Basal buffer systems for a newly glycosylated recombinant human interferon-β with biophysical stability and DoE approaches

European Journal of Pharmaceutical Sciences 78 (2015) 177–189

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European Journal of Pharmaceutical Sciences journal homepage: www.elsevier.com/locate/ejps

Basal buffer systems for a newly glycosylated recombinant human interferon-b with biophysical stability and DoE approaches Nam Ah Kim a, Kyoung Song b, Dae Gon Lim a, Shavron Hada a, Young Kee Shin b,c, Sangmun Shin d, Seong Hoon Jeong a,⇑ a

College of Pharmacy, Dongguk University-Seoul, Gyeonggi 410-820, Republic of Korea Abion Inc., Seoul, Republic of Korea College of Pharmacy, Seoul National University, Seoul, Republic of Korea d Department of Industrial & Management Systems Engineering, Dong-A University, Busan 604-714, Republic of Korea b c

a r t i c l e

i n f o

Article history: Received 20 May 2015 Received in revised form 19 July 2015 Accepted 23 July 2015 Available online 26 July 2015 Keywords: Interferon Design of experiment (DoE) Differential Scanning Calorimetry (DSC) FT-IR Protein formulation Biobetter

a b s t r a c t The purpose of this study was to develop a basal buffer system for a biobetter version of recombinant human interferon-b 1a (rhIFN-b 1a), termed R27T, to optimize its biophysical stability. The protein was pre-screened in solution as a function of pH (2–11) using differential scanning calorimetry (DSC) and dynamic light scattering (DLS). According to the result, its experimental pI and optimal pH range were 5.8 and 3.6–4.4, respectively. Design of experiment (DoE) approach was developed as a practical tool to aid formulation studies as a function of pH (2.9–5.7), buffer (phosphate, acetate, citrate, and histidine), and buffer concentration (20 mM and 50 mM). This method employed a weight-based procedure to interpret complex data sets and to investigate critical key factors representing protein stability. The factors used were Tm, enthalpy, and relative helix contents which were obtained by DSC and Fourier Transform Infrared spectroscopy (FT-IR). Although the weights changed by three responses, objective functions from a set of experimental designs based on four buffers were highest in 20 mM acetate buffer at pH 3.6 among all 19 scenarios tested. Size exclusion chromatography (SEC) was adopted to investigate accelerated storage stability in order to optimize the pH value with susceptible stability since the low pH was not patient-compliant. Interestingly, relative helix contents and storage stability (monomer remaining) increased with pH and was the highest at pH 4.0. On the other hand, relative helix contents and thermodynamic stability decreased at pH 4.2 and 4.4, suggesting protein aggregation issues. Therefore, the optimized basal buffer system for the novel biobetter was proposed to be 20 mM acetate buffer at pH 3.8 ± 0.2.  2015 Elsevier B.V. All rights reserved.

1. Introduction One of the major challenges in protein drug development is the preparation of a marketable formulation with sufficient chemical, physical, and biological stability to obtain product shelf life of 24–36 months (Schellekens and Jiskoot, 2013; Wang, 1999; Wang et al., 2010). Achieving this goal is still challenging due to the complexity of protein structures, with their large sizes and multiple levels (i.e. secondary, tertiary, and quaternary) as well as their intrinsic susceptibility to a variety of degradation pathways (Wang et al., 2010). Nevertheless, more than 30 years have

⇑ Corresponding author at: College of Pharmacy, Dongguk University-Seoul, Goyang, Gyeonggi 410-820, Republic of Korea. E-mail address: [email protected] (S.H. Jeong). http://dx.doi.org/10.1016/j.ejps.2015.07.020 0928-0987/ 2015 Elsevier B.V. All rights reserved.

passed since the first recombinant peptide hormone was successfully produced (Itakura et al., 1977). Many success stories have followed, including that of recombinant human insulin being the first approved genetically engineered biopharmaceutical in 1982 (Goeddel et al., 1979). The development process for therapeutic proteins is becoming faster and the number of biopharmaceuticals under development is growing significantly larger. However, the development process, especially the formulation, of biopharmaceuticals still faces several challenges due to protein aggregation, physicochemical instability, and pharmacokinetic properties with low half-life and solubility. Traditionally, protein formulation studies have been carried out in a semi-empirical manner (Fan et al., 2005). For example, a limited number of methods such as HPLC and DSC are utilized in an accelerated stability under a small number of solution conditions. A number of excipients are then added to overcome any observed

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instability issues. The viability of such methods is often less than ideal due to the aggressive nature of modern drug timelines. The resulting formulations are often suboptimal, although this approach has obviously led to many commercially successful products. In some cases, however, this minimal formulation strategy has been unsuccessful and led to product failures during development. Thus, it would seem that more intensive studies are desirable, especially if unexpected problems occur in later development stages with no detailed structural information available. Protein aggregation is one of the major problems in nearly all biopharmaceutical processes even during storage because therapeutic proteins are structurally and thermodynamically unstable in solution. Moreover, the proteins are susceptible to conformational changes due to various factors encountered during purification, processing, and storage (Aune and Tanford, 1969; Biltonen and Lumry, 1969; Chi et al., 2003; Cromwell et al., 2006; Pace, 1990; Rosenberg, 2006; Wang, 1999; Elkordy et al., 2004; Forbes et al., 2007). These issues may be exacerbated if the proteins are exposed to high temperatures, extreme pH, shear strain, and surface adsorption (Cromwell et al., 2006; Wang, 1999). Protein-based biopharmaceuticals have the potential to undergo physical degradation (i.e. unfolding, aggregation, insoluble particles due to non-native aggregation). To avoid protein aggregation or physical degradation and obtain maximum stability, optimization of the basal storage buffer might be required even at the early developmental stages, with the investigations of stable pH range, suitable buffer systems, and additives (Chi et al., 2003; Jeong, 2012; Ohtake et al., 2011). In previous studies, more rapid, rational, and exquisite approaches is investigated to develop protein formulations using human growth hormone, recombinant human epidermal growth factor, and etanercept with various biophysical methods such as DLS, DSC, circular dichroism (CD), SEC, and ATR-FTIR (Kim et al., 2013, 2014a,b; Lim et al., 2014a,b). A wider variety of analytical tools need to be employed to fully examine different aspects of proteins’ structures under a variety of solution conditions such as pH, buffer, buffer concentration, protein concentration, and excipients. The information can provide a rigorous basis for further formulation development. The large amount of data generated, however, can be difficult to interpret due to the diversity and complexity. Thus, novel data analysis methods are necessary to find consistency among the results and correlate them to define the physical states of target proteins. Design of experiment (DoE) and robust design optimization approaches developed by using response surface methodology (RSM) and weighted sum optimization model are employed in this experiment. Results obtained with several analytical methods can empirically be combined and processed through a series of mathematical procedures to obtain the optimal pH and buffer (Kim et al., 2012). In this study, the DoE approach is applied to a novel protein, a glycoengineered form of recombinant human interferon-b 1a (rhIFN-b 1a, R27T), to examine its biophysical stability as a practical tool in formulation applications and also during production and purification (Song et al., 2014). IFN-b 1a is an approved therapeutic protein for the treatment of multiple sclerosis (Leary et al., 2003). IFNs are known to participate in various cellular pathways that involve antiviral, anti-proliferative, anti-infective, and immuno-modulating activities (Gutterman, 1994; Karpusas et al., 1997). IFN-b is a 166 amino acid glycoprotein with a 4-helix bundle domain as its main structural component (Fan et al., 2005; Tyring, 1995). However, R27T is designed with Thr substituted for Arg at position 27th in rhIFN-b 1a, resulting in additional glycosylation at the 25th position. It is known that protein glycosylation can potentially affect many biochemical properties such as stability, solubility, intracellular trafficking activity, pharmacokinetics, and

