic structures of an IgG1 (the isoform of trastuzumab, used here; Protein Data Bank: 1HZH) and the staphylococcal protein A B domain (the native form of the recombinant structure contained in MabSelect SuRe resin; Protein Data Bank: 1BDD). We computed different binding conformations for different antibody to protein A stoichiometries (1:1, 2:1, and 3:1) and compared the normalized radial density distributions computed from 3D models with those obtained from the experimental data. In the linear range of the isotherm we favor a 1:1 ratio, with the antibody binding to the outer domains in the protein A chain at very low and high concentrations. In the saturation region, a 2:1 ratio is more likely to occur. A 3:1 stoichiometry is excluded because of steric effects.

B-domain with point mutations to improve alkaline stability (Ghose et al., 2005).
Despite having physical-chemical properties that make it prone to establishing hydrogen bonds and electrostatic interactions, it is because of its exposed hydrophobic moiety, the consensus binding site shows preferential binding with the protein A ligands (Salvalaglio et al., 2009). Irrespective of the abundant information regarding Fc recognition by protein A, antibody structural rearrangement upon adsorption to protein A ligands and the associated stoichiometry are not fully understood. However, some authors have reported the possibility of multiple binding to protein A chains, but with protein A in solution (Ghose, Hubbard, & Cramer, 2007). Others have also addressed this issue, reporting the possible antibody binding orientations of an IgG4 to immobilized protein A in silica (Mazzer et al., 2017).
Molecular models have been applied to study antibody form and flexibility in aqueous solutions (Brandt, Patapoff, & Aragon, 2010;Sandin, Öfverstedt, Wikström, Wrange, & Skoglund, 2004) for a better understanding of antibody aggregate adsorption to protein A resins (Yu et al., 2016) and to characterize the nature of antibody binding to protein A (Salvalaglio et al., 2009;Zamolo, Busini, Moiani, Moscatelli, & Cavallotti, 2008). Salvalaglio et al. (2009) and Zamolo et al. (2008) have described that regions and amino acids play a major role in the interaction with chromatography matrices based on the crystal structure of CH 2 and CH 3 of an IgG1 coupled with fragment B of protein A determined by Deisenhofer (1981) (PDB: 1FC2). However, despite this high economic value, a real three-dimensional (3D) structure of the antibody-staphylococcal protein A complex based on experimental data at different antibody loadings has not been elucidated. The current state-of-the-art on antibody-protein A conformations is solely attributed to the computational simulations (Busini, Moiani, Moscatelli, Zamolo, & Cavallotti, 2006;Salvalaglio et al., 2009).
Here we presented a methodology capable to experimentally assess normalized radial densities of antibody-protein A conformations at a resin surface by small-angle X-ray scattering (SAXS). SAXS provided information at the structural level of particle systems of the colloidal size (to thousands of angstroms, Å), such as antibodies (Boldon, Laliberte, & Liu, 2015). SAXS is based on the concept that a particle of relatively greater size than the X-ray wavelength will scatter the incident X-ray. On the basis of the scattering intensity, it is possible to assess form, shape, and size of the scatterer. Therefore, it would be possible to establish an approximation of the "spatial extension of the particle". SAXS can provide information from a dynamic system and take into account molecular flexibility and different configurations (Boldon et al., 2015).
In this work we investigated the adsorption of a monoclonal antibody to MabSelect SuRe. More concisely, we sought to obtain an overview of the structural rearrangement of the antibodies in the tetrameric protein A and to estimate the evolution of surface layer thickness with antibody concentration, as well as the antibody-ligand stoichiometry. We compared the antibody-protein A complex radial densities provided by SAXS with theoretical configurations (protein AB domain from the crystal structure 1BDD and the antibody from the crystal structure 1HZH from Protein Data Bank [PDB]) and spatial rearrangement of antibodies and staphylococcal Protein A ligands using a molecular model approach. We implemented this model to simulate different binding orientations of a crystallographic structure of an IgG1 to a tetrameric B-domain protein A chain attached to an agarose structure to mimic the experimental system of a monoclonal antibody to MabSelect SuRe. In the current study, the methodology is explored on this very specific system of high industrial relevance, but it is also applicable to a broad range of protein-surface adsorption systems and can improve the understanding of protein binding in those systems.

