Rational and Systematic Approach for Biotherapeutic Formulation Development Using MicroCal VP Capillary DSC

Standard analytics and real-time stability studies are traditionally used to develop drug formulations with the two-year shelf life, a prerequisite for drug products. While they have been shown to be effective, it is necessary to have methods capable of determining the structural stability of biopharmaceuticals as their progress from the development stage through final products is largely affected by the complicated structures of biopolymers as well as by their multiple degradation pathways. Differential scanning calorimetry (DSC) is a useful method for protein stability studies, providing results in line with real-time stability analyses.

Whatever may be the technique used for the analysis of protein structure or stability, an organized approach is required for the stringent statistical assessment of pre-formulation data. This article discusses a systematic method to develop pre-formulations using standard analytics and biophysical characterization. The use of a statistical design not only reduces the pharmaceutical development cycle, but also improves the quality of the development studies.

Tools for Pharmaceutical Development

The secondary and tertiary structures of proteins can be monitored using a number of techniques, such as DSC, fluorescence spectroscopy, Fourier-transform infrared spectroscopy (FTIR), and circular dichroism (CD); however, each method has its own pros and cons. Nevertheless, DSC is a better biophysical technique for preformulation development as:

  • The method is immune to a range of potential buffer components.
  • It can provide valuable preformulation data without the need to have information on structure or   relative proportion of alpha-helical or beta-sheet segments of proteins.
  • Most proteins show traceable thermal transitions while unfolding.

Moreover, comprehensive product quality analysis is performed using standard analytical techniques such as size exclusion chromatography (SEC) for aggregate detection, ion exchange (IEX) for observation of charge variants and deamidation, mass spectrometry to perform peptide mapping for chemical degradants, and capillary gel electrophoresis (CGE) or SDS-PAGE for observation of covalent aggregates and degradants. Design of experiments (DOE) optimizes the quantitative nature of most of the standard analytical methods and DSC employed during preformulation.

Critical factors and their interactions are determined and statistically evaluated with the help of DOE (Figure 1). They are determined from a large set of potential factors using factorial or fractional factorial designs for preclinical products or products with limited data. Central composite, Box-Behnken or other response surface designs are used for later-stage products or products for which initial studies have been carried out for critical factor identification, due to their ability to provide better granularity in the design space and applicability in modeling complex or quadratic surfaces. Figure 2 illustrates a typical preformulation project, defining scope, deliverables, and timelines at every stage of the development process.

Interaction plot showing the effect of buffer type on aggregation. For each sample, the percent high molecular weigh (HMW) species were summed and then reported in the statistical design as a function of pH and buffer type. For the first buffer system (shown in red), the percent HMW species varies as a function of pH and is generally higher than is observed for the second buffer system. The black data points show the same data for the second buffer system, but in this case the percent HMW species is generally lower and also does not appear to vary as a function of pH, suggesting that buffer two is a more suitable formulation both from a stability standpoint and from a manufacturing standpoint, since in this buffer system the pH would not have to be as tightly controlled to produce a product of acceptable quality.

Figure 1. Interaction plot showing the effect of buffer type on aggregation. For each sample, the percent high molecular weigh (HMW) species were summed and then reported in the statistical design as a function of pH and buffer type. For the first buffer system (shown in red), the percent HMW species varies as a function of pH and is generally higher than is observed for the second buffer system. The black data points show the same data for the second buffer system, but in this case the percent HMW species is generally lower and also does not appear to vary as a function of pH, suggesting that buffer two is a more suitable formulation both from a stability standpoint and from a manufacturing standpoint, since in this buffer system the pH would not have to be as tightly controlled to produce a product of acceptable quality.

Example workflow for preformulation projects  Baseline Biophysical Studies

Figure 2. Example workflow for preformulation projects

Baseline Biophysical Studies

The early stage of preformulation development evaluates the applicability of various potential biophysical techniques as part of a “baseline biophysical” study (Figures 3 and 4). The chosen technique that is suitable for a specific molecule is then employed as part of a screening design. Since DSC can handle a range of proteins, buffers, and excipients, it is used in preliminary as well as in subsequent preformulation studies. Besides identifying the appropriate technique for a specific molecule, this initial task helps the formulation scientist to standardize and improve the acquisition parameters and remove unsuitable categorical factors for a specific molecule. Therefore, subsequent designs are based on the pH, buffer, and ionic strength regions where the stability of the molecule appears to be at the highest level.