antigenicity (Liu, 1992). R27T demonstrated improved stability, a lower propensity for protein aggregation and an increased half-life compared to human interferon-b 1a (Rebif, IFN-b 1a) (Song et al., 2014). However, the effect of glycosylation on the stability of therapeutic proteins is pH-dependent (Wang et al., 2008). Basal buffer system would be necessary to suppress any degradation or to avoid undergoing destabilizations during the development process. In this work, R27T was characterized as a function of pH, temperature, various buffers, and buffer concentration. Biophysical methods are used to evaluate its stability in order to interpret complex data sets, with the expectation of an optimal basal system of R27T in solution. 2. Material and methods 2.1. R27T and sample preparation R27T is a recombinant human interferon-b 1a (rhIFN-b) with extra glycosylation and is genetically constructed by performing site-directed mutagenesis via PCR on wild-type human IFN-b (Shin et al., 2010). The purified R27T is stored at 4 C in a phosphate buffer (storage buffer, pH 2.9). Its molecular weight is about 24,742.69 Da. The buffer was used to dilute R27T serially to various concentrations. The solution is dialyzed for 24 h at 4 C in a Cellu Sep H1 cellulose membrane, with a MW cut-off of 5000 Da (Membrane Filtration Products, Seguin, TX, USA). One liter of each buffer is prepared to the desired pH: phosphate (pH 2.9, 3.6, 4.3, and 5.0), citrate (pH 3.6, 4.3, 5.0, and 5.7), acetate (pH 3.6, 4.3, 5.0, and 5.7) and histidine (pH 4.3, 5.0, 5.7, and 6.4). Each buffer’s concentration is adjusted to 20 mM and 50 mM. During dialysis, the buffers are exchanged three times, with an 8 h interval between exchanges, to achieve the desired pH level and complete desalting. R27T are recovered and filtered to remove particulates using a sterile syringe filter (0.22 lm cellulose acetate; Advantec Toyo Kaisha, Ltd., Tokyo, Japan). The final dialysis buffers are also filtered and used for all sample dilutions and as a reference solution for DSC studies. The protein concentration in each buffer is determined by ultraviolet spectroscopy using an Optizen Pop apparatus (Mecasys, Seoul, Korea) at 282 nm. All chemicals are of analytical grade from Sigma-Aldrich (St. Louis, MO, USA) and are used without further purification. 2.2. Design of experiment (DoE) and robust design (RD) for protein formulations One of the main objectives of this study is to determine the optimal operating pH for the protein in the selected buffers (i.e., phosphate, acetate, citrate, and histidine) by simultaneously considering three responses: transition temperature (Tm), calorimetric enthalpy (DH), and relative helix contents (a/b). An initial data transformation step is required to optimize the three responses simultaneously as each had a different scale of measurement for the optimal pH conditions in the four different buffer environments. Therefore, all of the responses are transformed linearly into the same range between 1 and 2 as follows:

ytransformed ¼

y  ymin þ 1: ymax  ymin

where ymin and ymax represent the minimum and maximum response values. To express the relationships between responses y (i.e., Tm, relative helix content, and enthalpy) and buffer concentration or pH using the available data from Table 1, a reduced second-order model using RSM associated with concentration and pH in all buffers. The reduced second-order model is identified as follows:

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N.A. Kim et al. / European Journal of Pharmaceutical Sciences 78 (2015) 177–189 Table 1 DSC and FT-IR results of R27T at different buffers and various pH. S.O.

R.O.

Buffer

Conc. (mM)

pH

Tm (C)

Enthalpy (kJ/mol)

a-Helix (%)

b-Sheet (%)

b-Turn (%)