| Adsorption isotherms
The antibody solutions were prepared in 0.02 M phosphate buffer with 0.15 M sodium chloride at pH 7.4 in a range from 0.01 to 10 mg/ml. A volume of 0.025 ml of resin was added to the antibody solution with a total volume of 0.25 ml. The samples were incubated for 24 hr in a thermomixer (Thermo Fisher Scientific, Waltham, MA) at 20°C and 900 rpm. After incubation, the bulk concentration was measured at Abs 280 nm using a UV plate reader (Tecan, Männedorf, Switzerland).

| Scanning electron microscopy
The MabSelect SuRe beads were first submerged in a cryoprotectant 2.3 M sucrose solution. The sample was then frozen with liquid nitrogen and the beads were cut into slices 30-µm thick using a tungsten carbide knife in an MT-990 Motorized Precision Microtome (RMC Boeckeler). The bead slices were dehydrated with ethanol series and then dried with CO 2 in a Critical Point Dryer Leica EM CPD030. For the visualization, we used a Scanning Electron Microscope Quanta™ 250 FEG, and the dried slices were placed on an aluminum slab and coated with a gold layer.

| SAXS
The SAXS measurements were performed in the beamlines BM26B (Portale et al., 2013) and BM29 (Pernot et al., 2013) at the European Synchrotron Radiation Facility (Grenoble, France). The antibody sample preparation followed the same procedure as for the adsorption isotherm measurements. After the incubation, the solution was resuspended, and 100 µL of incubated sample was loaded into a quartz capillary. The capillary was then placed aligned to the beam. The scattering images were collected in 10 frames at 1-s exposure each using Pilatus 1 M detector at 12 keV (λ = 1.033 Å).

| Modeling
SAXS is a powerful and effective technique for determining molecule shapes and sizes at the nanoscale length. This approach measures the scattering intensity I Q ( ) function of a scattering vector Q resulting from a scattering angle θ 2 , at a given wavelength λ, where θ λ = Q π 4 sin / . Q values are correlated to real-space distances d with Hayter & Penfold, 1983;Zhang et al., 2007).

| A fractal pearl necklace model
The antibody binds to protein A ligands and a complex is formed. This complex could be described by its characteristic pair density distribution. The Fourier transform of the pair density distribution gives the form factor, P Q ( ), which is the scattering intensity of the complex according to its characteristics, such as shape, size, or concentration. In addition, pair density distributions of complexes randomly arranged in the fractal network of the agarose resin contribute to the structure factor, S Q ( ), and can be described by . Under the assumptions of the scattering theory, the scattering intensity of the whole system is not more than the product of the form and structure factor: scattering intensity curve is obtained by where J 1/2 is a Bessel function of the first kind of order ½. The form factor is the Fourier transform of the radial density distribution: 1/2 . The structure factor is the Fourier transform of the pair density distribution of the fractal network: It is challenging to normalize any scattering intensity. The scattering intensity depends on the chemical contrast of each entity and may decrease despite the increasing number of scatterers. We shift the normalization issue to real space. We introduce the normalized pair density distribution of spherical hulls and hereby enforce radial symmetry. It is an essential step that solves the normalization problem in a very elegant way. We define our working equation as The variable R is the measured distance from the scattering site relative to the backbone of the agarose. This mathematical model resembles a fractal folded pearl necklace, made from pearls with an average radial density distribution of matter, ′ p R ( ).
2.7 | The fractal network of the resin imposes a fractal structure factor, S(Q) In the present case, we monitor antibody adsorption at high concentrations. Thus, the antibody concentration in the proximity of the surface is high. This is the reason why the infinite dilution argument no longer holds true. We have to take into account complex-complex pair density distributions. It seems appropriate to characterize their structure by the fractal pair density distribution: , with κ as the screening length. Then, the structure factor of protein-ligand complexes resembles: It is a sin-transform of the fractal pair density distribution: where C D f is a proportional constant of the gamma function Γ:

| Bi-Langmuir adsorption
MabSelect SuRe is known for its tetrameric chain of B-domain-derived ligands. These four theoretical antibody binding domains may be a source of energetic heterogeneity. Therefore, the Langmuir isotherm may incorrectly predict adsorption for this system. High energy adsorption sites become saturated (i.e., are occupied first) at low concentrations, while at high concentrations, molecules adsorb to high and low energy sites (Gritti & Guiochon, 2010). The system is better described by a bi-Langmuir model, which takes into account this possible heterogeneous adsorption as it is based on the coexistence of two (or more) independent noncooperative sites (Bellot & Condoret, 1993;Gritti & Guiochon, 2005). The amount of adsorbed protein q in equilibrium with equilibrium solution concentration C is modeled by where q i m , gives the maximum adsorbed capacity at any site, and b i values are the sample equilibrium constants between the bulk solution and the multiple adsorption sites and > b 0 i .

| RESULTS AND DISCUSSION
The scope of this study is to understand the rearrangement and orientation of antibodies on MabSelect SuRe. SAXS is the fingerprint technique used here, and antibody-protein A interaction data interpretation was done in terms of radial density distribution. We computed hypothetical 3D models and thereof radial density distributions. We compared the results to radial density distributions we computed from experimental scattering data. We found favored binding orientations and stoichiometry. Scanning electron microscopy (SEM) imaging was used to validate the determination of the structure factor of a defined fractal network composed by the resin's cross-linked agarose.
3.1 | Determination of the antibody-protein A 3D complex and its structural rearrangement

| Scattering profiles
The SAXS data were analyzed according to the mathematical framework drafted in the theory section and outlined in Figure 1.
In the current study, we assumed that the scattering intensities could be split into a product of form and structure factors. This simplification was made because of the different scales of the radial density distribution of both the antibody-protein A ligand complex and the distribution of these particular complexes in the resin. The form factor, P Q ( ), computed from the radial density distribution, mimics the statistics of the distances measured within the antibodyprotein A ligand complex, with a typical distribution as depicted in Figure 1a. The red disk is a schematic representation of the protein A ligand; the larger green disk mimics the immobilized antibody. The structure factor, S Q ( ), takes into account the distribution of these complexes throughout the resin network, with a possible arrangement shown in red in Figure 1b. We assume a random distribution of ligands, and it is the particular structure of the resin that imposes the characteristic shape of the pair density distribution from which the antibody-ligand complex structure factor is computed.

| Scanning electron microscoe (SEM)
Parallel to SAXS data, we used SEM to visualize the agarose beads of MabSelect SuRe. From the SEM image, we could computationally generate the structure factor of the fractal network and compare it with the obtained value from SAXS. Figure 2a shows a SEM image of MabSelect SuRe resin's network.
The magnification indicate a typical diameter of approx. 34 nm. The SEM image was binarized, resulting in Figure 2b, where gray areas indicated the agarose network and white areas mark the pores. From the binarized image, we chose 10,000 sites randomly distributed in two zones. First, we constrained the site choices to the gray areas, that is, the agarose network, and displayed them with red dots in Figure 2c.
Then, we randomly chose pixels from both the gray and the white areas (random noise over the whole picture), marking them with blue dots in

| Antibody solution
To appropriately describe the adsorption mechanism, it is essential to evaluate the antibody state of aggregation at the used solution concentrations. The form factor of the antibody was computed from measurements of antibody in solutions at 8, 16, and 30 mg/ml.

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hydrodynamic radius of an IgG1 and would match well with the experimental data for the antibody in solution (Gagnon et al., 2015). Pair densities could be found up to relative distances of 12 nm. Indeed, this value is an estimation of the hydrodynamic diameter of the antibody in solution considering all the associated intrinsic flexibility (Gagnon & Nian, 2016;Gagnon et al., 2015).
The relative pair density p r ( ) P from the experimental samples a tailing profile up to relative distances larger than the antibody size. This Consequently, we anticipate, that antibody monomers adsorb and that the tailings in pair densities are due to parasitic background. For 30 mg/ml, we indeed do monitor a factor of − Q 0.3 . However, 16 mg/ml is well above the antibody starting concentration at which we perform our measurements.