Differential Scanning Calorimetry for several different buffer types. The selected monoclonal antibody was exhaustively dialyzed into four different buffer types prior to analysis. Sample concentrations were approximately 2mg/mL with a scan rate of 60°C/h. Data presented here are after buffer subtraction and concentration normalization

Figure 3. Differential Scanning Calorimetry for several different buffer types. The selected monoclonal antibody was exhaustively dialyzed into four different buffer types prior to analysis. Sample concentrations were approximately 2mg/mL with a scan rate of 60°C/h. Data presented here are after buffer subtraction and concentration normalization

Example circular dichroism scans for baseline biophysical screening. Although CD is not as generally applicable as DSC because of potential buffer incompatibilities, certain proteins lend themselves well to CD analysis. Data above show far-UV CD scans of a protein that contains an appreciable amount of alpha-helix, a secondary structural element for which CD is well-suited.

Figure 4. Example circular dichroism scans for baseline biophysical screening. Although CD is not as generally applicable as DSC because of potential buffer incompatibilities, certain proteins lend themselves well to CD analysis. Data above show far-UV CD scans of a protein that contains an appreciable amount of alpha-helix, a secondary structural element for which CD is well-suited.

Solubility

Solubility studies are increasingly required to produce high concentration (>100 mg/mL) therapeutics due to the possibility of in-home administration with more concentrated drug products rather than limiting administration exclusively to a clinical setting. However, stability of the protein therapeutic is highly critical in cases involving the delivery of high concentrations in small volumes. Various buffer and pH candidates with a range of potential excipients are often used in these studies. Nevertheless, it is not necessary to have the same conditions for the most stable molecule and the most soluble molecule.

Statistical Design

Industries and regulatory bodies such as ICH and FDA widely recommend the implementation of statistical design during the development of pharmaceuticals for better characterization of critical factors affecting product stability and interactions between them that cannot be characterized by a one factor at a time (OFAT) approach. Factorial designs and response surface designs are the two common types of statistical designs. Critical factors and interactions between them can be effectively identified using factorial designs, whereas response surface designs are used by the development scientists for process optimization and establishment of suitable ranges for production after the identification of critical factors.

DSC is suitable for both of these statistical designs and can identify and optimize even factors that have very less effect on product stability. For every numeric factor in the design, low (-) and high-coded (+) values are selected by the user to set up factorial designs based on a battery of initial analyses such as the baseline biophysical studies. For a two-level factorial design:

No. runs = 2x where x = the number of factors

Based on the aforementioned expression, at least eight runs are required for a two-level, three-factor design. The below mentioned equation helps estimate the effect of a factor by averaging the runs set to each level.

Where, Y = the response at the low (-) and high (+) levels; n = the number of data points acquired at each level.

The curvature is recommended to be estimated using three to four center points along with the core design, in the design space, in order to achieve more robust error estimation. Central composite designs are nothing but response surface designs, which are basically “augmented” factorial designs. They are developed based on a core factorial design by adding a sequence of “star” or axial points, which project externally of the core factorial design space. Therefore, the effects can be more easily identified, as central composite designs cover a broader range when compared to the factorial design.

More replicates are also included at the center points, which enable error estimation. Five levels are available in total between the center points, axial points, and factorial design for central composite designs. As they differ for each individual factor, central composite designs have a very high degree of granularity so that quadratic functions can be used while optimizing preformulation. Either the central composite or factorial designs can be used as accelerated stability studies, where the selection of stress conditions relies on the thermal transitions for a specific therapeutic. Antibodies are typically characterized by two to three unfolding transitions with respect to the FAb, CH2, and CH3 domains. For the study, the stress temperature applied is expected to be below the start of the initial unfolding transition observed.

Applying the optimum stress without reaching the region where the onset of unfolding of a therapeutic or antibody would occur is the objective of the study because degradation or stress may be induced by unfolding, which is not representing stability under standard storage conditions. This can be achieved with DSC, which allows discerning initial unfolding events from baseline fluctuations due to its superior baseline stability. After the completion of the accelerated stability and preformulation stage of a project, the next step is performing the formulation development tasks such as container closure compatibility and syringeability to assess whether the administration of the proposed product formulation is as expected and to identify whether the container closure system faces any major compatibility problems.

Case Study

This study involved the use of the general approach for the development of a high-concentration monoclonal. A baseline biophysical study was the first step of the initial preformulation development, wherein the effect of buffer type and pH was evaluated against a standard set of excipients (Figure 5). The unsuitability of acetate as a buffer system was apparent even a simple rank-ordering approach was used. Sodium chloride had a moderate effect on the initial thermal transition, but the interaction in the acetate buffer system made the acetate and sodium chloride as a poor combination with respect to thermal stability (Figure 6).