Rd. Coil (%)

a/b

2 5 8 4 1 7 3 6

1 2 3 4 5 6 7 8

Acetate

20 50 50 20 20 50 20 50

4.3 3.6 5.7 5.7 3.6 5.0 5.0 4.3

59.84 63.70 59.38 57.74 62.59 59.71 58.52 59.06

20.99 13.64 11.82 15.20 21.51 15.11 30.68 10.63

30.31 23.65 30.43 25.97 48.20 32.53 29.59 21.33

26.33 32.22 31.98 26.56 17.24 24.49 30.59 32.77

23.70 28.23 30.09 29.73 11.29 30.62 28.00 29.82

19.66 15.90 7.50 17.74 23.27 12.36 12.05 16.07

1.15 0.73 0.95 0.98 2.80 1.33 0.97 0.65

7 3 2 5 8 6 4 1

1 2 3 4 5 6 7 8

Histidine

50 20 20 50 50 50 20 20

5.7 5.7 5.0 4.3 6.4 5.0 6.4 4.3

59.93 58.65 59.20 58.54 57.95 60.15 56.90 59.64

13.31 12.21 10.40 14.05 11.61 12.10 11.03 1.52

30.38 31.16 22.99 5.60 9.89 20.5 33.45 22.3

30.96 39.76 37.99 31.81 29.51 41.58 29.28 33.97

26.04 19.49 22.96 41.53 38.87 21.30 23.12 23.86

12.61 9.59 16.06 21.06 21.72 16.62 14.12 19.88

0.98 0.78 0.60 0.18 0.34 0.49 1.14 0.66

7 6 4 5 3 8 2 1

1 2 3 4 5 6 7 8

Phosphate

50 50 20 50 20 50 20 20

4.3 3.6 5.0 2.9 4.3 5.0 3.6 2.9

61.36 60.46 59.59 62.00 60.43 60.99 60.67 63.40

32.02 17.80 16.56 24.94 20.60 15.23 18.09 16.07

14.72 22.72 8.52 38.66 7.36 21.34 19.62 41.50

23.25 26.31 24.23 21.51 24.96 25.07 23.79 26.81

37.20 30.39 42.63 12.43 42.56 31.26 34.35 17.35

24.83 20.57 24.62 27.41 25.13 22.32 22.24 14.34

0.63 0.86 0.35 1.80 0.29 0.85 0.82 1.55

4 1 3 6 8 5 2 7

1 2 3 4 5 6 7 8

Citrate

20 20 20 50 50 50 20 50

5.7 3.6 5.0 4.3 5.7 3.6 4.3 5.0

60.43 55.75 60.80 57.98 62.84 54.51 58.27 61.51

11.31 5.36 9.00 10.46 15.78 9.28 9.47 15.48

9.65 38.32 18.20 33.15 18.79 19.00 51.92 34.32

26.95 27.01 20.28 43.07 26.68 40.70 21.35 40.91

37.14 16.83 30.57 23.65 26.18 29.67 21.12 14.91

26.26 17.85 30.95 0.13 28.35 10.63 5.61 9.87

0.36 1.42 0.90 0.77 0.70 0.47 2.43 0.84

^ ¼ m0 þ m1  C þ m2  pH þ m11  C 2 þ m22  pH2 þ m12  C  pH y where m0, m1, m2, m12, m11, m22, and m12 denote coefficients of the proposed second-order RSM model. RSM is typically used to optimize simultaneously a number of output responses and their associated input factors by using estimated response functions when the exact functional relationship is not known or is very complicated. For a comprehensive presentation of RSM, insightful comments on the current status and future direction of RSM are available (Box et al., 1988; Shin and Cho, 2005). To optimize the three responses simultaneously, a multi-objective RD principle is proposed utilizing the weighted-sum method, which is the most common technique for generating efficient solutions for multi-objective RD (Cho et al., 2000; Koksoy, 2003; Lin and Tu, 1995; Memtsas, 2003; Shin and Cho, 2009; Tang and Xu, 2002). Based on the weighted-sum method, the proposed multi-objective RD optimization model using decreasing importance levels from w1 to w3 (i.e., Tm > relative helix contents > enthalpy) for all weight scenarios shown in Table 2 can be generated as:

Table 2 Weighting scenarios simultaneously incorporating three responses (Tm, relative helix content, and enthalpy). Scenarios

Tm

Relative helix content

Enthalpy

w1

w2

w3

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19

0.65 0.65 0.65 0.60 0.60 0.60 0.55 0.55 0.55 0.55 0.50 0.50 0.50 0.50 0.45 0.45 0.45 0.40 0.33

0.30 0.25 0.20 0.35 0.30 0.25 0.40 0.35 0.30 0.25 0.45 0.40 0.35 0.30 0.40 0.35 0.30 0.35 0.33

0.05 0.10 0.15 0.05 0.10 0.15 0.05 0.10 0.15 0.20 0.05 0.10 0.15 0.20 0.15 0.20 0.25 0.25 0.33

Minimize w1  T m þ w2  relative helix content þ w3  enthalpy subject to

4 X wi ¼ 1 i¼1

x2X

stability, a sensitivity analysis for unknown weights need to be considered as shown in Table 2. 2.3. Dynamic Light Scattering (DLS) and Zeta potential

where X = {x e R2: g j ðxÞ 6 0 "j} and gj(x) represents the jth constraint. The set X of feasible solutions is closed and bounded. The optimal solutions for all buffer environments are evaluated to determine the best solutions. Generally, weights in this optimization model are known or given. However, in order to identify basal buffer systems for a newly glycosylated rhIFN-b with biophysical

Electrostatic interactions of the prepared R27T solutions are evaluated using a Zetasizer Nano ZS90 apparatus (Malvern Instruments, Worcestershire, UK). The apparatus is set at a temperature of 10 C and 1 mL of each prepared sample is measured out using a disposable sizing cuvette (Sarstedt, Numbrecht, Germany)