| The structure factor
The scattering intensity of the antibody adsorbed to the protein A ligand at the resin surface was measured and plotted in Figure 4a.
The black curve corresponds to the MabSelect SuRe resin scattering intensity. Brighter red curves correspond to scattering intensity of MabSelect SuRe resins that have been incubated with a range of antibody concentrations.
The structure factor dimension could be determined by calculating the absolute tangential slope to the low Q range of the scattering intensity of the resin. We found We propose a modified approach for background correction.
First, we corrected all scattering intensities by the structure factor, S Q ( ), and then computed the radial density distributions: . Figure 4b shows the radial density distributions,  ( ) c , from the scattering intensity profiles of antibody adsorption to MabSelect SuRe at the concentrations displayed in Figure 4a. As in Figure 4a, the curves go from black to red with increasing antibody concentration, with the black curve indicating the radial density distribution of the antibody-free resin sample. The resin signal (black line) has a maximum at 5-6 nm, which can be interpreted as the minimum radius of an agarose strand. We assume that the antibody molecules bind to the protein A ligands and do not penetrate the cross-linked agarose strand. Therefore, the radial density distributions for every antibody concentration needs to match until 6 nm, resulting in the normalization of the radial density distributions.

| Assessing the surface excess
If plotted with respect to the equilibrium bulk concentration of the antibody, the net area of the normalized radial density distribution profiles give a surface excess adsorption isotherm. The normalization of the isotherm was done in respect to the value at the highest concentration ( Figure 5b). The normalized surface excess values computed from the radial density distributions are shown in red, and the normalized isotherm derived from the equilibrium state of the samples before X-ray exposure is given in blue. This match supports the approach of how to assess radial density distributions from the scattering intensity data. Figure 5b also shows an insert with the experimentally determined adsorption isotherm. It follows the favorable binding rectangular profile characteristic of protein A resins in the antibody uptake. At equilibrium, the data show a second plateau to greater q max . The data were fitted with a bi-Langmuir model, as described in the theory section. Results favor a multipoint attachment due to heterogeneous binding sites with a weaker binding of a second antibody molecule to the protein A ligand (Bellot & Condoret, 1993).
What needs to be further explored is how in particular does it bind and how is the form of the antibody-protein A complex affected.

| A form of antibody-protein A 3D complex by molecular simulation
MabSelect SuRe is a tetrameric protein A chain, thus multipoint attachment is theoretically possible (Gagnon & Nian, 2016;Ghose et al., 2007;Mazzer et al., 2017). Focused research is lacking. We have addressed it on basis of the normalized and backgroundcorrected radial pair densities at different antibody bulk concentrations.

| Antibody-protein A-agarose complex
To visualize SAXS data, and to ease or support their interpretation, we perform reverse Monte Carlo simulations. They are a powerful tool to help the interpretation of the nature of the present data. In this study, we modeled the antibody-protein A complex form by a rigid body approach.
As already mentioned, MabSelect SuRe protein A chain is a recombinant polymer of four units of staphylococcal protein AB domain called the Z-domain. This Z-domain is engineered through a point mutation of the B-domain to give the ligand improved alkaline stability (Ghose et al., 2005). To mimic as closely as possible the chromatographic system involved in this study, we used the crystallographic structure of protein AB domain (PDB: 1BDD) and built a four-fragment chain.
The model used to represent MabSelect SuRe agarose was kindly provided by Carlo Cavallotti from his group's publication (Salvalaglio et al., 2009), because they have recently used such a model to predict which amino acid residues contribute the most to IgG binding to protein A. The construct of this agarose model is described in detail in Ref. (Busini et al., 2006). The tetrameric protein A chain was covalently linked to the agarose with an ester bond, and no spacer was introduced.
F I G U R E 5 (a) Background-corrected radial density distributions, ′ p R ( ). (b) Surface excess computed from the normalized areas from ′ R p R ( ) 2 as a function of antibody equilibrium concentration (red disks). The adsorbed amount derived from the equilibrium state of the samples before X-ray exposure (blue disks). The insert shows the experimentally determined adsorption isotherm of antibody adsorption to MabSelect SuRe (blue disks) [Color figure can be viewed at wileyonlinelibrary.com] F I G U R E 6 Rigid body models and radial density distributions of 1:1 antibody to protein A stoichiometry. (a) Selected configuration of the complex; the gray bead model indicates the resin; the red bead models mimic the MabSelect SuRe protein A tetrameric chain; the green bead model marks the antibody. (b) Radial density distributions computed from SAXS data (dark red to bright red lines) are compared to radial density distributions (blue line) computed from random walk models. The data enumerated 1-9 correspond to different antibody bulk concentrations, with the correspondence given in the text [Color figure can be viewed at wileyonlinelibrary.com] F I G U R E 7 Rigid body models and radial density distributions of 2:1 antibody-Protein A stoichiometry. (a) Selected configuration of the complex; the gray bead model indicates the resin; the red bead models mimic the MabSelect SuRe Protein A tetrameric chain; the green bead model marks the antibody. (b) Radial density distributions computed from SAXS data (dark red to bright red lines) are compared to radial density distributions (blue line) computed from random walk models. The data enumerated 1-9 correspond to different antibody bulk concentrations, with the correspondence given in the text [Color figure can be viewed at wileyonlinelibrary.com] The final part of the model was the antibodies. We used the crystal structure of an IgG1 (PDB: 1HZH) and bound them through the consensus binding site located between the CH2 and CH3 Fc domains to the Fc binding site of one of the protein AB domains.
Deisenhofer has determined a complex of one half of the antibody Fc fragment and one protein AB domain (PDB: 1FC2; Deisenhofer, 1981). Our model matched Deisenhofer's proposed structure.