Temperature of the first unfolding transition (Tm1) as a function of buffer type & excipient. The study presented above is typically input as a general factorial design. Although this design type does not lend itself to numerical optimization, it is particularly good at screening a large design space and ruling out buffer types and excipients that do not appear suitable

Figure 5. Temperature of the first unfolding transition (Tm1) as a function of buffer type & excipient. The study presented above is typically input as a general factorial design. Although this design type does not lend itself to numerical optimization, it is particularly good at screening a large design space and ruling out buffer types and excipients that do not appear suitable

Example thermograms for a monoclonal antibody. The data shows selected thermograms as a function of buffer type. Additional studies were performed to confirm that the first observed transition was reversible (data not shown).

Figure 6. Example thermograms for a monoclonal antibody. The data shows selected thermograms as a function of buffer type. Additional studies were performed to confirm that the first observed transition was reversible (data not shown).

Thermal stability of phosphate-containing buffers seemed to be the best, but they were found to be inferior to histidine-containing formulations based on accelerated stability studies. These data as well as previous study results were used to construct a response surface design for the optimization of the buffer concentration, excipient concentration, and pH within a more narrow design space, as listed in Table 1.

Table 1. Statistical design for formulation optimization

pH

Buffer Conc. (mM)

Excipient Conc. (mM)

6

35

115

6

35

115

6.75

20

150

5

35

115

6.75

50

80

5.25

50

150

6

35

115

5.25

20

80

5.25

20

150

6.75

20

80

6

35

115

6

55

115

6

15

115

7

35

115

5.25

50

80

6

35

50

6

35

115

6.75

50

150

6

35

115

6

35

180

Significant degradation was not observed in early studies for some of the lower temperature stress conditions and therefore, a temperature of around 50°C was targeted using DSC data in order to stress the molecule with no partial unfolding of the antibody. All of the samples and the center point formulations underwent DSC analysis at time zero and after a four-week accelerated stability study, respectively. Stressed (50°C) and control (4°C) samples incubated for four weeks were used for all other analytical methods (Table 2). A typical set of assays used for an accelerated stability study is shown in Table 2.

Table 2. Testing summary for accelerated stability study

Test

Method

Time - 0

Time – 4 Weeks

Physico-Chemical

DSC

X

Viscosity

Appearance

X

X

pH

X

X

Purity

Peptide map (ESI-MS and UV)

X

SDS - PAGE (reduced and non-reduced)

X

CEX-HPLC

X

SEC-HPLC

X

Strength

UVA280

X

Potency

ELISA

X

While DSC can be used to determine a range of thermodynamic parameters, the analysis of the data in Table 1 focused on the midpoints of the thermal transitions utilizing Design-Expert. Every single factor was determined to be important along with its quadratic terms, based on the model. Conversely, all of the interaction terms were revealed to be trivial with 0.05 alpha (Table 3). Any deviations or outliers from a normal distribution were not revealed by the diagnostic plots. Capillary DSC has the accuracy, precision and throughput to perform the statistical study of relatively subtle effects and trends (Figure 7).

Table 3. ANOVA for DSC data. While identification of effects is suitably performed by taking the difference between the average response at the high and low-coded conditions, confirmation of the validity of the selected factors should be confirmed using statistical analysis. In this case, the overall model and each of the factors are statistically significant at a 95% confidence level. Equally important, the “lack of fit” is not statistically significant, indicating that the data fit the model well

Source

Sum of squares

df

Mean square

F value

p-value Prob>F

Model

246.0185

6

41.00309

1089.692

<0.0001

A-pH

236.2554

1

236.2554

6278.691

<0.0001

B-Buffer conc.

0.287701

1

0.287701

7.645901

0.0161

C-Excipient Conc.

0.695344

1

0.695344

18.47936

0.0009

A2

8.286562

1

8.286562

220.2225

<0.0001

B2

0.619167

1

0.619167

16.45491

0.0014

C2

0.2832

1

0.2832

7.526291

0.0167

Residual

0.489166

13

0.037628

Lack of Fit

0.341832

8

0.042729

1.450081

0.3546

Pure Error

0.147333

5

0.029467

Cor Total

246.5077

19

Statistical analysis of the typical precision from MicroCal VP Capillary DSC. Due to the baseline stability of the MicroCal VP Capillary DSC system, data such as these are routinely observed, where a difference of 0.2°C between candidate formulations is statistically significant.