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to obtain the hydrodynamic size. A disposable capillary cell (Malvern Instruments) is used to measure the zeta potential. Each sample is measured five times with an intervening interval of 30 s. Hydrodynamic average size, polydispersity index (PDI), and zeta potential are calculated from the auto-correlated function using Zetasizer software version 7.11 (Malvern Instruments). 2.4. Differential Scanning Calorimetry (DSC) DSC thermograms are collected with a VP-DSC Microcalorimeter (Microcal, Northampton, MA, USA), using 0.51471 cm3 twin cells for the reference and sample solutions. Prior to the DSC measurements, the sample and the reference buffers are degassed under vacuum while being stirred. The final dialysis buffers are used as a reference to obtain the baseline. Measurements are repeated three times at a scan rate of 1 C/min from 15 C to 120 C. The resulting DSC data are evaluated using the Microcal LLC DSC plug-in for the Origin 7.0 software package provided with the equipment. Raw DSC data are corrected by subtracting the last buffer scan to obtain the instrumental baseline, which is then normalized by the total protein concentration. Following normalization, R27T scans are corrected by applying a linear baseline fit to obtain the non-zero baselines. The choice of an appropriate sample baseline correction is complicated by the presence of a limited post-transition baseline region followed by aggregation and precipitation events that occurs after the thermal denaturation envelope. All of the applied baseline correction options are evaluated within the analysis software. Linear baseline is selected until it gives the most consistent results when tested on repeated measurements, different samples and by independent user determinations. Final thermograms are plotted as excess specific heat capacity (cal/ C mol) versus temperature (C). These results are fit to a multistate model to calculate transition melting point (Tm) values and calorimetric enthalpies (DH). 2.5. Size-Exclusion Chromatography (SEC) Samples of R27T are analyzed using an Agilent high performance liquid chromatography system (Agilent HPLC 1260, Santa Clara, CA, USA) with a diode array detector at an ultraviolet wavelength of 282 nm as well as with a TSK-GEL G3000SWXL SEC column (TOSOH Bioscience, King of Prussia, PA, USA). To separate soluble R27T according to size, a mobile phase A (0.1% trifluoroacetic acid (TFA) in water with 150 mM NaCl) and a mobile phase B (0.1% TFA in acetonitrile (ACN) with 150 mM NaCl) is used, with the ratio of 4:6 and a flow rate of 0.5 mL/min. The injection volume is 20 lL. The peak areas for multimers are combined to calculate the total amount of soluble aggregates. The difference in the total area of R27T (sum of all SEC peaks in each chromatogram) at any time point versus time zero is defined as the point of insoluble aggregate formation. The percentage of each species (insoluble aggregate, monomer, and fragment) remaining relative to the total area at time zero is calculated and plotted against the incubation time using the following equation:

%Remaining ¼ ðat  A0 Þ  100

with an iD5 diamond ATR attachment. For each spectrum, a total of 100 interferograms is collected in single beam mode from 4000 cm1 to 600 cm1 with a resolution of 4 cm1. Ten microliters of each sample solution is placed directly on an iD5 diamond crystal plate. The sample spectrum is collected after determining the background and subtracting the buffer spectrum from the sample spectrum. Nicolet Omnic software is used for peak resolution. The a-helix, b-sheet, b-turn, and random coil contents of the protein solutions are estimated from the amide I region of the IR spectra. Peak resolution and curve-fitting are performed as follows: peaks of the amide I region are first subjected to Fourier self-deconvolution and then curve-fitted using the Gauss and Lorentz formula with OMNIC Peak Resolve software (Thermo Fischer Scientific, Waltham, MA, USA). The area corresponding to each secondary structure is calculated accordingly and expressed as a percentage of the sum of areas (Sakudo et al., 2009). 3. Results 3.1. Hydrodynamic size and zeta potential of R27T R27T is a biobetter version of rhIFN-b 1a, created by additional glycosylation using site-directed mutagenesis. Glycoengineering of interferon-b improved its biophysical properties, such as aggregation, stability, and pharmacokinetic properties without jeopardizing its activity (Karpusas et al., 1998; Runkel et al., 1998; Song et al., 2014). The work by Song et al. demonstrated enhanced biophysical stability following glycosylation at an additional 25th amino acid resulting from a mutation of Thr for Arg at position 27th in rhIFN-b 1a. This study is the continuation of the previous work, with the goal of improving the biophysical stability of R27T in solution. DLS measurements are performed to investigate the effects of protein concentration, pH, and buffer concentration on the electrostatic interactions between R27T and the formation of aggregates in the nano-size range. Table 3 summarizes the hydrodynamic size, zeta potential, and polydispersity index (PDI) at different concentrations of R27T. Fig. 1a shows the size distribution of R27T in a storage buffer at a concentration of 0.80 mg/mL. The hydrodynamic size of R27T is about 5.37 ± 0.27 nm in diameter. The size increased gradually as the concentration decreased. Protein aggregations are observed at 0.10 and 0.05 mg/mL (Fig. 1b). In addition, absolute zeta potential also decreases from 21.16 mV to 3.17 mV as the concentration decreased, suggesting that decreasing electrostatic interactions between neighboring proteins induced protein aggregation. PDI is dimensionless and scales from 0 to 1. Values below 0.05 are rarely seen, except in highly monodispersed standards. Values above 0.7 suggest that the sample has a very polydispersed distribution and may be unsuitable for DLS measurement. In this study, PDI increased at lower protein concentrations, suggesting an aggregation issue in the storage buffer (phosphate buffer at pH 2.9) (Fig. 1c, d, and Table 3).

Table 3 Hydrodynamic size (diameter, nm) and zeta potential of R27T at different concentrations in a storage buffer.

where at is the area of individual species on any given day and A0 is the total area of all the species at time zero. The error bars represents standard deviation (SD) of three different measurements. Different formulations of R27T are incubated at 37 C. 2.6. Attenuated total reflectance (ATR) FT-IR spectroscopy Infrared spectra are recorded using a Nicolet iS5 spectrophotometer (Thermo Fisher Scientific, Waltham, MA, USA) equipped

a b

R27T (mg/mL)

Size (d.nma, Vol.%)

Std.

Zeta potential (mVb)

Std.

PDI

0.80 0.50 0.30 0.10 0.05

5.37 (99.99) 7.00 (99.99) 8.22 (99.80) – –

0.27 0.33 0.44 – –

21.16 13.24 11.84 3.60 3.17

0.90 0.13 0.80 1.15 1.80

0.63 0.78 0.93 1.00 1.00

d, nm – diameter in nanometer. mV – milli volts.