| Random sampling
After designing the model complex, we ran simulations. We designed a rigid body random walk model. We define systems component: agaroese backbone plus first protein A ligand, adjacent protein A ligands and antibody. All system components were considered rigid entities. Different orientations of the protein A fragments and the antibodies were allowed. Each protein A fragment is considered as a single-point attachment domain to the antibody. Therefore, in the whole chain there are four potential binding sites; one per fragment.
The first set of simulations regarded the binding of one antibody molecule to the protein A chain. We simulated a library of at least 10,000 potential forms of antibody in complex with the protein A tetramer ligands and the agarose strand. From the conformations, we assessed the respective radial density distributions. Figure 6a shows the rigid body models of a selected conformation and Figure 6b shows the radial density distributions of 1:1 antibody to protein A chain stoichiometry.
With increasing equilibrium concentration (we number them from 1 to 9 in Figure 6b), the best results for 1:1 antibody-protein A stoichiometry show a binding preference for the: fourth (solution 1: C= 0.01 mg/ml; q= 25.8 mg/ml resin), third (solution 2: C = 0.01 mg/ml; q = 25.8 mg/ml resin), first (solution 3: C = 0.01 mg/ml; q= 25.8 mg/ml resin), first (solution 4: C = 0.01 mg/ml; q = 38.7 mg/ml resin), third (solution 5: C = 0.1 mg/ml; q = 64.5 mg/ml resin), third (solution 6: C = 1.2 mg/ml; q = 80.0 mg/ml resin), third (solution 7: C = 2.7 mg/ml; q = 80.0 mg/ml resin), fourth (solution 8: C = 4.7 mg/ml; q = 80.0 mg/ml resin), and third (solution 9: C = 5.6 mg/ml; q = 80.0 mg/ml resin) domain counting from the agarose surface. The obtained preferential binding is speculative as it does not take into account any energy minimization. Simulations indicate that at low bulk concentrations and very low surface concentrations (solutions 1 and 2) the antibody binds to the outermost ligands (fourth and third) but finds itself in the proximity of the first ligand. Engineered protein A in commercial media has a tentacle form and the chain could be extended in the surface (Gagnon & Nian, 2016). We have implemented the possibility for the protein A chain for a loop-like conformation (see its form in Figures 6,7, or 8). Within our random walk model the protein A chain is flexible and a transfer from the outermost to the innermost ligand seems plausible. Biologically, antibody dual-site binding to protein A is possible (Gagnon & Nian, 2016). With increasing surface concentration but still at low equilibrium concentration (solutions 3 and 4), the first ligand is the most favored. At elevated concentrations (solutions 5-9) the F I G U R E 8 Rigid body models and radial density distributions of 3:1 antibody-protein A stoichiometry. (a) Selected configuration of the complex; the gray bead model indicates the resin; the red bead models mimic the MabSelect SuRe protein A tetrameric chain; the green bead model marks the antibody. (b) Radial density distributions computed from SAXS data (dark red to bright red lines) are compared to radial density distributions (blue line) computed from random walk models. The data enumerated 1-9 correspond to different antibody bulk concentrations, with the correspondence given in the text [Color figure can be viewed at wileyonlinelibrary.com] outermost become favored again. At these concentrations, the radial densities of the simulated configurations lack the tailing we find in the experimental data, as seen in Figure 6b. A second antibody molecule is added to recover the partiuclar tailing.
Following a 2:1 stoichiometry, we attached two antibody molecules to every possible combination of B fragments and allowed every possible orientation. Figure 7a shows possible orientations of two antibody molecules bound to the inner and outermost fragments in the protein A. Again, the radial density distributions of these models were determined and scanned for similarity to radial densities computed from the experimental SAXS data. Figure 7b shows the radial density distributions of this 2:1 stoichiometry.
Antibody molecules bound to the two outermost fragments returned the best radial density distributions, matching the distribution at high antibody concentration provided by SAXS. It could be assumed that the steric hindrance from the agarose would be greater in comparison to the resulting hindrance of the close proximity of another antibody molecule. The radial density distributions at this moment showed a maximum detected relative distance at around 21 nm. This value could correspond to the largest possible distance between the two antibody molecules (approximately the sum of two hydrodynamic radii) or the distance from the most external antibody to the agarose.
These modeled data support the isotherm prediction. At saturation, more than one antibody molecule can bind to the protein A ligands with the support of binding heterogeneity proposed by bi-Langmuir isotherm model. Data based on equilibrium binding capacities definitely support the idea that two antibody molecules could be bound to the MabSelect SuRe ligand. This was already suggested by other authors with protein A in solution studies (Ghose et al., 2007) and with neutron reflectivity studies with protein A attached to silica (Mazzer et al., 2017).
Finally, we ran models with three antibody molecules bound to different protein A fragments within the same chain. The configurations computed are densely packed. Figure 8a shows a selected configuration of these models. Figure 8b shows the radial density distributions of this stoichiometry overlapped with the experimental data.
We are argue that a 3:1 stoichiometry possibility could be excluded because of the steric effects. We would need to consider all atomistic pairwise interactions to argue their feasibility. In the current study, we limited ourselves to geometrical considerations.