Figure 7. Statistical analysis of the typical precision from MicroCal VP Capillary DSC. Due to the baseline stability of the MicroCal VP Capillary DSC system, data such as these are routinely observed, where a difference of 0.2°C between candidate formulations is statistically significant.

The precision of the DSC method was also demonstrated by the analysis of the DOE 3-D and one-factor plots because despite the statistical significance of pH, excipient concentration, and buffer concentration, pH factor was shown to have more pronounced effect (appreciable slope) on Tm1 (Figure 8). Conversely, the slope for buffer concentration was close to zero as depicted in Figure 9, which implies that buffer and excipient concentrations are not operationally critical factors with respect to product stability despite of their statistical significance.

3D surface showing the effect of pH and buffer concentration on thermal stability. The data demonstrates the relative effects of pH and buffer strength on the midpoint of the first thermal transition for this antibody. The thermal stability of the antibody changes approximately 9°C as the pH is varied from 5.25 to 6.75

Figure 8. 3D surface showing the effect of pH and buffer concentration on thermal stability. The data demonstrates the relative effects of pH and buffer strength on the midpoint of the first thermal transition for this antibody. The thermal stability of the antibody changes approximately 9°C as the pH is varied from 5.25 to 6.75

Thermal stability as a function of buffer concentration. In contrast to the data shown in Figure 8, the change in the midpoint of the first thermal transition is negligible (0.3°C) as buffer strength is varied from 20 to 50mM. These data indicate that while the effect may be statistically significant, it is unlikely that buffer strength will have a dramatic effect on product stability. Other assays used in support of this preformulation study showed similar trends.

Figure 9. Thermal stability as a function of buffer concentration. In contrast to the data shown in Figure 8, the change in the midpoint of the first thermal transition is negligible (0.3°C) as buffer strength is varied from 20 to 50mM. These data indicate that while the effect may be statistically significant, it is unlikely that buffer strength will have a dramatic effect on product stability. Other assays used in support of this preformulation study showed similar trends.

A universal trend of thermal stability increase as a function of pH was shown by DSC (Figure 8), but a saddle plot centering on the pH of 6.3 was shown by the SEC analysis of the percent monomer (Figure 10). Several potential formulations that exhibited the desired chemical and conformational stability were targeted by combining the data obtained from various assays. However, isotonicity requirements essential for low-volume injectable drug products were not fulfilled by most of these candidate formulations. Therefore, data reevaluation was carried out in terms of excipient and buffer concentration so that an isotonic formulation can be created. However, since product quality was not affected by either buffer or excipient concentration subsequent to accelerated stability (Figure 9), an isotonic formulation with the desired stability can be created for this antibody by adding a small amount of excipient.

3D surface plot showing the effect of pH and buffer concentration on the percent monomer following accelerated stability. To a first order, the SEC data confirm the DSC results but there are some apparent differences: both assays indicate that pH appears to be the most significant factor with a more limited contribution from buffer or excipient concentration. The effect of pH on thermal stability, however, is markedly different than the effect of pH on purity by SEC because while thermal stability tends to increase linearly as a function of increasing pH, the SEC data indicate that above pH 6.4 increasing pH has the effect of decreasing stability as assessed by SEC.

Figure 10. 3D surface plot showing the effect of pH and buffer concentration on the percent monomer following accelerated stability. To a first order, the SEC data confirm the DSC results but there are some apparent differences: both assays indicate that pH appears to be the most significant factor with a more limited contribution from buffer or excipient concentration. The effect of pH on thermal stability, however, is markedly different than the effect of pH on purity by SEC because while thermal stability tends to increase linearly as a function of increasing pH, the SEC data indicate that above pH 6.4 increasing pH has the effect of decreasing stability as assessed by SEC.

Conclusion

A rational and systematic approach for biotherapeutic formulation development has been described in this article. Such an approach largely depends on automated quantitative methods such as MicroCal VP Capillary DSC, which can efficiently analyze a large design space region with adequate precision, thus enabling critical factors and their interactions to be identified and analyzed. DSC is suitable for both factorial and response surface design approaches owing to its intermediate precision and outstanding repeatability. As a result, it is possible to identify and optimize even those factors that slightly affect product stability.

Acknowledgements

Produced from content authored by Alex Tracy; Pooja Arora; Steven Cottle from Malvern Panalytical

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