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(a)

Size Distribution by Volume

Volume (Percent)

50 40 30 20 10 0 0.01

0.1

1

10

100

1000

10000

Size (d.nm) Record 162: 0.8 mg/ml 1 Record 164: 0.8 mg/ml 3 Record 166: 0.8 mg/ml 5

Record 163: 0.8 mg/ml 2 Record 165: 0.8 mg/ml 4

Size Distribution by Volume

(b) Volume (Percent)

20

Aggregates 15 10 5 0 0.01

0.1

1

10

100

1000

10000

Size (d.nm) Record 182: 0.05 mg/ml 1 Record 184: 0.05 mg/ml 3 Record 186: 0.05 mg/ml 5

Record 183: 0.05 mg/ml 2 Record 185: 0.05 mg/ml 4

Zeta Potential Distribution

(c) Total Counts

400000 300000 200000 100000 0

-1 00

0

1 00

200

Apparent Zeta Potential (mV) Record 96: DS01 (0.8mg/ml) 5 Record 98: DS01 (0.8mg/ml) 7 Record 100: DS01 (0.8mg/ml) 9

Record 97: DS01 (0.8mg/ml) 6 Record 99: DS01 (0.8mg/ml) 8

Zeta Potential Distribution

(d) Total Counts

800000 600000 400000 200000 0 -1 0 0

0

1 00

2 00

Apparent Zeta Potential (mV) Record 6: DS01 (0.05mg/ml) 1 Record 8: DS01 (0.05mg/ml) 3 Record 10: DS01 (0.05mg/ml) 5

Record 7: DS01 (0.05mg/ml) 2 Record 9: DS01 (0.05mg/ml) 4

Fig. 1. Hydrodynamic size distribution of R27T with its different concentration at (a) 0.80 mg/mL and (b) 0.05 mg/mL measured by DLS. Zeta potential of R27T with its different concentration at (c) 0.80 mg/mL and (d) 0.05 mg/mL measured by DLS.

From the results, high absolute value zeta potentials may be a key factor in suppressing protein aggregation. A set of samples is prepared to evaluate the change in zeta potential and to determine the experimental isoelectrical point (pI) where the zeta potential reached zero. Fig. 2a shows the changes in zeta potential according to pH. The results indicate relatively high electrical repulsion at acidic pH. The experimental pI, where the zeta potential reached zero on the x-axis, exhibits about 5.84.

3.2. Thermal properties of R27T During the DSC measurement, two parameters are evaluated: the transition temperature (Tm) and the enthalpy which represents the energy that maintains the folded tertiary structure at constant pressure and volume. These parameters may play an important role in maintaining the tertiary structure of the protein. Typical DSC thermograms of R27T at different concentrations are

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(a)

Zeta potential (mV)

30 20

Experimental pI = 5.84 10 pH value 0

0

1

2

2.4

3.2

3

4

5

6

7

8

9

10

11

12

10.4

11.2

-10 -20 -30

(b)

65

Temperature (°C)

60

55

50

45 pH value 40

1.6

4.0

4.8

5.6

6.4

7.2

8.0

8.8

9.6

12.0

Fig. 2. Changes of zeta potential with 0.80 mg/mL R27T at various pH values indicating relatively high electrical repulsion at acidic pH and the experimental pI of 5.84 (a). Changes of Tm plotted against various pH values with the pH adjustment from 2.0 to 11.2 (b).

presented in Fig. 3. The thermograms show changing conformational stability in the storage buffer which is in phosphate buffer at pH 2.9 depending on protein concentration from 0.80 mg/mL to 0.05 mg/mL. The Tm of R27T at a concentration of 0.8 mg/mL is 60.07 C (Fig. 3a). A single endothermic peak is observed at 40– 80 C. However, a decrease in Tm is observed at 60.07–50.68 C as the concentration decreased from 0.80 mg/mL to 0.05 mg/mL. This decrease may indicate a decrease in conformational stability (Tm values) at lower concentrations. This result seems to be consistent with the DLS. Optimal formulation development may be necessary to improve the conformational stability of R27T at therapeutic doses since the therapeutic dose concentration of R27T is expected to be at least 0.05 mg/mL (Rebif; 22 lg and 44 lg in 0.5 mL). The pH effect on R27T is further evaluated because the exposure of proteins to extreme pH can result in structure loss due to the disruption of both internal electrostatic forces and charge-charge interactions (Wang, 1999). Fig. 2b is a graph of Tm plotted against various pH values. The pH range is adjusted from 2 to 11 using HCl and NaOH during dialysis. The results show that the optimal pH stability range is between pH 3.6 and pH 4.4 while the highest conformational stability (Tm values) is observed at about 61 C. Furthermore, DSC studies are performed with additional acidic buffers – phosphate, acetate, citrate, and histidine at various pH ranges to determine the optimal buffer system. Fig. 4 displays the endothermic peaks of R27T at different pH in corresponding buffers. The figure confirms that the conformational stability of R27T changes significantly with different pH and buffers. More detailed information is listed in Table 1. The highest Tm is observed at 50 mM acetate pH 3.6, followed by 20 mM phosphate pH 2.9, 50 mM citrate pH 5.7, and 20 mM acetate pH 3.6. 3.3. Secondary structural stability – ATR FTIR The thermodynamic properties obtained from the DSC may be insufficient for decision making during formulation development.

As more protein is carefully characterized both experimentally and computationally, correlations between the thermodynamics and structure should be considered (Jeong, 2012). The extent of protonation of the side chain groups may reflect the stability of the native protein structure in the solution (Yang et al., 2004). The ionization state of individual amino acid side-chains is subject to the environmental pH, which can cause conformational changes (Krimm and Bandekar, 1986). Fig. 5 shows the Fourier self-deconvoluted spectra within the region of the amide I (1700–1600 cm1) bands of R27T in 20 mM phosphate buffer at (a) pH 2.9, (b) pH 3.6, and (c) pH 5.0. It is well known that rhIFN-b is dominated by a-helix with a strong peak at 1650 cm1 (Fan et al., 2005; Kumar et al., 2009). However, the signal significantly decreases as the pH increases from 2.9 to 5.0, resulting in a decrease in a-helix contents. According to the DSC result, the Tm of R27T in 20 mM phosphate buffer decreased as the pH increased, suggesting a close relationship between the thermodynamics and the secondary structural stability. As the Tm decreased with pH, the absorbance of a band near 1623 cm1 increased, representing the formation of an intermolecular b-sheet (Dong et al., 1995). Interestingly, the peak at 1665 cm1 appeared at pH 3.6 and the height of the peak increased when the pH increased (also when the a-helix content was below 30%) (Fig. 5b and c). This peak may be a b-turn and/or antiparallel b-sheets, aggregates and/or side chains (Bandekar, 1992). For R27T in 20 mM histidine buffer, visible protein aggregations are observed (Fig. 6a) – This phenomenon appeared to be related to pH, since the non-native aggregation also increased. Fig. 6b is the ATR FT-IR spectra of R27T in 20 mM histidine buffer at pH 5.7, which shows significantly increased signal at 1623 cm1. The ATR spectra were separated into nine peaks. Each ratio of composite area represents the corresponding percentage of each structure from peaks 1 to 9: peaks #1, 2, 3, 4, and 9 (1605 cm1, 1615 cm1, 1623 cm1, 1633 cm1, and 1690 cm1, respectively; b-sheet), peak #5 (1646 cm1; random coil), peak #6 (1655 cm1; a-helix), peaks #7 and 8 (1662 cm1 and