| CONCLUSIONS
In this study we experimentally assessed radial density distributions and, on basis of this experimental value, hypothesized possible antibodyprotein A forms and configurations in a chromatographic resin.
We used small-angle X-ray scattering as an experimental method to model and speculate on the 3D form of antibody in solution and after binding to tetrameric staphylococcal protein A in MabSelect SuRe. We compared the experimentally assessed radial density distribution with ones computed from molecular simulations.
Computational models were restricted to crystallographic data and to data derived from the molecular dynamic simulations.
We reason that the antibodies bind to the protein A ligand at different stoichiometries because of the existence of heterogeneous binding sites. At low antibody concentrations (<40 mg/ml resin) we argue that the probable binding stoichiometry is 1:1, whereas at higher concentrations (>40 mg/ml resin) a 2:1 stoichiometry is favored.
At low concentrations, and assuming a 1:1 stoichiometry, the random walk models point toward configurations where the antibody binds at the outermost ligands at very low and at high concentrations and in the perpendicular form in respect to the surface. At 2:1 stoichiometry, we favor propeller-like configurations of the immobilized antibodies, which are more preferentially bound to the first and fourth ligand.
From our data, a 3:1 stoichiometry, albeit theoretically possible, is excluded here because of the steric effects.
We are convinced that our study, in which we outlined how to rationally assess 3D forms of the antibody-protein A complexes at different antibody concentrations next to a resin surface, will trigger the rational design of this technology of high industrial relevance.
Our experimental design could be potentially used to investigate molecule binding on other chromatographic systems in terms of stoichiometry, binding configurations, and distal spacing. Therefore, it can be implemented as a monitoring tool in industrial applications where it is necessary to purify large amounts of the product while obeying to certain Quality by Design parameters.

CONFLICTS OF INTEREST
The authors declare that there are no conflicts of interest.