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183

Fig. 3. DSC thermograms of R27T at various protein concentrations; (a) 0.80 mg/mL, (b) 0.50 mg/mL, (c) 0.30 mg/mL, and (d) 0.05 mg/mL.

Cp (kcal/mole/ƒC)

pH 6.4 Histidine buffer pH 2.9 Phosphate buffer pH 3.6 Acetate buffer pH 5.0 Citrate buffer

provided and are listed in Table 1. In addition, the relative helix content is calculated as a-helix content divided by b-sheet content. The result shows that R27T in 20 mM acetate buffer at pH 3.6 had the highest relative helix contents, followed by R27T in 50 mM phosphate buffer at pH 2.9. 3.4. Buffer selection by robust design (RD)

Tm

Increasing stability

Temperature (ƒC) Fig. 4. DSC thermograms of R27T at various pHs with corresponding buffers suggesting increased stability with increased Tm.

1678 cm1; respectively; b-turn) are indicated accordingly. After resolving the peaks, the relative percentages of a-helix, b-sheet, b-turn, and random coil contents are calculated using the software

Nineteen different weighting scenarios are investigated as shown in Table 2 for the purpose of selecting the best-performing buffer, buffer concentration, and the respective pH value while evaluating the three responses simultaneously. The three responses include Tm, relative helix contents, and calorimetric enthalpy, which represent conformational stability, secondary structural stability, and apparent stability with respect to heat capacity. Since Tm is considered to be the most important factor representing protein thermodynamic stability, it may carry the highest weight. The weight of Tm decreases from 0.65 to 0.33 in the different scenarios. However, the protein solution stability is evaluated with Tm as well as with relative helix contents and enthalpy. 19 scenarios with different weights assigned to the three factors are chosen and investigated to identify reasonable correlations

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Fig. 5. Representative amide I region of ATR FT-IR spectra of R27T in 20 mM phosphate buffer at (a) pH 2.9, (b) pH 3.6, and (c) pH 5.0. The amide I region of ATR spectra (composite) was separated into nine peaks (Peaks #1–9). The resolution of each peak was calculated by Fourier self-deconvolution and curve fitting based on the Gauss and Lorentz formula. The ratio of the composite area represents the corresponding percentage of each structure.

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185

(a) Container : E-tubes 20 mM Histidine buffers

pH 4.3

pH 5.0

pH 5.7

pH 6.4

Protein aggregation increases

(b)

Fig. 6. Pictures of R27T in histidine buffer showing (a) visible protein aggregation with increasing pH, stored at 37 C for 2 weeks, and (b) representative amide I region of the ATR FT-IR spectra for R27T in 20 mM histidine buffer at pH 5.7.

between the protein solution stability and different pH values or buffers. Optimization results for the 19 scenarios are shown in Table 4. By comparing the optimal values of objective functions obtained from the four buffer environments (i.e., citrate, acetate, histidine, and phosphate), the optimal pH value and the corresponding buffer that produced the optimal formulation can be selected. 20 mM acetate buffer at pH 3.6 exhibited the highest responses and the objective functions in Table 4 also suggest that this buffer might be the optimal formulation in this study. When the weights of three responses contributed equally to the objective function, the acetate buffer exhibited the highest objective values.

3.5. Accelerated stability by SEC and pH optimization by FT-IR and DSC Although pH 3.6 is the optimized pH value within the pH range values tested (3.6, 4.3, 5.0, and 5.7) in acetate buffer, further study is needed to increase the pH of the formulation with acceptable stability since low pH can cause skin burns in subcutaneous injections. In addition, to understand the effects of pH on the accelerated storage stability, R27T formulations at pHs ranging from 3.4 to 4.4 are stored at 4 C and 37 C for 11 days. SEC chromatograms of the remaining monomer in the protein solutions are compared daily. Fig. 7a is the overlaid chromatogram of R27T at pH 4.2 stored at 37 C. The image shows changes in the available monomer and in protein aggregations depending on time and heat. However,

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Table 4 The multi-objective RD optimization results represented by transformed values in four buffers (acetate, citrate, histidine, and phosphate) based on 19 weighting scenarios. Optimal solutions

a

Buffers

Acetate

Scenarios

Ca

pH

C

Citrate pH

C

Histidine pH

C

Phosphate pH

Objective functions

Acetate

Citrate

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19

20.00 20.00 20.00 20.00 20.00 20.00 20.00 20.00 20.00 20.00 20.00 20.00 20.00 20.00 20.00 20.00 20.00 20.00 20.00

3.60 3.60 3.60 3.60 3.60 3.60 3.60 3.60 3.60 3.60 3.60 3.60 3.60 3.60 3.60 3.60 3.60 3.60 3.60

50.00 50.00 50.00 50.00 50.00 50.00 50.00 50.00 50.00 50.00 50.00 50.00 50.00 50.00 50.00 50.00 50.00 50.00 50.00

5.67 5.70 5.70 5.59 5.66 5.70 5.51 5.57 5.64 5.70 5.43 5.49 5.55 5.62 5.46 5.53 5.60 5.51 5.49

50.00 50.00 50.00 20.00 50.00 50.00 20.00 50.00 50.00 50.00 20.00 20.00 50.00 50.00 50.00 50.00 50.00 50.00 50.00

5.32 5.30 5.29 5.14 5.32 5.30 5.19 5.34 5.32 5.30 5.24 5.26 5.34 5.32 5.36 5.34 5.32 5.34 5.34

20.00 20.00 20.00 20.00 20.00 20.00 20.00 20.00 20.00 20.00 20.00 20.00 20.00 20.00 20.00 50.00 50.00 50.00 50.00

2.90 2.90 2.90 2.90 2.90 2.90 2.90 2.90 2.90 2.90 2.90 2.90 2.90 2.90 2.90 2.90 2.90 2.90 2.90

1.887 1.875 1.864 1.886 1.874 1.863 1.884 1.873 1.862 1.851 1.883 1.872 1.861 1.849 1.860 1.848 1.837 1.836 1.816

1.659 1.675 1.692 1.623 1.637 1.654 1.588 1.602 1.616 1.632 1.556 1.568 1.581 1.595 1.548 1.560 1.574 1.539 1.516

Histidine

Phosphate

1.449 1.463 1.476 1.430 1.441 1.454 1.415 1.419 1.433 1.446 1.401 1.402 1.411 1.424 1.389 1.402 1.416 1.394 1.387

1.799 1.794 1.789 1.779 1.774 1.769 1.759 1.754 1.750 1.745 1.740 1.735 1.730 1.725 1.710 1.710 1.715 1.705 1.699

C: Buffer concentration.

the aggregation of R27T also decreased on the 11th day compared to the 3rd and 7th days, suggesting non-native aggregation issues. As the pH increases from 3.4 to 4.4 with 0.2 pH increments, the amount of remaining monomer increased during storage (Fig. 7b). The accelerated stability results for R27T indicate low stability at pH 3.4, with only 4% monomer remaining on day 11. However, as the pH increases from 3.6 to 3.8, 4.0, 4.2, and 4.4, the amount of monomer remaining on day 11 increases to 21.35%, 22.59%, 23.42%, 24.93% and 25.10%, respectively. The change might be insignificant but indicates continuous increase by pH. On the other hand, the monomer remaining at 4 C shows the highest at pH 3.8 which is 75.63% (data not shown).

According to the results, the accelerated storage stability increases with pH between the pH range of 3.6 and 4.4. However, the pH range should be narrower for early stage formulation development. Fig. 8 shows the FT-IR and DSC results for R27T at various pHs in acetate buffer. R27T at pH 4.0 had the highest a-helix and the lowest b-sheet content. However, R27T at pH 4.2 and 4.4 had lower a-helix and higher b-sheet contents. The result correlates well with DSC result, which indicated increasing Tm and enthalpy as the pH increased from pH 3.6 to pH 4.0, suggesting an increase in conformational stability. pH 4.2 and 4.4 exhibited irregular thermograms after transitions. 4. Discussion

Absorbance at 214 nm (mAU)

(a)

R27T at pH 4.2

80

Monomer

0 day 70

3 day 7 day

60

11 day 50 40 30 20 10

Aggregation

0 0

5

10

15

20

Time (min)

(b) 100

R27T at 37 °C

pH3.4_H pH3.6_H pH3.8_H pH4.0_H pH4.2_H pH4.4_H Control_H

Monomer contents (%)

90 80 70 60 50 40 30 20 10 0 0

2

4

6

8

10

12

Time (days) Fig. 7. SEC chromatograms showing (a) 20 mM acetate buffer at pH 4.0 before and after thermal stress, stored in an oven at 37 C for 11 days and (b) plotted graph of monomer remaining after storage.

One of the most fundamental challenges when designing a protein-based formulation involves obtaining the desired therapeutic protein concentration in solution (Shire et al., 2004; Wang, 1999). This might be due to the fact that protein stability is not only dependent on the protein concentration but also on the solution’s pH, temperature, ionic strength, and excipient concentration (Kim et al., 2013, 2014b; Shire et al., 2004; Wang, 1999). Therefore, protein-based formulations should be suitable for stabilizing the therapeutic protein and avoid critical issues such as aggregation, precipitation, or fragmentation (Mahler et al., 2009; Shire et al., 2004). In this study, the basal buffer system for a newly constructed biobetter version of rhIFN-b 1a, termed R27T, is investigated using various analytical methods including DSC, DLS, FT-IR, and SEC. R27T contains Thr substituted for Arg at position 27th in rhIFN-b 1a, resulting in additional glycosylation at the 25th position (Song et al., 2014). The successful glycosylation hints at the potential of R27T as a long acting IFN analogue compared to Rebif. Glycosylation is known to increase the solubility of many proteins and the stability of the glycosylated protein is pH-dependent (Mitra et al., 2006; Wang et al., 2008). However, the conformational stability of R27T and the zeta potential decreased as the protein concentration decreased (Figs. 2 and 3), ultimately causing an increase in the size of protein. Protein aggregation is also observed using DLS, since the electrical repulsion by neighboring proteins decreased from 21.16 to 3.17 mV (Table 3). The therapeutic dose of this biobetter is expected to be

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(a) Secondary structural contents (%)

60

α-helix

Optimal pH range

β-sheet

50

51.58

40 42.09

42.03

40.98

30 20 20.95

23.76

24.28

33.83

34.18

26.07

25.98

4.2

4.4

20.24

10 0 3.4

3.6

3.8

4.0 pH value

(b)

Fig. 8. Optimal pH ranges suggested based on the contents of a-helix and b-sheet plotted against pH (a) and overlaid thermograms by pH (b).

nearly 50 lg/mL, similar to Rebif (44 and 88 lg/mL). Therefore, identifying a suitable pH and buffer is important to overcome stability issues and obtain optimal stability at different stages of the development processes, including drug production, purification, storage, and release. The propensity of proteins to unfold varies according to the electrostatic repulsions between similarly charged atoms. Likewise, disruption of the protein structure will occur when the protein is exposed to extreme pHs (Wang, 1999). According to the DSC result, the conformational stability (Tm values) of R27T has a convex pattern at acidic pHs, which might indicate the optimal pH range. In addition, the optimal pH range appeared to be relatively close to the apparent pI (5.84) where the zeta potential is zero. Once the pI and optimal pH range of R27T are identified, a DoE approach is used to obtain a basal buffer system at the pH range from 2.9 to 5.7 using acetate, histidine, phosphate, and citrate buffer with varying buffer concentrations. The results of the DoE indicated a high objective function for 20 mM acetate buffer at pH 3.6, despite varying weights for the 19 scenarios (Table 4). The next best objective function is for phosphate buffer at pH 2.9. In other words, the results also indicate the stability of R27T according to the pH and buffer with respect to thermal stability (Tm and enthalpy) and secondary structural stability (relative helix contents). Nevertheless, the formulation obtained from DoE is insufficient for subcutaneous injection, since the pH was too low and may cause patient compliance issue. Further study is necessary to increase the pH value of R27T with acetate buffer, taking into

consideration the thermal, secondary structural, and storage stabilities. Interestingly, the amount of monomer retained after storage in a 37 C oven increased with pH from 3.4 to 4.4 (Fig. 7). Protein aggregation also increased continuously until the 7th day. However, both monomer and aggregation decreased on the 11th day, suggesting the loss of R27T from non-native aggregation. On the other hand, after unfolding from heat, the baseline of the thermograms for the samples at pH 4.2 and 4.4 dropped significantly below the pre-unfolding level (Fig. 8b). The downward shift after unfolding reflects an exothermic event, possibly due to non-native aggregation or precipitation. Since the DSC thermograms suggests R27T instability at pH 4.2 and 4.4 and the same issue is observed with SEC, the secondary structural stabilities of the samples are analyzed by ATR FT-IR.rhIFN-b is a 166 amino acid glycoprotein with a 4-helix bundle domain as its main structural component (Tyring, 1995). Increasing b-sheet contents may indicate the formation of intermolecular b-sheets that can induce protein aggregation (Dong et al., 1995; Fan et al., 2005). In a previous study, ATR FT-IR results suggested that a significant amount of a-helical structure is retained as the temperature increased. The amount of intermolecular b-sheets also increases, reflecting aggregation (Fan et al., 2005). Tertiary structure changes, in which more apolar sites become accessible at high temperatures and induce intermolecular hydrophobic interactions, can also be a major force in rhIFN-b aggregation (Fan et al., 2005; Utsumi et al., 1989). Likewise, the increase of b-sheets in R27T is regarded as an

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indication of instability, a phenomenon that induces protein aggregation. Fig. 8a is the plotted graph of the a-helix and b-sheet contents of R27T at the optimal pH range in the acetate buffer. The graph shows that the secondary structural stability is highest at pH 4.0, with high a-helix and low b-sheet contents. However, increased b-sheet and decreased a-helix are observed at pH 4.2 and 4.4, which confirmed the aggregation issues indicated by the DSC result. Therefore, the optimal pH value for thermal and secondary structural stability of R27T is at pH 3.8 ± 0.2. In addition, the pH range exhibited high electrical repulsion (zeta potential, Fig. 2a) that may suppress protein aggregation with susceptible storage stability. However, pH 4.2 is disregarded due to the potential for protein aggregation. 5. Conclusions There are many factors that can affect the biophysical stability of protein formulations. In this study, a novel biobetter is investigated to determine the optimal pH for the development as a preliminary formulation using different established and validated biophysical analytical methods – DSC, DLS, FT-IR, SEC, as well as a statistical method -DoE. The applied methods provided an optimal pH and buffer for R27T, mediated by electrostatic interactions induced by pH. Decreasing conformational stability of R27T at lower concentrations is also observed, since low concentration is a destabilizing factor. However, the stability of glycosylated proteins is pH-dependent and these observations clarified the optimal pH for the protein without any excipients or salts. Therefore, the investigation of future glycosylated proteins by pH and buffer is recommended at earlier stages of formulation development in order to avoid undesirable results when developing the formulation with excipients to stabilize the protein. Acknowledgements This research was supported by the Bio & Medical Technology Development Program of the NRF funded by the Korean government, MSIP (NRF-2014M3A9A9073811) and the National Research Foundation of Korea (NRF) Grant funded by the Korean government, MSIP (NRF-2014R1A2A1A11049845). References Aune, K.C., Tanford, C., 1969. Thermodynamics of the denaturation of lysozyme by guanidine hydrochloride. II. Dependence on denaturant concentration at 25 degrees. Biochemistry 8, 4586–4590. Bandekar, J., 1992. Amide modes and protein conformation. Biochim. Biophys. Acta 1120, 123–143. Biltonen, R., Lumry, R., 1969. Studies of the chymotrypsinogen family of proteins. VII. Thermodynamic analysis of transition I of alpha-chymotrypsin. J. Am. Chem. Soc. 91, 4256–4264. Box, G., Bisgaard, S., Fung, C., 1988. An explanation and critique of taguchi’s contribution to quality engineering. Qual. Reliab. Eng. Int. 4, 123–131. Chi, E.Y., Krishnan, S., Randolph, T.W., Carpenter, J.F., 2003. Physical stability of proteins in aqueous solution: mechanism and driving forces in nonnative protein aggregation. Pharm. Res. 20, 1325–1336. Cho, B.R., Kim, Y.J., Kimbler, D.L., Phillips, M.D., 2000. An integrated joint optimization procedure for robust and tolerance design. Int. J. Prod. Res. 38, 2309–2325. Cromwell, M.E., Hilario, E., Jacobson, F., 2006. Protein aggregation and bioprocessing. AAPS J. 8, E572–E579. Dong, A., Prestrelski, S.J., Allison, S.D., Carpenter, J.F., 1995. Infrared spectroscopic studies of lyophilization- and temperature-induced protein aggregation. J. Pharm. Sci. 84, 415–424. Elkordy, A.A., Forbes, R.T., Barry, B.W., 2004. Stability of crystallised and spray-dried lysozyme. Int. J. Pharm. 278, 209–219. Fan, H., Ralston, J., Dibiase, M., Faulkner, E., Middaugh, C.R., 2005. Solution behavior of IFN-beta-1a: an empirical phase diagram based approach. J. Pharm. Sci. 94, 1893–1911. Forbes, R.T., Barry, B.W., Elkordy, A.A., 2007. Preparation and characterisation of spray-dried and crystallised trypsin: FT-Raman study to detect protein denaturation after thermal stress. Eur. J. Pharm. Sci. 30, 315–323.

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